def test_spec_name(self):
magic2_keys = {'er_synthetic_name'}
magic3_keys = {'specimen'}
magic2_dict = {key: '' for key in magic2_keys}
output = map_magic.mapping(magic2_dict,
map_magic.spec_magic2_2_magic3_map)
self.assertEqual(sorted(magic3_keys), sorted(output.keys()))
output = map_magic.mapping(output, map_magic.spec_magic3_2_magic2_map)
self.assertEqual(['er_specimen_name'], sorted(output.keys()))
def test_samp_map_with_adjusted_vocab(self):
magic2_keys = {'er_specimen_names', 'sample_comp_name',
'sample_date', 'external_database_ids'}
magic3_keys = {'specimens', 'dir_comp_name',
'timestamp', 'external_database_ids'}
magic3_dict = {key: '' for key in magic3_keys}
output = map_magic.mapping(magic3_dict,
map_magic.samp_magic3_2_magic2_map)
self.assertEqual(sorted(magic2_keys), sorted(output.keys()))
def calculate_all_statistics(self):
#print "calling calculate_all_statistics in spd.py"
if self.n < 3:
print("-W- Cannot run statistics with only {} points".format(self.n))
self.pars = {}
return None
self.York_Regression()
self.get_vds()
self.get_FRAC()
self.get_curve()
self.get_curve_prime()
self.get_SCAT()
self.get_R_corr2()
self.get_R_det2()
self.get_Z()
self.get_Zstar()
self.get_IZZI_MD()
# directional statistics
self.get_dec_and_inc()
self.get_ptrm_dec_and_inc()
self.get_MAD()
self.get_ptrm_MAD()
self.get_alpha()
self.get_DANG()
self.get_NRM_dev()
self.get_theta()
self.get_gamma()
# ptrm check statistics
self.get_n_ptrm()
if self.pars['n_ptrm'] == 0:
self.pars['max_ptrm_check'], self.pars['delta_CK'], self.pars['get_DRAT'], self.pars['DRAT'], self.pars['DRATS'], self.pars['CDRAT'], self.pars['mean_DRAT'], self.pars['mean_DEV'], self.pars['max_DEV'], self.pars['delta_pal'] = 0, 0, 0, 0, 0, 0, 0, 0, 0, 0
self.get_length_best_fit_line()
else:
self.get_max_ptrm_check()
self.get_delta_CK()
self.get_DRAT()
self.get_max_DEV()
self.get_CDRAT()
self.get_DRATS()
self.get_mean_DRAT()
self.get_mean_DEV()
self.get_delta_pal()
# tail check statistics
self.get_n_tail()
self.get_max_tail_check()
self.get_DRAT_tail()
self.get_delta_TR()
self.get_MD_VDS()
# additivity check statistics
self.get_n_add()
self.get_delta_AC()
# stats that require an independent chrm or are not yet coded
self.get_alpha_prime()
self.get_CRM_percent()
self.get_delta_t_star()
if self.mapping == 'magic':
self.pars = map_magic.mapping(self.pars, map_magic.spd2magic_map)
def calculate_all_statistics(self):
#print "calling calculate_all_statistics in spd.py"
if self.n < 3:
print("-W- Cannot run statistics with only {} points".format(self.n))
self.pars = {}
return None
self.York_Regression()
self.get_vds()
self.get_FRAC()
self.get_curve()
self.get_curve_prime()
self.get_SCAT()
self.get_R_corr2()
self.get_R_det2()
self.get_Z()
self.get_Zstar()
self.get_IZZI_MD()
# directional statistics
self.get_dec_and_inc()
self.get_ptrm_dec_and_inc()
self.get_MAD()
self.get_ptrm_MAD()
self.get_alpha()
self.get_DANG()
self.get_NRM_dev()
self.get_theta()
self.get_gamma()
# ptrm check statistics
self.get_n_ptrm()
if self.pars['n_ptrm'] == 0:
self.pars['max_ptrm_check'], self.pars['delta_CK'], self.pars['get_DRAT'], self.pars['DRAT'], self.pars['DRATS'], self.pars['CDRAT'], self.pars['mean_DRAT'], self.pars['mean_DEV'], self.pars['max_DEV'], self.pars['delta_pal'] = 0, 0, 0, 0, 0, 0, 0, 0, 0, 0
self.get_length_best_fit_line()
else:
self.get_max_ptrm_check()
self.get_delta_CK()
self.get_DRAT()
self.get_max_DEV()
self.get_CDRAT()
self.get_DRATS()
self.get_mean_DRAT()
self.get_mean_DEV()
self.get_delta_pal()
# tail check statistics
self.get_n_tail()
self.get_max_tail_check()
self.get_DRAT_tail()
self.get_delta_TR()
self.get_MD_VDS()
# additivity check statistics
self.get_n_add()
self.get_delta_AC()
# stats that require an independent chrm or are not yet coded
self.get_alpha_prime()
self.get_CRM_percent()
self.get_delta_t_star()
if self.mapping == 'magic':
self.pars = map_magic.mapping(self.pars, map_magic.spd2magic_map)
def test_site_map(self):
magic2_keys = {'er_site_name', 'er_sample_names',
'magic_experiment_names', 'site_igsn',
'er_citation_names', 'site_cooling_rate',
'site_inferred_age', 'int_rel_chi_sigma_perc',
'er_scientist_mail_names'}
magic2_dict = {key: '' for key in magic2_keys}
magic3_keys = {'site', 'samples', 'experiments', 'igsn', 'citations',
'cooling_rate', 'age', 'int_rel_chi_sigma_perc',
'scientists'}
magic3_dict = {key: '' for key in magic3_keys}
output = map_magic.mapping(magic2_dict,
map_magic.site_magic2_2_magic3_map)
self.assertEqual(sorted(magic3_keys), sorted(output.keys()))
# check that all output values are 3.0 valid
for val in list(output.keys()):
self.assertIn(val, DM.dm['sites'].index)
# try reverse operation (2.5 --> 3.0)
output = map_magic.mapping(magic3_dict,
map_magic.site_magic3_2_magic2_map)
self.assertEqual(sorted(magic2_keys), sorted(output.keys()))
def test_loc_map(self):
magic2_keys = {'er_location_name', 'location_type', 'location_url',
'location_end_elevation', 'location_description'}
magic2_dict = {key: '' for key in magic2_keys}
magic3_keys = {'location', 'location_type', 'expedition_url',
'elevation_high', 'description'}
output = map_magic.mapping(magic2_dict,
map_magic.loc_magic2_2_magic3_map)
self.assertEqual(sorted(magic3_keys), sorted(output.keys()))
# check that all output values are 3.0 valid
for val in list(output.keys()):
self.assertIn(val, DM.dm['locations'].index)
def test_samp_map(self):
magic2_keys = {'er_sample_name', 'er_specimen_names', 'er_site_name',
'magic_method_codes', 'sample_texture',
'sample_bed_dip', 'sample_int_rel_sigma'}
magic2_dict = {key: '' for key in magic2_keys}
magic3_keys = {'sample', 'specimens', 'site', 'method_codes',
'texture', 'bed_dip', 'int_rel_sigma'}
output = map_magic.mapping(magic2_dict,
map_magic.samp_magic2_2_magic3_map)
self.assertEqual(sorted(magic3_keys), sorted(output.keys()))
# check that all output values are 3.0 valid
for val in list(output.keys()):
self.assertIn(val, DM.dm['samples'].index)
def test_spec_map(self):
magic2_keys = {'er_specimen_name', 'er_sample_name', 'specimen_weight',
'result_type', 'specimen_type', 'specimen_azimuth',
'specimen_tilt_correction', 'specimen_int_ptrm_n'}
magic2_dict = {key: '' for key in magic2_keys}
magic3_keys = {'specimen', 'sample', 'weight', 'result_type',
'geologic_types', 'azimuth', 'dir_tilt_correction',
'int_n_ptrm'}
output = map_magic.mapping(magic2_dict,
map_magic.spec_magic2_2_magic3_map)
self.assertEqual(sorted(magic3_keys), sorted(output.keys()))
# check that all output values are 3.0 valid
for val in list(output.keys()):
self.assertIn(val, DM.dm['specimens'].index)
def test_meas_map(self):
magic2_keys = {'er_analyst_mail_names', 'measurement_number',
'measurement_flag', 'treatment_temp',
'measurement_pos_z'}
magic2_dict = {key: '' for key in magic2_keys}
magic3_keys = {'analysts', 'measurement',
'quality', 'treat_temp',
'meas_pos_z'}
output = map_magic.mapping(magic2_dict,
map_magic.meas_magic2_2_magic3_map)
# check that translation worked
self.assertEqual(sorted(magic3_keys), sorted(output.keys()))
# check that all output values are 3.0 valid
for val in list(output.keys()):
self.assertIn(val, DM.dm['measurements'].index)
def reqd_stats(self):
#print 'do REQD STATS'
if self.n < 3:
print("-W- Cannot run statistics with only {} points".format(
self.n))
self.pars = {}
return None
stats_run = []
#print 'self.calculate: ', self.calculate
if 'best_fit_vector_Free' not in self.calculate:
self.calculate.append('best_fit_vector_Free')
for stat in self.calculate: # iterate through all stats that should be calculated
func = self.statistics[stat]
#print 'func', func
if func: # sometimes this will be none, since statistics like tmin are generated during __init__ and don't require a function to be run
if func.__name__ == 'York_Regression':
#print 'York_Regression'
if 'York_Regression' not in stats_run:
func(self)
stats_run.append(func.__name__)
elif func.__name__ in self.dependencies:
for d in self.dependencies[func.__name__]:
if d == None: continue
#print 'd: ', d,
if d.__name__ not in stats_run: # if the dependency has not already been run, run it
d(self)
stats_run.append(d.__name__)
if func.__name__ not in stats_run:
func(
self
) # all dependencies have been satisfied, so run the main function
stats_run.append(func.__name__)
else:
#print 'alert: did not find dependencies, now attempting to run'
try:
func(
self
) # this will work if the function has no dependencies
stats_run.append(func.__name__)
except:
self.calculate_all_statistics(
) # if no dependency info can be found, just run all statistics
return 0 # since all possible statistics have been run, the function ends
#print 'stats run: ', stats_run
if self.mapping == 'magic':
self.pars = map_magic.mapping(self.pars, map_magic.spd2magic_map)
if len(stats_run) != len(set(stats_run)):
raise Exception('lengths were off')
def reqd_stats(self):
#print 'do REQD STATS'
if self.n < 3:
print("-W- Cannot run statistics with only {} points".format(self.n))
self.pars = {}
return None
stats_run = []
#print 'self.calculate: ', self.calculate
if 'best_fit_vector_Free' not in self.calculate:
self.calculate.append('best_fit_vector_Free')
for stat in self.calculate: # iterate through all stats that should be calculated
func = self.statistics[stat]
#print 'func', func
if func: # sometimes this will be none, since statistics like tmin are generated during __init__ and don't require a function to be run
if func.__name__ == 'York_Regression':
#print 'York_Regression'
if 'York_Regression' not in stats_run:
func(self)
stats_run.append(func.__name__)
elif func.__name__ in self.dependencies:
for d in self.dependencies[func.__name__]:
if d == None: continue
#print 'd: ', d,
if d.__name__ not in stats_run: # if the dependency has not already been run, run it
d(self)
stats_run.append(d.__name__)
if func.__name__ not in stats_run:
func(self) # all dependencies have been satisfied, so run the main function
stats_run.append(func.__name__)
else:
#print 'alert: did not find dependencies, now attempting to run'
try:
func(self) # this will work if the function has no dependencies
stats_run.append(func.__name__)
except:
self.calculate_all_statistics() # if no dependency info can be found, just run all statistics
return 0 # since all possible statistics have been run, the function ends
#print 'stats run: ', stats_run
if self.mapping == 'magic':
self.pars = map_magic.mapping(self.pars, map_magic.spd2magic_map)
if len(stats_run) != len(set(stats_run)):
raise Exception('lengths were off')
def main():
"""
NAME
atrm_magic.py
DESCRIPTION
Converts ATRM data to best-fit tensor (6 elements plus sigma)
Original program ARMcrunch written to accomodate ARM anisotropy data
collected from 6 axial directions (+X,+Y,+Z,-X,-Y,-Z) using the
off-axis remanence terms to construct the tensor. A better way to
do the anisotropy of ARMs is to use 9,12 or 15 measurements in
the Hext rotational scheme.
SYNTAX
atrm_magic.py [-h][command line options]
OPTIONS
-h prints help message and quits
-usr USER: identify user, default is ""
-f FILE: specify input file, default is atrm_measurements.txt
-Fa FILE: specify anisotropy output file, default is trm_anisotropy.txt (MagIC 2.5 only)
-Fr FILE: specify results output file, default is atrm_results.txt (MagIC 2.5 only)
-Fsi FILE: specify output file, default is specimens.txt (MagIC 3 only)
-DM DATA_MODEL: specify MagIC 2 or MagIC 3, default is 3
INPUT
Input for the present program is a TRM acquisition data with an optional baseline.
The order of the measurements is:
Decs=[0,90,0,180,270,0,0,90,0]
Incs=[0,0,90,0,0,-90,0,0,90]
The last two measurements are optional
"""
# initialize some parameters
args = sys.argv
user = ""
meas_file = "atrm_measurements.txt"
rmag_anis = "trm_anisotropy.txt"
rmag_res = "atrm_results.txt"
dir_path = '.'
#
# get name of file from command line
#
if '-WD' in args:
ind = args.index('-WD')
dir_path = args[ind + 1]
if "-h" in args:
print(main.__doc__)
sys.exit()
if "-usr" in args:
ind = args.index("-usr")
user = sys.argv[ind + 1]
if "-f" in args:
ind = args.index("-f")
meas_file = sys.argv[ind + 1]
if "-Fa" in args:
ind = args.index("-Fa")
rmag_anis = args[ind + 1]
if "-Fr" in args:
ind = args.index("-Fr")
rmag_res = args[ind + 1]
data_model_num = int(pmag.get_named_arg_from_sys("-DM", 3))
spec_file = pmag.get_named_arg_from_sys("-Fsi", "specimens.txt")
spec_file = os.path.join(dir_path, spec_file)
meas_file = dir_path + '/' + meas_file
rmag_anis = dir_path + '/' + rmag_anis
rmag_res = dir_path + '/' + rmag_res
# read in data
if data_model_num == 3:
meas_data = []
meas_data3, file_type = pmag.magic_read(meas_file)
if file_type != 'measurements':
print(file_type, "This is not a valid measurements file ")
sys.exit()
# convert meas_data to 2.5
for rec in meas_data3:
meas_map = map_magic.meas_magic3_2_magic2_map
meas_data.append(map_magic.mapping(rec, meas_map))
else:
meas_data, file_type = pmag.magic_read(meas_file)
if file_type != 'magic_measurements':
print(file_type, "This is not a valid magic_measurements file ")
sys.exit()
meas_data = pmag.get_dictitem(
meas_data, 'magic_method_codes', 'LP-AN-TRM', 'has')
#
#
# get sorted list of unique specimen names
ssort = []
for rec in meas_data:
spec = rec["er_specimen_name"]
if spec not in ssort:
ssort.append(spec)
sids = sorted(ssort)
#
#
# work on each specimen
#
specimen, npos = 0, 6
RmagSpecRecs, RmagResRecs = [], []
SpecRecs, SpecRecs3 = [], []
while specimen < len(sids):
nmeas = 0
s = sids[specimen]
RmagSpecRec = {}
RmagResRec = {}
BX, X = [], []
method_codes = []
Spec0 = ""
#
# find the data from the meas_data file for this sample
# and get dec, inc, int and convert to x,y,z
#
# fish out data for this specimen name
data = pmag.get_dictitem(meas_data, 'er_specimen_name', s, 'T')
if len(data) > 5:
RmagSpecRec["rmag_anisotropy_name"] = data[0]["er_specimen_name"]
RmagSpecRec["er_location_name"] = data[0].get("er_location_name", "")
RmagSpecRec["er_specimen_name"] = data[0]["er_specimen_name"]
RmagSpecRec["er_sample_name"] = data[0].get("er_sample_name", "")
RmagSpecRec["er_site_name"] = data[0].get("er_site_name", "")
RmagSpecRec["magic_experiment_names"] = RmagSpecRec["rmag_anisotropy_name"] + ":ATRM"
RmagSpecRec["er_citation_names"] = "This study"
RmagResRec["rmag_result_name"] = data[0]["er_specimen_name"] + ":ATRM"
RmagResRec["er_location_names"] = data[0].get("er_location_names", "")
RmagResRec["er_specimen_names"] = data[0]["er_specimen_name"]
RmagResRec["er_sample_names"] = data[0].get("er_sample_name", "")
RmagResRec["er_site_names"] = data[0].get("er_site_name", "")
RmagResRec["magic_experiment_names"] = RmagSpecRec["rmag_anisotropy_name"] + ":ATRM"
RmagResRec["er_citation_names"] = "This study"
RmagSpecRec["anisotropy_type"] = "ATRM"
if "magic_instrument_codes" in list(data[0].keys()):
RmagSpecRec["magic_instrument_codes"] = data[0]["magic_instrument_codes"]
else:
RmagSpecRec["magic_instrument_codes"] = ""
RmagSpecRec["anisotropy_description"] = "Hext statistics adapted to ATRM"
for rec in data:
meths = rec['magic_method_codes'].strip().split(':')
Dir = []
Dir.append(float(rec["measurement_dec"]))
Dir.append(float(rec["measurement_inc"]))
Dir.append(float(rec["measurement_magn_moment"]))
if "LT-T-Z" in meths:
BX.append(pmag.dir2cart(Dir)) # append baseline steps
elif "LT-T-I" in meths:
X.append(pmag.dir2cart(Dir))
nmeas += 1
#
if len(BX) == 1:
for i in range(len(X) - 1):
BX.append(BX[0]) # assume first 0 field step as baseline
elif len(BX) == 0: # assume baseline is zero
for i in range(len(X)):
BX.append([0., 0., 0.]) # assume baseline of 0
elif len(BX) != len(X): # if BX isn't just one measurement or one in between every infield step, just assume it is zero
print('something odd about the baselines - just assuming zero')
for i in range(len(X)):
BX.append([0., 0., 0.]) # assume baseline of 0
if nmeas < 6: # must have at least 6 measurements right now -
print('skipping specimen ', s, ' too few measurements')
specimen += 1
else:
# B matrix made from design matrix for positions
B, H, tmpH = pmag.designATRM(npos)
#
# subtract optional baseline and put in a work array
#
work = numpy.zeros((nmeas, 3), 'f')
for i in range(nmeas):
for j in range(3):
# subtract baseline, if available
work[i][j] = X[i][j] - BX[i][j]
#
# calculate tensor elements
# first put ARM components in w vector
#
w = numpy.zeros((npos * 3), 'f')
index = 0
for i in range(npos):
for j in range(3):
w[index] = work[i][j]
index += 1
s = numpy.zeros((6), 'f') # initialize the s matrix
for i in range(6):
for j in range(len(w)):
s[i] += B[i][j] * w[j]
trace = s[0] + s[1] + s[2] # normalize by the trace
for i in range(6):
s[i] = old_div(s[i], trace)
a = pmag.s2a(s)
#------------------------------------------------------------
# Calculating dels is different than in the Kappabridge
# routine. Use trace normalized tensor (a) and the applied
# unit field directions (tmpH) to generate model X,Y,Z
# components. Then compare these with the measured values.
#------------------------------------------------------------
S = 0.
comp = numpy.zeros((npos * 3), 'f')
for i in range(npos):
for j in range(3):
index = i * 3 + j
compare = a[j][0] * tmpH[i][0] + a[j][1] * \
tmpH[i][1] + a[j][2] * tmpH[i][2]
comp[index] = compare
for i in range(npos * 3):
d = old_div(w[i], trace) - comp[i] # del values
S += d * d
nf = float(npos * 3. - 6.) # number of degrees of freedom
if S > 0:
sigma = numpy.sqrt(old_div(S, nf))
else:
sigma = 0
hpars = pmag.dohext(nf, sigma, s)
#
# prepare for output
#
RmagSpecRec["anisotropy_s1"] = '%8.6f' % (s[0])
RmagSpecRec["anisotropy_s2"] = '%8.6f' % (s[1])
RmagSpecRec["anisotropy_s3"] = '%8.6f' % (s[2])
RmagSpecRec["anisotropy_s4"] = '%8.6f' % (s[3])
RmagSpecRec["anisotropy_s5"] = '%8.6f' % (s[4])
RmagSpecRec["anisotropy_s6"] = '%8.6f' % (s[5])
RmagSpecRec["anisotropy_mean"] = '%8.3e' % (old_div(trace, 3))
RmagSpecRec["anisotropy_sigma"] = '%8.6f' % (sigma)
RmagSpecRec["anisotropy_unit"] = "Am^2"
RmagSpecRec["anisotropy_n"] = '%i' % (npos)
RmagSpecRec["anisotropy_tilt_correction"] = '-1'
# used by thellier_gui - must be taken out for uploading
RmagSpecRec["anisotropy_F"] = '%7.1f ' % (hpars["F"])
# used by thellier_gui - must be taken out for uploading
RmagSpecRec["anisotropy_F_crit"] = hpars["F_crit"]
RmagResRec["anisotropy_t1"] = '%8.6f ' % (hpars["t1"])
RmagResRec["anisotropy_t2"] = '%8.6f ' % (hpars["t2"])
RmagResRec["anisotropy_t3"] = '%8.6f ' % (hpars["t3"])
RmagResRec["anisotropy_v1_dec"] = '%7.1f ' % (hpars["v1_dec"])
RmagResRec["anisotropy_v2_dec"] = '%7.1f ' % (hpars["v2_dec"])
RmagResRec["anisotropy_v3_dec"] = '%7.1f ' % (hpars["v3_dec"])
RmagResRec["anisotropy_v1_inc"] = '%7.1f ' % (hpars["v1_inc"])
RmagResRec["anisotropy_v2_inc"] = '%7.1f ' % (hpars["v2_inc"])
RmagResRec["anisotropy_v3_inc"] = '%7.1f ' % (hpars["v3_inc"])
RmagResRec["anisotropy_ftest"] = '%7.1f ' % (hpars["F"])
RmagResRec["anisotropy_ftest12"] = '%7.1f ' % (hpars["F12"])
RmagResRec["anisotropy_ftest23"] = '%7.1f ' % (hpars["F23"])
RmagResRec["result_description"] = 'Critical F: ' + \
hpars["F_crit"] + ';Critical F12/F13: ' + hpars["F12_crit"]
if hpars["e12"] > hpars["e13"]:
RmagResRec["anisotropy_v1_zeta_semi_angle"] = '%7.1f ' % (
hpars['e12'])
RmagResRec["anisotropy_v1_zeta_dec"] = '%7.1f ' % (
hpars['v2_dec'])
RmagResRec["anisotropy_v1_zeta_inc"] = '%7.1f ' % (
hpars['v2_inc'])
RmagResRec["anisotropy_v2_zeta_semi_angle"] = '%7.1f ' % (
hpars['e12'])
RmagResRec["anisotropy_v2_zeta_dec"] = '%7.1f ' % (
hpars['v1_dec'])
RmagResRec["anisotropy_v2_zeta_inc"] = '%7.1f ' % (
hpars['v1_inc'])
RmagResRec["anisotropy_v1_eta_semi_angle"] = '%7.1f ' % (
hpars['e13'])
RmagResRec["anisotropy_v1_eta_dec"] = '%7.1f ' % (
hpars['v3_dec'])
RmagResRec["anisotropy_v1_eta_inc"] = '%7.1f ' % (
hpars['v3_inc'])
RmagResRec["anisotropy_v3_eta_semi_angle"] = '%7.1f ' % (
hpars['e13'])
RmagResRec["anisotropy_v3_eta_dec"] = '%7.1f ' % (
hpars['v1_dec'])
RmagResRec["anisotropy_v3_eta_inc"] = '%7.1f ' % (
hpars['v1_inc'])
else:
RmagResRec["anisotropy_v1_zeta_semi_angle"] = '%7.1f ' % (
hpars['e13'])
RmagResRec["anisotropy_v1_zeta_dec"] = '%7.1f ' % (
hpars['v3_dec'])
RmagResRec["anisotropy_v1_zeta_inc"] = '%7.1f ' % (
hpars['v3_inc'])
RmagResRec["anisotropy_v3_zeta_semi_angle"] = '%7.1f ' % (
hpars['e13'])
RmagResRec["anisotropy_v3_zeta_dec"] = '%7.1f ' % (
hpars['v1_dec'])
RmagResRec["anisotropy_v3_zeta_inc"] = '%7.1f ' % (
hpars['v1_inc'])
RmagResRec["anisotropy_v1_eta_semi_angle"] = '%7.1f ' % (
hpars['e12'])
RmagResRec["anisotropy_v1_eta_dec"] = '%7.1f ' % (
hpars['v2_dec'])
RmagResRec["anisotropy_v1_eta_inc"] = '%7.1f ' % (
hpars['v2_inc'])
RmagResRec["anisotropy_v2_eta_semi_angle"] = '%7.1f ' % (
hpars['e12'])
RmagResRec["anisotropy_v2_eta_dec"] = '%7.1f ' % (
hpars['v1_dec'])
RmagResRec["anisotropy_v2_eta_inc"] = '%7.1f ' % (
hpars['v1_inc'])
if hpars["e23"] > hpars['e12']:
RmagResRec["anisotropy_v2_zeta_semi_angle"] = '%7.1f ' % (
hpars['e23'])
RmagResRec["anisotropy_v2_zeta_dec"] = '%7.1f ' % (
hpars['v3_dec'])
RmagResRec["anisotropy_v2_zeta_inc"] = '%7.1f ' % (
hpars['v3_inc'])
RmagResRec["anisotropy_v3_zeta_semi_angle"] = '%7.1f ' % (
hpars['e23'])
RmagResRec["anisotropy_v3_zeta_dec"] = '%7.1f ' % (
hpars['v2_dec'])
RmagResRec["anisotropy_v3_zeta_inc"] = '%7.1f ' % (
hpars['v2_inc'])
RmagResRec["anisotropy_v3_eta_semi_angle"] = '%7.1f ' % (
hpars['e13'])
RmagResRec["anisotropy_v3_eta_dec"] = '%7.1f ' % (
hpars['v1_dec'])
RmagResRec["anisotropy_v3_eta_inc"] = '%7.1f ' % (
hpars['v1_inc'])
RmagResRec["anisotropy_v2_eta_semi_angle"] = '%7.1f ' % (
hpars['e12'])
RmagResRec["anisotropy_v2_eta_dec"] = '%7.1f ' % (
hpars['v1_dec'])
RmagResRec["anisotropy_v2_eta_inc"] = '%7.1f ' % (
hpars['v1_inc'])
else:
RmagResRec["anisotropy_v2_zeta_semi_angle"] = '%7.1f ' % (
hpars['e12'])
RmagResRec["anisotropy_v2_zeta_dec"] = '%7.1f ' % (
hpars['v1_dec'])
RmagResRec["anisotropy_v2_zeta_inc"] = '%7.1f ' % (
hpars['v1_inc'])
RmagResRec["anisotropy_v3_eta_semi_angle"] = '%7.1f ' % (
hpars['e23'])
RmagResRec["anisotropy_v3_eta_dec"] = '%7.1f ' % (
hpars['v2_dec'])
RmagResRec["anisotropy_v3_eta_inc"] = '%7.1f ' % (
hpars['v2_inc'])
RmagResRec["anisotropy_v3_zeta_semi_angle"] = '%7.1f ' % (
hpars['e13'])
RmagResRec["anisotropy_v3_zeta_dec"] = '%7.1f ' % (
hpars['v1_dec'])
RmagResRec["anisotropy_v3_zeta_inc"] = '%7.1f ' % (
hpars['v1_inc'])
RmagResRec["anisotropy_v2_eta_semi_angle"] = '%7.1f ' % (
hpars['e23'])
RmagResRec["anisotropy_v2_eta_dec"] = '%7.1f ' % (
hpars['v3_dec'])
RmagResRec["anisotropy_v2_eta_inc"] = '%7.1f ' % (
hpars['v3_inc'])
RmagResRec["tilt_correction"] = '-1'
RmagResRec["anisotropy_type"] = 'ATRM'
RmagResRec["magic_method_codes"] = 'LP-AN-TRM:AE-H'
RmagSpecRec["magic_method_codes"] = 'LP-AN-TRM:AE-H'
RmagResRec["magic_software_packages"] = pmag.get_version()
RmagSpecRec["magic_software_packages"] = pmag.get_version()
RmagSpecRecs.append(RmagSpecRec)
RmagResRecs.append(RmagResRec)
specimen += 1
if data_model_num == 3:
SpecRec = RmagResRec.copy()
SpecRec.update(RmagSpecRec)
SpecRecs.append(SpecRec)
# finished iterating through specimens,
# now we need to write out the data to files
if data_model_num == 3:
# translate records
for rec in SpecRecs:
rec3 = map_magic.convert_aniso('magic3', rec)
SpecRecs3.append(rec3)
# write output to 3.0 specimens file
pmag.magic_write(spec_file, SpecRecs3, 'specimens')
print("specimen data stored in {}".format(spec_file))
else:
# write output to 2.5 rmag_ files
pmag.magic_write(rmag_anis, RmagSpecRecs, 'rmag_anisotropy')
print("specimen tensor elements stored in ", rmag_anis)
pmag.magic_write(rmag_res, RmagResRecs, 'rmag_results')
print("specimen statistics and eigenparameters stored in ", rmag_res)
def main():
"""
NAME
aarm_magic.py
DESCRIPTION
Converts AARM data to best-fit tensor (6 elements plus sigma)
Original program ARMcrunch written to accomodate ARM anisotropy data
collected from 6 axial directions (+X,+Y,+Z,-X,-Y,-Z) using the
off-axis remanence terms to construct the tensor. A better way to
do the anisotropy of ARMs is to use 9,12 or 15 measurements in
the Hext rotational scheme.
SYNTAX
aarm_magic.py [-h][command line options]
OPTIONS
-h prints help message and quits
-usr USER: identify user, default is ""
-f FILE: specify input file, default is aarm_measurements.txt
-crd [s,g,t] specify coordinate system, requires samples file
-fsa FILE: specify er_samples.txt file, default is er_samples.txt (2.5) or samples.txt (3.0)
-Fa FILE: specify anisotropy output file, default is arm_anisotropy.txt (MagIC 2.5 only)
-Fr FILE: specify results output file, default is aarm_results.txt (MagIC 2.5 only)
-Fsi FILE: specify output file, default is specimens.txt (MagIC 3 only)
-DM DATA_MODEL: specify MagIC 2 or MagIC 3, default is 3
INPUT
Input for the present program is a series of baseline, ARM pairs.
The baseline should be the AF demagnetized state (3 axis demag is
preferable) for the following ARM acquisition. The order of the
measurements is:
positions 1,2,3, 6,7,8, 11,12,13 (for 9 positions)
positions 1,2,3,4, 6,7,8,9, 11,12,13,14 (for 12 positions)
positions 1-15 (for 15 positions)
"""
# initialize some parameters
args = sys.argv
if "-h" in args:
print(main.__doc__)
sys.exit()
user = ""
meas_file = "aarm_measurements.txt"
rmag_anis = "arm_anisotropy.txt"
rmag_res = "aarm_results.txt"
dir_path = '.'
#
# get name of file from command line
#
data_model_num = int(pmag.get_named_arg_from_sys("-DM", 3))
spec_file = pmag.get_named_arg_from_sys("-Fsi", "specimens.txt")
if data_model_num == 3:
samp_file = pmag.get_named_arg_from_sys("-fsa", "samples.txt")
else:
samp_file = pmag.get_named_arg_from_sys("-fsa", "er_samples.txt")
if '-WD' in args:
ind = args.index('-WD')
dir_path = args[ind + 1]
if "-usr" in args:
ind = args.index("-usr")
user = sys.argv[ind + 1]
if "-f" in args:
ind = args.index("-f")
meas_file = sys.argv[ind + 1]
coord = '-1'
if "-crd" in sys.argv:
ind = sys.argv.index("-crd")
coord = sys.argv[ind + 1]
if coord == 's':
coord = '-1'
if coord == 'g':
coord = '0'
if coord == 't':
coord = '100'
if "-Fa" in args:
ind = args.index("-Fa")
rmag_anis = args[ind + 1]
if "-Fr" in args:
ind = args.index("-Fr")
rmag_res = args[ind + 1]
meas_file = dir_path + '/' + meas_file
samp_file = dir_path + '/' + samp_file
rmag_anis = dir_path + '/' + rmag_anis
rmag_res = dir_path + '/' + rmag_res
spec_file = os.path.join(dir_path, spec_file)
# read in data
# read in data
if data_model_num == 3:
meas_data = []
meas_data3, file_type = pmag.magic_read(meas_file)
if file_type != 'measurements':
print(file_type,
"This is not a valid MagIC 3.0. measurements file ")
sys.exit()
# convert meas_data to 2.5
for rec in meas_data3:
meas_map = map_magic.meas_magic3_2_magic2_map
meas_data.append(map_magic.mapping(rec, meas_map))
else:
meas_data, file_type = pmag.magic_read(meas_file)
if file_type != 'magic_measurements':
print(file_type,
"This is not a valid MagIC 2.5 magic_measurements file ")
sys.exit()
# fish out relevant data
meas_data = pmag.get_dictitem(meas_data, 'magic_method_codes', 'LP-AN-ARM',
'has')
# figure out how to do this with 3 vs. 2.5
if coord != '-1': # need to read in sample data
if data_model_num == 3:
samp_data3, file_type = pmag.magic_read(samp_file)
if file_type != 'samples':
print(file_type, "This is not a valid er_samples file ")
print("Only specimen coordinates will be calculated")
coord = '-1'
else:
# translate to 2
samp_data = []
samp_map = map_magic.samp_magic3_2_magic2_map
for rec in samp_data3:
samp_data.append(map_magic.mapping(rec, samp_map))
else:
samp_data, file_type = pmag.magic_read(samp_file)
if file_type != 'er_samples':
print(file_type, "This is not a valid er_samples file ")
print("Only specimen coordinates will be calculated")
coord = '-1'
#
# sort the specimen names
#
ssort = []
for rec in meas_data:
spec = rec["er_specimen_name"]
if spec not in ssort:
ssort.append(spec)
if len(ssort) > 1:
sids = sorted(ssort)
else:
sids = ssort
#
# work on each specimen
#
specimen = 0
RmagSpecRecs, RmagResRecs = [], []
SpecRecs, SpecRecs3 = [], []
while specimen < len(sids):
s = sids[specimen]
data = []
RmagSpecRec = {}
RmagResRec = {}
method_codes = []
#
# find the data from the meas_data file for this sample
#
data = pmag.get_dictitem(meas_data, 'er_specimen_name', s, 'T')
#
# find out the number of measurements (9, 12 or 15)
#
npos = old_div(len(data), 2)
if npos == 9:
#
# get dec, inc, int and convert to x,y,z
#
# B matrix made from design matrix for positions
B, H, tmpH = pmag.designAARM(npos)
X = []
for rec in data:
Dir = []
Dir.append(float(rec["measurement_dec"]))
Dir.append(float(rec["measurement_inc"]))
Dir.append(float(rec["measurement_magn_moment"]))
X.append(pmag.dir2cart(Dir))
#
# subtract baseline and put in a work array
#
work = numpy.zeros((npos, 3), 'f')
for i in range(npos):
for j in range(3):
work[i][j] = X[2 * i + 1][j] - X[2 * i][j]
#
# calculate tensor elements
# first put ARM components in w vector
#
w = numpy.zeros((npos * 3), 'f')
index = 0
for i in range(npos):
for j in range(3):
w[index] = work[i][j]
index += 1
s = numpy.zeros((6), 'f') # initialize the s matrix
for i in range(6):
for j in range(len(w)):
s[i] += B[i][j] * w[j]
trace = s[0] + s[1] + s[2] # normalize by the trace
for i in range(6):
s[i] = old_div(s[i], trace)
a = pmag.s2a(s)
#------------------------------------------------------------
# Calculating dels is different than in the Kappabridge
# routine. Use trace normalized tensor (a) and the applied
# unit field directions (tmpH) to generate model X,Y,Z
# components. Then compare these with the measured values.
#------------------------------------------------------------
S = 0.
comp = numpy.zeros((npos * 3), 'f')
for i in range(npos):
for j in range(3):
index = i * 3 + j
compare = a[j][0] * tmpH[i][0] + a[j][1] * \
tmpH[i][1] + a[j][2] * tmpH[i][2]
comp[index] = compare
for i in range(npos * 3):
d = old_div(w[i], trace) - comp[i] # del values
S += d * d
nf = float(npos * 3 - 6) # number of degrees of freedom
if S > 0:
sigma = numpy.sqrt(old_div(S, nf))
else:
sigma = 0
RmagSpecRec["rmag_anisotropy_name"] = data[0]["er_specimen_name"]
RmagSpecRec["er_location_name"] = data[0].get(
"er_location_name", "")
RmagSpecRec["er_specimen_name"] = data[0]["er_specimen_name"]
RmagSpecRec["er_sample_name"] = data[0].get("er_sample_name", "")
RmagSpecRec["er_site_name"] = data[0].get("er_site_name", "")
RmagSpecRec["magic_experiment_names"] = RmagSpecRec[
"rmag_anisotropy_name"] + ":AARM"
RmagSpecRec["er_citation_names"] = "This study"
RmagResRec[
"rmag_result_name"] = data[0]["er_specimen_name"] + ":AARM"
RmagResRec["er_location_names"] = data[0].get(
"er_location_name", "")
RmagResRec["er_specimen_names"] = data[0]["er_specimen_name"]
RmagResRec["er_sample_names"] = data[0].get("er_sample_name", "")
RmagResRec["er_site_names"] = data[0].get("er_site_name", "")
RmagResRec["magic_experiment_names"] = RmagSpecRec[
"rmag_anisotropy_name"] + ":AARM"
RmagResRec["er_citation_names"] = "This study"
if "magic_instrument_codes" in list(data[0].keys()):
RmagSpecRec["magic_instrument_codes"] = data[0][
"magic_instrument_codes"]
else:
RmagSpecRec["magic_instrument_codes"] = ""
RmagSpecRec["anisotropy_type"] = "AARM"
RmagSpecRec[
"anisotropy_description"] = "Hext statistics adapted to AARM"
if coord != '-1': # need to rotate s
# set orientation priorities
SO_methods = []
for rec in samp_data:
if "magic_method_codes" not in rec:
rec['magic_method_codes'] = 'SO-NO'
if "magic_method_codes" in rec:
methlist = rec["magic_method_codes"]
for meth in methlist.split(":"):
if "SO" in meth and "SO-POM" not in meth.strip():
if meth.strip() not in SO_methods:
SO_methods.append(meth.strip())
SO_priorities = pmag.set_priorities(SO_methods, 0)
# continue here
redo, p = 1, 0
if len(SO_methods) <= 1:
az_type = SO_methods[0]
orient = pmag.find_samp_rec(RmagSpecRec["er_sample_name"],
samp_data, az_type)
if orient["sample_azimuth"] != "":
method_codes.append(az_type)
redo = 0
while redo == 1:
if p >= len(SO_priorities):
print("no orientation data for ", s)
orient["sample_azimuth"] = ""
orient["sample_dip"] = ""
method_codes.append("SO-NO")
redo = 0
else:
az_type = SO_methods[SO_methods.index(
SO_priorities[p])]
orient = pmag.find_samp_rec(
RmagSpecRec["er_sample_name"], samp_data, az_type)
if orient["sample_azimuth"] != "":
method_codes.append(az_type)
redo = 0
p += 1
az, pl = orient['sample_azimuth'], orient['sample_dip']
s = pmag.dosgeo(s, az, pl) # rotate to geographic coordinates
if coord == '100':
sample_bed_dir, sample_bed_dip = orient[
'sample_bed_dip_direction'], orient['sample_bed_dip']
# rotate to geographic coordinates
s = pmag.dostilt(s, sample_bed_dir, sample_bed_dip)
hpars = pmag.dohext(nf, sigma, s)
#
# prepare for output
#
RmagSpecRec["anisotropy_s1"] = '%8.6f' % (s[0])
RmagSpecRec["anisotropy_s2"] = '%8.6f' % (s[1])
RmagSpecRec["anisotropy_s3"] = '%8.6f' % (s[2])
RmagSpecRec["anisotropy_s4"] = '%8.6f' % (s[3])
RmagSpecRec["anisotropy_s5"] = '%8.6f' % (s[4])
RmagSpecRec["anisotropy_s6"] = '%8.6f' % (s[5])
RmagSpecRec["anisotropy_mean"] = '%8.3e' % (old_div(trace, 3))
RmagSpecRec["anisotropy_sigma"] = '%8.6f' % (sigma)
RmagSpecRec["anisotropy_unit"] = "Am^2"
RmagSpecRec["anisotropy_n"] = '%i' % (npos)
RmagSpecRec["anisotropy_tilt_correction"] = coord
# used by thellier_gui - must be taken out for uploading
RmagSpecRec["anisotropy_F"] = '%7.1f ' % (hpars["F"])
# used by thellier_gui - must be taken out for uploading
RmagSpecRec["anisotropy_F_crit"] = hpars["F_crit"]
RmagResRec["anisotropy_t1"] = '%8.6f ' % (hpars["t1"])
RmagResRec["anisotropy_t2"] = '%8.6f ' % (hpars["t2"])
RmagResRec["anisotropy_t3"] = '%8.6f ' % (hpars["t3"])
RmagResRec["anisotropy_v1_dec"] = '%7.1f ' % (hpars["v1_dec"])
RmagResRec["anisotropy_v2_dec"] = '%7.1f ' % (hpars["v2_dec"])
RmagResRec["anisotropy_v3_dec"] = '%7.1f ' % (hpars["v3_dec"])
RmagResRec["anisotropy_v1_inc"] = '%7.1f ' % (hpars["v1_inc"])
RmagResRec["anisotropy_v2_inc"] = '%7.1f ' % (hpars["v2_inc"])
RmagResRec["anisotropy_v3_inc"] = '%7.1f ' % (hpars["v3_inc"])
RmagResRec["anisotropy_ftest"] = '%7.1f ' % (hpars["F"])
RmagResRec["anisotropy_ftest12"] = '%7.1f ' % (hpars["F12"])
RmagResRec["anisotropy_ftest23"] = '%7.1f ' % (hpars["F23"])
RmagResRec["result_description"] = 'Critical F: ' + \
hpars["F_crit"] + ';Critical F12/F13: ' + hpars["F12_crit"]
if hpars["e12"] > hpars["e13"]:
RmagResRec["anisotropy_v1_zeta_semi_angle"] = '%7.1f ' % (
hpars['e12'])
RmagResRec["anisotropy_v1_zeta_dec"] = '%7.1f ' % (
hpars['v2_dec'])
RmagResRec["anisotropy_v1_zeta_inc"] = '%7.1f ' % (
hpars['v2_inc'])
RmagResRec["anisotropy_v2_zeta_semi_angle"] = '%7.1f ' % (
hpars['e12'])
RmagResRec["anisotropy_v2_zeta_dec"] = '%7.1f ' % (
hpars['v1_dec'])
RmagResRec["anisotropy_v2_zeta_inc"] = '%7.1f ' % (
hpars['v1_inc'])
RmagResRec["anisotropy_v1_eta_semi_angle"] = '%7.1f ' % (
hpars['e13'])
RmagResRec["anisotropy_v1_eta_dec"] = '%7.1f ' % (
hpars['v3_dec'])
RmagResRec["anisotropy_v1_eta_inc"] = '%7.1f ' % (
hpars['v3_inc'])
RmagResRec["anisotropy_v3_eta_semi_angle"] = '%7.1f ' % (
hpars['e13'])
RmagResRec["anisotropy_v3_eta_dec"] = '%7.1f ' % (
hpars['v1_dec'])
RmagResRec["anisotropy_v3_eta_inc"] = '%7.1f ' % (
hpars['v1_inc'])
else:
RmagResRec["anisotropy_v1_zeta_semi_angle"] = '%7.1f ' % (
hpars['e13'])
RmagResRec["anisotropy_v1_zeta_dec"] = '%7.1f ' % (
hpars['v3_dec'])
RmagResRec["anisotropy_v1_zeta_inc"] = '%7.1f ' % (
hpars['v3_inc'])
RmagResRec["anisotropy_v3_zeta_semi_angle"] = '%7.1f ' % (
hpars['e13'])
RmagResRec["anisotropy_v3_zeta_dec"] = '%7.1f ' % (
hpars['v1_dec'])
RmagResRec["anisotropy_v3_zeta_inc"] = '%7.1f ' % (
hpars['v1_inc'])
RmagResRec["anisotropy_v1_eta_semi_angle"] = '%7.1f ' % (
hpars['e12'])
RmagResRec["anisotropy_v1_eta_dec"] = '%7.1f ' % (
hpars['v2_dec'])
RmagResRec["anisotropy_v1_eta_inc"] = '%7.1f ' % (
hpars['v2_inc'])
RmagResRec["anisotropy_v2_eta_semi_angle"] = '%7.1f ' % (
hpars['e12'])
RmagResRec["anisotropy_v2_eta_dec"] = '%7.1f ' % (
hpars['v1_dec'])
RmagResRec["anisotropy_v2_eta_inc"] = '%7.1f ' % (
hpars['v1_inc'])
if hpars["e23"] > hpars['e12']:
RmagResRec["anisotropy_v2_zeta_semi_angle"] = '%7.1f ' % (
hpars['e23'])
RmagResRec["anisotropy_v2_zeta_dec"] = '%7.1f ' % (
hpars['v3_dec'])
RmagResRec["anisotropy_v2_zeta_inc"] = '%7.1f ' % (
hpars['v3_inc'])
RmagResRec["anisotropy_v3_zeta_semi_angle"] = '%7.1f ' % (
hpars['e23'])
RmagResRec["anisotropy_v3_zeta_dec"] = '%7.1f ' % (
hpars['v2_dec'])
RmagResRec["anisotropy_v3_zeta_inc"] = '%7.1f ' % (
hpars['v2_inc'])
RmagResRec["anisotropy_v3_eta_semi_angle"] = '%7.1f ' % (
hpars['e13'])
RmagResRec["anisotropy_v3_eta_dec"] = '%7.1f ' % (
hpars['v1_dec'])
RmagResRec["anisotropy_v3_eta_inc"] = '%7.1f ' % (
hpars['v1_inc'])
RmagResRec["anisotropy_v2_eta_semi_angle"] = '%7.1f ' % (
hpars['e12'])
RmagResRec["anisotropy_v2_eta_dec"] = '%7.1f ' % (
hpars['v1_dec'])
RmagResRec["anisotropy_v2_eta_inc"] = '%7.1f ' % (
hpars['v1_inc'])
else:
RmagResRec["anisotropy_v2_zeta_semi_angle"] = '%7.1f ' % (
hpars['e12'])
RmagResRec["anisotropy_v2_zeta_dec"] = '%7.1f ' % (
hpars['v1_dec'])
RmagResRec["anisotropy_v2_zeta_inc"] = '%7.1f ' % (
hpars['v1_inc'])
RmagResRec["anisotropy_v3_eta_semi_angle"] = '%7.1f ' % (
hpars['e23'])
RmagResRec["anisotropy_v3_eta_dec"] = '%7.1f ' % (
hpars['v2_dec'])
RmagResRec["anisotropy_v3_eta_inc"] = '%7.1f ' % (
hpars['v2_inc'])
RmagResRec["anisotropy_v3_zeta_semi_angle"] = '%7.1f ' % (
hpars['e13'])
RmagResRec["anisotropy_v3_zeta_dec"] = '%7.1f ' % (
hpars['v1_dec'])
RmagResRec["anisotropy_v3_zeta_inc"] = '%7.1f ' % (
hpars['v1_inc'])
RmagResRec["anisotropy_v2_eta_semi_angle"] = '%7.1f ' % (
hpars['e23'])
RmagResRec["anisotropy_v2_eta_dec"] = '%7.1f ' % (
hpars['v3_dec'])
RmagResRec["anisotropy_v2_eta_inc"] = '%7.1f ' % (
hpars['v3_inc'])
RmagResRec["tilt_correction"] = '-1'
RmagResRec["anisotropy_type"] = 'AARM'
RmagResRec["magic_method_codes"] = 'LP-AN-ARM:AE-H'
RmagSpecRec["magic_method_codes"] = 'LP-AN-ARM:AE-H'
RmagResRec["magic_software_packages"] = pmag.get_version()
RmagSpecRec["magic_software_packages"] = pmag.get_version()
specimen += 1
RmagSpecRecs.append(RmagSpecRec)
RmagResRecs.append(RmagResRec)
if data_model_num == 3:
SpecRec = RmagResRec.copy()
SpecRec.update(RmagSpecRec)
SpecRecs.append(SpecRec)
else:
print('skipping specimen ', s, ' only 9 positions supported',
'; this has ', npos)
specimen += 1
if data_model_num == 3:
# translate records
for rec in SpecRecs:
rec3 = map_magic.convert_aniso('magic3', rec)
SpecRecs3.append(rec3)
# write output to 3.0 specimens file
pmag.magic_write(spec_file, SpecRecs3, 'specimens')
print("specimen data stored in {}".format(spec_file))
else:
if rmag_anis == "":
rmag_anis = "rmag_anisotropy.txt"
pmag.magic_write(rmag_anis, RmagSpecRecs, 'rmag_anisotropy')
print("specimen tensor elements stored in ", rmag_anis)
if rmag_res == "":
rmag_res = "rmag_results.txt"
pmag.magic_write(rmag_res, RmagResRecs, 'rmag_results')
print("specimen statistics and eigenparameters stored in ", rmag_res)
def main():
"""
NAME
atrm_magic.py
DESCRIPTION
Converts ATRM data to best-fit tensor (6 elements plus sigma)
Original program ARMcrunch written to accomodate ARM anisotropy data
collected from 6 axial directions (+X,+Y,+Z,-X,-Y,-Z) using the
off-axis remanence terms to construct the tensor. A better way to
do the anisotropy of ARMs is to use 9,12 or 15 measurements in
the Hext rotational scheme.
SYNTAX
atrm_magic.py [-h][command line options]
OPTIONS
-h prints help message and quits
-usr USER: identify user, default is ""
-f FILE: specify input file, default is atrm_measurements.txt
-Fa FILE: specify anisotropy output file, default is trm_anisotropy.txt (MagIC 2.5 only)
-Fr FILE: specify results output file, default is atrm_results.txt (MagIC 2.5 only)
-Fsi FILE: specify output file, default is specimens.txt (MagIC 3 only)
-DM DATA_MODEL: specify MagIC 2 or MagIC 3, default is 3
INPUT
Input for the present program is a TRM acquisition data with an optional baseline.
The order of the measurements is:
Decs=[0,90,0,180,270,0,0,90,0]
Incs=[0,0,90,0,0,-90,0,0,90]
The last two measurements are optional
"""
# initialize some parameters
args = sys.argv
user = ""
meas_file = "atrm_measurements.txt"
rmag_anis = "trm_anisotropy.txt"
rmag_res = "atrm_results.txt"
dir_path = '.'
#
# get name of file from command line
#
if '-WD' in args:
ind = args.index('-WD')
dir_path = args[ind + 1]
if "-h" in args:
print(main.__doc__)
sys.exit()
if "-usr" in args:
ind = args.index("-usr")
user = sys.argv[ind + 1]
if "-f" in args:
ind = args.index("-f")
meas_file = sys.argv[ind + 1]
if "-Fa" in args:
ind = args.index("-Fa")
rmag_anis = args[ind + 1]
if "-Fr" in args:
ind = args.index("-Fr")
rmag_res = args[ind + 1]
data_model_num = int(pmag.get_named_arg_from_sys("-DM", 3))
spec_file = pmag.get_named_arg_from_sys("-Fsi", "specimens.txt")
spec_file = os.path.join(dir_path, spec_file)
meas_file = dir_path + '/' + meas_file
rmag_anis = dir_path + '/' + rmag_anis
rmag_res = dir_path + '/' + rmag_res
# read in data
if data_model_num == 3:
meas_data = []
meas_data3, file_type = pmag.magic_read(meas_file)
if file_type != 'measurements':
print(file_type, "This is not a valid measurements file ")
sys.exit()
# convert meas_data to 2.5
for rec in meas_data3:
meas_map = map_magic.meas_magic3_2_magic2_map
meas_data.append(map_magic.mapping(rec, meas_map))
else:
meas_data, file_type = pmag.magic_read(meas_file)
if file_type != 'magic_measurements':
print(file_type, "This is not a valid magic_measurements file ")
sys.exit()
meas_data = pmag.get_dictitem(meas_data, 'magic_method_codes', 'LP-AN-TRM',
'has')
#
#
# get sorted list of unique specimen names
ssort = []
for rec in meas_data:
spec = rec["er_specimen_name"]
if spec not in ssort:
ssort.append(spec)
sids = sorted(ssort)
#
#
# work on each specimen
#
specimen, npos = 0, 6
RmagSpecRecs, RmagResRecs = [], []
SpecRecs, SpecRecs3 = [], []
while specimen < len(sids):
nmeas = 0
s = sids[specimen]
RmagSpecRec = {}
RmagResRec = {}
BX, X = [], []
method_codes = []
Spec0 = ""
#
# find the data from the meas_data file for this sample
# and get dec, inc, int and convert to x,y,z
#
# fish out data for this specimen name
data = pmag.get_dictitem(meas_data, 'er_specimen_name', s, 'T')
if len(data) > 5:
RmagSpecRec["rmag_anisotropy_name"] = data[0]["er_specimen_name"]
RmagSpecRec["er_location_name"] = data[0].get(
"er_location_name", "")
RmagSpecRec["er_specimen_name"] = data[0]["er_specimen_name"]
RmagSpecRec["er_sample_name"] = data[0].get("er_sample_name", "")
RmagSpecRec["er_site_name"] = data[0].get("er_site_name", "")
RmagSpecRec["magic_experiment_names"] = RmagSpecRec[
"rmag_anisotropy_name"] + ":ATRM"
RmagSpecRec["er_citation_names"] = "This study"
RmagResRec[
"rmag_result_name"] = data[0]["er_specimen_name"] + ":ATRM"
RmagResRec["er_location_names"] = data[0].get(
"er_location_names", "")
RmagResRec["er_specimen_names"] = data[0]["er_specimen_name"]
RmagResRec["er_sample_names"] = data[0].get("er_sample_name", "")
RmagResRec["er_site_names"] = data[0].get("er_site_name", "")
RmagResRec["magic_experiment_names"] = RmagSpecRec[
"rmag_anisotropy_name"] + ":ATRM"
RmagResRec["er_citation_names"] = "This study"
RmagSpecRec["anisotropy_type"] = "ATRM"
if "magic_instrument_codes" in list(data[0].keys()):
RmagSpecRec["magic_instrument_codes"] = data[0][
"magic_instrument_codes"]
else:
RmagSpecRec["magic_instrument_codes"] = ""
RmagSpecRec[
"anisotropy_description"] = "Hext statistics adapted to ATRM"
for rec in data:
meths = rec['magic_method_codes'].strip().split(':')
Dir = []
Dir.append(float(rec["measurement_dec"]))
Dir.append(float(rec["measurement_inc"]))
Dir.append(float(rec["measurement_magn_moment"]))
if "LT-T-Z" in meths:
BX.append(pmag.dir2cart(Dir)) # append baseline steps
elif "LT-T-I" in meths:
X.append(pmag.dir2cart(Dir))
nmeas += 1
#
if len(BX) == 1:
for i in range(len(X) - 1):
BX.append(BX[0]) # assume first 0 field step as baseline
elif len(BX) == 0: # assume baseline is zero
for i in range(len(X)):
BX.append([0., 0., 0.]) # assume baseline of 0
elif len(BX) != len(
X
): # if BX isn't just one measurement or one in between every infield step, just assume it is zero
print('something odd about the baselines - just assuming zero')
for i in range(len(X)):
BX.append([0., 0., 0.]) # assume baseline of 0
if nmeas < 6: # must have at least 6 measurements right now -
print('skipping specimen ', s, ' too few measurements')
specimen += 1
else:
# B matrix made from design matrix for positions
B, H, tmpH = pmag.designATRM(npos)
#
# subtract optional baseline and put in a work array
#
work = numpy.zeros((nmeas, 3), 'f')
for i in range(nmeas):
for j in range(3):
# subtract baseline, if available
work[i][j] = X[i][j] - BX[i][j]
#
# calculate tensor elements
# first put ARM components in w vector
#
w = numpy.zeros((npos * 3), 'f')
index = 0
for i in range(npos):
for j in range(3):
w[index] = work[i][j]
index += 1
s = numpy.zeros((6), 'f') # initialize the s matrix
for i in range(6):
for j in range(len(w)):
s[i] += B[i][j] * w[j]
trace = s[0] + s[1] + s[2] # normalize by the trace
for i in range(6):
s[i] = old_div(s[i], trace)
a = pmag.s2a(s)
#------------------------------------------------------------
# Calculating dels is different than in the Kappabridge
# routine. Use trace normalized tensor (a) and the applied
# unit field directions (tmpH) to generate model X,Y,Z
# components. Then compare these with the measured values.
#------------------------------------------------------------
S = 0.
comp = numpy.zeros((npos * 3), 'f')
for i in range(npos):
for j in range(3):
index = i * 3 + j
compare = a[j][0] * tmpH[i][0] + a[j][1] * \
tmpH[i][1] + a[j][2] * tmpH[i][2]
comp[index] = compare
for i in range(npos * 3):
d = old_div(w[i], trace) - comp[i] # del values
S += d * d
nf = float(npos * 3. - 6.) # number of degrees of freedom
if S > 0:
sigma = numpy.sqrt(old_div(S, nf))
else:
sigma = 0
hpars = pmag.dohext(nf, sigma, s)
#
# prepare for output
#
RmagSpecRec["anisotropy_s1"] = '%8.6f' % (s[0])
RmagSpecRec["anisotropy_s2"] = '%8.6f' % (s[1])
RmagSpecRec["anisotropy_s3"] = '%8.6f' % (s[2])
RmagSpecRec["anisotropy_s4"] = '%8.6f' % (s[3])
RmagSpecRec["anisotropy_s5"] = '%8.6f' % (s[4])
RmagSpecRec["anisotropy_s6"] = '%8.6f' % (s[5])
RmagSpecRec["anisotropy_mean"] = '%8.3e' % (old_div(trace, 3))
RmagSpecRec["anisotropy_sigma"] = '%8.6f' % (sigma)
RmagSpecRec["anisotropy_unit"] = "Am^2"
RmagSpecRec["anisotropy_n"] = '%i' % (npos)
RmagSpecRec["anisotropy_tilt_correction"] = '-1'
# used by thellier_gui - must be taken out for uploading
RmagSpecRec["anisotropy_F"] = '%7.1f ' % (hpars["F"])
# used by thellier_gui - must be taken out for uploading
RmagSpecRec["anisotropy_F_crit"] = hpars["F_crit"]
RmagResRec["anisotropy_t1"] = '%8.6f ' % (hpars["t1"])
RmagResRec["anisotropy_t2"] = '%8.6f ' % (hpars["t2"])
RmagResRec["anisotropy_t3"] = '%8.6f ' % (hpars["t3"])
RmagResRec["anisotropy_v1_dec"] = '%7.1f ' % (hpars["v1_dec"])
RmagResRec["anisotropy_v2_dec"] = '%7.1f ' % (hpars["v2_dec"])
RmagResRec["anisotropy_v3_dec"] = '%7.1f ' % (hpars["v3_dec"])
RmagResRec["anisotropy_v1_inc"] = '%7.1f ' % (hpars["v1_inc"])
RmagResRec["anisotropy_v2_inc"] = '%7.1f ' % (hpars["v2_inc"])
RmagResRec["anisotropy_v3_inc"] = '%7.1f ' % (hpars["v3_inc"])
RmagResRec["anisotropy_ftest"] = '%7.1f ' % (hpars["F"])
RmagResRec["anisotropy_ftest12"] = '%7.1f ' % (hpars["F12"])
RmagResRec["anisotropy_ftest23"] = '%7.1f ' % (hpars["F23"])
RmagResRec["result_description"] = 'Critical F: ' + \
hpars["F_crit"] + ';Critical F12/F13: ' + hpars["F12_crit"]
if hpars["e12"] > hpars["e13"]:
RmagResRec["anisotropy_v1_zeta_semi_angle"] = '%7.1f ' % (
hpars['e12'])
RmagResRec["anisotropy_v1_zeta_dec"] = '%7.1f ' % (
hpars['v2_dec'])
RmagResRec["anisotropy_v1_zeta_inc"] = '%7.1f ' % (
hpars['v2_inc'])
RmagResRec["anisotropy_v2_zeta_semi_angle"] = '%7.1f ' % (
hpars['e12'])
RmagResRec["anisotropy_v2_zeta_dec"] = '%7.1f ' % (
hpars['v1_dec'])
RmagResRec["anisotropy_v2_zeta_inc"] = '%7.1f ' % (
hpars['v1_inc'])
RmagResRec["anisotropy_v1_eta_semi_angle"] = '%7.1f ' % (
hpars['e13'])
RmagResRec["anisotropy_v1_eta_dec"] = '%7.1f ' % (
hpars['v3_dec'])
RmagResRec["anisotropy_v1_eta_inc"] = '%7.1f ' % (
hpars['v3_inc'])
RmagResRec["anisotropy_v3_eta_semi_angle"] = '%7.1f ' % (
hpars['e13'])
RmagResRec["anisotropy_v3_eta_dec"] = '%7.1f ' % (
hpars['v1_dec'])
RmagResRec["anisotropy_v3_eta_inc"] = '%7.1f ' % (
hpars['v1_inc'])
else:
RmagResRec["anisotropy_v1_zeta_semi_angle"] = '%7.1f ' % (
hpars['e13'])
RmagResRec["anisotropy_v1_zeta_dec"] = '%7.1f ' % (
hpars['v3_dec'])
RmagResRec["anisotropy_v1_zeta_inc"] = '%7.1f ' % (
hpars['v3_inc'])
RmagResRec["anisotropy_v3_zeta_semi_angle"] = '%7.1f ' % (
hpars['e13'])
RmagResRec["anisotropy_v3_zeta_dec"] = '%7.1f ' % (
hpars['v1_dec'])
RmagResRec["anisotropy_v3_zeta_inc"] = '%7.1f ' % (
hpars['v1_inc'])
RmagResRec["anisotropy_v1_eta_semi_angle"] = '%7.1f ' % (
hpars['e12'])
RmagResRec["anisotropy_v1_eta_dec"] = '%7.1f ' % (
hpars['v2_dec'])
RmagResRec["anisotropy_v1_eta_inc"] = '%7.1f ' % (
hpars['v2_inc'])
RmagResRec["anisotropy_v2_eta_semi_angle"] = '%7.1f ' % (
hpars['e12'])
RmagResRec["anisotropy_v2_eta_dec"] = '%7.1f ' % (
hpars['v1_dec'])
RmagResRec["anisotropy_v2_eta_inc"] = '%7.1f ' % (
hpars['v1_inc'])
if hpars["e23"] > hpars['e12']:
RmagResRec["anisotropy_v2_zeta_semi_angle"] = '%7.1f ' % (
hpars['e23'])
RmagResRec["anisotropy_v2_zeta_dec"] = '%7.1f ' % (
hpars['v3_dec'])
RmagResRec["anisotropy_v2_zeta_inc"] = '%7.1f ' % (
hpars['v3_inc'])
RmagResRec["anisotropy_v3_zeta_semi_angle"] = '%7.1f ' % (
hpars['e23'])
RmagResRec["anisotropy_v3_zeta_dec"] = '%7.1f ' % (
hpars['v2_dec'])
RmagResRec["anisotropy_v3_zeta_inc"] = '%7.1f ' % (
hpars['v2_inc'])
RmagResRec["anisotropy_v3_eta_semi_angle"] = '%7.1f ' % (
hpars['e13'])
RmagResRec["anisotropy_v3_eta_dec"] = '%7.1f ' % (
hpars['v1_dec'])
RmagResRec["anisotropy_v3_eta_inc"] = '%7.1f ' % (
hpars['v1_inc'])
RmagResRec["anisotropy_v2_eta_semi_angle"] = '%7.1f ' % (
hpars['e12'])
RmagResRec["anisotropy_v2_eta_dec"] = '%7.1f ' % (
hpars['v1_dec'])
RmagResRec["anisotropy_v2_eta_inc"] = '%7.1f ' % (
hpars['v1_inc'])
else:
RmagResRec["anisotropy_v2_zeta_semi_angle"] = '%7.1f ' % (
hpars['e12'])
RmagResRec["anisotropy_v2_zeta_dec"] = '%7.1f ' % (
hpars['v1_dec'])
RmagResRec["anisotropy_v2_zeta_inc"] = '%7.1f ' % (
hpars['v1_inc'])
RmagResRec["anisotropy_v3_eta_semi_angle"] = '%7.1f ' % (
hpars['e23'])
RmagResRec["anisotropy_v3_eta_dec"] = '%7.1f ' % (
hpars['v2_dec'])
RmagResRec["anisotropy_v3_eta_inc"] = '%7.1f ' % (
hpars['v2_inc'])
RmagResRec["anisotropy_v3_zeta_semi_angle"] = '%7.1f ' % (
hpars['e13'])
RmagResRec["anisotropy_v3_zeta_dec"] = '%7.1f ' % (
hpars['v1_dec'])
RmagResRec["anisotropy_v3_zeta_inc"] = '%7.1f ' % (
hpars['v1_inc'])
RmagResRec["anisotropy_v2_eta_semi_angle"] = '%7.1f ' % (
hpars['e23'])
RmagResRec["anisotropy_v2_eta_dec"] = '%7.1f ' % (
hpars['v3_dec'])
RmagResRec["anisotropy_v2_eta_inc"] = '%7.1f ' % (
hpars['v3_inc'])
RmagResRec["tilt_correction"] = '-1'
RmagResRec["anisotropy_type"] = 'ATRM'
RmagResRec["magic_method_codes"] = 'LP-AN-TRM:AE-H'
RmagSpecRec["magic_method_codes"] = 'LP-AN-TRM:AE-H'
RmagResRec["magic_software_packages"] = pmag.get_version()
RmagSpecRec["magic_software_packages"] = pmag.get_version()
RmagSpecRecs.append(RmagSpecRec)
RmagResRecs.append(RmagResRec)
specimen += 1
if data_model_num == 3:
SpecRec = RmagResRec.copy()
SpecRec.update(RmagSpecRec)
SpecRecs.append(SpecRec)
# finished iterating through specimens,
# now we need to write out the data to files
if data_model_num == 3:
# translate records
for rec in SpecRecs:
rec3 = map_magic.convert_aniso('magic3', rec)
SpecRecs3.append(rec3)
# write output to 3.0 specimens file
pmag.magic_write(spec_file, SpecRecs3, 'specimens')
print("specimen data stored in {}".format(spec_file))
else:
# write output to 2.5 rmag_ files
pmag.magic_write(rmag_anis, RmagSpecRecs, 'rmag_anisotropy')
print("specimen tensor elements stored in ", rmag_anis)
pmag.magic_write(rmag_res, RmagResRecs, 'rmag_results')
print("specimen statistics and eigenparameters stored in ", rmag_res)