def main():
"""
NAME
di_tilt.py
DESCRIPTION
rotates geographic coordinate dec, inc data to stratigraphic
coordinates using the dip and dip direction (strike+90, dip if dip to right of strike)
INPUT FORMAT
declination inclination dip_direction dip
SYNTAX
di_tilt.py [-h][-i][-f FILE] [< filename ]
OPTIONS
-h prints help message and quits
-i for interactive data entry
-f FILE command line entry of file name
-F OFILE, specify output file, default is standard output
OUTPUT:
declination inclination
"""
if '-h' in sys.argv:
print main.__doc__
sys.exit()
if '-F' in sys.argv:
ind = sys.argv.index('-F')
ofile = sys.argv[ind + 1]
out = open(ofile, 'w')
print ofile, ' opened for output'
else:
ofile = ""
if '-i' in sys.argv: # interactive flag
while 1:
try:
Dec = float(raw_input("Declination: <cntl-D> to quit "))
except:
print "\n Good-bye\n"
sys.exit()
Inc = float(raw_input("Inclination: "))
Dip_dir = float(raw_input("Dip direction: "))
Dip = float(raw_input("Dip: "))
print '%7.1f %7.1f' % (pmag.dotilt(Dec, Inc, Dip_dir, Dip))
elif '-f' in sys.argv:
ind = sys.argv.index('-f')
file = sys.argv[ind + 1]
data = numpy.loadtxt(file)
else:
data = numpy.loadtxt(
sys.stdin, dtype=numpy.float) # read in the data from the datafile
D, I = pmag.dotilt_V(data)
for k in range(len(D)):
if ofile == "":
print '%7.1f %7.1f' % (D[k], I[k])
else:
out.write('%7.1f %7.1f\n' % (D[k], I[k]))
def main():
"""
NAME
di_tilt.py
DESCRIPTION
rotates geographic coordinate dec, inc data to stratigraphic
coordinates using the dip and dip direction (strike+90, dip if dip to right of strike)
INPUT FORMAT
declination inclination dip_direction dip
SYNTAX
di_tilt.py [-h][-i][-f FILE] [< filename ]
OPTIONS
-h prints help message and quits
-i for interactive data entry
-f FILE command line entry of file name
-F OFILE, specify output file, default is standard output
OUTPUT:
declination inclination
"""
if "-h" in sys.argv:
print main.__doc__
sys.exit()
if "-F" in sys.argv:
ind = sys.argv.index("-F")
ofile = sys.argv[ind + 1]
out = open(ofile, "w")
print ofile, " opened for output"
else:
ofile = ""
if "-i" in sys.argv: # interactive flag
while 1:
try:
Dec = float(raw_input("Declination: <cntl-D> to quit "))
except:
print "\n Good-bye\n"
sys.exit()
Inc = float(raw_input("Inclination: "))
Dip_dir = float(raw_input("Dip direction: "))
Dip = float(raw_input("Dip: "))
print "%7.1f %7.1f" % (pmag.dotilt(Dec, Inc, Dip_dir, Dip))
elif "-f" in sys.argv:
ind = sys.argv.index("-f")
file = sys.argv[ind + 1]
data = numpy.loadtxt(file)
else:
data = numpy.loadtxt(sys.stdin, dtype=numpy.float) # read in the data from the datafile
D, I = pmag.dotilt_V(data)
for k in range(len(D)):
if ofile == "":
print "%7.1f %7.1f" % (D[k], I[k])
else:
out.write("%7.1f %7.1f\n" % (D[k], I[k]))
def main():
"""
NAME
zeq_magic.py
DESCRIPTION
reads in magic_measurements formatted file, makes plots of remanence decay
during demagnetization experiments. Reads in prior interpretations saved in
a pmag_specimens formatted file [and allows re-interpretations of best-fit lines
and planes and saves (revised or new) interpretations in a pmag_specimens file.
interpretations are saved in the coordinate system used. Also allows judicious editting of
measurements to eliminate "bad" measurements. These are marked as such in the magic_measurements
input file. they are NOT deleted, just ignored. ] Bracketed part not yet implemented
SYNTAX
zeq_magic.py [command line options]
OPTIONS
-h prints help message and quits
-f MEASFILE: sets measurements format input file, default: measurements.txt
-fsp SPECFILE: sets specimens format file with prior interpreations, default: specimens.txt
-fsa SAMPFILE: sets samples format file sample=>site information, default: samples.txt
-fsi SITEFILE: sets sites format file with site=>location informationprior interpreations, default: samples.txt
-Fp PLTFILE: sets filename for saved plot, default is name_type.fmt (where type is zijd, eqarea or decay curve)
-crd [s,g,t]: sets coordinate system, g=geographic, t=tilt adjusted, default: specimen coordinate system
-spc SPEC plots single specimen SPEC, saves plot with specified format
with optional -dir settings and quits
-dir [L,P,F][beg][end]: sets calculation type for principal component analysis, default is none
beg: starting step for PCA calculation
end: ending step for PCA calculation
[L,P,F]: calculation type for line, plane or fisher mean
must be used with -spc option
-fmt FMT: set format of saved plot [png,svg,jpg]
-A: suppresses averaging of replicate measurements, default is to average
-sav: saves all plots without review
SCREEN OUTPUT:
Specimen, N, a95, StepMin, StepMax, Dec, Inc, calculation type
"""
# initialize some variables
doave, e, b = 1, 0, 0 # average replicates, initial end and beginning step
intlist = ['magn_moment', 'magn_volume', 'magn_mass', 'magnitude']
plots, coord = 0, 's'
noorient = 0
version_num = pmag.get_version()
verbose = pmagplotlib.verbose
calculation_type, fmt = "", "svg"
user, spec_keys, locname = "", [], ''
geo, tilt, ask = 0, 0, 0
PriorRecs = [] # empty list for prior interpretations
backup = 0
specimen = "" # can skip everything and just plot one specimen with bounds e,b
if '-h' in sys.argv:
print(main.__doc__)
sys.exit()
dir_path = pmag.get_named_arg_from_sys("-WD", default_val=os.getcwd())
meas_file = pmag.get_named_arg_from_sys("-f",
default_val="measurements.txt")
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")
#meas_file = os.path.join(dir_path, meas_file)
#spec_file = os.path.join(dir_path, spec_file)
#samp_file = os.path.join(dir_path, samp_file)
#site_file = os.path.join(dir_path, site_file)
plot_file = pmag.get_named_arg_from_sys("-Fp", default_val="")
crd = pmag.get_named_arg_from_sys("-crd", default_val="s")
if crd == "s":
coord = "-1"
elif crd == "t":
coord = "100"
else:
coord = "0"
fmt = pmag.get_named_arg_from_sys("-fmt", "svg")
specimen = pmag.get_named_arg_from_sys("-spc", default_val="")
beg_pca, end_pca = "", ""
if '-dir' in sys.argv:
ind = sys.argv.index('-dir')
direction_type = sys.argv[ind + 1]
beg_pca = int(sys.argv[ind + 2])
end_pca = int(sys.argv[ind + 3])
if direction_type == 'L':
calculation_type = 'DE-BFL'
if direction_type == 'P':
calculation_type = 'DE-BFP'
if direction_type == 'F':
calculation_type = 'DE-FM'
if '-A' in sys.argv:
doave = 0
if '-sav' in sys.argv:
plots, verbose = 1, 0
#
first_save = 1
fnames = {
'measurements': meas_file,
'specimens': spec_file,
'samples': samp_file,
'sites': site_file
}
contribution = nb.Contribution(
dir_path,
custom_filenames=fnames,
read_tables=['measurements', 'specimens', 'samples', 'sites'])
#
# import specimens
specimen_cols = [
'analysts', 'aniso_ftest', 'aniso_ftest12', 'aniso_ftest23', 'aniso_s',
'aniso_s_mean', 'aniso_s_n_measurements', 'aniso_s_sigma',
'aniso_s_unit', 'aniso_tilt_correction', 'aniso_type', 'aniso_v1',
'aniso_v2', 'aniso_v3', 'citations', 'description', 'dir_alpha95',
'dir_comp', 'dir_dec', 'dir_inc', 'dir_mad_free', 'dir_n_measurements',
'dir_tilt_correction', 'experiments', 'geologic_classes',
'geologic_types', 'hyst_bc', 'hyst_bcr', 'hyst_mr_moment',
'hyst_ms_moment', 'int_abs', 'int_b', 'int_b_beta', 'int_b_sigma',
'int_corr', 'int_dang', 'int_drats', 'int_f', 'int_fvds', 'int_gamma',
'int_mad_free', 'int_md', 'int_n_measurements', 'int_n_ptrm', 'int_q',
'int_rsc', 'int_treat_dc_field', 'lithologies', 'meas_step_max',
'meas_step_min', 'meas_step_unit', 'method_codes', 'sample',
'software_packages', 'specimen'
]
if 'specimens' in contribution.tables:
# contribution.propagate_name_down('sample','measurements')
spec_container = contribution.tables['specimens']
prior_spec_data = spec_container.get_records_for_code(
'LP-DIR', strict_match=False
) # look up all prior directional interpretations
#
# tie sample names to measurement data
#
else:
spec_container, prior_spec_data = None, []
#
# import samples for orientation info
#
if 'samples' in contribution.tables:
# contribution.propagate_name_down('site','measurements')
contribution.propagate_cols(
col_names=['azimuth', 'dip', 'orientation_flag'],
target_df_name='measurements',
source_df_name='samples')
#
# define figure numbers for equal area, zijderveld,
# and intensity vs. demagnetiztion step respectively
#
ZED = {}
ZED['eqarea'], ZED['zijd'], ZED['demag'] = 1, 2, 3
pmagplotlib.plot_init(ZED['eqarea'], 6, 6)
pmagplotlib.plot_init(ZED['zijd'], 6, 6)
pmagplotlib.plot_init(ZED['demag'], 6, 6)
# save_pca=0
angle, direction_type, setangle = "", "", 0
# create measurement dataframe
#
meas_container = contribution.tables['measurements']
meas_data = meas_container.df
#
meas_data = meas_data[meas_data['method_codes'].str.contains(
'LT-NO|LT-AF-Z|LT-T-Z|LT-M-Z') == True] # fish out steps for plotting
meas_data = meas_data[meas_data['method_codes'].str.contains(
'AN|ARM|LP-TRM|LP-PI-ARM') == False] # strip out unwanted experiments
intensity_types = [
col_name for col_name in meas_data.columns if col_name in intlist
]
# plot first intensity method found - normalized to initial value anyway -
# doesn't matter which used
int_key = intensity_types[0]
# get all the non-null intensity records of the same type
meas_data = meas_data[meas_data[int_key].notnull()]
if 'flag' not in meas_data.columns:
meas_data['flag'] = 'g' # set the default flag to good
# need to treat LP-NO specially for af data, treatment should be zero,
# otherwise 273.
meas_data['treatment'] = meas_data['treat_ac_field'].where(
cond=meas_data['treat_ac_field'] != '0', other=meas_data['treat_temp'])
meas_data['ZI'] = 1 # initialize these to one
meas_data['instrument_codes'] = "" # initialize these to blank
# for unusual case of microwave power....
if 'treat_mw_power' in meas_data.columns:
meas_data.loc[
meas_data.treat_mw_power != 0,
'treatment'] = meas_data.treat_mw_power * meas_data.treat_mw_time
#
# get list of unique specimen names from measurement data
#
# this is a list of all the specimen names
specimen_names = meas_data.specimen.unique()
specimen_names = specimen_names.tolist()
specimen_names.sort()
#
# set up new DataFrame for this sessions specimen interpretations
#
data_container = nb.MagicDataFrame(dtype='specimens',
columns=specimen_cols)
# this is for interpretations from this session
current_spec_data = data_container.df
locname = 'LookItUp'
if specimen == "":
k = 0
else:
k = specimen_names.index(specimen)
# let's look at the data now
while k < len(specimen_names):
# set the current specimen for plotting
this_specimen = specimen_names[k]
if verbose and this_specimen != "":
print(this_specimen, k + 1, 'out of ', len(specimen_names))
if setangle == 0:
angle = ""
this_specimen_measurements = meas_data[
meas_data['specimen'].str.contains(
this_specimen) == True] # fish out this specimen
this_specimen_measurements = this_specimen_measurements[
this_specimen_measurements['flag'].str.contains(
'g') == True] # fish out this specimen
if len(this_specimen_measurements) != 0: # if there are measurements
#
# set up datablock [[treatment,dec, inc, int, direction_type],[....]]
#
#
# figure out the method codes
#
units, methods, title = "", "", this_specimen
# this is a list of all the specimen method codes`
meas_meths = this_specimen_measurements.method_codes.unique()
tr = pd.to_numeric(this_specimen_measurements.treatment).tolist()
if set(tr) == set([0]):
k += 1
continue
for m in meas_meths:
if 'LT-AF-Z' in m:
units = 'T' # units include tesla
tr[0] = 0
if 'LT-T-Z' in m:
units = units + ":K" # units include kelvin
if 'LT-M-Z' in m:
units = units + ':J' # units include joules
tr[0] = 0
units = units.strip(':') # strip off extra colons
if 'LP-' in m:
methods = methods + ":" + m
decs = pd.to_numeric(this_specimen_measurements.dir_dec).tolist()
incs = pd.to_numeric(this_specimen_measurements.dir_inc).tolist()
#
# fix the coordinate system
#
if coord != '-1': # need to transform coordinates to geographic
azimuths = pd.to_numeric(this_specimen_measurements.azimuth
).tolist() # get the azimuths
# get the azimuths
dips = pd.to_numeric(this_specimen_measurements.dip).tolist()
dirs = [decs, incs, azimuths, dips]
# this transposes the columns and rows of the list of lists
dirs_geo = np.array(list(map(list, list(zip(*dirs)))))
decs, incs = pmag.dogeo_V(dirs_geo)
if coord == '100': # need to do tilt correction too
bed_dip_dirs = pd.to_numeric(
this_specimen_measurements.bed_dip_dir).tolist(
) # get the azimuths
bed_dips = pd.to_numeric(this_specimen_measurements.bed_dip
).tolist() # get the azimuths
dirs = [decs, incs, bed_dip_dirs, bed_dips]
# this transposes the columns and rows of the list of lists
dirs_tilt = np.array(list(map(list, list(zip(*dirs)))))
decs, incs = pmag.dotilt_V(dirs_tilt)
title = title + '_t'
else:
title = title + '_g'
if angle == "":
angle = decs[0]
ints = pd.to_numeric(this_specimen_measurements[int_key]).tolist()
ZI = this_specimen_measurements.ZI.tolist()
flags = this_specimen_measurements.flag.tolist()
codes = this_specimen_measurements.instrument_codes.tolist()
datalist = [tr, decs, incs, ints, ZI, flags, codes]
# this transposes the columns and rows of the list of lists
datablock = list(map(list, list(zip(*datalist))))
pmagplotlib.plotZED(ZED, datablock, angle, title, units)
if verbose:
pmagplotlib.drawFIGS(ZED)
#
# collect info for current_specimen_interpretation dictionary
#
if beg_pca == "" and len(prior_spec_data) != 0:
#
# find prior interpretation
#
prior_specimen_interpretations = prior_spec_data[
prior_spec_data['specimen'].str.contains(
this_specimen) == True]
beg_pcas = pd.to_numeric(prior_specimen_interpretations.
meas_step_min.values).tolist()
end_pcas = pd.to_numeric(prior_specimen_interpretations.
meas_step_max.values).tolist()
spec_methods = prior_specimen_interpretations.method_codes.tolist(
)
# step through all prior interpretations and plot them
for ind in range(len(beg_pcas)):
spec_meths = spec_methods[ind].split(':')
for m in spec_meths:
if 'DE-BFL' in m:
calculation_type = 'DE-BFL' # best fit line
if 'DE-BFP' in m:
calculation_type = 'DE-BFP' # best fit plane
if 'DE-FM' in m:
calculation_type = 'DE-FM' # fisher mean
if 'DE-BFL-A' in m:
calculation_type = 'DE-BFL-A' # anchored best fit line
start, end = tr.index(beg_pcas[ind]), tr.index(
end_pcas[ind]
) # getting the starting and ending points
# calculate direction/plane
mpars = pmag.domean(datablock, start, end,
calculation_type)
if mpars["specimen_direction_type"] != "Error":
# put it on the plot
pmagplotlib.plotDir(ZED, mpars, datablock, angle)
if verbose:
pmagplotlib.drawFIGS(ZED)
else:
start, end = int(beg_pca), int(end_pca)
# calculate direction/plane
mpars = pmag.domean(datablock, start, end, calculation_type)
if mpars["specimen_direction_type"] != "Error":
# put it on the plot
pmagplotlib.plotDir(ZED, mpars, datablock, angle)
if verbose:
pmagplotlib.drawFIGS(ZED)
if plots == 1 or specimen != "":
if plot_file == "":
basename = title
else:
basename = plot_file
files = {}
for key in list(ZED.keys()):
files[key] = basename + '_' + key + '.' + fmt
pmagplotlib.saveP(ZED, files)
if specimen != "":
sys.exit()
if verbose:
recnum = 0
for plotrec in datablock:
if units == 'T':
print('%s: %i %7.1f %s %8.3e %7.1f %7.1f %s' %
(plotrec[5], recnum, plotrec[0] * 1e3, " mT",
plotrec[3], plotrec[1], plotrec[2], plotrec[6]))
if units == "K":
print('%s: %i %7.1f %s %8.3e %7.1f %7.1f %s' %
(plotrec[5], recnum, plotrec[0] - 273, ' C',
plotrec[3], plotrec[1], plotrec[2], plotrec[6]))
if units == "J":
print('%s: %i %7.1f %s %8.3e %7.1f %7.1f %s' %
(plotrec[5], recnum, plotrec[0], ' J',
plotrec[3], plotrec[1], plotrec[2], plotrec[6]))
if 'K' in units and 'T' in units:
if plotrec[0] >= 1.:
print('%s: %i %7.1f %s %8.3e %7.1f %7.1f %s' %
(plotrec[5], recnum, plotrec[0] - 273, ' C',
plotrec[3], plotrec[1], plotrec[2],
plotrec[6]))
if plotrec[0] < 1.:
print('%s: %i %7.1f %s %8.3e %7.1f %7.1f %s' %
(plotrec[5], recnum, plotrec[0] * 1e3, " mT",
plotrec[3], plotrec[1], plotrec[2],
plotrec[6]))
recnum += 1
# we have a current interpretation
elif mpars["specimen_direction_type"] != "Error":
#
# create a new specimen record for the interpreation for this
# specimen
this_specimen_interpretation = {
col: ""
for col in specimen_cols
}
# this_specimen_interpretation["analysts"]=user
this_specimen_interpretation['software_packages'] = version_num
this_specimen_interpretation['specimen'] = this_specimen
this_specimen_interpretation["method_codes"] = calculation_type
this_specimen_interpretation["meas_step_unit"] = units
this_specimen_interpretation["meas_step_min"] = tr[start]
this_specimen_interpretation["meas_step_max"] = tr[end]
this_specimen_interpretation["dir_dec"] = '%7.1f' % (
mpars['specimen_dec'])
this_specimen_interpretation["dir_inc"] = '%7.1f' % (
mpars['specimen_inc'])
this_specimen_interpretation["dir_dang"] = '%7.1f' % (
mpars['specimen_dang'])
this_specimen_interpretation["dir_n_measurements"] = '%i' % (
mpars['specimen_n'])
this_specimen_interpretation["dir_tilt_correction"] = coord
methods = methods.replace(" ", "")
if "T" in units:
methods = methods + ":LP-DIR-AF"
if "K" in units:
methods = methods + ":LP-DIR-T"
if "J" in units:
methods = methods + ":LP-DIR-M"
this_specimen_interpretation["method_codes"] = methods.strip(
':')
this_specimen_interpretation[
"experiments"] = this_specimen_measurements.experiment.unique(
)[0]
#
# print some stuff
#
if calculation_type != 'DE-FM':
this_specimen_interpretation["dir_mad_free"] = '%7.1f' % (
mpars['specimen_mad'])
this_specimen_interpretation["dir_alpha95"] = ''
if verbose:
if units == 'K':
print(
'%s %i %7.1f %7.1f %7.1f %7.1f %7.1f %7.1f %s \n'
%
(this_specimen_interpretation["specimen"],
int(this_specimen_interpretation[
"dir_n_measurements"]),
float(this_specimen_interpretation[
"dir_mad_free"]),
float(
this_specimen_interpretation["dir_dang"]),
float(this_specimen_interpretation[
"meas_step_min"]) - 273,
float(this_specimen_interpretation[
"meas_step_max"]) - 273,
float(
this_specimen_interpretation["dir_dec"]),
float(
this_specimen_interpretation["dir_inc"]),
calculation_type))
elif units == 'T':
print(
'%s %i %7.1f %7.1f %7.1f %7.1f %7.1f %7.1f %s \n'
%
(this_specimen_interpretation["specimen"],
int(this_specimen_interpretation[
"dir_n_measurements"]),
float(this_specimen_interpretation[
"dir_mad_free"]),
float(
this_specimen_interpretation["dir_dang"]),
float(this_specimen_interpretation[
"meas_step_min"]) * 1e3,
float(this_specimen_interpretation[
"meas_step_max"]) * 1e3,
float(
this_specimen_interpretation["dir_dec"]),
float(
this_specimen_interpretation["dir_inc"]),
calculation_type))
elif 'T' in units and 'K' in units:
if float(this_specimen_interpretation[
'meas_step_min']) < 1.0:
min = float(this_specimen_interpretation[
'meas_step_min']) * 1e3
else:
min = float(this_specimen_interpretation[
'meas_step_min']) - 273
if float(this_specimen_interpretation[
'meas_step_max']) < 1.0:
max = float(this_specimen_interpretation[
'meas_step_max']) * 1e3
else:
max = float(this_specimen_interpretation[
'meas_step_max']) - 273
print(
'%s %i %7.1f %i %i %7.1f %7.1f %7.1f %s \n' %
(this_specimen_interpretation["specimen"],
int(this_specimen_interpretation[
"dir_n_measurements"]),
float(this_specimen_interpretation[
"dir_mad_free"]),
float(
this_specimen_interpretation["dir_dang"]),
min, max,
float(
this_specimen_interpretation["dir_dec"]),
float(
this_specimen_interpretation["dir_inc"]),
calculation_type))
else:
print(
'%s %i %7.1f %7.1f %7.1f %7.1f %7.1f %7.1f %s \n'
%
(this_specimen_interpretation["specimen"],
int(this_specimen_interpretation[
"dir_n_measurements"]),
float(this_specimen_interpretation[
"dir_mad_free"]),
float(
this_specimen_interpretation["dir_dang"]),
float(this_specimen_interpretation[
"meas_step_min"]),
float(this_specimen_interpretation[
"meas_step_max"]),
float(
this_specimen_interpretation["dir_dec"]),
float(
this_specimen_interpretation["dir_inc"]),
calculation_type))
else:
this_specimen_interpretation["dir_alpha95"] = '%7.1f' % (
mpars['specimen_alpha95'])
this_specimen_interpretation["dir_mad_free"] = ''
if verbose:
if 'K' in units:
print(
'%s %i %7.1f %7.1f %7.1f %7.1f %7.1f %7.1f %s \n'
%
(this_specimen_interpretation["specimen"],
int(this_specimen_interpretation[
"dir_n_measurments"]),
float(this_specimen_interpretation[
"dir_mad_free"]),
float(
this_specimen_interpretation["dir_dang"]),
float(this_specimen_interpretation[
"meas_step_min"]) - 273,
float(this_specimen_interpretation[
"meas_step_max"]) - 273,
float(
this_specimen_interpretation["dir_dec"]),
float(
this_specimen_interpretation["dir_inc"]),
calculation_type))
elif 'T' in units:
print(
'%s %i %7.1f %7.1f %7.1f %7.1f %7.1f %7.1f %s \n'
%
(this_specimen_interpretation["specimen"],
int(this_specimen_interpretation[
"dir_n_measurements"]),
float(this_specimen_interpretation[
"dir_alpha95"]),
float(
this_specimen_interpretation["dir_dang"]),
float(this_specimen_interpretation[
"meas_step_min"]) * 1e3,
float(this_specimen_interpretation[
"meas_step_max"]) * 1e3,
float(
this_specimen_interpretation["dir_dec"]),
float(
this_specimen_interpretation["dir_inc"]),
calculation_type))
elif 'T' in units and 'K' in units:
if float(this_specimen_interpretation[
'meas_step_min']) < 1.0:
min = float(this_specimen_interpretation[
'meas_step_min']) * 1e3
else:
min = float(this_specimen_interpretation[
'meas_step_min']) - 273
if float(this_specimen_interpretation[
'meas_step_max']) < 1.0:
max = float(this_specimen_interpretation[
'meas_step_max']) * 1e3
else:
max = float(this_specimen_interpretation[
'meas_step_max']) - 273
print('%s %i %7.1f %i %i %7.1f %7.1f %s \n' % (
this_specimen_interpretation["specimen"],
int(this_specimen_interpretation[
"dir_n_measurements"]),
float(
this_specimen_interpretation["dir_alpha95"]
), min, max,
float(this_specimen_interpretation["dir_dec"]),
float(this_specimen_interpretation["dir_inc"]),
calculation_type))
else:
print(
'%s %i %7.1f %7.1f %7.1f %7.1f %7.1f %s \n' %
(this_specimen_interpretation["specimen"],
int(this_specimen_interpretation[
"dir_n_measurements"]),
float(this_specimen_interpretation[
"dir_alpha95"]),
float(this_specimen_interpretation[
"meas_step_min"]),
float(this_specimen_interpretation[
"meas_step_max"]),
float(
this_specimen_interpretation["dir_dec"]),
float(
this_specimen_interpretation["dir_inc"]),
calculation_type))
if verbose:
saveit = input("Save this interpretation? [y]/n \n")
# START HERE
#
# if len(current_spec_data)==0: # no interpretations yet for this session
# print "no current interpretation"
# beg_pca,end_pca="",""
# calculation_type=""
# get the ones that meet the current coordinate system
else:
print("no data")
if verbose:
input('Ready for next specimen ')
k += 1
def main():
"""
NAME
foldtest.py
DESCRIPTION
does a fold test (Tauxe, 2010) on data
INPUT FORMAT
dec inc dip_direction dip
SYNTAX
foldtest.py [command line options]
OPTIONS
-h prints help message and quits
-f FILE file with input data
-F FILE for confidence bounds on fold test
-u ANGLE (circular standard deviation) for uncertainty on bedding poles
-b MIN MAX bounds for quick search of percent untilting [default is -10 to 150%]
-n NB number of bootstrap samples [default is 1000]
-fmt FMT, specify format - default is svg
-sav save figures and quit
INPUT FILE
Dec Inc Dip_Direction Dip in space delimited file
OUTPUT PLOTS
Geographic: is an equal area projection of the input data in
original coordinates
Stratigraphic: is an equal area projection of the input data in
tilt adjusted coordinates
% Untilting: The dashed (red) curves are representative plots of
maximum eigenvalue (tau_1) as a function of untilting
The solid line is the cumulative distribution of the
% Untilting required to maximize tau for all the
bootstrapped data sets. The dashed vertical lines
are 95% confidence bounds on the % untilting that yields
the most clustered result (maximum tau_1).
Command line: prints out the bootstrapped iterations and
finally the confidence bounds on optimum untilting.
If the 95% conf bounds include 0, then a post-tilt magnetization is indicated
If the 95% conf bounds include 100, then a pre-tilt magnetization is indicated
If the 95% conf bounds exclude both 0 and 100, syn-tilt magnetization is
possible as is vertical axis rotation or other pathologies
Geographic: is an equal area projection of the input data in
OPTIONAL OUTPUT FILE:
The output file has the % untilting within the 95% confidence bounds
nd the number of bootstrap samples
"""
kappa=0
fmt,plot='svg',0
nb=1000 # number of bootstraps
min,max=-10,150
if '-h' in sys.argv: # check if help is needed
print(main.__doc__)
sys.exit() # graceful quit
if '-F' in sys.argv:
ind=sys.argv.index('-F')
outfile=open(sys.argv[ind+1],'w')
else:
outfile=""
if '-f' in sys.argv:
ind=sys.argv.index('-f')
file=sys.argv[ind+1]
DIDDs=numpy.loadtxt(file)
else:
print(main.__doc__)
sys.exit()
if '-fmt' in sys.argv:
ind=sys.argv.index('-fmt')
fmt=sys.argv[ind+1]
if '-sav' in sys.argv:plot=1
if '-b' in sys.argv:
ind=sys.argv.index('-b')
min=int(sys.argv[ind+1])
max=int(sys.argv[ind+2])
if '-n' in sys.argv:
ind=sys.argv.index('-n')
nb=int(sys.argv[ind+1])
if '-u' in sys.argv:
ind=sys.argv.index('-u')
csd=float(sys.argv[ind+1])
kappa=(81. / csd)**2
#
# get to work
#
PLTS={'geo':1,'strat':2,'taus':3} # make plot dictionary
pmagplotlib.plot_init(PLTS['geo'],5,5)
pmagplotlib.plot_init(PLTS['strat'],5,5)
pmagplotlib.plot_init(PLTS['taus'],5,5)
pmagplotlib.plot_eq(PLTS['geo'],DIDDs,'Geographic')
D,I=pmag.dotilt_V(DIDDs)
TCs=numpy.array([D,I]).transpose()
pmagplotlib.plot_eq(PLTS['strat'],TCs,'Stratigraphic')
if not set_env.IS_WIN:
if plot==0:pmagplotlib.draw_figs(PLTS)
Percs=list(range(min,max))
Cdf,Untilt=[],[]
pylab.figure(num=PLTS['taus'])
print('doing ',nb,' iterations...please be patient.....')
for n in range(nb): # do bootstrap data sets - plot first 25 as dashed red line
if n%50==0:print(n)
Taus=[] # set up lists for taus
PDs=pmag.pseudo(DIDDs)
if kappa!=0:
for k in range(len(PDs)):
d,i=pmag.fshdev(kappa)
dipdir,dip=pmag.dodirot(d,i,PDs[k][2],PDs[k][3])
PDs[k][2]=dipdir
PDs[k][3]=dip
for perc in Percs:
tilt=numpy.array([1.,1.,1.,0.01*perc])
D,I=pmag.dotilt_V(PDs*tilt)
TCs=numpy.array([D,I]).transpose()
ppars=pmag.doprinc(TCs) # get principal directions
Taus.append(ppars['tau1'])
if n<25:pylab.plot(Percs,Taus,'r--')
Untilt.append(Percs[Taus.index(numpy.max(Taus))]) # tilt that gives maximum tau
Cdf.append(float(n) / float(nb))
pylab.plot(Percs,Taus,'k')
pylab.xlabel('% Untilting')
pylab.ylabel('tau_1 (red), CDF (green)')
Untilt.sort() # now for CDF of tilt of maximum tau
pylab.plot(Untilt,Cdf,'g')
lower=int(.025*nb)
upper=int(.975*nb)
pylab.axvline(x=Untilt[lower],ymin=0,ymax=1,linewidth=1,linestyle='--')
pylab.axvline(x=Untilt[upper],ymin=0,ymax=1,linewidth=1,linestyle='--')
tit= '%i - %i %s'%(Untilt[lower],Untilt[upper],'Percent Unfolding')
print(tit)
print('range of all bootstrap samples: ', Untilt[0], ' - ', Untilt[-1])
pylab.title(tit)
outstring= '%i - %i; %i\n'%(Untilt[lower],Untilt[upper],nb)
if outfile!="":outfile.write(outstring)
files={}
for key in list(PLTS.keys()):
files[key]=('foldtest_'+'%s'%(key.strip()[:2])+'.'+fmt)
if plot==0:
pmagplotlib.draw_figs(PLTS)
ans= input('S[a]ve all figures, <Return> to quit ')
if ans!='a':
print("Good bye")
sys.exit()
pmagplotlib.save_plots(PLTS,files)
def main():
"""
NAME
foldtest_magic.py
DESCRIPTION
does a fold test (Tauxe, 2010) on data
INPUT FORMAT
pmag_specimens format file, er_samples.txt format file (for bedding)
SYNTAX
foldtest_magic.py [command line options]
OPTIONS
-h prints help message and quits
-f sites formatted file [default for 3.0 is sites.txt, for 2.5, pmag_sites.txt]
-fsa samples formatted file
-fsi sites formatted file
-exc use criteria to set acceptance criteria (supported only for data model 3)
-n NB, set number of bootstraps, default is 1000
-b MIN, MAX, set bounds for untilting, default is -10, 150
-fmt FMT, specify format - default is svg
-sav saves plots and quits
-DM NUM MagIC data model number (2 or 3, default 3)
OUTPUT
Geographic: is an equal area projection of the input data in
original coordinates
Stratigraphic: is an equal area projection of the input data in
tilt adjusted coordinates
% Untilting: The dashed (red) curves are representative plots of
maximum eigenvalue (tau_1) as a function of untilting
The solid line is the cumulative distribution of the
% Untilting required to maximize tau for all the
bootstrapped data sets. The dashed vertical lines
are 95% confidence bounds on the % untilting that yields
the most clustered result (maximum tau_1).
Command line: prints out the bootstrapped iterations and
finally the confidence bounds on optimum untilting.
If the 95% conf bounds include 0, then a pre-tilt magnetization is indicated
If the 95% conf bounds include 100, then a post-tilt magnetization is indicated
If the 95% conf bounds exclude both 0 and 100, syn-tilt magnetization is
possible as is vertical axis rotation or other pathologies
"""
if '-h' in sys.argv: # check if help is needed
print(main.__doc__)
sys.exit() # graceful quit
kappa = 0
dir_path = pmag.get_named_arg("-WD", ".")
nboot = int(float(pmag.get_named_arg("-n", 1000))) # number of bootstraps
fmt = pmag.get_named_arg("-fmt", "svg")
data_model_num = int(float(pmag.get_named_arg("-DM", 3)))
if data_model_num == 3:
infile = pmag.get_named_arg("-f", 'sites.txt')
orfile = 'samples.txt'
site_col = 'site'
dec_col = 'dir_dec'
inc_col = 'dir_inc'
tilt_col = 'dir_tilt_correction'
dipkey, azkey = 'bed_dip', 'bed_dip_direction'
crit_col = 'criterion'
critfile = 'criteria.txt'
else:
infile = pmag.get_named_arg("-f", 'pmag_sites.txt')
orfile = 'er_samples.txt'
site_col = 'er_site_name'
dec_col = 'site_dec'
inc_col = 'site_inc'
tilt_col = 'site_tilt_correction'
dipkey, azkey = 'sample_bed_dip', 'sample_bed_dip_direction'
crit_col = 'pmag_criteria_code'
critfile = 'pmag_criteria.txt'
if '-sav' in sys.argv:
plot = 1
else:
plot = 0
if '-b' in sys.argv:
ind = sys.argv.index('-b')
untilt_min = int(sys.argv[ind+1])
untilt_max = int(sys.argv[ind+2])
else:
untilt_min, untilt_max = -10, 150
if '-fsa' in sys.argv:
orfile = pmag.get_named_arg("-fsa", "")
elif '-fsi' in sys.argv:
orfile = pmag.get_named_arg("-fsi", "")
if data_model_num == 3:
dipkey, azkey = 'bed_dip', 'bed_dip_direction'
else:
dipkey, azkey = 'site_bed_dip', 'site_bed_dip_direction'
else:
if data_model_num == 3:
orfile = 'sites.txt'
else:
orfile = 'pmag_sites.txt'
orfile = pmag.resolve_file_name(orfile, dir_path)
infile = pmag.resolve_file_name(infile, dir_path)
critfile = pmag.resolve_file_name(critfile, dir_path)
df = pd.read_csv(infile, sep='\t', header=1)
# keep only records with tilt_col
data = df.copy()
data = data[data[tilt_col].notnull()]
data = data.where(data.notnull(), "")
# turn into pmag data list
data = list(data.T.apply(dict))
# get orientation data
if data_model_num == 3:
# often orientation will be in infile (sites table)
if os.path.split(orfile)[1] == os.path.split(infile)[1]:
ordata = df[df[azkey].notnull()]
ordata = ordata[ordata[dipkey].notnull()]
ordata = list(ordata.T.apply(dict))
# sometimes orientation might be in a sample file instead
else:
ordata = pd.read_csv(orfile, sep='\t', header=1)
ordata = list(ordata.T.apply(dict))
else:
ordata, file_type = pmag.magic_read(orfile)
if '-exc' in sys.argv:
crits, file_type = pmag.magic_read(critfile)
SiteCrits = []
for crit in crits:
if crit[crit_col] == "DE-SITE":
SiteCrits.append(crit)
#break
# get to work
#
PLTS = {'geo': 1, 'strat': 2, 'taus': 3} # make plot dictionary
if not set_env.IS_WIN:
pmagplotlib.plot_init(PLTS['geo'], 5, 5)
pmagplotlib.plot_init(PLTS['strat'], 5, 5)
pmagplotlib.plot_init(PLTS['taus'], 5, 5)
if data_model_num == 2:
GEOrecs = pmag.get_dictitem(data, tilt_col, '0', 'T')
else:
GEOrecs = data
if len(GEOrecs) > 0: # have some geographic data
num_dropped = 0
DIDDs = [] # set up list for dec inc dip_direction, dip
for rec in GEOrecs: # parse data
dip, dip_dir = 0, -1
Dec = float(rec[dec_col])
Inc = float(rec[inc_col])
orecs = pmag.get_dictitem(
ordata, site_col, rec[site_col], 'T')
if len(orecs) > 0:
if orecs[0][azkey] != "":
dip_dir = float(orecs[0][azkey])
if orecs[0][dipkey] != "":
dip = float(orecs[0][dipkey])
if dip != 0 and dip_dir != -1:
if '-exc' in sys.argv:
keep = 1
for site_crit in SiteCrits:
crit_name = site_crit['table_column'].split('.')[1]
if crit_name and crit_name in rec.keys() and rec[crit_name]:
# get the correct operation (<, >=, =, etc.)
op = OPS[site_crit['criterion_operation']]
# then make sure the site record passes
if op(float(rec[crit_name]), float(site_crit['criterion_value'])):
keep = 0
if keep == 1:
DIDDs.append([Dec, Inc, dip_dir, dip])
else:
num_dropped += 1
else:
DIDDs.append([Dec, Inc, dip_dir, dip])
if num_dropped:
print("-W- Dropped {} records because each failed one or more criteria".format(num_dropped))
else:
print('no geographic directional data found')
sys.exit()
pmagplotlib.plot_eq(PLTS['geo'], DIDDs, 'Geographic')
data = np.array(DIDDs)
D, I = pmag.dotilt_V(data)
TCs = np.array([D, I]).transpose()
pmagplotlib.plot_eq(PLTS['strat'], TCs, 'Stratigraphic')
if plot == 0:
pmagplotlib.draw_figs(PLTS)
Percs = list(range(untilt_min, untilt_max))
Cdf, Untilt = [], []
plt.figure(num=PLTS['taus'])
print('doing ', nboot, ' iterations...please be patient.....')
for n in range(nboot): # do bootstrap data sets - plot first 25 as dashed red line
if n % 50 == 0:
print(n)
Taus = [] # set up lists for taus
PDs = pmag.pseudo(DIDDs)
if kappa != 0:
for k in range(len(PDs)):
d, i = pmag.fshdev(kappa)
dipdir, dip = pmag.dodirot(d, i, PDs[k][2], PDs[k][3])
PDs[k][2] = dipdir
PDs[k][3] = dip
for perc in Percs:
tilt = np.array([1., 1., 1., 0.01*perc])
D, I = pmag.dotilt_V(PDs*tilt)
TCs = np.array([D, I]).transpose()
ppars = pmag.doprinc(TCs) # get principal directions
Taus.append(ppars['tau1'])
if n < 25:
plt.plot(Percs, Taus, 'r--')
# tilt that gives maximum tau
Untilt.append(Percs[Taus.index(np.max(Taus))])
Cdf.append(float(n) / float(nboot))
plt.plot(Percs, Taus, 'k')
plt.xlabel('% Untilting')
plt.ylabel('tau_1 (red), CDF (green)')
Untilt.sort() # now for CDF of tilt of maximum tau
plt.plot(Untilt, Cdf, 'g')
lower = int(.025*nboot)
upper = int(.975*nboot)
plt.axvline(x=Untilt[lower], ymin=0, ymax=1, linewidth=1, linestyle='--')
plt.axvline(x=Untilt[upper], ymin=0, ymax=1, linewidth=1, linestyle='--')
tit = '%i - %i %s' % (Untilt[lower], Untilt[upper], 'Percent Unfolding')
print(tit)
plt.title(tit)
if plot == 0:
pmagplotlib.draw_figs(PLTS)
ans = input('S[a]ve all figures, <Return> to quit \n ')
if ans != 'a':
print("Good bye")
sys.exit()
files = {}
for key in list(PLTS.keys()):
files[key] = ('foldtest_'+'%s' % (key.strip()[:2])+'.'+fmt)
pmagplotlib.save_plots(PLTS, files)
def main():
"""
NAME
di_rot.py
DESCRIPTION
rotates set of directions to new coordinate system
SYNTAX
di_rot.py [command line options]
OPTIONS
-h prints help message and quits
-f specify input file, default is standard input
-F specify output file, default is standard output
-D D specify Dec of new coordinate system, default is 0
-I I specify Inc of new coordinate system, default is 90
INTPUT/OUTPUT
dec inc [space delimited]
"""
D,I=0.,90.
outfile=""
infile=""
if '-h' in sys.argv:
print main.__doc__
sys.exit()
if '-f' in sys.argv:
ind=sys.argv.index('-f')
infile=sys.argv[ind+1]
data=numpy.loadtxt(infile)
else:
data=numpy.loadtxt(sys.stdin,dtype=numpy.float)
if '-F' in sys.argv:
ind=sys.argv.index('-F')
outfile=sys.argv[ind+1]
out=open(outfile,'w')
if '-D' in sys.argv:
ind=sys.argv.index('-D')
D=float(sys.argv[ind+1])
if '-I' in sys.argv:
ind=sys.argv.index('-I')
I=float(sys.argv[ind+1])
if len(data.shape)>1: # 2-D array
N=data.shape[0]
DipDir,Dip=numpy.ones(N,dtype=numpy.float).transpose()*(D-180.),numpy.ones(N,dtype=numpy.float).transpose()*(90.-I)
data=data.transpose()
data=numpy.array([data[0],data[1],DipDir ,Dip]).transpose()
drot,irot=pmag.dotilt_V(data)
drot=(drot-180.)%360. #
for k in range(N):
if outfile=="":
print '%7.1f %7.1f ' % (drot[k],irot[k])
else:
out.write('%7.1f %7.1f\n' % (drot[k],irot[k]))
else:
d,i=pmag.dotilt(data[0],data[1],(D-180.),90.-I)
if outfile=="":
print '%7.1f %7.1f ' % ((d-180.)%360.,i)
else:
out.write('%7.1f %7.1f\n' % ((d-180.)%360.,i))
def main():
"""
NAME
foldtest.py
DESCRIPTION
does a fold test (Tauxe, 2010) on data
INPUT FORMAT
dec inc dip_direction dip
SYNTAX
foldtest.py [command line options]
OPTIONS
-h prints help message and quits
-f FILE file with input data
-F FILE for confidence bounds on fold test
-u ANGLE (circular standard deviation) for uncertainty on bedding poles
-b MIN MAX bounds for quick search of percent untilting [default is -10 to 150%]
-n NB number of bootstrap samples [default is 1000]
-fmt FMT, specify format - default is svg
-sav save figures and quit
INPUT FILE
Dec Inc Dip_Direction Dip in space delimited file
OUTPUT PLOTS
Geographic: is an equal area projection of the input data in
original coordinates
Stratigraphic: is an equal area projection of the input data in
tilt adjusted coordinates
% Untilting: The dashed (red) curves are representative plots of
maximum eigenvalue (tau_1) as a function of untilting
The solid line is the cumulative distribution of the
% Untilting required to maximize tau for all the
bootstrapped data sets. The dashed vertical lines
are 95% confidence bounds on the % untilting that yields
the most clustered result (maximum tau_1).
Command line: prints out the bootstrapped iterations and
finally the confidence bounds on optimum untilting.
If the 95% conf bounds include 0, then a post-tilt magnetization is indicated
If the 95% conf bounds include 100, then a pre-tilt magnetization is indicated
If the 95% conf bounds exclude both 0 and 100, syn-tilt magnetization is
possible as is vertical axis rotation or other pathologies
Geographic: is an equal area projection of the input data in
OPTIONAL OUTPUT FILE:
The output file has the % untilting within the 95% confidence bounds
nd the number of bootstrap samples
"""
kappa = 0
fmt, plot = 'svg', 0
nb = 1000 # number of bootstraps
min, max = -10, 150
if '-h' in sys.argv: # check if help is needed
print main.__doc__
sys.exit() # graceful quit
if '-F' in sys.argv:
ind = sys.argv.index('-F')
outfile = open(sys.argv[ind + 1], 'w')
else:
outfile = ""
if '-f' in sys.argv:
ind = sys.argv.index('-f')
file = sys.argv[ind + 1]
DIDDs = numpy.loadtxt(file)
else:
print main.__doc__
sys.exit()
if '-fmt' in sys.argv:
ind = sys.argv.index('-fmt')
fmt = sys.argv[ind + 1]
if '-sav' in sys.argv: plot = 1
if '-b' in sys.argv:
ind = sys.argv.index('-b')
min = int(sys.argv[ind + 1])
max = int(sys.argv[ind + 2])
if '-n' in sys.argv:
ind = sys.argv.index('-n')
nb = int(sys.argv[ind + 1])
if '-u' in sys.argv:
ind = sys.argv.index('-u')
csd = float(sys.argv[ind + 1])
kappa = (81. / csd)**2
#
# get to work
#
PLTS = {'geo': 1, 'strat': 2, 'taus': 3} # make plot dictionary
pmagplotlib.plot_init(PLTS['geo'], 5, 5)
pmagplotlib.plot_init(PLTS['strat'], 5, 5)
pmagplotlib.plot_init(PLTS['taus'], 5, 5)
pmagplotlib.plotEQ(PLTS['geo'], DIDDs, 'Geographic')
D, I = pmag.dotilt_V(DIDDs)
TCs = numpy.array([D, I]).transpose()
pmagplotlib.plotEQ(PLTS['strat'], TCs, 'Stratigraphic')
if plot == 0: pmagplotlib.drawFIGS(PLTS)
Percs = range(min, max)
Cdf, Untilt = [], []
pylab.figure(num=PLTS['taus'])
print 'doing ', nb, ' iterations...please be patient.....'
for n in range(
nb): # do bootstrap data sets - plot first 25 as dashed red line
if n % 50 == 0: print n
Taus = [] # set up lists for taus
PDs = pmag.pseudo(DIDDs)
if kappa != 0:
for k in range(len(PDs)):
d, i = pmag.fshdev(kappa)
dipdir, dip = pmag.dodirot(d, i, PDs[k][2], PDs[k][3])
PDs[k][2] = dipdir
PDs[k][3] = dip
for perc in Percs:
tilt = numpy.array([1., 1., 1., 0.01 * perc])
D, I = pmag.dotilt_V(PDs * tilt)
TCs = numpy.array([D, I]).transpose()
ppars = pmag.doprinc(TCs) # get principal directions
Taus.append(ppars['tau1'])
if n < 25: pylab.plot(Percs, Taus, 'r--')
Untilt.append(Percs[Taus.index(
numpy.max(Taus))]) # tilt that gives maximum tau
Cdf.append(float(n) / float(nb))
pylab.plot(Percs, Taus, 'k')
pylab.xlabel('% Untilting')
pylab.ylabel('tau_1 (red), CDF (green)')
Untilt.sort() # now for CDF of tilt of maximum tau
pylab.plot(Untilt, Cdf, 'g')
lower = int(.025 * nb)
upper = int(.975 * nb)
pylab.axvline(x=Untilt[lower], ymin=0, ymax=1, linewidth=1, linestyle='--')
pylab.axvline(x=Untilt[upper], ymin=0, ymax=1, linewidth=1, linestyle='--')
tit = '%i - %i %s' % (Untilt[lower], Untilt[upper], 'Percent Unfolding')
print tit
print 'range of all bootstrap samples: ', Untilt[0], ' - ', Untilt[-1]
pylab.title(tit)
outstring = '%i - %i; %i\n' % (Untilt[lower], Untilt[upper], nb)
if outfile != "": outfile.write(outstring)
files = {}
for key in PLTS.keys():
files[key] = ('foldtest_' + '%s' % (key.strip()[:2]) + '.' + fmt)
if plot == 0:
pmagplotlib.drawFIGS(PLTS)
ans = raw_input('S[a]ve all figures, <Return> to quit ')
if ans != 'a':
print "Good bye"
sys.exit()
pmagplotlib.saveP(PLTS, files)
def main():
"""
NAME
foldtest_magic.py
DESCRIPTION
does a fold test (Tauxe, 2010) on data
INPUT FORMAT
pmag_specimens format file, er_samples.txt format file (for bedding)
SYNTAX
foldtest_magic.py [command line options]
OPTIONS
-h prints help message and quits
-f sites formatted file [default for 3.0 is sites.txt, for 2.5, pmag_sites.txt]
-fsa samples formatted file
-fsi sites formatted file
-exc use criteria to set acceptance criteria (supported only for data model 3)
-n NB, set number of bootstraps, default is 1000
-b MIN, MAX, set bounds for untilting, default is -10, 150
-fmt FMT, specify format - default is svg
-sav saves plots and quits
-DM NUM MagIC data model number (2 or 3, default 3)
OUTPUT
Geographic: is an equal area projection of the input data in
original coordinates
Stratigraphic: is an equal area projection of the input data in
tilt adjusted coordinates
% Untilting: The dashed (red) curves are representative plots of
maximum eigenvalue (tau_1) as a function of untilting
The solid line is the cumulative distribution of the
% Untilting required to maximize tau for all the
bootstrapped data sets. The dashed vertical lines
are 95% confidence bounds on the % untilting that yields
the most clustered result (maximum tau_1).
Command line: prints out the bootstrapped iterations and
finally the confidence bounds on optimum untilting.
If the 95% conf bounds include 0, then a pre-tilt magnetization is indicated
If the 95% conf bounds include 100, then a post-tilt magnetization is indicated
If the 95% conf bounds exclude both 0 and 100, syn-tilt magnetization is
possible as is vertical axis rotation or other pathologies
"""
if '-h' in sys.argv: # check if help is needed
print(main.__doc__)
sys.exit() # graceful quit
kappa = 0
dir_path = pmag.get_named_arg("-WD", ".")
nboot = int(float(pmag.get_named_arg("-n", 1000))) # number of bootstraps
fmt = pmag.get_named_arg("-fmt", "svg")
data_model_num = int(float(pmag.get_named_arg("-DM", 3)))
if data_model_num == 3:
infile = pmag.get_named_arg("-f", 'sites.txt')
orfile = 'samples.txt'
site_col = 'site'
dec_col = 'dir_dec'
inc_col = 'dir_inc'
tilt_col = 'dir_tilt_correction'
dipkey, azkey = 'bed_dip', 'bed_dip_direction'
crit_col = 'criterion'
critfile = 'criteria.txt'
else:
infile = pmag.get_named_arg("-f", 'pmag_sites.txt')
orfile = 'er_samples.txt'
site_col = 'er_site_name'
dec_col = 'site_dec'
inc_col = 'site_inc'
tilt_col = 'site_tilt_correction'
dipkey, azkey = 'sample_bed_dip', 'sample_bed_dip_direction'
crit_col = 'pmag_criteria_code'
critfile = 'pmag_criteria.txt'
if '-sav' in sys.argv:
plot = 1
else:
plot = 0
if '-b' in sys.argv:
ind = sys.argv.index('-b')
untilt_min = int(sys.argv[ind + 1])
untilt_max = int(sys.argv[ind + 2])
else:
untilt_min, untilt_max = -10, 150
if '-fsa' in sys.argv:
orfile = pmag.get_named_arg("-fsa", "")
elif '-fsi' in sys.argv:
orfile = pmag.get_named_arg("-fsi", "")
if data_model_num == 3:
dipkey, azkey = 'bed_dip', 'bed_dip_direction'
else:
dipkey, azkey = 'site_bed_dip', 'site_bed_dip_direction'
else:
if data_model_num == 3:
orfile = 'sites.txt'
else:
orfile = 'pmag_sites.txt'
orfile = pmag.resolve_file_name(orfile, dir_path)
infile = pmag.resolve_file_name(infile, dir_path)
critfile = pmag.resolve_file_name(critfile, dir_path)
df = pd.read_csv(infile, sep='\t', header=1)
# keep only records with tilt_col
data = df.copy()
data = data[data[tilt_col].notnull()]
data = data.where(data.notnull(), "")
# turn into pmag data list
data = list(data.T.apply(dict))
# get orientation data
if data_model_num == 3:
# often orientation will be in infile (sites table)
if os.path.split(orfile)[1] == os.path.split(infile)[1]:
ordata = df[df[azkey].notnull()]
ordata = ordata[ordata[dipkey].notnull()]
ordata = list(ordata.T.apply(dict))
# sometimes orientation might be in a sample file instead
else:
ordata = pd.read_csv(orfile, sep='\t', header=1)
ordata = list(ordata.T.apply(dict))
else:
ordata, file_type = pmag.magic_read(orfile)
if '-exc' in sys.argv:
crits, file_type = pmag.magic_read(critfile)
SiteCrits = []
for crit in crits:
if crit[crit_col] == "DE-SITE":
SiteCrits.append(crit)
#break
# get to work
#
PLTS = {'geo': 1, 'strat': 2, 'taus': 3} # make plot dictionary
if not set_env.IS_WIN:
pmagplotlib.plot_init(PLTS['geo'], 5, 5)
pmagplotlib.plot_init(PLTS['strat'], 5, 5)
pmagplotlib.plot_init(PLTS['taus'], 5, 5)
if data_model_num == 2:
GEOrecs = pmag.get_dictitem(data, tilt_col, '0', 'T')
else:
GEOrecs = data
if len(GEOrecs) > 0: # have some geographic data
num_dropped = 0
DIDDs = [] # set up list for dec inc dip_direction, dip
for rec in GEOrecs: # parse data
dip, dip_dir = 0, -1
Dec = float(rec[dec_col])
Inc = float(rec[inc_col])
orecs = pmag.get_dictitem(ordata, site_col, rec[site_col], 'T')
if len(orecs) > 0:
if orecs[0][azkey] != "":
dip_dir = float(orecs[0][azkey])
if orecs[0][dipkey] != "":
dip = float(orecs[0][dipkey])
if dip != 0 and dip_dir != -1:
if '-exc' in sys.argv:
keep = 1
for site_crit in SiteCrits:
crit_name = site_crit['table_column'].split('.')[1]
if crit_name and crit_name in rec.keys(
) and rec[crit_name]:
# get the correct operation (<, >=, =, etc.)
op = OPS[site_crit['criterion_operation']]
# then make sure the site record passes
if op(float(rec[crit_name]),
float(site_crit['criterion_value'])):
keep = 0
if keep == 1:
DIDDs.append([Dec, Inc, dip_dir, dip])
else:
num_dropped += 1
else:
DIDDs.append([Dec, Inc, dip_dir, dip])
if num_dropped:
print(
"-W- Dropped {} records because each failed one or more criteria"
.format(num_dropped))
else:
print('no geographic directional data found')
sys.exit()
pmagplotlib.plot_eq(PLTS['geo'], DIDDs, 'Geographic')
data = np.array(DIDDs)
D, I = pmag.dotilt_V(data)
TCs = np.array([D, I]).transpose()
pmagplotlib.plot_eq(PLTS['strat'], TCs, 'Stratigraphic')
if plot == 0:
pmagplotlib.draw_figs(PLTS)
Percs = list(range(untilt_min, untilt_max))
Cdf, Untilt = [], []
plt.figure(num=PLTS['taus'])
print('doing ', nboot, ' iterations...please be patient.....')
for n in range(
nboot
): # do bootstrap data sets - plot first 25 as dashed red line
if n % 50 == 0:
print(n)
Taus = [] # set up lists for taus
PDs = pmag.pseudo(DIDDs)
if kappa != 0:
for k in range(len(PDs)):
d, i = pmag.fshdev(kappa)
dipdir, dip = pmag.dodirot(d, i, PDs[k][2], PDs[k][3])
PDs[k][2] = dipdir
PDs[k][3] = dip
for perc in Percs:
tilt = np.array([1., 1., 1., 0.01 * perc])
D, I = pmag.dotilt_V(PDs * tilt)
TCs = np.array([D, I]).transpose()
ppars = pmag.doprinc(TCs) # get principal directions
Taus.append(ppars['tau1'])
if n < 25:
plt.plot(Percs, Taus, 'r--')
# tilt that gives maximum tau
Untilt.append(Percs[Taus.index(np.max(Taus))])
Cdf.append(float(n) / float(nboot))
plt.plot(Percs, Taus, 'k')
plt.xlabel('% Untilting')
plt.ylabel('tau_1 (red), CDF (green)')
Untilt.sort() # now for CDF of tilt of maximum tau
plt.plot(Untilt, Cdf, 'g')
lower = int(.025 * nboot)
upper = int(.975 * nboot)
plt.axvline(x=Untilt[lower], ymin=0, ymax=1, linewidth=1, linestyle='--')
plt.axvline(x=Untilt[upper], ymin=0, ymax=1, linewidth=1, linestyle='--')
tit = '%i - %i %s' % (Untilt[lower], Untilt[upper], 'Percent Unfolding')
print(tit)
plt.title(tit)
if plot == 0:
pmagplotlib.draw_figs(PLTS)
ans = input('S[a]ve all figures, <Return> to quit \n ')
if ans != 'a':
print("Good bye")
sys.exit()
files = {}
for key in list(PLTS.keys()):
files[key] = ('foldtest_' + '%s' % (key.strip()[:2]) + '.' + fmt)
pmagplotlib.save_plots(PLTS, files)
def main():
"""
NAME
zeq_magic.py
DESCRIPTION
reads in magic_measurements formatted file, makes plots of remanence decay
during demagnetization experiments. Reads in prior interpretations saved in
a pmag_specimens formatted file [and allows re-interpretations of best-fit lines
and planes and saves (revised or new) interpretations in a pmag_specimens file.
interpretations are saved in the coordinate system used. Also allows judicious editting of
measurements to eliminate "bad" measurements. These are marked as such in the magic_measurements
input file. they are NOT deleted, just ignored. ] Bracketed part not yet implemented
SYNTAX
zeq_magic.py [command line options]
OPTIONS
-h prints help message and quits
-f MEASFILE: sets measurements format input file, default: measurements.txt
-fsp SPECFILE: sets specimens format file with prior interpreations, default: specimens.txt
-fsa SAMPFILE: sets samples format file sample=>site information, default: samples.txt
-fsi SITEFILE: sets sites format file with site=>location informationprior interpreations, default: samples.txt
-Fp PLTFILE: sets filename for saved plot, default is name_type.fmt (where type is zijd, eqarea or decay curve)
-crd [s,g,t]: sets coordinate system, g=geographic, t=tilt adjusted, default: specimen coordinate system
-spc SPEC plots single specimen SPEC, saves plot with specified format
with optional -dir settings and quits
-dir [L,P,F][beg][end]: sets calculation type for principal component analysis, default is none
beg: starting step for PCA calculation
end: ending step for PCA calculation
[L,P,F]: calculation type for line, plane or fisher mean
must be used with -spc option
-fmt FMT: set format of saved plot [png,svg,jpg]
-A: suppresses averaging of replicate measurements, default is to average
-sav: saves all plots without review
SCREEN OUTPUT:
Specimen, N, a95, StepMin, StepMax, Dec, Inc, calculation type
"""
# initialize some variables
doave, e, b = 1, 0, 0 # average replicates, initial end and beginning step
intlist = ['magn_moment', 'magn_volume', 'magn_mass', 'magnitude']
plots, coord = 0, 's'
noorient = 0
version_num = pmag.get_version()
verbose = pmagplotlib.verbose
calculation_type, fmt = "", "svg"
user, spec_keys, locname = "", [], ''
geo, tilt, ask = 0, 0, 0
PriorRecs = [] # empty list for prior interpretations
backup = 0
specimen = "" # can skip everything and just plot one specimen with bounds e,b
if '-h' in sys.argv:
print(main.__doc__)
sys.exit()
dir_path = pmag.get_named_arg_from_sys("-WD", default_val=os.getcwd())
meas_file = pmag.get_named_arg_from_sys(
"-f", default_val="measurements.txt")
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")
#meas_file = os.path.join(dir_path, meas_file)
#spec_file = os.path.join(dir_path, spec_file)
#samp_file = os.path.join(dir_path, samp_file)
#site_file = os.path.join(dir_path, site_file)
plot_file = pmag.get_named_arg_from_sys("-Fp", default_val="")
crd = pmag.get_named_arg_from_sys("-crd", default_val="s")
if crd == "s":
coord = "-1"
elif crd == "t":
coord = "100"
else:
coord = "0"
fmt = pmag.get_named_arg_from_sys("-fmt", "svg")
specimen = pmag.get_named_arg_from_sys("-spc", default_val="")
beg_pca, end_pca = "", ""
if '-dir' in sys.argv:
ind = sys.argv.index('-dir')
direction_type = sys.argv[ind + 1]
beg_pca = int(sys.argv[ind + 2])
end_pca = int(sys.argv[ind + 3])
if direction_type == 'L':
calculation_type = 'DE-BFL'
if direction_type == 'P':
calculation_type = 'DE-BFP'
if direction_type == 'F':
calculation_type = 'DE-FM'
if '-A' in sys.argv:
doave = 0
if '-sav' in sys.argv:
plots, verbose = 1, 0
#
first_save = 1
fnames = {'measurements': meas_file, 'specimens': spec_file,
'samples': samp_file, 'sites': site_file}
contribution = nb.Contribution(dir_path, custom_filenames=fnames, read_tables=[
'measurements', 'specimens', 'samples', 'sites'])
#
# import specimens
specimen_cols = ['analysts', 'aniso_ftest', 'aniso_ftest12', 'aniso_ftest23', 'aniso_s', 'aniso_s_mean', 'aniso_s_n_measurements', 'aniso_s_sigma', 'aniso_s_unit', 'aniso_tilt_correction', 'aniso_type', 'aniso_v1', 'aniso_v2', 'aniso_v3', 'citations', 'description', 'dir_alpha95', 'dir_comp', 'dir_dec', 'dir_inc', 'dir_mad_free', 'dir_n_measurements', 'dir_tilt_correction', 'experiments', 'geologic_classes',
'geologic_types', 'hyst_bc', 'hyst_bcr', 'hyst_mr_moment', 'hyst_ms_moment', 'int_abs', 'int_b', 'int_b_beta', 'int_b_sigma', 'int_corr', 'int_dang', 'int_drats', 'int_f', 'int_fvds', 'int_gamma', 'int_mad_free', 'int_md', 'int_n_measurements', 'int_n_ptrm', 'int_q', 'int_rsc', 'int_treat_dc_field', 'lithologies', 'meas_step_max', 'meas_step_min', 'meas_step_unit', 'method_codes', 'sample', 'software_packages', 'specimen']
if 'specimens' in contribution.tables:
# contribution.propagate_name_down('sample','measurements')
spec_container = contribution.tables['specimens']
if 'method_codes' not in spec_container.df.columns:
spec_container.df['method_codes'] = None
prior_spec_data = spec_container.get_records_for_code(
'LP-DIR', strict_match=False) # look up all prior directional interpretations
#
# tie sample names to measurement data
#
else:
spec_container, prior_spec_data = None, []
#
# import samples for orientation info
#
if ('samples' in contribution.tables) and ('specimens' in contribution.tables):
# contribution.propagate_name_down('site','measurements')
contribution.propagate_cols(col_names=[
'azimuth', 'dip', 'orientation_quality'], target_df_name='measurements', source_df_name='samples')
#
# define figure numbers for equal area, zijderveld,
# and intensity vs. demagnetiztion step respectively
#
ZED = {}
ZED['eqarea'], ZED['zijd'], ZED['demag'] = 1, 2, 3
pmagplotlib.plot_init(ZED['eqarea'], 6, 6)
pmagplotlib.plot_init(ZED['zijd'], 6, 6)
pmagplotlib.plot_init(ZED['demag'], 6, 6)
# save_pca=0
angle, direction_type, setangle = "", "", 0
# create measurement dataframe
#
meas_container = contribution.tables['measurements']
meas_data = meas_container.df
#
meas_data = meas_data[meas_data['method_codes'].str.contains(
'LT-NO|LT-AF-Z|LT-T-Z|LT-M-Z') == True] # fish out steps for plotting
meas_data = meas_data[meas_data['method_codes'].str.contains(
'AN|ARM|LP-TRM|LP-PI-ARM') == False] # strip out unwanted experiments
intensity_types = [
col_name for col_name in meas_data.columns if col_name in intlist]
intensity_types = [
col_name for col_name in intensity_types if any(meas_data[col_name])]
if not len(intensity_types):
print('-W- No intensity columns found')
return
# plot first non-empty intensity method found - normalized to initial value anyway -
# doesn't matter which used
int_key = intensity_types[0]
# get all the non-null intensity records of the same type
meas_data = meas_data[meas_data[int_key].notnull()]
if 'flag' not in meas_data.columns:
meas_data['flag'] = 'g' # set the default flag to good
# need to treat LP-NO specially for af data, treatment should be zero,
# otherwise 273.
meas_data['treatment'] = meas_data['treat_ac_field'].where(
cond=meas_data['treat_ac_field'] != 0, other=meas_data['treat_temp'])
meas_data['ZI'] = 1 # initialize these to one
meas_data['instrument_codes'] = "" # initialize these to blank
# for unusual case of microwave power....
if 'treat_mw_power' in meas_data.columns:
meas_data.loc[
(meas_data.treat_mw_power != 0) &
(meas_data.treat_mw_power) &
(meas_data.treat_mw_time),
'treatment'] = meas_data.treat_mw_power * meas_data.treat_mw_time
#
# get list of unique specimen names from measurement data
#
# this is a list of all the specimen names
specimen_names = meas_data.specimen.unique()
specimen_names = specimen_names.tolist()
specimen_names.sort()
#
# set up new DataFrame for this sessions specimen interpretations
#
data_container = nb.MagicDataFrame(
dtype='specimens', columns=specimen_cols)
# this is for interpretations from this session
current_spec_data = data_container.df
locname = 'LookItUp'
if specimen == "":
k = 0
else:
k = specimen_names.index(specimen)
# let's look at the data now
while k < len(specimen_names):
mpars = None
# set the current specimen for plotting
this_specimen = specimen_names[k]
# reset beginning/end pca if plotting more than one specimen
if not specimen:
beg_pca, end_pca = "", ""
if verbose and this_specimen != "":
print(this_specimen, k + 1, 'out of ', len(specimen_names))
if setangle == 0:
angle = ""
this_specimen_measurements = meas_data[meas_data['specimen'].str.contains(
this_specimen) == True] # fish out this specimen
this_specimen_measurements = this_specimen_measurements[this_specimen_measurements['flag'].str.contains(
'g') == True] # fish out this specimen
if len(this_specimen_measurements) != 0: # if there are measurements
#
# set up datablock [[treatment,dec, inc, int, direction_type],[....]]
#
#
# figure out the method codes
#
units, methods, title = "", "", this_specimen
# this is a list of all the specimen method codes`
meas_meths = this_specimen_measurements.method_codes.unique()
tr = pd.to_numeric(this_specimen_measurements.treatment).tolist()
if set(tr) == set([0]):
k += 1
continue
for m in meas_meths:
if 'LT-AF-Z' in m:
units = 'T' # units include tesla
tr[0] = 0
if 'LT-T-Z' in m:
units = units + ":K" # units include kelvin
if 'LT-M-Z' in m:
units = units + ':J' # units include joules
tr[0] = 0
units = units.strip(':') # strip off extra colons
if 'LP-' in m:
methods = methods + ":" + m
decs = pd.to_numeric(this_specimen_measurements.dir_dec).tolist()
incs = pd.to_numeric(this_specimen_measurements.dir_inc).tolist()
#
# fix the coordinate system
#
if coord != '-1': # need to transform coordinates to geographic
azimuths = pd.to_numeric(
this_specimen_measurements.azimuth).tolist() # get the azimuths
# get the azimuths
dips = pd.to_numeric(this_specimen_measurements.dip).tolist()
dirs = [decs, incs, azimuths, dips]
# this transposes the columns and rows of the list of lists
dirs_geo = np.array(list(map(list, list(zip(*dirs)))))
decs, incs = pmag.dogeo_V(dirs_geo)
if coord == '100': # need to do tilt correction too
bed_dip_dirs = pd.to_numeric(
this_specimen_measurements.bed_dip_dir).tolist() # get the azimuths
bed_dips = pd.to_numeric(
this_specimen_measurements.bed_dip).tolist() # get the azimuths
dirs = [decs, incs, bed_dip_dirs, bed_dips]
# this transposes the columns and rows of the list of lists
dirs_tilt = np.array(list(map(list, list(zip(*dirs)))))
decs, incs = pmag.dotilt_V(dirs_tilt)
title = title + '_t'
else:
title = title + '_g'
if angle == "":
angle = decs[0]
ints = pd.to_numeric(this_specimen_measurements[int_key]).tolist()
ZI = this_specimen_measurements.ZI.tolist()
flags = this_specimen_measurements.flag.tolist()
codes = this_specimen_measurements.instrument_codes.tolist()
datalist = [tr, decs, incs, ints, ZI, flags, codes]
# this transposes the columns and rows of the list of lists
datablock = list(map(list, list(zip(*datalist))))
pmagplotlib.plotZED(ZED, datablock, angle, title, units)
if verbose:
pmagplotlib.drawFIGS(ZED)
#
# collect info for current_specimen_interpretation dictionary
#
#
# find prior interpretation
#
prior_specimen_interpretations = prior_spec_data[prior_spec_data['specimen'].str.contains(this_specimen) == True]
if (beg_pca == "") and (len(prior_specimen_interpretations) != 0):
if len(prior_specimen_interpretations)>0:
beg_pcas = pd.to_numeric(
prior_specimen_interpretations.meas_step_min.values).tolist()
end_pcas = pd.to_numeric(
prior_specimen_interpretations.meas_step_max.values).tolist()
spec_methods = prior_specimen_interpretations.method_codes.tolist()
# step through all prior interpretations and plot them
for ind in range(len(beg_pcas)):
spec_meths = spec_methods[ind].split(':')
for m in spec_meths:
if 'DE-BFL' in m:
calculation_type = 'DE-BFL' # best fit line
if 'DE-BFP' in m:
calculation_type = 'DE-BFP' # best fit plane
if 'DE-FM' in m:
calculation_type = 'DE-FM' # fisher mean
if 'DE-BFL-A' in m:
calculation_type = 'DE-BFL-A' # anchored best fit line
start, end = tr.index(beg_pcas[ind]), tr.index(
end_pcas[ind]) # getting the starting and ending points
# calculate direction/plane
mpars = pmag.domean(
datablock, start, end, calculation_type)
if mpars["specimen_direction_type"] != "Error":
# put it on the plot
pmagplotlib.plotDir(ZED, mpars, datablock, angle)
if verbose:
pmagplotlib.drawFIGS(ZED)
else:
try:
start, end = int(beg_pca), int(end_pca)
except ValueError:
beg_pca = 0
end_pca = len(datablock) - 1
start, end = int(beg_pca), int(end_pca)
# calculate direction/plane
try:
mpars = pmag.domean(datablock, start, end, calculation_type)
except Exception as ex:
print('-I- Problem with {}'.format(this_specimen))
print(' ', ex)
print(' Skipping')
k += 1
continue
if mpars["specimen_direction_type"] != "Error":
# put it on the plot
pmagplotlib.plotDir(ZED, mpars, datablock, angle)
if verbose:
pmagplotlib.drawFIGS(ZED)
if plots == 1 or specimen != "":
if plot_file == "":
basename = title
else:
basename = plot_file
files = {}
for key in list(ZED.keys()):
files[key] = basename + '_' + key + '.' + fmt
pmagplotlib.saveP(ZED, files)
if specimen != "":
sys.exit()
if verbose:
recnum = 0
for plotrec in datablock:
if units == 'T':
print('%s: %i %7.1f %s %8.3e %7.1f %7.1f %s' % (
plotrec[5], recnum, plotrec[0] * 1e3, " mT", plotrec[3], plotrec[1], plotrec[2], plotrec[6]))
if units == "K":
print('%s: %i %7.1f %s %8.3e %7.1f %7.1f %s' % (
plotrec[5], recnum, plotrec[0] - 273, ' C', plotrec[3], plotrec[1], plotrec[2], plotrec[6]))
if units == "J":
print('%s: %i %7.1f %s %8.3e %7.1f %7.1f %s' % (
plotrec[5], recnum, plotrec[0], ' J', plotrec[3], plotrec[1], plotrec[2], plotrec[6]))
if 'K' in units and 'T' in units:
if plotrec[0] >= 1.:
print('%s: %i %7.1f %s %8.3e %7.1f %7.1f %s' % (
plotrec[5], recnum, plotrec[0] - 273, ' C', plotrec[3], plotrec[1], plotrec[2], plotrec[6]))
if plotrec[0] < 1.:
print('%s: %i %7.1f %s %8.3e %7.1f %7.1f %s' % (
plotrec[5], recnum, plotrec[0] * 1e3, " mT", plotrec[3], plotrec[1], plotrec[2], plotrec[6]))
recnum += 1
# we have a current interpretation
elif mpars["specimen_direction_type"] != "Error":
#
# create a new specimen record for the interpreation for this
# specimen
this_specimen_interpretation = {
col: "" for col in specimen_cols}
# this_specimen_interpretation["analysts"]=user
this_specimen_interpretation['software_packages'] = version_num
this_specimen_interpretation['specimen'] = this_specimen
this_specimen_interpretation["method_codes"] = calculation_type
this_specimen_interpretation["meas_step_unit"] = units
this_specimen_interpretation["meas_step_min"] = tr[start]
this_specimen_interpretation["meas_step_max"] = tr[end]
this_specimen_interpretation["dir_dec"] = '%7.1f' % (
mpars['specimen_dec'])
this_specimen_interpretation["dir_inc"] = '%7.1f' % (
mpars['specimen_inc'])
this_specimen_interpretation["dir_dang"] = '%7.1f' % (
mpars['specimen_dang'])
this_specimen_interpretation["dir_n_measurements"] = '%i' % (
mpars['specimen_n'])
this_specimen_interpretation["dir_tilt_correction"] = coord
methods = methods.replace(" ", "")
if "T" in units:
methods = methods + ":LP-DIR-AF"
if "K" in units:
methods = methods + ":LP-DIR-T"
if "J" in units:
methods = methods + ":LP-DIR-M"
this_specimen_interpretation["method_codes"] = methods.strip(
':')
this_specimen_interpretation["experiments"] = this_specimen_measurements.experiment.unique()[
0]
#
# print some stuff
#
if calculation_type != 'DE-FM':
this_specimen_interpretation["dir_mad_free"] = '%7.1f' % (
mpars['specimen_mad'])
this_specimen_interpretation["dir_alpha95"] = ''
if verbose:
if units == 'K':
print('%s %i %7.1f %7.1f %7.1f %7.1f %7.1f %7.1f %s \n' % (this_specimen_interpretation["specimen"], int(this_specimen_interpretation["dir_n_measurements"]), float(this_specimen_interpretation["dir_mad_free"]), float(this_specimen_interpretation["dir_dang"]), float(
this_specimen_interpretation["meas_step_min"]) - 273, float(this_specimen_interpretation["meas_step_max"]) - 273, float(this_specimen_interpretation["dir_dec"]), float(this_specimen_interpretation["dir_inc"]), calculation_type))
elif units == 'T':
print('%s %i %7.1f %7.1f %7.1f %7.1f %7.1f %7.1f %s \n' % (this_specimen_interpretation["specimen"], int(this_specimen_interpretation["dir_n_measurements"]), float(this_specimen_interpretation["dir_mad_free"]), float(this_specimen_interpretation["dir_dang"]), float(
this_specimen_interpretation["meas_step_min"]) * 1e3, float(this_specimen_interpretation["meas_step_max"]) * 1e3, float(this_specimen_interpretation["dir_dec"]), float(this_specimen_interpretation["dir_inc"]), calculation_type))
elif 'T' in units and 'K' in units:
if float(this_specimen_interpretation['meas_step_min']) < 1.0:
min = float(
this_specimen_interpretation['meas_step_min']) * 1e3
else:
min = float(
this_specimen_interpretation['meas_step_min']) - 273
if float(this_specimen_interpretation['meas_step_max']) < 1.0:
max = float(
this_specimen_interpretation['meas_step_max']) * 1e3
else:
max = float(
this_specimen_interpretation['meas_step_max']) - 273
print('%s %i %7.1f %i %i %7.1f %7.1f %7.1f %s \n' % (this_specimen_interpretation["specimen"], int(this_specimen_interpretation["dir_n_measurements"]), float(this_specimen_interpretation["dir_mad_free"]), float(
this_specimen_interpretation["dir_dang"]), min, max, float(this_specimen_interpretation["dir_dec"]), float(this_specimen_interpretation["dir_inc"]), calculation_type))
else:
print('%s %i %7.1f %7.1f %7.1f %7.1f %7.1f %7.1f %s \n' % (this_specimen_interpretation["specimen"], int(this_specimen_interpretation["dir_n_measurements"]), float(this_specimen_interpretation["dir_mad_free"]), float(this_specimen_interpretation["dir_dang"]), float(
this_specimen_interpretation["meas_step_min"]), float(this_specimen_interpretation["meas_step_max"]), float(this_specimen_interpretation["dir_dec"]), float(this_specimen_interpretation["dir_inc"]), calculation_type))
else:
this_specimen_interpretation["dir_alpha95"] = '%7.1f' % (
mpars['specimen_alpha95'])
this_specimen_interpretation["dir_mad_free"] = ''
if verbose:
if 'K' in units:
print('%s %i %7.1f %7.1f %7.1f %7.1f %7.1f %7.1f %s \n' % (this_specimen_interpretation["specimen"], int(this_specimen_interpretation["dir_n_measurments"]), float(this_specimen_interpretation["dir_mad_free"]), float(this_specimen_interpretation["dir_dang"]), float(
this_specimen_interpretation["meas_step_min"]) - 273, float(this_specimen_interpretation["meas_step_max"]) - 273, float(this_specimen_interpretation["dir_dec"]), float(this_specimen_interpretation["dir_inc"]), calculation_type))
elif 'T' in units:
print('%s %i %7.1f %7.1f %7.1f %7.1f %7.1f %7.1f %s \n' % (this_specimen_interpretation["specimen"], int(this_specimen_interpretation["dir_n_measurements"]), float(this_specimen_interpretation["dir_alpha95"]), float(this_specimen_interpretation["dir_dang"]), float(
this_specimen_interpretation["meas_step_min"]) * 1e3, float(this_specimen_interpretation["meas_step_max"]) * 1e3, float(this_specimen_interpretation["dir_dec"]), float(this_specimen_interpretation["dir_inc"]), calculation_type))
elif 'T' in units and 'K' in units:
if float(this_specimen_interpretation['meas_step_min']) < 1.0:
min = float(
this_specimen_interpretation['meas_step_min']) * 1e3
else:
min = float(
this_specimen_interpretation['meas_step_min']) - 273
if float(this_specimen_interpretation['meas_step_max']) < 1.0:
max = float(
this_specimen_interpretation['meas_step_max']) * 1e3
else:
max = float(
this_specimen_interpretation['meas_step_max']) - 273
print('%s %i %7.1f %i %i %7.1f %7.1f %s \n' % (this_specimen_interpretation["specimen"], int(this_specimen_interpretation["dir_n_measurements"]), float(
this_specimen_interpretation["dir_alpha95"]), min, max, float(this_specimen_interpretation["dir_dec"]), float(this_specimen_interpretation["dir_inc"]), calculation_type))
else:
print('%s %i %7.1f %7.1f %7.1f %7.1f %7.1f %s \n' % (this_specimen_interpretation["specimen"], int(this_specimen_interpretation["dir_n_measurements"]), float(this_specimen_interpretation["dir_alpha95"]), float(
this_specimen_interpretation["meas_step_min"]), float(this_specimen_interpretation["meas_step_max"]), float(this_specimen_interpretation["dir_dec"]), float(this_specimen_interpretation["dir_inc"]), calculation_type))
if verbose:
saveit = input("Save this interpretation? [y]/n \n")
# START HERE
#
# if len(current_spec_data)==0: # no interpretations yet for this session
# print "no current interpretation"
# beg_pca,end_pca="",""
# calculation_type=""
# get the ones that meet the current coordinate system
else:
print("no data")
if verbose:
res = input(' <return> for next specimen, [q]uit ')
if res == 'q':
return
k += 1
def main():
"""
NAME
zeq_magic.py
DESCRIPTION
reads in magic_measurements formatted file, makes plots of remanence decay
during demagnetization experiments. Reads in prior interpretations saved in
a pmag_specimens formatted file [and allows re-interpretations of best-fit lines
and planes and saves (revised or new) interpretations in a pmag_specimens file.
interpretations are saved in the coordinate system used. Also allows judicious editting of
measurements to eliminate "bad" measurements. These are marked as such in the magic_measurements
input file. they are NOT deleted, just ignored. ] Bracketed part not yet implemented
SYNTAX
zeq_magic.py [command line options]
OPTIONS
-h prints help message and quits
-f MEASFILE: sets measurements format input file, default: measurements.txt
-fsp SPECFILE: sets specimens format file with prior interpreations, default: specimens.txt
-fsa SAMPFILE: sets samples format file sample=>site information, default: samples.txt
-fsi SITEFILE: sets sites format file with site=>location informationprior interpreations, default: samples.txt
-Fp PLTFILE: sets filename for saved plot, default is name_type.fmt (where type is zijd, eqarea or decay curve)
-crd [s,g,t]: sets coordinate system, g=geographic, t=tilt adjusted, default: specimen coordinate system
-spc SPEC plots single specimen SPEC, saves plot with specified format
with optional -dir settings and quits
-dir [L,P,F][beg][end]: sets calculation type for principal component analysis, default is none
beg: starting step for PCA calculation
end: ending step for PCA calculation
[L,P,F]: calculation type for line, plane or fisher mean
must be used with -spc option
-fmt FMT: set format of saved plot [png,svg,jpg]
-A: suppresses averaging of replicate measurements, default is to average
-sav: saves all plots without review
SCREEN OUTPUT:
Specimen, N, a95, StepMin, StepMax, Dec, Inc, calculation type
"""
# initialize some variables
doave, e, b = 1, 0, 0 # average replicates, initial end and beginning step
intlist = ['magn_moment', 'magn_volume', 'magn_mass', 'magnitude']
plots, coord = 0, 's'
noorient = 0
version_num = pmag.get_version()
verbose = pmagplotlib.verbose
calculation_type, fmt = "", "svg"
spec_keys = []
geo, tilt, ask = 0, 0, 0
PriorRecs = [] # empty list for prior interpretations
backup = 0
specimen = "" # can skip everything and just plot one specimen with bounds e,b
if '-h' in sys.argv:
print(main.__doc__)
sys.exit()
dir_path = pmag.get_named_arg("-WD", default_val=os.getcwd())
meas_file = pmag.get_named_arg("-f", default_val="measurements.txt")
spec_file = pmag.get_named_arg("-fsp", default_val="specimens.txt")
samp_file = pmag.get_named_arg("-fsa", default_val="samples.txt")
site_file = pmag.get_named_arg("-fsi", default_val="sites.txt")
#meas_file = os.path.join(dir_path, meas_file)
#spec_file = os.path.join(dir_path, spec_file)
#samp_file = os.path.join(dir_path, samp_file)
#site_file = os.path.join(dir_path, site_file)
plot_file = pmag.get_named_arg("-Fp", default_val="")
crd = pmag.get_named_arg("-crd", default_val="s")
if crd == "s":
coord = "-1"
elif crd == "t":
coord = "100"
else:
coord = "0"
saved_coord = coord
fmt = pmag.get_named_arg("-fmt", "svg")
specimen = pmag.get_named_arg("-spc", default_val="")
#if specimen: # just save plot and exit
# plots, verbose = 1, 0
beg_pca, end_pca = "", ""
if '-dir' in sys.argv:
ind = sys.argv.index('-dir')
direction_type = sys.argv[ind + 1]
beg_pca = int(sys.argv[ind + 2])
end_pca = int(sys.argv[ind + 3])
if direction_type == 'L':
calculation_type = 'DE-BFL'
if direction_type == 'P':
calculation_type = 'DE-BFP'
if direction_type == 'F':
calculation_type = 'DE-FM'
if '-A' in sys.argv:
doave = 0
if '-sav' in sys.argv:
plots, verbose = 1, 0
#
first_save = 1
fnames = {
'measurements': meas_file,
'specimens': spec_file,
'samples': samp_file,
'sites': site_file
}
contribution = cb.Contribution(
dir_path,
custom_filenames=fnames,
read_tables=['measurements', 'specimens', 'samples', 'sites'])
#
# import specimens
if 'measurements' not in contribution.tables:
print('-W- No measurements table found in your working directory')
return
specimen_cols = [
'analysts', 'aniso_ftest', 'aniso_ftest12', 'aniso_ftest23', 'aniso_s',
'aniso_s_mean', 'aniso_s_n_measurements', 'aniso_s_sigma',
'aniso_s_unit', 'aniso_tilt_correction', 'aniso_type', 'aniso_v1',
'aniso_v2', 'aniso_v3', 'citations', 'description', 'dir_alpha95',
'dir_comp', 'dir_dec', 'dir_inc', 'dir_mad_free', 'dir_n_measurements',
'dir_tilt_correction', 'experiments', 'geologic_classes',
'geologic_types', 'hyst_bc', 'hyst_bcr', 'hyst_mr_moment',
'hyst_ms_moment', 'int_abs', 'int_b', 'int_b_beta', 'int_b_sigma',
'int_corr', 'int_dang', 'int_drats', 'int_f', 'int_fvds', 'int_gamma',
'int_mad_free', 'int_md', 'int_n_measurements', 'int_n_ptrm', 'int_q',
'int_rsc', 'int_treat_dc_field', 'lithologies', 'meas_step_max',
'meas_step_min', 'meas_step_unit', 'method_codes', 'sample',
'software_packages', 'specimen'
]
if 'specimens' in contribution.tables:
contribution.propagate_name_down('sample', 'measurements')
# add location/site info to measurements table for naming plots
if pmagplotlib.isServer:
contribution.propagate_name_down('site', 'measurements')
contribution.propagate_name_down('location', 'measurements')
spec_container = contribution.tables['specimens']
if 'method_codes' not in spec_container.df.columns:
spec_container.df['method_codes'] = None
prior_spec_data = spec_container.get_records_for_code(
'LP-DIR', strict_match=False
) # look up all prior directional interpretations
#
# tie sample names to measurement data
#
else:
spec_container, prior_spec_data = None, []
#
# import samples for orientation info
#
if 'samples' in contribution.tables:
samp_container = contribution.tables['samples']
samps = samp_container.df
samp_data = samps.to_dict(
'records'
) # convert to list of dictionaries for use with get_orient
else:
samp_data = []
#if ('samples' in contribution.tables) and ('specimens' in contribution.tables):
# # contribution.propagate_name_down('site','measurements')
# contribution.propagate_cols(col_names=[
# 'azimuth', 'dip', 'orientation_quality','bed_dip','bed_dip_direction'], target_df_name='measurements', source_df_name='samples')
##
# define figure numbers for equal area, zijderveld,
# and intensity vs. demagnetiztion step respectively
#
ZED = {}
ZED['eqarea'], ZED['zijd'], ZED['demag'] = 1, 2, 3
pmagplotlib.plot_init(ZED['eqarea'], 6, 6)
pmagplotlib.plot_init(ZED['zijd'], 6, 6)
pmagplotlib.plot_init(ZED['demag'], 6, 6)
# save_pca=0
angle, direction_type, setangle = "", "", 0
# create measurement dataframe
#
meas_container = contribution.tables['measurements']
meas_data = meas_container.df
#
meas_data = meas_data[meas_data['method_codes'].str.contains(
'LT-NO|LT-AF-Z|LT-T-Z|LT-M-Z') == True] # fish out steps for plotting
meas_data = meas_data[meas_data['method_codes'].str.contains(
'AN|ARM|LP-TRM|LP-PI-ARM') == False] # strip out unwanted experiments
intensity_types = [
col_name for col_name in meas_data.columns if col_name in intlist
]
intensity_types = [
col_name for col_name in intensity_types if any(meas_data[col_name])
]
if not len(intensity_types):
print('-W- No intensity columns found')
return
# plot first non-empty intensity method found - normalized to initial value anyway -
# doesn't matter which used
int_key = intensity_types[0]
# get all the non-null intensity records of the same type
meas_data = meas_data[meas_data[int_key].notnull()]
if 'quality' not in meas_data.columns:
meas_data['quality'] = 'g' # set the default flag to good
# need to treat LP-NO specially for af data, treatment should be zero,
# otherwise 273.
#meas_data['treatment'] = meas_data['treat_ac_field'].where(
# cond=meas_data['treat_ac_field'] != 0, other=meas_data['treat_temp'])
meas_data['treatment'] = meas_data['treat_ac_field'].where(
cond=meas_data['treat_ac_field'].astype(bool),
other=meas_data['treat_temp'])
meas_data['ZI'] = 1 # initialize these to one
meas_data['instrument_codes'] = "" # initialize these to blank
# for unusual case of microwave power....
if 'treat_mw_power' in meas_data.columns:
meas_data.loc[
(meas_data.treat_mw_power != 0) & (meas_data.treat_mw_power) &
(meas_data.treat_mw_time),
'treatment'] = meas_data.treat_mw_power * meas_data.treat_mw_time
#
# get list of unique specimen names from measurement data
#
# this is a list of all the specimen names
specimen_names = meas_data.specimen.unique()
specimen_names = specimen_names.tolist()
specimen_names.sort()
#
# set up new DataFrame for this sessions specimen interpretations
#
data_container = cb.MagicDataFrame(dtype='specimens',
columns=specimen_cols)
# this is for interpretations from this session
current_spec_data = data_container.df
if specimen == "":
k = 0
else:
k = specimen_names.index(specimen)
# let's look at the data now
while k < len(specimen_names):
mpars = {"specimen_direction_type": "Error"}
# set the current specimen for plotting
this_specimen = specimen_names[k]
# reset beginning/end pca if plotting more than one specimen
if not specimen:
beg_pca, end_pca = "", ""
if verbose and this_specimen != "":
print(this_specimen, k + 1, 'out of ', len(specimen_names))
if setangle == 0:
angle = ""
this_specimen_measurements = meas_data[
meas_data['specimen'].str.contains(this_specimen).astype(
bool)] # fish out this specimen
this_specimen_measurements = this_specimen_measurements[
-this_specimen_measurements['quality'].str.contains('b').astype(
bool)] # remove bad measurements
if len(this_specimen_measurements) != 0: # if there are measurements
meas_list = this_specimen_measurements.to_dict(
'records') # get a list of dictionaries
this_sample = ""
if coord != '-1' and 'sample' in meas_list[0].keys(
): # look up sample name
this_sample = pmag.get_dictitem(meas_list, 'specimen',
this_specimen, 'T')
if len(this_sample) > 0:
this_sample = this_sample[0]['sample']
#
# set up datablock [[treatment,dec, inc, int, direction_type],[....]]
#
#
# figure out the method codes
#
units, methods, title = "", "", this_specimen
if pmagplotlib.isServer:
try:
loc = this_specimen_measurements.loc[:,
'location'].values[0]
except:
loc = ""
try:
site = this_specimen_measurements.loc[:, 'site'].values[0]
except:
site = ""
try:
samp = this_specimen_measurements.loc[:,
'sample'].values[0]
except:
samp = ""
title = "LO:_{}_SI:_{}_SA:_{}_SP:_{}_".format(
loc, site, samp, this_specimen)
# this is a list of all the specimen method codes
meas_meths = this_specimen_measurements.method_codes.unique()
tr = pd.to_numeric(this_specimen_measurements.treatment).tolist()
if any(cb.is_null(treat, False) for treat in tr):
print(
'-W- Missing required values in measurements.treatment for {}, skipping'
.format(this_specimen))
if specimen:
return
k += 1
continue
if set(tr) == set([0]):
print(
'-W- Missing required values in measurements.treatment for {}, skipping'
.format(this_specimen))
if specimen:
return
k += 1
continue
for m in meas_meths:
if 'LT-AF-Z' in m and 'T' not in units:
units = 'T' # units include tesla
tr[0] = 0
if 'LT-T-Z' in m and 'K' not in units:
units = units + ":K" # units include kelvin
if 'LT-M-Z' in m and 'J' not in units:
units = units + ':J' # units include joules
tr[0] = 0
units = units.strip(':') # strip off extra colons
if 'LP-' in m:
methods = methods + ":" + m
decs = pd.to_numeric(this_specimen_measurements.dir_dec).tolist()
incs = pd.to_numeric(this_specimen_measurements.dir_inc).tolist()
#
# fix the coordinate system
#
# revert to original coordinate system
coord = saved_coord
if coord != '-1': # need to transform coordinates to geographic
# get the azimuth
or_info, az_type = pmag.get_orient(samp_data,
this_sample,
data_model=3)
if 'azimuth' in or_info.keys() and cb.not_null(
or_info['azimuth']):
#azimuths = pd.to_numeric(
# this_specimen_measurements.azimuth).tolist()
#dips = pd.to_numeric(this_specimen_measurements.dip).tolist()
azimuths = len(decs) * [or_info['azimuth']]
dips = len(decs) * [or_info['dip']]
# if azimuth/dip is missing, plot using specimen coordinates instead
else:
azimuths, dips = [], []
if any([cb.is_null(az) for az in azimuths if az != 0]):
coord = '-1'
print("-W- Couldn't find azimuth and dip for {}".format(
this_specimen))
print(" Plotting with specimen coordinates instead")
elif any([cb.is_null(dip) for dip in dips if dip != 0]):
coord = '-1'
print("-W- Couldn't find azimuth and dip for {}".format(
this_specimen))
print(" Plotting with specimen coordinates instead")
else:
coord = saved_coord
# if azimuth and dip were found, continue with geographic coordinates
if coord != "-1" and len(azimuths) > 0:
dirs = [decs, incs, azimuths, dips]
# this transposes the columns and rows of the list of lists
dirs_geo = np.array(list(map(list, list(zip(*dirs)))))
decs, incs = pmag.dogeo_V(dirs_geo)
if coord == '100' and 'bed_dip_direction' in or_info.keys(
) and or_info[
'bed_dip_direction'] != "": # need to do tilt correction too
bed_dip_dirs = len(decs) * [
or_info['bed_dip_direction']
]
bed_dips = len(decs) * [or_info['bed_dip']]
#bed_dip_dirs = pd.to_numeric(
# this_specimen_measurements.bed_dip_direction).tolist() # get the azimuths
#bed_dips = pd.to_numeric(
# this_specimen_measurements.bed_dip).tolist() # get the azimuths
dirs = [decs, incs, bed_dip_dirs, bed_dips]
## this transposes the columns and rows of the list of lists
dirs_tilt = np.array(list(map(list, list(zip(*dirs)))))
decs, incs = pmag.dotilt_V(dirs_tilt)
if pmagplotlib.isServer:
title = title + "CO:_t_"
else:
title = title + '_t'
else:
if pmagplotlib.isServer:
title = title + "CO:_g_"
else:
title = title + '_g'
if angle == "":
angle = decs[0]
ints = pd.to_numeric(this_specimen_measurements[int_key]).tolist()
ZI = this_specimen_measurements.ZI.tolist()
flags = this_specimen_measurements.quality.tolist()
codes = this_specimen_measurements.instrument_codes.tolist()
datalist = [tr, decs, incs, ints, ZI, flags, codes]
# this transposes the columns and rows of the list of lists
datablock = list(map(list, list(zip(*datalist))))
pmagplotlib.plot_zed(ZED, datablock, angle, title, units)
if verbose and not set_env.IS_WIN:
pmagplotlib.draw_figs(ZED)
#
# collect info for current_specimen_interpretation dictionary
#
#
# find prior interpretation
#
prior_specimen_interpretations = []
if len(prior_spec_data):
prior_specimen_interpretations = prior_spec_data[
prior_spec_data['specimen'].str.contains(
this_specimen) == True]
if (beg_pca == "") and (len(prior_specimen_interpretations) != 0):
if len(prior_specimen_interpretations) > 0:
beg_pcas = pd.to_numeric(prior_specimen_interpretations.
meas_step_min.values).tolist()
end_pcas = pd.to_numeric(prior_specimen_interpretations.
meas_step_max.values).tolist()
spec_methods = prior_specimen_interpretations.method_codes.tolist(
)
# step through all prior interpretations and plot them
for ind in range(len(beg_pcas)):
spec_meths = spec_methods[ind].split(':')
for m in spec_meths:
if 'DE-BFL' in m:
calculation_type = 'DE-BFL' # best fit line
if 'DE-BFP' in m:
calculation_type = 'DE-BFP' # best fit plane
if 'DE-FM' in m:
calculation_type = 'DE-FM' # fisher mean
if 'DE-BFL-A' in m:
calculation_type = 'DE-BFL-A' # anchored best fit line
if len(beg_pcas) != 0:
try:
# getting the starting and ending points
start, end = tr.index(beg_pcas[ind]), tr.index(
end_pcas[ind])
mpars = pmag.domean(datablock, start, end,
calculation_type)
except ValueError:
print(
'-W- Specimen record contains invalid start/stop bounds:'
)
mpars['specimen_direction_type'] = "Error"
# calculate direction/plane
if mpars["specimen_direction_type"] != "Error":
# put it on the plot
pmagplotlib.plot_dir(ZED, mpars, datablock,
angle)
if verbose and not set_env.IS_WIN:
pmagplotlib.draw_figs(ZED)
### SKIP if no prior interpretation - this section should not be used:
# else:
# try:
# start, end = int(beg_pca), int(end_pca)
# except ValueError:
# beg_pca = 0
# end_pca = len(datablock) - 1
# start, end = int(beg_pca), int(end_pca)
# # # calculate direction/plane
# try:
# mpars = pmag.domean(datablock, start, end, calculation_type)
# except Exception as ex:
# print('-I- Problem with {}'.format(this_specimen))
# print(' ', ex)
# print(' Skipping')
# continue
# k += 1
# if mpars["specimen_direction_type"] != "Error":
# # put it on the plot
# pmagplotlib.plot_dir(ZED, mpars, datablock, angle)
# if verbose:
# pmagplotlib.draw_figs(ZED)
if plots == 1 or specimen != "":
if plot_file == "":
basename = title
else:
basename = plot_file
files = {}
for key in list(ZED.keys()):
files[key] = basename + '_' + key + '.' + fmt
if pmagplotlib.isServer:
files[key] = basename + "TY:_{}_.".format(key) + fmt
pmagplotlib.save_plots(ZED, files)
if specimen != "":
sys.exit()
if verbose:
recnum = 0
for plotrec in datablock:
if units == 'T':
print('%s: %i %7.1f %s %8.3e %7.1f %7.1f %s' %
(plotrec[5], recnum, plotrec[0] * 1e3, " mT",
plotrec[3], plotrec[1], plotrec[2], plotrec[6]))
if units == "K":
print('%s: %i %7.1f %s %8.3e %7.1f %7.1f %s' %
(plotrec[5], recnum, plotrec[0] - 273, ' C',
plotrec[3], plotrec[1], plotrec[2], plotrec[6]))
if units == "J":
print('%s: %i %7.1f %s %8.3e %7.1f %7.1f %s' %
(plotrec[5], recnum, plotrec[0], ' J',
plotrec[3], plotrec[1], plotrec[2], plotrec[6]))
if 'K' in units and 'T' in units:
if plotrec[0] >= 1.:
print('%s: %i %7.1f %s %8.3e %7.1f %7.1f %s' %
(plotrec[5], recnum, plotrec[0] - 273, ' C',
plotrec[3], plotrec[1], plotrec[2],
plotrec[6]))
if plotrec[0] < 1.:
print('%s: %i %7.1f %s %8.3e %7.1f %7.1f %s' %
(plotrec[5], recnum, plotrec[0] * 1e3, " mT",
plotrec[3], plotrec[1], plotrec[2],
plotrec[6]))
recnum += 1
# we have a current interpretation
elif mpars["specimen_direction_type"] != "Error":
#
# create a new specimen record for the interpreation for this
# specimen
this_specimen_interpretation = {
col: ""
for col in specimen_cols
}
# this_specimen_interpretation["analysts"]=user
this_specimen_interpretation['software_packages'] = version_num
this_specimen_interpretation['specimen'] = this_specimen
this_specimen_interpretation["method_codes"] = calculation_type
this_specimen_interpretation["meas_step_unit"] = units
this_specimen_interpretation["meas_step_min"] = tr[start]
this_specimen_interpretation["meas_step_max"] = tr[end]
this_specimen_interpretation["dir_dec"] = '%7.1f' % (
mpars['specimen_dec'])
this_specimen_interpretation["dir_inc"] = '%7.1f' % (
mpars['specimen_inc'])
this_specimen_interpretation["dir_dang"] = '%7.1f' % (
mpars['specimen_dang'])
this_specimen_interpretation["dir_n_measurements"] = '%i' % (
mpars['specimen_n'])
this_specimen_interpretation["dir_tilt_correction"] = coord
methods = methods.replace(" ", "")
if "T" in units:
methods = methods + ":LP-DIR-AF"
if "K" in units:
methods = methods + ":LP-DIR-T"
if "J" in units:
methods = methods + ":LP-DIR-M"
this_specimen_interpretation["method_codes"] = methods.strip(
':')
this_specimen_interpretation[
"experiments"] = this_specimen_measurements.experiment.unique(
)[0]
#
# print some stuff
#
if calculation_type != 'DE-FM':
this_specimen_interpretation["dir_mad_free"] = '%7.1f' % (
mpars['specimen_mad'])
this_specimen_interpretation["dir_alpha95"] = ''
if verbose:
if units == 'K':
print(
'%s %i %7.1f %7.1f %7.1f %7.1f %7.1f %7.1f %s \n'
%
(this_specimen_interpretation["specimen"],
int(this_specimen_interpretation[
"dir_n_measurements"]),
float(this_specimen_interpretation[
"dir_mad_free"]),
float(
this_specimen_interpretation["dir_dang"]),
float(this_specimen_interpretation[
"meas_step_min"]) - 273,
float(this_specimen_interpretation[
"meas_step_max"]) - 273,
float(
this_specimen_interpretation["dir_dec"]),
float(
this_specimen_interpretation["dir_inc"]),
calculation_type))
elif units == 'T':
print(
'%s %i %7.1f %7.1f %7.1f %7.1f %7.1f %7.1f %s \n'
%
(this_specimen_interpretation["specimen"],
int(this_specimen_interpretation[
"dir_n_measurements"]),
float(this_specimen_interpretation[
"dir_mad_free"]),
float(
this_specimen_interpretation["dir_dang"]),
float(this_specimen_interpretation[
"meas_step_min"]) * 1e3,
float(this_specimen_interpretation[
"meas_step_max"]) * 1e3,
float(
this_specimen_interpretation["dir_dec"]),
float(
this_specimen_interpretation["dir_inc"]),
calculation_type))
elif 'T' in units and 'K' in units:
if float(this_specimen_interpretation[
'meas_step_min']) < 1.0:
min = float(this_specimen_interpretation[
'meas_step_min']) * 1e3
else:
min = float(this_specimen_interpretation[
'meas_step_min']) - 273
if float(this_specimen_interpretation[
'meas_step_max']) < 1.0:
max = float(this_specimen_interpretation[
'meas_step_max']) * 1e3
else:
max = float(this_specimen_interpretation[
'meas_step_max']) - 273
print(
'%s %i %7.1f %i %i %7.1f %7.1f %7.1f %s \n' %
(this_specimen_interpretation["specimen"],
int(this_specimen_interpretation[
"dir_n_measurements"]),
float(this_specimen_interpretation[
"dir_mad_free"]),
float(
this_specimen_interpretation["dir_dang"]),
min, max,
float(
this_specimen_interpretation["dir_dec"]),
float(
this_specimen_interpretation["dir_inc"]),
calculation_type))
else:
print(
'%s %i %7.1f %7.1f %7.1f %7.1f %7.1f %7.1f %s \n'
%
(this_specimen_interpretation["specimen"],
int(this_specimen_interpretation[
"dir_n_measurements"]),
float(this_specimen_interpretation[
"dir_mad_free"]),
float(
this_specimen_interpretation["dir_dang"]),
float(this_specimen_interpretation[
"meas_step_min"]),
float(this_specimen_interpretation[
"meas_step_max"]),
float(
this_specimen_interpretation["dir_dec"]),
float(
this_specimen_interpretation["dir_inc"]),
calculation_type))
else:
this_specimen_interpretation["dir_alpha95"] = '%7.1f' % (
mpars['specimen_alpha95'])
this_specimen_interpretation["dir_mad_free"] = ''
if verbose:
if 'K' in units:
print(
'%s %i %7.1f %7.1f %7.1f %7.1f %7.1f %7.1f %s \n'
%
(this_specimen_interpretation["specimen"],
int(this_specimen_interpretation[
"dir_n_measurments"]),
float(this_specimen_interpretation[
"dir_mad_free"]),
float(
this_specimen_interpretation["dir_dang"]),
float(this_specimen_interpretation[
"meas_step_min"]) - 273,
float(this_specimen_interpretation[
"meas_step_max"]) - 273,
float(
this_specimen_interpretation["dir_dec"]),
float(
this_specimen_interpretation["dir_inc"]),
calculation_type))
elif 'T' in units:
print(
'%s %i %7.1f %7.1f %7.1f %7.1f %7.1f %7.1f %s \n'
%
(this_specimen_interpretation["specimen"],
int(this_specimen_interpretation[
"dir_n_measurements"]),
float(this_specimen_interpretation[
"dir_alpha95"]),
float(
this_specimen_interpretation["dir_dang"]),
float(this_specimen_interpretation[
"meas_step_min"]) * 1e3,
float(this_specimen_interpretation[
"meas_step_max"]) * 1e3,
float(
this_specimen_interpretation["dir_dec"]),
float(
this_specimen_interpretation["dir_inc"]),
calculation_type))
elif 'T' in units and 'K' in units:
if float(this_specimen_interpretation[
'meas_step_min']) < 1.0:
min = float(this_specimen_interpretation[
'meas_step_min']) * 1e3
else:
min = float(this_specimen_interpretation[
'meas_step_min']) - 273
if float(this_specimen_interpretation[
'meas_step_max']) < 1.0:
max = float(this_specimen_interpretation[
'meas_step_max']) * 1e3
else:
max = float(this_specimen_interpretation[
'meas_step_max']) - 273
print('%s %i %7.1f %i %i %7.1f %7.1f %s \n' % (
this_specimen_interpretation["specimen"],
int(this_specimen_interpretation[
"dir_n_measurements"]),
float(
this_specimen_interpretation["dir_alpha95"]
), min, max,
float(this_specimen_interpretation["dir_dec"]),
float(this_specimen_interpretation["dir_inc"]),
calculation_type))
else:
print(
'%s %i %7.1f %7.1f %7.1f %7.1f %7.1f %s \n' %
(this_specimen_interpretation["specimen"],
int(this_specimen_interpretation[
"dir_n_measurements"]),
float(this_specimen_interpretation[
"dir_alpha95"]),
float(this_specimen_interpretation[
"meas_step_min"]),
float(this_specimen_interpretation[
"meas_step_max"]),
float(
this_specimen_interpretation["dir_dec"]),
float(
this_specimen_interpretation["dir_inc"]),
calculation_type))
if verbose:
saveit = input("Save this interpretation? [y]/n \n")
else:
print("no data", this_specimen)
if verbose:
pmagplotlib.draw_figs(ZED)
#res = input(' <return> for next specimen, [q]uit ')
res = input("S[a]ve plots, [q]uit, or <return> to continue ")
if res == 'a':
files = {
plot_type: this_specimen + "_" + plot_type + "." + fmt
for plot_type in ZED
}
pmagplotlib.save_plots(ZED, files)
print("")
if res == 'q':
return
k += 1
def main():
"""
NAME
foldtest_magic.py
DESCRIPTION
does a fold test (Tauxe, 2010) on data
INPUT FORMAT
pmag_specimens format file, er_samples.txt format file (for bedding)
SYNTAX
foldtest_magic.py [command line options]
OPTIONS
-h prints help message and quits
-f pmag_sites formatted file [default is pmag_sites.txt]
-fsa er_samples formatted file [default is er_samples.txt]
-fsi er_sites formatted file
-exc use pmag_criteria.txt to set acceptance criteria
-n NB, set number of bootstraps, default is 1000
-b MIN, MAX, set bounds for untilting, default is -10, 150
-fmt FMT, specify format - default is svg
-sav saves plots and quits
OUTPUT
Geographic: is an equal area projection of the input data in
original coordinates
Stratigraphic: is an equal area projection of the input data in
tilt adjusted coordinates
% Untilting: The dashed (red) curves are representative plots of
maximum eigenvalue (tau_1) as a function of untilting
The solid line is the cumulative distribution of the
% Untilting required to maximize tau for all the
bootstrapped data sets. The dashed vertical lines
are 95% confidence bounds on the % untilting that yields
the most clustered result (maximum tau_1).
Command line: prints out the bootstrapped iterations and
finally the confidence bounds on optimum untilting.
If the 95% conf bounds include 0, then a pre-tilt magnetization is indicated
If the 95% conf bounds include 100, then a post-tilt magnetization is indicated
If the 95% conf bounds exclude both 0 and 100, syn-tilt magnetization is
possible as is vertical axis rotation or other pathologies
"""
kappa = 0
nb = 1000 # number of bootstraps
min, max = -10, 150
dir_path = '.'
infile, orfile = 'pmag_sites.txt', 'er_samples.txt'
critfile = 'pmag_criteria.txt'
dipkey, azkey = 'sample_bed_dip', 'sample_bed_dip_direction'
fmt = 'svg'
plot = 0
if '-WD' in sys.argv:
ind = sys.argv.index('-WD')
dir_path = sys.argv[ind + 1]
if '-h' in sys.argv: # check if help is needed
print(main.__doc__)
sys.exit() # graceful quit
if '-n' in sys.argv:
ind = sys.argv.index('-n')
nb = int(sys.argv[ind + 1])
if '-fmt' in sys.argv:
ind = sys.argv.index('-fmt')
fmt = sys.argv[ind + 1]
if '-sav' in sys.argv: plot = 1
if '-b' in sys.argv:
ind = sys.argv.index('-b')
min = int(sys.argv[ind + 1])
max = int(sys.argv[ind + 2])
if '-f' in sys.argv:
ind = sys.argv.index('-f')
infile = sys.argv[ind + 1]
if '-fsa' in sys.argv:
ind = sys.argv.index('-fsa')
orfile = sys.argv[ind + 1]
elif '-fsi' in sys.argv:
ind = sys.argv.index('-fsi')
orfile = sys.argv[ind + 1]
dipkey, azkey = 'site_bed_dip', 'site_bed_dip_direction'
orfile = dir_path + '/' + orfile
infile = dir_path + '/' + infile
critfile = dir_path + '/' + critfile
data, file_type = pmag.magic_read(infile)
ordata, file_type = pmag.magic_read(orfile)
if '-exc' in sys.argv:
crits, file_type = pmag.magic_read(critfile)
for crit in crits:
if crit['pmag_criteria_code'] == "DE-SITE":
SiteCrit = crit
break
# get to work
#
PLTS = {'geo': 1, 'strat': 2, 'taus': 3} # make plot dictionary
pmagplotlib.plot_init(PLTS['geo'], 5, 5)
pmagplotlib.plot_init(PLTS['strat'], 5, 5)
pmagplotlib.plot_init(PLTS['taus'], 5, 5)
GEOrecs = pmag.get_dictitem(data, 'site_tilt_correction', '0', 'T')
if len(GEOrecs) > 0: # have some geographic data
DIDDs = [] # set up list for dec inc dip_direction, dip
for rec in GEOrecs: # parse data
dip, dip_dir = 0, -1
Dec = float(rec['site_dec'])
Inc = float(rec['site_inc'])
orecs = pmag.get_dictitem(ordata, 'er_site_name',
rec['er_site_name'], 'T')
if len(orecs) > 0:
if orecs[0][azkey] != "": dip_dir = float(orecs[0][azkey])
if orecs[0][dipkey] != "": dip = float(orecs[0][dipkey])
if dip != 0 and dip_dir != -1:
if '-exc' in sys.argv:
keep = 1
for key in list(SiteCrit.keys()):
if 'site' in key and SiteCrit[key] != "" and rec[
key] != "" and key != 'site_alpha95':
if float(rec[key]) < float(SiteCrit[key]):
keep = 0
print(rec['er_site_name'], key, rec[key])
if key == 'site_alpha95' and SiteCrit[
key] != "" and rec[key] != "":
if float(rec[key]) > float(SiteCrit[key]):
keep = 0
if keep == 1: DIDDs.append([Dec, Inc, dip_dir, dip])
else:
DIDDs.append([Dec, Inc, dip_dir, dip])
else:
print('no geographic directional data found')
sys.exit()
pmagplotlib.plotEQ(PLTS['geo'], DIDDs, 'Geographic')
data = numpy.array(DIDDs)
D, I = pmag.dotilt_V(data)
TCs = numpy.array([D, I]).transpose()
pmagplotlib.plotEQ(PLTS['strat'], TCs, 'Stratigraphic')
if plot == 0: pmagplotlib.drawFIGS(PLTS)
Percs = list(range(min, max))
Cdf, Untilt = [], []
pylab.figure(num=PLTS['taus'])
print('doing ', nb, ' iterations...please be patient.....')
for n in range(
nb): # do bootstrap data sets - plot first 25 as dashed red line
if n % 50 == 0: print(n)
Taus = [] # set up lists for taus
PDs = pmag.pseudo(DIDDs)
if kappa != 0:
for k in range(len(PDs)):
d, i = pmag.fshdev(kappa)
dipdir, dip = pmag.dodirot(d, i, PDs[k][2], PDs[k][3])
PDs[k][2] = dipdir
PDs[k][3] = dip
for perc in Percs:
tilt = numpy.array([1., 1., 1., 0.01 * perc])
D, I = pmag.dotilt_V(PDs * tilt)
TCs = numpy.array([D, I]).transpose()
ppars = pmag.doprinc(TCs) # get principal directions
Taus.append(ppars['tau1'])
if n < 25: pylab.plot(Percs, Taus, 'r--')
Untilt.append(Percs[Taus.index(
numpy.max(Taus))]) # tilt that gives maximum tau
Cdf.append(old_div(float(n), float(nb)))
pylab.plot(Percs, Taus, 'k')
pylab.xlabel('% Untilting')
pylab.ylabel('tau_1 (red), CDF (green)')
Untilt.sort() # now for CDF of tilt of maximum tau
pylab.plot(Untilt, Cdf, 'g')
lower = int(.025 * nb)
upper = int(.975 * nb)
pylab.axvline(x=Untilt[lower], ymin=0, ymax=1, linewidth=1, linestyle='--')
pylab.axvline(x=Untilt[upper], ymin=0, ymax=1, linewidth=1, linestyle='--')
tit = '%i - %i %s' % (Untilt[lower], Untilt[upper], 'Percent Unfolding')
print(tit)
pylab.title(tit)
if plot == 0:
pmagplotlib.drawFIGS(PLTS)
ans = input('S[a]ve all figures, <Return> to quit \n ')
if ans != 'a':
print("Good bye")
sys.exit()
files = {}
for key in list(PLTS.keys()):
files[key] = ('foldtest_' + '%s' % (key.strip()[:2]) + '.' + fmt)
pmagplotlib.saveP(PLTS, files)
def main():
"""
NAME
di_rot.py
DESCRIPTION
rotates set of directions to new coordinate system
SYNTAX
di_rot.py [command line options]
OPTIONS
-h prints help message and quits
-f specify input file, default is standard input
-F specify output file, default is standard output
-D D specify Dec of new coordinate system, default is 0
-I I specify Inc of new coordinate system, default is 90
INTPUT/OUTPUT
dec inc [space delimited]
"""
D, I = 0., 90.
outfile = ""
infile = ""
if '-h' in sys.argv:
print main.__doc__
sys.exit()
if '-f' in sys.argv:
ind = sys.argv.index('-f')
infile = sys.argv[ind + 1]
data = numpy.loadtxt(infile)
else:
data = numpy.loadtxt(sys.stdin, dtype=numpy.float)
if '-F' in sys.argv:
ind = sys.argv.index('-F')
outfile = sys.argv[ind + 1]
out = open(outfile, 'w')
if '-D' in sys.argv:
ind = sys.argv.index('-D')
D = float(sys.argv[ind + 1])
if '-I' in sys.argv:
ind = sys.argv.index('-I')
I = float(sys.argv[ind + 1])
if len(data.shape) > 1: # 2-D array
N = data.shape[0]
DipDir, Dip = numpy.ones(N, dtype=numpy.float).transpose() * (
D - 180.), numpy.ones(N, dtype=numpy.float).transpose() * (90. - I)
data = data.transpose()
data = numpy.array([data[0], data[1], DipDir, Dip]).transpose()
drot, irot = pmag.dotilt_V(data)
drot = (drot - 180.) % 360. #
for k in range(N):
if outfile == "":
print '%7.1f %7.1f ' % (drot[k], irot[k])
else:
out.write('%7.1f %7.1f\n' % (drot[k], irot[k]))
else:
d, i = pmag.dotilt(data[0], data[1], (D - 180.), 90. - I)
if outfile == "":
print '%7.1f %7.1f ' % ((d - 180.) % 360., i)
else:
out.write('%7.1f %7.1f\n' % ((d - 180.) % 360., i))
def main():
"""
NAME
foldtest_magic.py
DESCRIPTION
does a fold test (Tauxe, 2010) on data
INPUT FORMAT
pmag_specimens format file, er_samples.txt format file (for bedding)
SYNTAX
foldtest_magic.py [command line options]
OPTIONS
-h prints help message and quits
-f pmag_sites formatted file [default is pmag_sites.txt]
-fsa er_samples formatted file [default is er_samples.txt]
-fsi er_sites formatted file
-exc use pmag_criteria.txt to set acceptance criteria
-n NB, set number of bootstraps, default is 1000
-b MIN, MAX, set bounds for untilting, default is -10, 150
-fmt FMT, specify format - default is svg
-sav saves plots and quits
OUTPUT
Geographic: is an equal area projection of the input data in
original coordinates
Stratigraphic: is an equal area projection of the input data in
tilt adjusted coordinates
% Untilting: The dashed (red) curves are representative plots of
maximum eigenvalue (tau_1) as a function of untilting
The solid line is the cumulative distribution of the
% Untilting required to maximize tau for all the
bootstrapped data sets. The dashed vertical lines
are 95% confidence bounds on the % untilting that yields
the most clustered result (maximum tau_1).
Command line: prints out the bootstrapped iterations and
finally the confidence bounds on optimum untilting.
If the 95% conf bounds include 0, then a pre-tilt magnetization is indicated
If the 95% conf bounds include 100, then a post-tilt magnetization is indicated
If the 95% conf bounds exclude both 0 and 100, syn-tilt magnetization is
possible as is vertical axis rotation or other pathologies
"""
kappa=0
nb=1000 # number of bootstraps
min,max=-10,150
dir_path='.'
infile,orfile='pmag_sites.txt','er_samples.txt'
critfile='pmag_criteria.txt'
dipkey,azkey='sample_bed_dip','sample_bed_dip_direction'
fmt='svg'
plot=0
if '-WD' in sys.argv:
ind=sys.argv.index('-WD')
dir_path=sys.argv[ind+1]
if '-h' in sys.argv: # check if help is needed
print main.__doc__
sys.exit() # graceful quit
if '-n' in sys.argv:
ind=sys.argv.index('-n')
nb=int(sys.argv[ind+1])
if '-fmt' in sys.argv:
ind=sys.argv.index('-fmt')
fmt=sys.argv[ind+1]
if '-sav' in sys.argv:plot=1
if '-b' in sys.argv:
ind=sys.argv.index('-b')
min=int(sys.argv[ind+1])
max=int(sys.argv[ind+2])
if '-f' in sys.argv:
ind=sys.argv.index('-f')
infile=sys.argv[ind+1]
if '-fsa' in sys.argv:
ind=sys.argv.index('-fsa')
orfile=sys.argv[ind+1]
elif '-fsi' in sys.argv:
ind=sys.argv.index('-fsi')
orfile=sys.argv[ind+1]
dipkey,azkey='site_bed_dip','site_bed_dip_direction'
orfile=dir_path+'/'+orfile
infile=dir_path+'/'+infile
critfile=dir_path+'/'+critfile
data,file_type=pmag.magic_read(infile)
ordata,file_type=pmag.magic_read(orfile)
if '-exc' in sys.argv:
crits,file_type=pmag.magic_read(critfile)
for crit in crits:
if crit['pmag_criteria_code']=="DE-SITE":
SiteCrit=crit
break
# get to work
#
PLTS={'geo':1,'strat':2,'taus':3} # make plot dictionary
pmagplotlib.plot_init(PLTS['geo'],5,5)
pmagplotlib.plot_init(PLTS['strat'],5,5)
pmagplotlib.plot_init(PLTS['taus'],5,5)
GEOrecs=pmag.get_dictitem(data,'site_tilt_correction','0','T')
if len(GEOrecs)>0: # have some geographic data
DIDDs= [] # set up list for dec inc dip_direction, dip
for rec in GEOrecs: # parse data
dip,dip_dir=0,-1
Dec=float(rec['site_dec'])
Inc=float(rec['site_inc'])
orecs=pmag.get_dictitem(ordata,'er_site_name',rec['er_site_name'],'T')
if len(orecs)>0:
if orecs[0][azkey]!="":dip_dir=float(orecs[0][azkey])
if orecs[0][dipkey]!="":dip=float(orecs[0][dipkey])
if dip!=0 and dip_dir!=-1:
if '-exc' in sys.argv:
keep=1
for key in SiteCrit.keys():
if 'site' in key and SiteCrit[key]!="" and rec[key]!="" and key!='site_alpha95':
if float(rec[key])<float(SiteCrit[key]):
keep=0
print rec['er_site_name'],key,rec[key]
if key=='site_alpha95' and SiteCrit[key]!="" and rec[key]!="":
if float(rec[key])>float(SiteCrit[key]):
keep=0
if keep==1: DIDDs.append([Dec,Inc,dip_dir,dip])
else:
DIDDs.append([Dec,Inc,dip_dir,dip])
else:
print 'no geographic directional data found'
sys.exit()
pmagplotlib.plotEQ(PLTS['geo'],DIDDs,'Geographic')
data=numpy.array(DIDDs)
D,I=pmag.dotilt_V(data)
TCs=numpy.array([D,I]).transpose()
pmagplotlib.plotEQ(PLTS['strat'],TCs,'Stratigraphic')
if plot==0:pmagplotlib.drawFIGS(PLTS)
Percs=range(min,max)
Cdf,Untilt=[],[]
pylab.figure(num=PLTS['taus'])
print 'doing ',nb,' iterations...please be patient.....'
for n in range(nb): # do bootstrap data sets - plot first 25 as dashed red line
if n%50==0:print n
Taus=[] # set up lists for taus
PDs=pmag.pseudo(DIDDs)
if kappa!=0:
for k in range(len(PDs)):
d,i=pmag.fshdev(kappa)
dipdir,dip=pmag.dodirot(d,i,PDs[k][2],PDs[k][3])
PDs[k][2]=dipdir
PDs[k][3]=dip
for perc in Percs:
tilt=numpy.array([1.,1.,1.,0.01*perc])
D,I=pmag.dotilt_V(PDs*tilt)
TCs=numpy.array([D,I]).transpose()
ppars=pmag.doprinc(TCs) # get principal directions
Taus.append(ppars['tau1'])
if n<25:pylab.plot(Percs,Taus,'r--')
Untilt.append(Percs[Taus.index(numpy.max(Taus))]) # tilt that gives maximum tau
Cdf.append(float(n)/float(nb))
pylab.plot(Percs,Taus,'k')
pylab.xlabel('% Untilting')
pylab.ylabel('tau_1 (red), CDF (green)')
Untilt.sort() # now for CDF of tilt of maximum tau
pylab.plot(Untilt,Cdf,'g')
lower=int(.025*nb)
upper=int(.975*nb)
pylab.axvline(x=Untilt[lower],ymin=0,ymax=1,linewidth=1,linestyle='--')
pylab.axvline(x=Untilt[upper],ymin=0,ymax=1,linewidth=1,linestyle='--')
tit= '%i - %i %s'%(Untilt[lower],Untilt[upper],'Percent Unfolding')
print tit
pylab.title(tit)
if plot==0:
pmagplotlib.drawFIGS(PLTS)
ans= raw_input('S[a]ve all figures, <Return> to quit \n ')
if ans!='a':
print "Good bye"
sys.exit()
files={}
for key in PLTS.keys():
files[key]=('foldtest_'+'%s'%(key.strip()[:2])+'.'+fmt)
pmagplotlib.saveP(PLTS,files)
