def average_duplicates(duplicates):
'''
avarage replicate measurements.
'''
carts_s,carts_g,carts_t=[],[],[]
for rec in duplicates:
moment=float(rec['moment'])
if 'dec_s' in list(rec.keys()) and 'inc_s' in list(rec.keys()):
if rec['dec_s']!="" and rec['inc_s']!="":
dec_s=float(rec['dec_s'])
inc_s=float(rec['inc_s'])
cart_s=pmag.dir2cart([dec_s,inc_s,moment])
carts_s.append(cart_s)
if 'dec_g' in list(rec.keys()) and 'inc_g' in list(rec.keys()):
if rec['dec_g']!="" and rec['inc_g']!="":
dec_g=float(rec['dec_g'])
inc_g=float(rec['inc_g'])
cart_g=pmag.dir2cart([dec_g,inc_g,moment])
carts_g.append(cart_g)
if 'dec_t' in list(rec.keys()) and 'inc_t' in list(rec.keys()):
if rec['dec_t']!="" and rec['inc_t']!="":
dec_t=float(rec['dec_t'])
inc_t=float(rec['inc_t'])
cart_t=pmag.dir2cart([dec_t,inc_t,moment])
carts_t.append(cart_t)
if len(carts_s)>0:
carts=scipy.array(carts_s)
x_mean=scipy.mean(carts[:,0])
y_mean=scipy.mean(carts[:,1])
z_mean=scipy.mean(carts[:,2])
mean_dir=pmag.cart2dir([x_mean,y_mean,z_mean])
mean_dec_s="%.2f"%mean_dir[0]
mean_inc_s="%.2f"%mean_dir[1]
mean_moment="%10.3e"%mean_dir[2]
else:
mean_dec_s,mean_inc_s="",""
if len(carts_g)>0:
carts=scipy.array(carts_g)
x_mean=scipy.mean(carts[:,0])
y_mean=scipy.mean(carts[:,1])
z_mean=scipy.mean(carts[:,2])
mean_dir=pmag.cart2dir([x_mean,y_mean,z_mean])
mean_dec_g="%.2f"%mean_dir[0]
mean_inc_g="%.2f"%mean_dir[1]
mean_moment="%10.3e"%mean_dir[2]
else:
mean_dec_g,mean_inc_g="",""
if len(carts_t)>0:
carts=scipy.array(carts_t)
x_mean=scipy.mean(carts[:,0])
y_mean=scipy.mean(carts[:,1])
z_mean=scipy.mean(carts[:,2])
mean_dir=pmag.cart2dir([x_mean,y_mean,z_mean])
mean_dec_t="%.2f"%mean_dir[0]
mean_inc_t="%.2f"%mean_dir[1]
mean_moment="%10.3e"%mean_dir[2]
else:
mean_dec_t,mean_inc_t="",""
meanrec={}
for key in list(duplicates[0].keys()):
if key in ['dec_s','inc_s','dec_g','inc_g','dec_t','inc_t','moment']:
continue
else:
meanrec[key]=duplicates[0][key]
meanrec['dec_s']=mean_dec_s
meanrec['dec_g']=mean_dec_g
meanrec['dec_t']=mean_dec_t
meanrec['inc_s']=mean_inc_s
meanrec['inc_g']=mean_inc_g
meanrec['inc_t']=mean_inc_t
meanrec['moment']=mean_moment
return meanrec
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
-Fr FILE: specify results output file, default is atrm_results.txt
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]
meas_file = dir_path + '/' + meas_file
rmag_anis = dir_path + '/' + rmag_anis
rmag_res = dir_path + '/' + rmag_res
# read in data
meas_data, file_type = pmag.magic_read(meas_file)
meas_data = pmag.get_dictitem(meas_data, 'magic_method_codes', 'LP-AN-TRM',
'has')
if file_type != 'magic_measurements':
print(file_type)
print(file_type, "This is not a valid magic_measurements file ")
sys.exit()
#
#
# 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 = [], []
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
#
data = pmag.get_dictitem(meas_data, 'er_specimen_name', s,
'T') # fish out data for this specimen name
if len(data) > 5:
RmagSpecRec["rmag_anisotropy_name"] = data[0]["er_specimen_name"]
RmagSpecRec["er_location_name"] = data[0]["er_location_name"]
RmagSpecRec["er_specimen_name"] = data[0]["er_specimen_name"]
RmagSpecRec["er_sample_name"] = data[0]["er_sample_name"]
RmagSpecRec["er_site_name"] = data[0]["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]["er_location_name"]
RmagResRec["er_specimen_names"] = data[0]["er_specimen_name"]
RmagResRec["er_sample_names"] = data[0]["er_sample_name"]
RmagResRec["er_site_names"] = data[0]["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, H, tmpH = pmag.designATRM(
npos) # B matrix made from design matrix for positions
#
# 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):
work[i][j] = X[i][j] - BX[i][
j] # subtract baseline, if available
#
# 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'
RmagSpecRec["anisotropy_F"] = '%7.1f ' % (
hpars["F"]
) # used by thellier_gui - must be taken out for uploading
RmagSpecRec["anisotropy_F_crit"] = hpars[
"F_crit"] # used by thellier_gui - must be taken out for uploading
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
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 plotMT(datablock, s, num, units, norm):
Ints = []
for plotrec in datablock:
Ints.append(plotrec[3])
Ints.sort()
T, M, Tv, recnum = [], [], [], 0
Mex, Tex, Vdif = [], [], []
recbak = []
for rec in datablock:
if rec[5] == 'g':
if units == "T":
T.append(rec[0] * 1e3)
Tv.append(rec[0] * 1e3)
if recnum > 0: Tv.append(rec[0] * 1e3)
elif units == "U":
T.append(rec[0])
Tv.append(rec[0])
if recnum > 0: Tv.append(rec[0])
elif units == "K":
T.append(rec[0] - 273)
Tv.append(rec[0] - 273)
if recnum > 0: Tv.append(rec[0] - 273)
elif "T" in units and "K" in units:
if rec[0] < 1.:
T.append(rec[0] * 1e3)
Tv.append(rec[0] * 1e3)
else:
T.append(rec[0] - 273)
Tv.append(rec[0] - 273)
if recnum > 0: Tv.append(rec[0] - 273)
else:
T.append(rec[0])
Tv.append(rec[0])
if recnum > 0: Tv.append(rec[0])
if norm == 1:
M.append(rec[3] / Ints[-1])
else:
M.append(rec[3])
if recnum > 0 and len(rec) > 0 and len(recbak) > 0:
v = []
if recbak[0] != rec[0]:
V0 = pmag.dir2cart([recbak[1], recbak[2], recbak[3]])
V1 = pmag.dir2cart([rec[1], rec[2], rec[3]])
for el in range(3):
v.append(abs(V1[el] - V0[el]))
vdir = pmag.cart2dir(v)
Vdif.append(vdir[2] / Ints[-1]) # append vector difference
Vdif.append(vdir[2] / Ints[-1]) #
recbak = []
for el in rec:
recbak.append(el)
delta = .02 * M[0]
if num == 1:
if recnum % 2 == 0:
plt.text(T[-1] + delta,
M[-1],
' ' * 3 + str(int(datablock[recnum][0] - 273)) +
'$\degree$C',
fontsize=9)
recnum += 1
else:
if rec[0] < 200: Tex.append(rec[0] * 1e3)
if rec[0] >= 200: Tex.append(rec[0] - 273)
Mex.append(rec[3] / Ints[-1])
recnum += 1
MTlist = T
MTlisty = M
if len(Mex) > 0 and len(Tex) > 0:
plt.scatter(Tex, Mex, marker='d', color='k')
if len(Vdif) > 0:
Vdif.append(vdir[2] / Ints[-1]) #
Vdif.append(0)
Tv.append(Tv[-1])
plt.plot(T, M)
plt.plot(T, M, 'ro')
if len(Tv) == len(Vdif) and norm == 1: plt.plot(Tv, Vdif, 'g-')
if units == "T":
plt.xlabel("Step (mT)")
elif units == "K":
plt.xlabel("Step (C)")
elif units == "J":
plt.xlabel("Step (J)")
else:
plt.xlabel("Step [mT,C]")
if norm == 1: plt.ylabel("Fractional Magnetization")
if norm == 0: plt.ylabel("Magnetization")
plt.axvline(0, color='k')
plt.axhline(0, color='k')
tstring = s
plt.title(tstring)
def get_PI_parameters(Data, acceptance_criteria, preferences, s, tmin, tmax, GUI_log, THERMAL,MICROWAVE):
datablock = Data[s]['datablock']
pars=copy.deepcopy(Data[s]['pars']) # assignments to pars are assiging to Data[s]['pars']
Pint_pars = spd.PintPars(Data, str(s), tmin, tmax, 'magic', preferences['show_statistics_on_gui'],acceptance_criteria)
Pint_pars.reqd_stats() # calculate only statistics indicated in preferences
if not Pint_pars.pars:
print("Could not get any parameters for {}".format(Pint_pars))
return 0
pars.update(Pint_pars.pars)
t_Arai=Data[s]['t_Arai']
x_Arai=Data[s]['x_Arai']
y_Arai=Data[s]['y_Arai']
x_tail_check=Data[s]['x_tail_check']
y_tail_check=Data[s]['y_tail_check']
zijdblock=Data[s]['zijdblock']
z_temperatures=Data[s]['z_temp']
#print tmin,tmax,z_temperatures
# check tmin
if tmin not in t_Arai or tmin not in z_temperatures:
return(pars)
# check tmax
if tmax not in t_Arai or tmin not in z_temperatures:
return(pars)
start=t_Arai.index(tmin)
end=t_Arai.index(tmax)
zstart=z_temperatures.index(tmin)
zend=z_temperatures.index(tmax)
zdata_segment=Data[s]['zdata'][zstart:zend+1]
# replacing PCA for zdata and for ptrms here
## removed a bunch of Ron's commented out old code
#-------------------------------------------------
# York regresssion (York, 1967) following Coe (1978)
# calculate f,fvds,
# modified from pmag.py
#-------------------------------------------------
x_Arai_segment = x_Arai[start:end+1]
y_Arai_segment = y_Arai[start:end+1]
# replace thellier_gui code for york regression here
pars["specimen_int"]=-1*pars['lab_dc_field']*pars["specimen_b"]
# replace thellier_gui code for ptrm checks, DRAT etc. here
# also tail checks and SCAT
#-------------------------------------------------
# Add missing parts of code from old get_PI
#-------------------------------------------------
if MICROWAVE==True:
LP_code="LP-PI-M"
else:
LP_code="LP-PI-TRM"
count_IZ = Data[s]['steps_Arai'].count('IZ')
count_ZI = Data[s]['steps_Arai'].count('ZI')
if count_IZ > 1 and count_ZI > 1:
pars['magic_method_codes']=LP_code+":"+"LP-PI-BT-IZZI"
elif count_IZ < 1 and count_ZI > 1:
pars['magic_method_codes']=LP_code+":"+"LP-PI-ZI"
elif count_IZ > 1 and count_ZI < 1:
pars['magic_method_codes']=LP_code+":"+"LP-PI-IZ"
else:
pars['magic_method_codes']=LP_code
if 'ptrm_checks_temperatures' in list(Data[s].keys()) and len(Data[s]['ptrm_checks_temperatures'])>0:
if MICROWAVE==True:
pars['magic_method_codes']+=":LP-PI-ALT-PMRM"
else:
pars['magic_method_codes']+=":LP-PI-ALT-PTRM"
if 'tail_check_temperatures' in list(Data[s].keys()) and len(Data[s]['tail_check_temperatures'])>0:
pars['magic_method_codes']+=":LP-PI-BT-MD"
if 'additivity_check_temperatures' in list(Data[s].keys()) and len(Data[s]['additivity_check_temperatures'])>0:
pars['magic_method_codes']+=":LP-PI-BT"
#-------------------------------------------------
# Calculate anisotropy correction factor
#-------------------------------------------------
if "AniSpec" in list(Data[s].keys()):
pars["AC_WARNING"]=""
# if both aarm and atrm tensor axist, try first the aarm. if it fails use the atrm.
if 'AARM' in list(Data[s]["AniSpec"].keys()) and 'ATRM' in list(Data[s]["AniSpec"].keys()):
TYPES=['AARM','ATRM']
else:
TYPES=list(Data[s]["AniSpec"].keys())
for TYPE in TYPES:
red_flag=False
S_matrix=zeros((3,3),'f')
S_matrix[0,0]=Data[s]['AniSpec'][TYPE]['anisotropy_s1']
S_matrix[1,1]=Data[s]['AniSpec'][TYPE]['anisotropy_s2']
S_matrix[2,2]=Data[s]['AniSpec'][TYPE]['anisotropy_s3']
S_matrix[0,1]=Data[s]['AniSpec'][TYPE]['anisotropy_s4']
S_matrix[1,0]=Data[s]['AniSpec'][TYPE]['anisotropy_s4']
S_matrix[1,2]=Data[s]['AniSpec'][TYPE]['anisotropy_s5']
S_matrix[2,1]=Data[s]['AniSpec'][TYPE]['anisotropy_s5']
S_matrix[0,2]=Data[s]['AniSpec'][TYPE]['anisotropy_s6']
S_matrix[2,0]=Data[s]['AniSpec'][TYPE]['anisotropy_s6']
#Data[s]['AniSpec']['anisotropy_type']=Data[s]['AniSpec']['anisotropy_type']
Data[s]['AniSpec'][TYPE]['anisotropy_n']=int(float(Data[s]['AniSpec'][TYPE]['anisotropy_n']))
this_specimen_f_type=Data[s]['AniSpec'][TYPE]['anisotropy_type']+"_"+"%i"%(int(Data[s]['AniSpec'][TYPE]['anisotropy_n']))
Ftest_crit={}
Ftest_crit['ATRM_6']= 3.1059
Ftest_crit['AARM_6']= 3.1059
Ftest_crit['AARM_9']= 2.6848
Ftest_crit['AARM_15']= 2.4558
# threshold value for Ftest:
if 'AniSpec' in list(Data[s].keys()) and TYPE in list(Data[s]['AniSpec'].keys())\
and 'anisotropy_sigma' in list(Data[s]['AniSpec'][TYPE].keys()) \
and Data[s]['AniSpec'][TYPE]['anisotropy_sigma']!="":
# Calculate Ftest. If Ftest exceeds threshold value: set anistropy tensor to identity matrix
sigma=float(Data[s]['AniSpec'][TYPE]['anisotropy_sigma'])
nf = 3*int(Data[s]['AniSpec'][TYPE]['anisotropy_n'])-6
F=calculate_ftest(S_matrix,sigma,nf)
#print s,"F",F
Data[s]['AniSpec'][TYPE]['ftest']=F
#print "s,sigma,nf,F,Ftest_crit[this_specimen_f_type]"
#print s,sigma,nf,F,Ftest_crit[this_specimen_f_type]
if acceptance_criteria['specimen_aniso_ftest_flag']['value'] in ['g','1',1,True,'TRUE','True'] :
Ftest_threshold=Ftest_crit[this_specimen_f_type]
if Data[s]['AniSpec'][TYPE]['ftest'] < Ftest_crit[this_specimen_f_type]:
S_matrix=identity(3,'f')
pars["AC_WARNING"]=pars["AC_WARNING"]+"%s tensor fails F-test; "%(TYPE)
red_flag=True
else:
Data[s]['AniSpec'][TYPE]['anisotropy_sigma']=""
Data[s]['AniSpec'][TYPE]['ftest']=99999
if 'anisotropy_alt' in list(Data[s]['AniSpec'][TYPE].keys()) and Data[s]['AniSpec'][TYPE]['anisotropy_alt']!="" and Data[s]['AniSpec'][TYPE]['anisotropy_alt']!=None: # added 3/5/20 LT
if acceptance_criteria['anisotropy_alt']['value'] != -999 and \
(float(Data[s]['AniSpec'][TYPE]['anisotropy_alt']) > float(acceptance_criteria['anisotropy_alt']['value'])):
S_matrix=identity(3,'f')
pars["AC_WARNING"]=pars["AC_WARNING"]+"%s tensor fails alteration check: %.1f > %.1f; "%(TYPE,float(Data[s]['AniSpec'][TYPE]['anisotropy_alt']),float(acceptance_criteria['anisotropy_alt']['value']))
red_flag=True
else:
Data[s]['AniSpec'][TYPE]['anisotropy_alt']=""
Data[s]['AniSpec'][TYPE]['S_matrix']=S_matrix
#--------------------------
# if AARM passes all, use the AARM.
# if ATRM fail alteration use the AARM
# if both fail F-test: use AARM
#--------------------------
if len(TYPES)>1:
if "ATRM tensor fails alteration check" in pars["AC_WARNING"]:
TYPE='AARM'
elif "ATRM tensor fails F-test" in pars["AC_WARNING"]:
TYPE='AARM'
else:
TYPE=='AARM'
S_matrix= Data[s]['AniSpec'][TYPE]['S_matrix']
#---------------------------
TRM_anc_unit=array(pars['specimen_PCA_v1'])/sqrt(pars['specimen_PCA_v1'][0]**2+pars['specimen_PCA_v1'][1]**2+pars['specimen_PCA_v1'][2]**2)
B_lab_unit=pmag.dir2cart([ Data[s]['Thellier_dc_field_phi'], Data[s]['Thellier_dc_field_theta'],1])
#B_lab_unit=array([0,0,-1])
Anisotropy_correction_factor=linalg.norm(dot(inv(S_matrix),TRM_anc_unit.transpose()))*norm(dot(S_matrix,B_lab_unit))
pars["Anisotropy_correction_factor"]=Anisotropy_correction_factor
pars["AC_specimen_int"]= pars["Anisotropy_correction_factor"] * float(pars["specimen_int"])
pars["AC_anisotropy_type"]=Data[s]['AniSpec'][TYPE]["anisotropy_type"]
pars["specimen_int_uT"]=float(pars["AC_specimen_int"])*1e6
if TYPE=='AARM':
if ":LP-AN-ARM" not in pars['magic_method_codes']:
pars['magic_method_codes']+=":LP-AN-ARM:AE-H:DA-AC-AARM"
pars['specimen_correction']='c'
pars['specimen_int_corr_anisotropy']=Anisotropy_correction_factor
if TYPE=='ATRM':
if ":LP-AN-TRM" not in pars['magic_method_codes']:
pars['magic_method_codes']+=":LP-AN-TRM:AE-H:DA-AC-ATRM"
pars['specimen_correction']='c'
pars['specimen_int_corr_anisotropy']=Anisotropy_correction_factor
else:
pars["Anisotropy_correction_factor"]=1.0
pars["specimen_int_uT"]=float(pars["specimen_int"])*1e6
pars["AC_WARNING"]="No anistropy correction"
pars['specimen_correction']='u'
pars["specimen_int_corr_anisotropy"]=pars["Anisotropy_correction_factor"]
#-------------------------------------------------
# NLT and anisotropy correction together in one equation
# See Shaar et al (2010), Equation (3)
#-------------------------------------------------
if 'NLT_parameters' in list(Data[s].keys()):
alpha=Data[s]['NLT_parameters']['tanh_parameters'][0][0]
beta=Data[s]['NLT_parameters']['tanh_parameters'][0][1]
b=float(pars["specimen_b"])
Fa=pars["Anisotropy_correction_factor"]
if ((abs(b)*Fa)/alpha) <1.0:
Banc_NLT=math.atanh(((abs(b)*Fa)/alpha))/beta
pars["NLTC_specimen_int"]=Banc_NLT
pars["specimen_int_uT"]=Banc_NLT*1e6
if "AC_specimen_int" in list(pars.keys()):
pars["NLT_specimen_correction_factor"]=Banc_NLT/float(pars["AC_specimen_int"])
else:
pars["NLT_specimen_correction_factor"]=Banc_NLT/float(pars["specimen_int"])
if ":LP-TRM" not in pars['magic_method_codes']:
pars['magic_method_codes']+=":LP-TRM:DA-NL"
pars['specimen_correction']='c'
else:
GUI_log.write ("-W- WARNING: problematic NLT mesurements for specimens %s. Cant do NLT calculation. check data\n"%s)
pars["NLT_specimen_correction_factor"]=-1
else:
pars["NLT_specimen_correction_factor"]=-1
#-------------------------------------------------
# Calculate the final result with cooling rate correction
#-------------------------------------------------
pars["specimen_int_corr_cooling_rate"]=-999
if 'cooling_rate_data' in list(Data[s].keys()):
if 'CR_correction_factor' in list(Data[s]['cooling_rate_data'].keys()):
if Data[s]['cooling_rate_data']['CR_correction_factor'] != -1 and Data[s]['cooling_rate_data']['CR_correction_factor'] !=-999:
pars["specimen_int_corr_cooling_rate"]=Data[s]['cooling_rate_data']['CR_correction_factor']
pars['specimen_correction']='c'
pars["specimen_int_uT"]=pars["specimen_int_uT"]*pars["specimen_int_corr_cooling_rate"]
if ":DA-CR" not in pars['magic_method_codes']:
pars['magic_method_codes']+=":DA-CR"
if 'CR_correction_factor_flag' in list(Data[s]['cooling_rate_data'].keys()):
if Data[s]['cooling_rate_data']['CR_correction_factor_flag']=="calculated":
pars['CR_flag']="calculated"
else:
pars['CR_flag']=""
if 'CR_correction_factor_flag' in list(Data[s]['cooling_rate_data'].keys()) \
and Data[s]['cooling_rate_data']['CR_correction_factor_flag']!="calculated":
pars["CR_WARNING"]="inferred cooling rate correction"
else:
pars["CR_WARNING"]="no cooling rate correction"
def combine_dictionaries(d1, d2):
"""
combines dict1 and dict2 into a new dict.
if dict1 and dict2 share a key, the value from dict1 is used
"""
for key, value in d2.items():
if key not in list(d1.keys()):
d1[key] = value
return d1
Data[s]['pars'] = pars
#print pars.keys()
return(pars)
def average_duplicates(duplicates):
'''
avarage replicate measurements.
'''
carts_s,carts_g,carts_t=[],[],[]
for rec in duplicates:
moment=float(rec['moment'])
if 'dec_s' in list(rec.keys()) and 'inc_s' in list(rec.keys()):
if rec['dec_s']!="" and rec['inc_s']!="":
dec_s=float(rec['dec_s'])
inc_s=float(rec['inc_s'])
cart_s=pmag.dir2cart([dec_s,inc_s,moment])
carts_s.append(cart_s)
if 'dec_g' in list(rec.keys()) and 'inc_g' in list(rec.keys()):
if rec['dec_g']!="" and rec['inc_g']!="":
dec_g=float(rec['dec_g'])
inc_g=float(rec['inc_g'])
cart_g=pmag.dir2cart([dec_g,inc_g,moment])
carts_g.append(cart_g)
if 'dec_t' in list(rec.keys()) and 'inc_t' in list(rec.keys()):
if rec['dec_t']!="" and rec['inc_t']!="":
dec_t=float(rec['dec_t'])
inc_t=float(rec['inc_t'])
cart_t=pmag.dir2cart([dec_t,inc_t,moment])
carts_t.append(cart_t)
if len(carts_s)>0:
carts=scipy.array(carts_s)
x_mean=scipy.mean(carts[:,0])
y_mean=scipy.mean(carts[:,1])
z_mean=scipy.mean(carts[:,2])
mean_dir=pmag.cart2dir([x_mean,y_mean,z_mean])
mean_dec_s="%.2f"%mean_dir[0]
mean_inc_s="%.2f"%mean_dir[1]
mean_moment="%10.3e"%mean_dir[2]
else:
mean_dec_s,mean_inc_s="",""
if len(carts_g)>0:
carts=scipy.array(carts_g)
x_mean=scipy.mean(carts[:,0])
y_mean=scipy.mean(carts[:,1])
z_mean=scipy.mean(carts[:,2])
mean_dir=pmag.cart2dir([x_mean,y_mean,z_mean])
mean_dec_g="%.2f"%mean_dir[0]
mean_inc_g="%.2f"%mean_dir[1]
mean_moment="%10.3e"%mean_dir[2]
else:
mean_dec_g,mean_inc_g="",""
if len(carts_t)>0:
carts=scipy.array(carts_t)
x_mean=scipy.mean(carts[:,0])
y_mean=scipy.mean(carts[:,1])
z_mean=scipy.mean(carts[:,2])
mean_dir=pmag.cart2dir([x_mean,y_mean,z_mean])
mean_dec_t="%.2f"%mean_dir[0]
mean_inc_t="%.2f"%mean_dir[1]
mean_moment="%10.3e"%mean_dir[2]
else:
mean_dec_t,mean_inc_t="",""
meanrec={}
for key in list(duplicates[0].keys()):
if key in ['dec_s','inc_s','dec_g','inc_g','dec_t','inc_t','moment']:
continue
else:
meanrec[key]=duplicates[0][key]
meanrec['dec_s']=mean_dec_s
meanrec['dec_g']=mean_dec_g
meanrec['dec_t']=mean_dec_t
meanrec['inc_s']=mean_inc_s
meanrec['inc_g']=mean_inc_g
meanrec['inc_t']=mean_inc_t
meanrec['moment']=mean_moment
return meanrec
def main():
"""
NAME
lowrie_magic.py
DESCRIPTION
plots intensity decay curves for Lowrie experiments
SYNTAX
lowrie_magic.py -h [command line options]
INPUT
takes magic_measurements formatted input files
OPTIONS
-h prints help message and quits
-f FILE: specify input file, default is magic_measurements.txt
-N do not normalize by maximum magnetization
-fmt [svg, pdf, eps, png] specify fmt, default is svg
-sav saves plots and quits
"""
fmt, plot = 'svg', 0
FIG = {} # plot dictionary
FIG['lowrie'] = 1 # demag is figure 1
pmagplotlib.plot_init(FIG['lowrie'], 6, 6)
norm = 1 # default is to normalize by maximum axis
in_file, dir_path = 'magic_measurements.txt', '.'
if len(sys.argv) > 1:
if '-WD' in sys.argv:
ind = sys.argv.index('-WD')
dir_path = sys.argv[ind + 1]
if '-h' in sys.argv:
print(main.__doc__)
sys.exit()
if '-N' in sys.argv: norm = 0 # don't normalize
if '-sav' in sys.argv: plot = 1 # don't normalize
if '-fmt' in sys.argv: # sets input filename
ind = sys.argv.index("-fmt")
fmt = sys.argv[ind + 1]
if '-f' in sys.argv: # sets input filename
ind = sys.argv.index("-f")
in_file = sys.argv[ind + 1]
else:
print(main.__doc__)
print('you must supply a file name')
sys.exit()
in_file = dir_path + '/' + in_file
print(in_file)
PmagRecs, file_type = pmag.magic_read(in_file)
if file_type != "magic_measurements":
print('bad input file')
sys.exit()
PmagRecs = pmag.get_dictitem(PmagRecs, 'magic_method_codes', 'LP-IRM-3D',
'has') # get all 3D IRM records
if len(PmagRecs) == 0:
print('no records found')
sys.exit()
specs = pmag.get_dictkey(PmagRecs, 'er_specimen_name', '')
sids = []
for spec in specs:
if spec not in sids:
sids.append(spec) # get list of unique specimen names
for spc in sids: # step through the specimen names
print(spc)
specdata = pmag.get_dictitem(PmagRecs, 'er_specimen_name', spc,
'T') # get all this one's data
DIMs, Temps = [], []
for dat in specdata: # step through the data
DIMs.append([
float(dat['measurement_dec']),
float(dat['measurement_inc']),
float(dat['measurement_magn_moment'])
])
Temps.append(float(dat['treatment_temp']) - 273.)
carts = pmag.dir2cart(DIMs).transpose()
if norm == 1: # want to normalize
nrm = (DIMs[0][2]) # normalize by NRM
ylab = "M/M_o"
else:
nrm = 1. # don't normalize
ylab = "Magnetic moment (Am^2)"
xlab = "Temperature (C)"
pmagplotlib.plotXY(FIG['lowrie'],
Temps,
old_div(abs(carts[0]), nrm),
sym='r-')
pmagplotlib.plotXY(FIG['lowrie'],
Temps,
old_div(abs(carts[0]), nrm),
sym='ro') # X direction
pmagplotlib.plotXY(FIG['lowrie'],
Temps,
old_div(abs(carts[1]), nrm),
sym='c-')
pmagplotlib.plotXY(FIG['lowrie'],
Temps,
old_div(abs(carts[1]), nrm),
sym='cs') # Y direction
pmagplotlib.plotXY(FIG['lowrie'],
Temps,
old_div(abs(carts[2]), nrm),
sym='k-')
pmagplotlib.plotXY(FIG['lowrie'],
Temps,
old_div(abs(carts[2]), nrm),
sym='k^',
title=spc,
xlab=xlab,
ylab=ylab) # Z direction
files = {'lowrie': 'lowrie:_' + spc + '_.' + fmt}
if plot == 0:
pmagplotlib.drawFIGS(FIG)
ans = input('S[a]ve figure? [q]uit, <return> to continue ')
if ans == 'a':
pmagplotlib.saveP(FIG, files)
elif ans == 'q':
sys.exit()
else:
pmagplotlib.saveP(FIG, files)
pmagplotlib.clearFIG(FIG['lowrie'])
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
-Fr FILE: specify results output file, default is atrm_results.txt
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]
meas_file=dir_path+'/'+meas_file
rmag_anis=dir_path+'/'+rmag_anis
rmag_res=dir_path+'/'+rmag_res
# read in data
meas_data,file_type=pmag.magic_read(meas_file)
meas_data=pmag.get_dictitem(meas_data,'magic_method_codes','LP-AN-TRM','has')
if file_type != 'magic_measurements':
print file_type
print file_type,"This is not a valid magic_measurements file "
sys.exit()
#
#
# 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=[],[]
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
#
data=pmag.get_dictitem(meas_data,'er_specimen_name',s,'T') # fish out data for this specimen name
if len(data)>5:
RmagSpecRec["rmag_anisotropy_name"]=data[0]["er_specimen_name"]
RmagSpecRec["er_location_name"]=data[0]["er_location_name"]
RmagSpecRec["er_specimen_name"]=data[0]["er_specimen_name"]
RmagSpecRec["er_sample_name"]=data[0]["er_sample_name"]
RmagSpecRec["er_site_name"]=data[0]["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]["er_location_name"]
RmagResRec["er_specimen_names"]=data[0]["er_specimen_name"]
RmagResRec["er_sample_names"]=data[0]["er_sample_name"]
RmagResRec["er_site_names"]=data[0]["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 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,H,tmpH=pmag.designATRM(npos) # B matrix made from design matrix for positions
#
# 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):
work[i][j]=X[i][j]-BX[i][j] # subtract baseline, if available
#
# 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]=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=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(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'%(trace/3)
RmagSpecRec["anisotropy_sigma"]='%8.6f'%(sigma)
RmagSpecRec["anisotropy_unit"]="Am^2"
RmagSpecRec["anisotropy_n"]='%i'%(npos)
RmagSpecRec["anisotropy_tilt_correction"]='-1'
RmagSpecRec["anisotropy_F"]='%7.1f '%(hpars["F"]) # used by thellier_gui - must be taken out for uploading
RmagSpecRec["anisotropy_F_crit"]=hpars["F_crit"] # used by thellier_gui - must be taken out for uploading
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
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
lowrie.py
DESCRIPTION
plots intensity decay curves for Lowrie experiments
SYNTAX
lowrie -h [command line options]
INPUT
takes SIO formatted input files
OPTIONS
-h prints help message and quits
-f FILE: specify input file
-N do not normalize by maximum magnetization
-fmt [svg, pdf, eps, png] specify fmt, default is svg
-sav save plots and quit
"""
fmt, plot = "svg", 0
FIG = {} # plot dictionary
FIG["lowrie"] = 1 # demag is figure 1
pmagplotlib.plot_init(FIG["lowrie"], 6, 6)
norm = 1 # default is to normalize by maximum axis
if len(sys.argv) > 1:
if "-h" in sys.argv:
print main.__doc__
sys.exit()
if "-N" in sys.argv:
norm = 0 # don't normalize
if "-sav" in sys.argv:
plot = 1 # don't normalize
if "-fmt" in sys.argv: # sets input filename
ind = sys.argv.index("-fmt")
fmt = sys.argv[ind + 1]
if "-f" in sys.argv: # sets input filename
ind = sys.argv.index("-f")
in_file = sys.argv[ind + 1]
else:
print main.__doc__
print "you must supply a file name"
sys.exit()
else:
print main.__doc__
print "you must supply a file name"
sys.exit()
data = open(in_file).readlines() # open the SIO format file
PmagRecs = [] # set up a list for the results
keys = ["specimen", "treatment", "csd", "M", "dec", "inc"]
for line in data:
PmagRec = {}
rec = line.replace("\n", "").split()
for k in range(len(keys)):
PmagRec[keys[k]] = rec[k]
PmagRecs.append(PmagRec)
specs = pmag.get_dictkey(PmagRecs, "specimen", "")
sids = []
for spec in specs:
if spec not in sids:
sids.append(spec) # get list of unique specimen names
for spc in sids: # step through the specimen names
print spc
specdata = pmag.get_dictitem(PmagRecs, "specimen", spc, "T") # get all this one's data
DIMs, Temps = [], []
for dat in specdata: # step through the data
DIMs.append([float(dat["dec"]), float(dat["inc"]), float(dat["M"]) * 1e-3])
Temps.append(float(dat["treatment"]))
carts = pmag.dir2cart(DIMs).transpose()
# if norm==1: # want to normalize
# nrm=max(max(abs(carts[0])),max(abs(carts[1])),max(abs(carts[2]))) # by maximum of x,y,z values
# ylab="M/M_max"
if norm == 1: # want to normalize
nrm = DIMs[0][2] # normalize by NRM
ylab = "M/M_o"
else:
nrm = 1.0 # don't normalize
ylab = "Magnetic moment (Am^2)"
xlab = "Temperature (C)"
pmagplotlib.plotXY(FIG["lowrie"], Temps, abs(carts[0]) / nrm, sym="r-")
pmagplotlib.plotXY(FIG["lowrie"], Temps, abs(carts[0]) / nrm, sym="ro") # X direction
pmagplotlib.plotXY(FIG["lowrie"], Temps, abs(carts[1]) / nrm, sym="c-")
pmagplotlib.plotXY(FIG["lowrie"], Temps, abs(carts[1]) / nrm, sym="cs") # Y direction
pmagplotlib.plotXY(FIG["lowrie"], Temps, abs(carts[2]) / nrm, sym="k-")
pmagplotlib.plotXY(
FIG["lowrie"], Temps, abs(carts[2]) / nrm, sym="k^", title=spc, xlab=xlab, ylab=ylab
) # Z direction
files = {"lowrie": "lowrie:_" + spc + "_." + fmt}
if plot == 0:
pmagplotlib.drawFIGS(FIG)
ans = raw_input("S[a]ve figure? [q]uit, <return> to continue ")
if ans == "a":
pmagplotlib.saveP(FIG, files)
elif ans == "q":
sys.exit()
else:
pmagplotlib.saveP(FIG, files)
pmagplotlib.clearFIG(FIG["lowrie"])
def main():
"""
NAME
lowrie.py
DESCRIPTION
plots intensity decay curves for Lowrie experiments
SYNTAX
lowrie -h [command line options]
INPUT
takes SIO formatted input files
OPTIONS
-h prints help message and quits
-f FILE: specify input file
-N do not normalize by maximum magnetization
-fmt [svg, pdf, eps, png] specify fmt, default is svg
-sav save plots and quit
"""
fmt, plot = 'svg', 0
FIG = {} # plot dictionary
FIG['lowrie'] = 1 # demag is figure 1
pmagplotlib.plot_init(FIG['lowrie'], 6, 6)
norm = 1 # default is to normalize by maximum axis
if len(sys.argv) > 1:
if '-h' in sys.argv:
print(main.__doc__)
sys.exit()
if '-N' in sys.argv:
norm = 0 # don't normalize
if '-sav' in sys.argv:
plot = 1 # don't normalize
if '-fmt' in sys.argv: # sets input filename
ind = sys.argv.index("-fmt")
fmt = sys.argv[ind + 1]
if '-f' in sys.argv: # sets input filename
ind = sys.argv.index("-f")
in_file = sys.argv[ind + 1]
else:
print(main.__doc__)
print('you must supply a file name')
sys.exit()
else:
print(main.__doc__)
print('you must supply a file name')
sys.exit()
data = pmag.open_file(in_file)
PmagRecs = [] # set up a list for the results
keys = ['specimen', 'treatment', 'csd', 'M', 'dec', 'inc']
for line in data:
PmagRec = {}
rec = line.replace('\n', '').split()
for k in range(len(keys)):
PmagRec[keys[k]] = rec[k]
PmagRecs.append(PmagRec)
specs = pmag.get_dictkey(PmagRecs, 'specimen', '')
sids = []
for spec in specs:
if spec not in sids:
sids.append(spec) # get list of unique specimen names
for spc in sids: # step through the specimen names
print(spc)
specdata = pmag.get_dictitem(
PmagRecs, 'specimen', spc, 'T') # get all this one's data
DIMs, Temps = [], []
for dat in specdata: # step through the data
DIMs.append([float(dat['dec']), float(
dat['inc']), float(dat['M']) * 1e-3])
Temps.append(float(dat['treatment']))
carts = pmag.dir2cart(DIMs).transpose()
# if norm==1: # want to normalize
# nrm=max(max(abs(carts[0])),max(abs(carts[1])),max(abs(carts[2]))) # by maximum of x,y,z values
# ylab="M/M_max"
if norm == 1: # want to normalize
nrm = (DIMs[0][2]) # normalize by NRM
ylab = "M/M_o"
else:
nrm = 1. # don't normalize
ylab = "Magnetic moment (Am^2)"
xlab = "Temperature (C)"
pmagplotlib.plotXY(FIG['lowrie'], Temps, old_div(
abs(carts[0]), nrm), sym='r-')
pmagplotlib.plotXY(FIG['lowrie'], Temps, old_div(
abs(carts[0]), nrm), sym='ro') # X direction
pmagplotlib.plotXY(FIG['lowrie'], Temps, old_div(
abs(carts[1]), nrm), sym='c-')
pmagplotlib.plotXY(FIG['lowrie'], Temps, old_div(
abs(carts[1]), nrm), sym='cs') # Y direction
pmagplotlib.plotXY(FIG['lowrie'], Temps, old_div(
abs(carts[2]), nrm), sym='k-')
pmagplotlib.plotXY(FIG['lowrie'], Temps, old_div(
abs(carts[2]), nrm), sym='k^', title=spc, xlab=xlab, ylab=ylab) # Z direction
files = {'lowrie': 'lowrie:_' + spc + '_.' + fmt}
if plot == 0:
pmagplotlib.drawFIGS(FIG)
ans = input('S[a]ve figure? [q]uit, <return> to continue ')
if ans == 'a':
pmagplotlib.saveP(FIG, files)
elif ans == 'q':
sys.exit()
else:
pmagplotlib.saveP(FIG, files)
pmagplotlib.clearFIG(FIG['lowrie'])
def main():
"""
NAME
dir_cart.py
DESCRIPTION
converts geomagnetic elements to cartesian coordinates
INPUT (COMMAND LINE ENTRY)
declination inclination [magnitude]
or
longitude latitude
if only two columns, assumes magnitude of unity
OUTPUT
x1 x2 x3
SYNTAX
dir_cart.py [command line options] [< filename]
OPTIONS
-h print help and quit
-i for interactive data entry
-f FILE, input file
-F FILE, output file
"""
out=""
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')
if '-i' in sys.argv:
cont=1
while cont==1:
try:
dir=[]
ans=raw_input('Declination: [cntrl-D to quit] ')
dir.append(float(ans))
ans=raw_input('Inclination: ')
dir.append(float(ans))
ans=raw_input('Intensity [return for unity]: ')
if ans=='':ans='1'
dir.append(float(ans))
cart= pmag.dir2cart(dir) # send dir to dir2cart and spit out result
print '%8.4e %8.4e %8.4e'%(cart[0],cart[1],cart[2])
except:
print '\n Good-bye \n'
sys.exit()
elif '-f' in sys.argv:
ind=sys.argv.index('-f')
file=sys.argv[ind+1]
input=numpy.loadtxt(file)
else:
input=numpy.loadtxt(sys.stdin,dtype=numpy.float)
cart= pmag.dir2cart(input)
if len(cart.shape)==1:
line=cart
print '%8.4e %8.4e %8.4e'%(line[0],line[1],line[2])
if out!="":out.write('%8.4e %8.4e %8.4e\n'%(line[0],line[1],line[2]))
else:
for line in cart:
print '%8.4e %8.4e %8.4e'%(line[0],line[1],line[2])
if out!="":out.write('%8.4e %8.4e %8.4e\n'%(line[0],line[1],line[2]))
def main():
"""
NAME
lowrie_magic.py
DESCRIPTION
plots intensity decay curves for Lowrie experiments
SYNTAX
lowrie_magic.py -h [command line options]
INPUT
takes measurements formatted input files
OPTIONS
-h prints help message and quits
-f FILE: specify input file, default is magic_measurements.txt
-N do not normalize by maximum magnetization
-fmt [svg, pdf, eps, png] specify fmt, default is svg
-sav saves plots and quits
-DM [2, 3] MagIC data model number
"""
if '-h' in sys.argv:
print(main.__doc__)
sys.exit()
if len(sys.argv) <= 1:
print(main.__doc__)
print('you must supply a file name')
sys.exit()
FIG = {} # plot dictionary
FIG['lowrie'] = 1 # demag is figure 1
pmagplotlib.plot_init(FIG['lowrie'], 6, 6)
norm = 1 # default is to normalize by maximum axis
in_file = pmag.get_named_arg("-f", "measurements.txt")
dir_path = pmag.get_named_arg("-WD", ".")
in_file = pmag.resolve_file_name(in_file, dir_path)
data_model = pmag.get_named_arg("-DM", 3)
data_model = int(float(data_model))
fmt = pmag.get_named_arg("-fmt", "svg")
if '-N' in sys.argv:
norm = 0 # don't normalize
if '-sav' in sys.argv:
plot = 1 # silently save and quit
else:
plot = 0 # generate plots
print(in_file)
# read in data
PmagRecs, file_type = pmag.magic_read(in_file)
if data_model == 2 and file_type != "magic_measurements":
print('bad input file', file_type)
sys.exit()
if data_model == 3 and file_type != "measurements":
print('bad input file', file_type)
sys.exit()
if data_model == 2:
meth_code_col = 'magic_method_codes'
spec_col = 'er_specimen_name'
dec_col = "measurement_dec"
inc_col = 'measurement_inc'
moment_col = 'measurement_magn_moment'
temp_col = 'treatment_temp'
else:
meth_code_col = 'method_codes'
spec_col = 'specimen'
dec_col = 'dir_dec'
inc_col = 'dir_inc'
moment_col = 'magn_moment'
temp_col = "treat_temp"
PmagRecs = pmag.get_dictitem(
PmagRecs, meth_code_col, 'LP-IRM-3D', 'has') # get all 3D IRM records
if len(PmagRecs) == 0:
print('no records found with the method code LP-IRM-3D')
sys.exit()
specs = pmag.get_dictkey(PmagRecs, spec_col, '')
sids = []
for spec in specs:
if spec not in sids:
sids.append(spec) # get list of unique specimen names
for spc in sids: # step through the specimen names
print(spc)
specdata = pmag.get_dictitem(
PmagRecs, spec_col, spc, 'T') # get all this one's data
DIMs, Temps = [], []
for dat in specdata: # step through the data
DIMs.append([float(dat[dec_col]), float(
dat[inc_col]), float(dat[moment_col])])
Temps.append(float(dat[temp_col])-273.)
carts = pmag.dir2cart(DIMs).transpose()
if norm == 1: # want to normalize
nrm = (DIMs[0][2]) # normalize by NRM
ylab = "M/M_o"
else:
nrm = 1. # don't normalize
ylab = "Magnetic moment (Am^2)"
xlab = "Temperature (C)"
pmagplotlib.plot_xy(FIG['lowrie'], Temps, abs(carts[0]) / nrm, sym='r-')
pmagplotlib.plot_xy(FIG['lowrie'], Temps, abs(carts[0]) / nrm, sym='ro') # X direction
pmagplotlib.plot_xy(FIG['lowrie'], Temps, abs(carts[1]) / nrm, sym='c-')
pmagplotlib.plot_xy(FIG['lowrie'], Temps, abs(carts[1]) / nrm, sym='cs') # Y direction
pmagplotlib.plot_xy(FIG['lowrie'], Temps, abs(carts[2]) / nrm, sym='k-')
pmagplotlib.plot_xy(FIG['lowrie'], Temps, abs(carts[2]) / nrm, sym='k^', title=spc, xlab=xlab, ylab=ylab) # Z direction
files = {'lowrie': 'lowrie:_'+spc+'_.'+fmt}
if plot == 0:
pmagplotlib.draw_figs(FIG)
ans = input('S[a]ve figure? [q]uit, <return> to continue ')
if ans == 'a':
pmagplotlib.save_plots(FIG, files)
elif ans == 'q':
sys.exit()
else:
pmagplotlib.save_plots(FIG, files)
pmagplotlib.clearFIG(FIG['lowrie'])
def main():
"""
NAME
lowrie_magic.py
DESCRIPTION
plots intensity decay curves for Lowrie experiments
SYNTAX
lowrie_magic.py -h [command line options]
INPUT
takes measurements formatted input files
OPTIONS
-h prints help message and quits
-f FILE: specify input file, default is magic_measurements.txt
-N do not normalize by maximum magnetization
-fmt [svg, pdf, eps, png] specify fmt, default is svg
-sav saves plots and quits
-DM [2, 3] MagIC data model number
"""
if '-h' in sys.argv:
print(main.__doc__)
sys.exit()
if len(sys.argv) <= 1:
print(main.__doc__)
print('you must supply a file name')
sys.exit()
FIG = {} # plot dictionary
FIG['lowrie'] = 1 # demag is figure 1
pmagplotlib.plot_init(FIG['lowrie'], 6, 6)
norm = 1 # default is to normalize by maximum axis
in_file = pmag.get_named_arg("-f", "measurements.txt")
dir_path = pmag.get_named_arg("-WD", ".")
in_file = pmag.resolve_file_name(in_file, dir_path)
data_model = pmag.get_named_arg("-DM", 3)
data_model = int(float(data_model))
fmt = pmag.get_named_arg("-fmt", "svg")
if '-N' in sys.argv:
norm = 0 # don't normalize
if '-sav' in sys.argv:
plot = 1 # silently save and quit
else:
plot = 0 # generate plots
print(in_file)
# read in data
PmagRecs, file_type = pmag.magic_read(in_file)
if data_model == 2 and file_type != "magic_measurements":
print('bad input file', file_type)
sys.exit()
if data_model == 3 and file_type != "measurements":
print('bad input file', file_type)
sys.exit()
if data_model == 2:
meth_code_col = 'magic_method_codes'
spec_col = 'er_specimen_name'
dec_col = "measurement_dec"
inc_col = 'measurement_inc'
moment_col = 'measurement_magn_moment'
temp_col = 'treatment_temp'
else:
meth_code_col = 'method_codes'
spec_col = 'specimen'
dec_col = 'dir_dec'
inc_col = 'dir_inc'
moment_col = 'magn_moment'
temp_col = "treat_temp"
PmagRecs = pmag.get_dictitem(PmagRecs, meth_code_col, 'LP-IRM-3D',
'has') # get all 3D IRM records
if len(PmagRecs) == 0:
print('no records found with the method code LP-IRM-3D')
sys.exit()
specs = pmag.get_dictkey(PmagRecs, spec_col, '')
sids = []
for spec in specs:
if spec not in sids:
sids.append(spec) # get list of unique specimen names
for spc in sids: # step through the specimen names
print(spc)
specdata = pmag.get_dictitem(PmagRecs, spec_col, spc,
'T') # get all this one's data
DIMs, Temps = [], []
for dat in specdata: # step through the data
DIMs.append([
float(dat[dec_col]),
float(dat[inc_col]),
float(dat[moment_col])
])
Temps.append(float(dat[temp_col]) - 273.)
carts = pmag.dir2cart(DIMs).transpose()
if norm == 1: # want to normalize
nrm = (DIMs[0][2]) # normalize by NRM
ylab = "M/M_o"
else:
nrm = 1. # don't normalize
ylab = "Magnetic moment (Am^2)"
xlab = "Temperature (C)"
pmagplotlib.plot_xy(FIG['lowrie'],
Temps,
abs(carts[0]) / nrm,
sym='r-')
pmagplotlib.plot_xy(FIG['lowrie'],
Temps,
abs(carts[0]) / nrm,
sym='ro') # X direction
pmagplotlib.plot_xy(FIG['lowrie'],
Temps,
abs(carts[1]) / nrm,
sym='c-')
pmagplotlib.plot_xy(FIG['lowrie'],
Temps,
abs(carts[1]) / nrm,
sym='cs') # Y direction
pmagplotlib.plot_xy(FIG['lowrie'],
Temps,
abs(carts[2]) / nrm,
sym='k-')
pmagplotlib.plot_xy(FIG['lowrie'],
Temps,
abs(carts[2]) / nrm,
sym='k^',
title=spc,
xlab=xlab,
ylab=ylab) # Z direction
files = {'lowrie': 'lowrie:_' + spc + '_.' + fmt}
if plot == 0:
pmagplotlib.draw_figs(FIG)
ans = input('S[a]ve figure? [q]uit, <return> to continue ')
if ans == 'a':
pmagplotlib.save_plots(FIG, files)
elif ans == 'q':
sys.exit()
else:
pmagplotlib.save_plots(FIG, files)
pmagplotlib.clearFIG(FIG['lowrie'])
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 er_samples.txt file
-fsa FILE: specify er_samples.txt file, default is er_samples.txt
-Fa FILE: specify anisotropy output file, default is arm_anisotropy.txt
-Fr FILE: specify results output file, default is aarm_results.txt
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
user=""
meas_file="aarm_measurements.txt"
samp_file="er_samples.txt"
rmag_anis="arm_anisotropy.txt"
rmag_res="aarm_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]
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 "-fsa" in args:
ind=args.index("-fsa")
samp_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]
meas_file=dir_path+'/'+meas_file
samp_file=dir_path+'/'+samp_file
rmag_anis=dir_path+'/'+rmag_anis
rmag_res=dir_path+'/'+rmag_res
# read in data
meas_data,file_type=pmag.magic_read(meas_file)
meas_data=pmag.get_dictitem(meas_data,'magic_method_codes','LP-AN-ARM','has')
if file_type != 'magic_measurements':
print file_type
print file_type,"This is not a valid magic_measurements file "
sys.exit()
if coord!='-1': # need to read in sample data
samp_data,file_type=pmag.magic_read(samp_file)
if file_type != 'er_samples':
print file_type
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=[],[]
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=len(data)/2
if npos==9:
#
# get dec, inc, int and convert to x,y,z
#
B,H,tmpH=pmag.designAARM(npos) # B matrix made from design matrix for positions
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]=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=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(S/nf)
else: sigma=0
RmagSpecRec["rmag_anisotropy_name"]=data[0]["er_specimen_name"]
RmagSpecRec["er_location_name"]=data[0]["er_location_name"]
RmagSpecRec["er_specimen_name"]=data[0]["er_specimen_name"]
RmagSpecRec["er_sample_name"]=data[0]["er_sample_name"]
RmagSpecRec["er_site_name"]=data[0]["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]["er_location_name"]
RmagResRec["er_specimen_names"]=data[0]["er_specimen_name"]
RmagResRec["er_sample_names"]=data[0]["er_sample_name"]
RmagResRec["er_site_names"]=data[0]["er_site_name"]
RmagResRec["magic_experiment_names"]=RmagSpecRec["rmag_anisotropy_name"]+":AARM"
RmagResRec["er_citation_names"]="This study"
if "magic_instrument_codes" in 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(PmagSpecRec["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':
sampe_bed_dir,sample_bed_dip=orient['sample_bed_dip_direction'],orient['sample_bed_dip']
s=pmag.dostilt(s,bed_dir,bed_dip) # rotate to geographic coordinates
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'%(trace/3)
RmagSpecRec["anisotropy_sigma"]='%8.6f'%(sigma)
RmagSpecRec["anisotropy_unit"]="Am^2"
RmagSpecRec["anisotropy_n"]='%i'%(npos)
RmagSpecRec["anisotropy_tilt_correction"]=coord
RmagSpecRec["anisotropy_F"]='%7.1f '%(hpars["F"]) # used by thellier_gui - must be taken out for uploading
RmagSpecRec["anisotropy_F_crit"]=hpars["F_crit"] # used by thellier_gui - must be taken out for uploading
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)
else:
print 'skipping specimen ',s,' only 9 positions supported','; this has ',npos
specimen+=1
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 get_PI_parameters(Data,acceptance_criteria,preferences,s,tmin,tmax,GUI_log,THERMAL,MICROWAVE):
datablock = Data[s]['datablock']
pars=copy.deepcopy(Data[s]['pars']) # assignments to pars are assiging to Data[s]['pars']
# get MagIC mothod codes:
#pars['jmethod_codes']="LP-PI-TRM" # thellier Method
import SPD
import SPD.spd3_0 as spd
Pint_pars = spd.PintPars(Data, str(s), tmin, tmax, 'magic', preferences['show_statistics_on_gui'])
Pint_pars.reqd_stats() # calculate only statistics indicated in preferences
#print 'LIB1: ',Pint_pars
if not Pint_pars.pars:
print "Could not get any parameters for {}".format(Pint_pars)
return 0
#Pint_pars.calculate_all_statistics() # calculate every statistic available
#print "-D- Debag"
#print Pint_pars.keys()
pars.update(Pint_pars.pars) #
#print 'LIB2: ',pars
t_Arai=Data[s]['t_Arai']
x_Arai=Data[s]['x_Arai']
y_Arai=Data[s]['y_Arai']
x_tail_check=Data[s]['x_tail_check']
y_tail_check=Data[s]['y_tail_check']
zijdblock=Data[s]['zijdblock']
z_temperatures=Data[s]['z_temp']
#print tmin,tmax,z_temperatures
# check tmin
if tmin not in t_Arai or tmin not in z_temperatures:
return(pars)
# check tmax
if tmax not in t_Arai or tmin not in z_temperatures:
return(pars)
start=t_Arai.index(tmin)
end=t_Arai.index(tmax)
zstart=z_temperatures.index(tmin)
zend=z_temperatures.index(tmax)
zdata_segment=Data[s]['zdata'][zstart:zend+1]
# replacing PCA for zdata and for ptrms here
## removed a bunch of Ron's commented out old code
#lj
#-------------------------------------------------
# York regresssion (York, 1967) following Coe (1978)
# calculate f,fvds,
# modified from pmag.py
#-------------------------------------------------
x_Arai_segment= x_Arai[start:end+1]
y_Arai_segment= y_Arai[start:end+1]
# replace thellier_gui code for york regression here
pars["int_abs"]=-1*pars['treat_dc_field']*pars["int_b"]
# replace thellier_gui code for ptrm checks, DRAT etc. here
# also tail checks and SCAT
#-------------------------------------------------
# Add missing parts of code from old get_PI
#-------------------------------------------------
if MICROWAVE==True:
LP_code="LP-PI-M"
else:
LP_code="LP-PI-TRM"
count_IZ= Data[s]['steps_Arai'].count('IZ')
count_ZI= Data[s]['steps_Arai'].count('ZI')
if count_IZ >1 and count_ZI >1:
pars['method_codes']=LP_code+":"+"LP-PI-BT-IZZI"
elif count_IZ <1 and count_ZI >1:
pars['method_codes']=LP_code+":"+"LP-PI-ZI"
elif count_IZ >1 and count_ZI <1:
pars['method_codes']=LP_code+":"+"LP-PI-IZ"
else:
pars['method_codes']=LP_code
if 'ptrm_checks_temperatures' in Data[s].keys() and len(Data[s]['ptrm_checks_temperatures'])>0:
if MICROWAVE==True:
pars['method_codes']+=":LP-PI-ALT-PMRM"
else:
pars['method_codes']+=":LP-PI-ALT-PTRM"
if 'tail_check_temperatures' in Data[s].keys() and len(Data[s]['tail_check_temperatures'])>0:
pars['method_codes']+=":LP-PI-BT-MD"
if 'additivity_check_temperatures' in Data[s].keys() and len(Data[s]['additivity_check_temperatures'])>0:
pars['method_codes']+=":LP-PI-BT"
#-------------------------------------------------
# Calculate anistropy correction factor
#-------------------------------------------------
if "AniSpec" in Data[s].keys():
pars["AC_WARNING"]=""
# if both aarm and atrm tensor axist, try first the aarm. if it fails use the atrm.
if 'AARM' in Data[s]["AniSpec"].keys() and 'ATRM' in Data[s]["AniSpec"].keys():
TYPES=['AARM','ATRM']
else:
TYPES=Data[s]["AniSpec"].keys()
for TYPE in TYPES:
red_flag=False
S_matrix=zeros((3,3),'f')
S_matrix[0,0]=Data[s]['AniSpec'][TYPE]['aniso_s1']
S_matrix[1,1]=Data[s]['AniSpec'][TYPE]['aniso_s2']
S_matrix[2,2]=Data[s]['AniSpec'][TYPE]['aniso_s3']
S_matrix[0,1]=Data[s]['AniSpec'][TYPE]['aniso_s4']
S_matrix[1,0]=Data[s]['AniSpec'][TYPE]['aniso_s4']
S_matrix[1,2]=Data[s]['AniSpec'][TYPE]['aniso_s5']
S_matrix[2,1]=Data[s]['AniSpec'][TYPE]['aniso_s5']
S_matrix[0,2]=Data[s]['AniSpec'][TYPE]['aniso_s6']
S_matrix[2,0]=Data[s]['AniSpec'][TYPE]['aniso_s6']
#Data[s]['AniSpec']['aniso_type']=Data[s]['AniSpec']['aniso_type']
Data[s]['AniSpec'][TYPE]['aniso_n']=int(float(Data[s]['AniSpec'][TYPE]['aniso_n']))
this_specimen_f_type=Data[s]['AniSpec'][TYPE]['aniso_type']+"_"+"%i"%(int(Data[s]['AniSpec'][TYPE]['aniso_n']))
Ftest_crit={}
Ftest_crit['ATRM_6']= 3.1059
Ftest_crit['AARM_6']= 3.1059
Ftest_crit['AARM_9']= 2.6848
Ftest_crit['AARM_15']= 2.4558
# threshold value for Ftest:
if 'AniSpec' in Data[s].keys() and TYPE in Data[s]['AniSpec'].keys()\
and 'aniso_sigma' in Data[s]['AniSpec'][TYPE].keys() \
and Data[s]['AniSpec'][TYPE]['aniso_sigma']!="":
# Calculate Ftest. If Ftest exceeds threshold value: set anistropy tensor to identity matrix
sigma=float(Data[s]['AniSpec'][TYPE]['aniso_sigma'])
nf = 3*int(Data[s]['AniSpec'][TYPE]['aniso_n'])-6
F=calculate_ftest(S_matrix,sigma,nf)
#print s,"F",F
Data[s]['AniSpec'][TYPE]['aniso_ftest']=F
#print "s,sigma,nf,F,Ftest_crit[this_specimen_f_type]"
#print s,sigma,nf,F,Ftest_crit[this_specimen_f_type]
if acceptance_criteria['anisotropy_ftest_flag']['value'] in ['g','1',1,True,'TRUE','True'] :
Ftest_threshold=Ftest_crit[this_specimen_f_type]
if Data[s]['AniSpec'][TYPE]['ftest'] < Ftest_crit[this_specimen_f_type]:
S_matrix=identity(3,'f')
pars["AC_WARNING"]=pars["AC_WARNING"]+"%s tensor fails F-test; "%(TYPE)
red_flag=True
else:
Data[s]['AniSpec'][TYPE]['aniso_sigma']=""
Data[s]['AniSpec'][TYPE]['aniso_ftest']=99999
if 'aniso_alt' in Data[s]['AniSpec'][TYPE].keys() and Data[s]['AniSpec'][TYPE]['aniso_alt']!="":
if acceptance_criteria['aniso_alt']['value'] != -999 and \
(float(Data[s]['AniSpec'][TYPE]['aniso_alt']) > float(acceptance_criteria['aniso_alt']['value'])):
S_matrix=identity(3,'f')
pars["AC_WARNING"]=pars["AC_WARNING"]+"%s tensor fails alteration check: %.1f > %.1f; "%(TYPE,float(Data[s]['AniSpec'][TYPE]['aniso_alt']),float(acceptance_criteria['anisotropy_alt']['value']))
red_flag=True
else:
Data[s]['AniSpec'][TYPE]['aniso_alt']=""
Data[s]['AniSpec'][TYPE]['S_matrix']=S_matrix
#--------------------------
# if AARM passes all, use the AARM.
# if ATRM fail alteration use the AARM
# if both fail F-test: use AARM
if len(TYPES)>1:
if "ATRM tensor fails alteration check" in pars["AC_WARNING"]:
TYPE='AARM'
elif "ATRM tensor fails F-test" in pars["AC_WARNING"]:
TYPE='AARM'
else:
TYPE=='AARM'
S_matrix= Data[s]['AniSpec'][TYPE]['S_matrix']
#---------------------------
TRM_anc_unit=array(pars['specimen_PCA_v1'])/sqrt(pars['specimen_PCA_v1'][0]**2+pars['specimen_PCA_v1'][1]**2+pars['specimen_PCA_v1'][2]**2)
B_lab_unit=pmag.dir2cart([ Data[s]['treat_dc_field_phi'], Data[s]['treat_dc_field_theta'],1])
#B_lab_unit=array([0,0,-1])
Anisotropy_correction_factor=linalg.norm(dot(inv(S_matrix),TRM_anc_unit.transpose()))*norm(dot(S_matrix,B_lab_unit))
pars["Anisotropy_correction_factor"]=Anisotropy_correction_factor
pars["AC_specimen_int"]= pars["Anisotropy_correction_factor"] * float(pars["int_abs"])
pars["AC_anisotropy_type"]=Data[s]['AniSpec'][TYPE]["aniso_type"]
pars["specimen_int_uT"]=float(pars["AC_specimen_int"])*1e6
if TYPE=='AARM':
if ":LP-AN-ARM" not in pars['method_codes']:
pars['method_codes']+=":LP-AN-ARM:AE-H:DA-AC-AARM"
pars['specimen_correction']='c'
pars['specimen_int_corr_anisotropy']=Anisotropy_correction_factor
if TYPE=='ATRM':
if ":LP-AN-TRM" not in pars['jmethod_codes']:
pars['method_codes']+=":LP-AN-TRM:AE-H:DA-AC-ATRM"
pars['specimen_correction']='c'
pars['specimen_int_corr_anisotropy']=Anisotropy_correction_factor
else:
pars["Anisotropy_correction_factor"]=1.0
pars["specimen_int_uT"]=float(pars["int_abs"])*1e6
pars["AC_WARNING"]="No anistropy correction"
pars['specimen_correction']='u'
pars["specimen_int_corr_anisotropy"]=pars["Anisotropy_correction_factor"]
#-------------------------------------------------
# NLT and anisotropy correction together in one equation
# See Shaar et al (2010), Equation (3)
#-------------------------------------------------
if 'NLT_parameters' in Data[s].keys():
alpha=Data[s]['NLT_parameters']['tanh_parameters'][0][0]
beta=Data[s]['NLT_parameters']['tanh_parameters'][0][1]
b=float(pars["int__b"])
Fa=pars["Anisotropy_correction_factor"]
if ((abs(b)*Fa)/alpha) <1.0:
Banc_NLT=math.atanh( ((abs(b)*Fa)/alpha) ) / beta
pars["NLTC_specimen_int"]=Banc_NLT
pars["specimen_int_uT"]=Banc_NLT*1e6
if "AC_specimen_int" in pars.keys():
pars["NLT_specimen_correction_factor"]=Banc_NLT/float(pars["AC_specimen_int"])
else:
pars["NLT_specimen_correction_factor"]=Banc_NLT/float(pars["int_abs"])
if ":LP-TRM" not in pars['method_codes']:
pars['method_codes']+=":LP-TRM:DA-NL"
pars['specimen_correction']='c'
else:
GUI_log.write ("-W- WARNING: problematic NLT mesurements for specimens %s. Cant do NLT calculation. check data\n"%s)
pars["NLT_specimen_correction_factor"]=-1
else:
pars["NLT_specimen_correction_factor"]=-1
#-------------------------------------------------
# Calculate the final result with cooling rate correction
#-------------------------------------------------
pars["specimen_int_corr_cooling_rate"]=-999
if 'cooling_rate_data' in Data[s].keys():
if 'CR_correction_factor' in Data[s]['cooling_rate_data'].keys():
if Data[s]['cooling_rate_data']['CR_correction_factor'] != -1 and Data[s]['cooling_rate_data']['CR_correction_factor'] !=-999:
pars["specimen_int_corr_cooling_rate"]=Data[s]['cooling_rate_data']['CR_correction_factor']
pars['specimen_correction']='c'
pars["specimen_int_uT"]=pars["specimen_int_uT"]*pars["specimen_int_corr_cooling_rate"]
if ":DA-CR" not in pars['method_codes']:
pars['method_codes']+=":DA-CR"
if 'CR_correction_factor_flag' in Data[s]['cooling_rate_data'].keys():
if Data[s]['cooling_rate_data']['CR_correction_factor_flag']=="calculated":
pars['CR_flag']="calculated"
else:
pars['CR_flag']=""
if 'CR_correction_factor_flag' in Data[s]['cooling_rate_data'].keys() \
and Data[s]['cooling_rate_data']['CR_correction_factor_flag']!="calculated":
pars["CR_WARNING"]="inferred cooling rate correction"
else:
pars["CR_WARNING"]="no cooling rate correction"
def combine_dictionaries(d1, d2):
"""
combines dict1 and dict2 into a new dict.
if dict1 and dict2 share a key, the value from dict1 is used
"""
for key, value in d2.iteritems():
if key not in d1.keys():
d1[key] = value
return d1
Data[s]['pars'] = pars
#print pars.keys()
return(pars)
def main():
"""
NAME
remanence_aniso_magic.py
DESCRIPTION
This program is similar to aarm_magic.py and atrm_magic.py with minor modifications.
Converts magic measurement file with ATRM/AARM data to best-fit tensor (6 elements plus sigma)
following Hext (1963), and calculates F-test statistics.
Comments:
- infield steps are marked with method codes LT-T-I:LP-AN-TRM; LT-AF-I:LP-AN-ARM
- zerofield steps are marked with method codes LT-T-Z:LP-AN-TRM; LT-AF-Z:LP-AN-ARM
- alteration check is marked with method codes LT-PTRM-I:LP-AN-TRM
please notice;
- ATRM: The program uses treatment_dc_field_phi/treatment_dc_field_theta columns to infer the direction of the applied field
(this is a change from atrm_magic.py)
- ATRM: zerofield (baseline) magnetization is subtructed from all infield measurements
- AARM: The program uses measurement number (running number) to to infer the direction of the applied field
assuming the SIO protocol for 6,9,15 measurements scheme.
See cookbook for diagram and details.
- AARM: zerofield (baseline) are assumed to be before any infield, and the baseline is subtructed from the
subsequent infield magnetization.
SYNTAX
remanence_aniso_magic.py [-h] [command line options]
OPTIONS
-h prints help message and quits
-f FILE: specify input file, default is magic_measurements.txt
INPUT
magic measurement file with ATRM and/or AARM data.
if both types of measurements exist then the program calculates both.
OUTPUT
rmag_anisotropy.log
-I- information
-W- Warning
-E- Error
rmag_anistropy.txt:
This file contains in addition to some some magic information the following:
- anistropy tensor s1 to s6 normalized by the trace:
|Mx| |s1 s4 s6| |Bx|
|My| = |s4 s2 s5| . |By|
|Mz| |s6 s5 s3| |Bz|
- anisotropy_sigma (Hext, 1963)
- anisotropy_alt (altertion check for ATRM in units of %):
100* [abs(M_first-Mlast)/max(M_first,M_last)]
-
rmag_results.txt:
This file contains in addition to some magic information the follow(ing:
- anisotropy_t1,anisotropy_t2,anisotropy_t3 : eigenvalues
- anisotropy_v*_dec,anisotropy_v*_inc: declination/inclination of the eigenvectors
- anisotropy_ftest,anisotropy_ftest12,anisotropy_ftest13
- (the crtical F for 95% confidence level of anistropy is given in result_description column).
"""
#==================================================================================
meas_file="magic_measurements.txt"
args=sys.argv
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 "-f" in args:
ind=args.index("-f")
meas_file=sys.argv[ind+1]
else:
meas_file=dir_path+'/'+meas_file
WD=dir_path
#======================================
# functions
#======================================
def get_Data(magic_file):
#------------------------------------------------
# Read magic measurement file and sort to blocks
#------------------------------------------------
Data={}
try:
meas_data,file_type=pmag.magic_read(magic_file)
except:
print "-E- ERROR: Cant read magic_measurement.txt file. File is corrupted."
return Data
# get list of unique specimen names
#sids=pmag.get_specs(meas_data) # samples ID's
for rec in meas_data:
s=rec["er_specimen_name"]
method_codes= rec["magic_method_codes"].strip('\n')
method_codes.replace(" ","")
methods=method_codes.split(":")
if "LP-AN-TRM" in methods:
if s not in Data.keys():
Data[s]={}
if 'atrmblock' not in Data[s].keys():
Data[s]['atrmblock']=[]
Data[s]['atrmblock'].append(rec)
if "LP-AN-ARM" in methods:
if s not in Data.keys():
Data[s]={}
if 'aarmblock' not in Data[s].keys():
Data[s]['aarmblock']=[]
Data[s]['aarmblock'].append(rec)
return (Data)
#======================================
# better to put this one in pmagpy
#======================================
def calculate_aniso_parameters(B,K):
aniso_parameters={}
S_bs=dot(B,K)
# normalize by trace
trace=S_bs[0]+S_bs[1]+S_bs[2]
S_bs=S_bs/trace
s1,s2,s3,s4,s5,s6=S_bs[0],S_bs[1],S_bs[2],S_bs[3],S_bs[4],S_bs[5]
s_matrix=[[s1,s4,s6],[s4,s2,s5],[s6,s5,s3]]
# calculate eigen vector,
t,evectors=eig(s_matrix)
# sort vectors
t=list(t)
t1=max(t)
ix_1=t.index(t1)
t3=min(t)
ix_3=t.index(t3)
for tt in range(3):
if t[tt]!=t1 and t[tt]!=t3:
t2=t[tt]
ix_2=t.index(t2)
v1=[evectors[0][ix_1],evectors[1][ix_1],evectors[2][ix_1]]
v2=[evectors[0][ix_2],evectors[1][ix_2],evectors[2][ix_2]]
v3=[evectors[0][ix_3],evectors[1][ix_3],evectors[2][ix_3]]
DIR_v1=pmag.cart2dir(v1)
DIR_v2=pmag.cart2dir(v2)
DIR_v3=pmag.cart2dir(v3)
aniso_parameters['anisotropy_s1']="%f"%s1
aniso_parameters['anisotropy_s2']="%f"%s2
aniso_parameters['anisotropy_s3']="%f"%s3
aniso_parameters['anisotropy_s4']="%f"%s4
aniso_parameters['anisotropy_s5']="%f"%s5
aniso_parameters['anisotropy_s6']="%f"%s6
aniso_parameters['anisotropy_degree']="%f"%(t1/t3)
aniso_parameters['anisotropy_t1']="%f"%t1
aniso_parameters['anisotropy_t2']="%f"%t2
aniso_parameters['anisotropy_t3']="%f"%t3
aniso_parameters['anisotropy_v1_dec']="%.1f"%DIR_v1[0]
aniso_parameters['anisotropy_v1_inc']="%.1f"%DIR_v1[1]
aniso_parameters['anisotropy_v2_dec']="%.1f"%DIR_v2[0]
aniso_parameters['anisotropy_v2_inc']="%.1f"%DIR_v2[1]
aniso_parameters['anisotropy_v3_dec']="%.1f"%DIR_v3[0]
aniso_parameters['anisotropy_v3_inc']="%.1f"%DIR_v3[1]
# modified from pmagpy:
if len(K)/3==9 or len(K)/3==6 or len(K)/3==15:
n_pos=len(K)/3
tmpH = Matrices[n_pos]['tmpH']
a=s_matrix
S=0.
comp=zeros((n_pos*3),'f')
for i in range(n_pos):
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(n_pos*3):
d=K[i]/trace - comp[i] # del values
S+=d*d
nf=float(n_pos*3-6) # number of degrees of freedom
if S >0:
sigma=math.sqrt(S/nf)
hpars=pmag.dohext(nf,sigma,[s1,s2,s3,s4,s5,s6])
aniso_parameters['anisotropy_sigma']="%f"%sigma
aniso_parameters['anisotropy_ftest']="%f"%hpars["F"]
aniso_parameters['anisotropy_ftest12']="%f"%hpars["F12"]
aniso_parameters['anisotropy_ftest23']="%f"%hpars["F23"]
aniso_parameters['result_description']="Critical F: %s"%(hpars['F_crit'])
aniso_parameters['anisotropy_F_crit']="%f"%float(hpars['F_crit'])
aniso_parameters['anisotropy_n']=n_pos
return(aniso_parameters)
#======================================
# Main
#======================================
aniso_logfile=open(WD+"/rmag_anisotropy.log",'w')
aniso_logfile.write("------------------------\n")
aniso_logfile.write( "-I- Start rmag anisrotropy script\n")
aniso_logfile.write( "------------------------\n")
Data=get_Data(meas_file)
#try:
# Data=get_Data(meas_file)
#except:
# aniso_logfile.write( "-E- Cant open measurement file %s\n" %meas_file)
# print "-E- Cant open measurement file %s\n exiting" %meas_file
# exit()
aniso_logfile.write( "-I- Open measurement file %s\n" %meas_file)
Data_anisotropy={}
specimens=Data.keys()
specimens.sort()
#-----------------------------------
# Prepare rmag_anisotropy.txt file for writing
#-----------------------------------
rmag_anisotropy_file =open(WD+"/rmag_anisotropy.txt",'w')
rmag_anisotropy_file.write("tab\trmag_anisotropy\n")
rmag_results_file =open(WD+"/rmag_results.txt",'w')
rmag_results_file.write("tab\trmag_results\n")
rmag_anistropy_header=['er_specimen_name','er_sample_name','er_site_name','anisotropy_type','anisotropy_n','anisotropy_description','anisotropy_s1','anisotropy_s2','anisotropy_s3','anisotropy_s4','anisotropy_s5','anisotropy_s6','anisotropy_sigma','anisotropy_alt','magic_experiment_names','magic_method_codes']
String=""
for i in range (len(rmag_anistropy_header)):
String=String+rmag_anistropy_header[i]+'\t'
rmag_anisotropy_file.write(String[:-1]+"\n")
rmag_results_header=['er_specimen_names','er_sample_names','er_site_names','anisotropy_type','magic_method_codes','magic_experiment_names','result_description','anisotropy_t1','anisotropy_t2','anisotropy_t3','anisotropy_ftest','anisotropy_ftest12','anisotropy_ftest23',\
'anisotropy_v1_dec','anisotropy_v1_inc','anisotropy_v2_dec','anisotropy_v2_inc','anisotropy_v3_dec','anisotropy_v3_inc']
String=""
for i in range (len(rmag_results_header)):
String=String+rmag_results_header[i]+'\t'
rmag_results_file.write(String[:-1]+"\n")
#-----------------------------------
# Matrices definitions:
# A design matrix
# B dot(inv(dot(A.transpose(),A)),A.transpose())
# tmpH is used for sigma calculation (9,15 measurements only)
#
# Anisotropy tensor:
#
# |Mx| |s1 s4 s6| |Bx|
# |My| = |s4 s2 s5| . |By|
# |Mz| |s6 s5 s3| |Bz|
#
# A matrix (measurement matrix):
# Each mesurement yields three lines in "A" matrix
#
# |Mi | |Bx 0 0 By 0 Bz| |s1|
# |Mi+1| = |0 By 0 Bx Bz 0 | . |s2|
# |Mi+2| |0 0 Bz 0 By Bx| |s3|
# |s4|
# |s5|
#
#-----------------------------------
Matrices={}
for n_pos in [6,9,15]:
Matrices[n_pos]={}
A=zeros((n_pos*3,6),'f')
if n_pos==6:
positions=[[0.,0.,1.],[90.,0.,1.],[0.,90.,1.],\
[180.,0.,1.],[270.,0.,1.],[0.,-90.,1.]]
if n_pos==15:
positions=[[315.,0.,1.],[225.,0.,1.],[180.,0.,1.],[135.,0.,1.],[45.,0.,1.],\
[90.,-45.,1.],[270.,-45.,1.],[270.,0.,1.],[270.,45.,1.],[90.,45.,1.],\
[180.,45.,1.],[180.,-45.,1.],[0.,-90.,1.],[0,-45.,1.],[0,45.,1.]]
if n_pos==9:
positions=[[315.,0.,1.],[225.,0.,1.],[180.,0.,1.],\
[90.,-45.,1.],[270.,-45.,1.],[270.,0.,1.],\
[180.,45.,1.],[180.,-45.,1.],[0.,-90.,1.]]
tmpH=zeros((n_pos,3),'f') # define tmpH
for i in range(len(positions)):
CART=pmag.dir2cart(positions[i])
a=CART[0];b=CART[1];c=CART[2]
A[3*i][0]=a
A[3*i][3]=b
A[3*i][5]=c
A[3*i+1][1]=b
A[3*i+1][3]=a
A[3*i+1][4]=c
A[3*i+2][2]=c
A[3*i+2][4]=b
A[3*i+2][5]=a
tmpH[i][0]=CART[0]
tmpH[i][1]=CART[1]
tmpH[i][2]=CART[2]
B=dot(inv(dot(A.transpose(),A)),A.transpose())
Matrices[n_pos]['A']=A
Matrices[n_pos]['B']=B
Matrices[n_pos]['tmpH']=tmpH
for specimen in specimens:
if 'atrmblock' in Data[specimen].keys():
#-----------------------------------
# aTRM 6 positions
#-----------------------------------
aniso_logfile.write("-I- Start calculating ATRM tensor for specimen %s\n "%specimen)
atrmblock=Data[specimen]['atrmblock']
if len(atrmblock)<6:
aniso_logfile.write("-W- specimen %s has not enough measurementf for ATRM calculation\n"%specimen)
continue
B=Matrices[6]['B']
Reject_specimen = False
# The zero field step is a "baseline"
# Search the baseline in the ATRM measurement
baseline=""
Alteration_check=""
Alteration_check_index=""
baselines=[]
# search for baseline in atrm blocks
for rec in atrmblock:
dec=float(rec['measurement_dec'])
inc=float(rec['measurement_inc'])
moment=float(rec['measurement_magn_moment'])
# find the temperature of the atrm
if float(rec['treatment_dc_field'])!=0 and float(rec['treatment_temp'])!=273:
atrm_temperature=float(rec['treatment_temp'])
# find baseline
if float(rec['treatment_dc_field'])==0 and float(rec['treatment_temp'])!=273:
baselines.append(array(pmag.dir2cart([dec,inc,moment])))
# Find alteration check
#print rec['measurement_number']
if len(baselines)!=0:
aniso_logfile.write( "-I- found ATRM baseline for specimen %s\n"%specimen)
baselines=array(baselines)
baseline=array([mean(baselines[:,0]),mean(baselines[:,1]),mean(baselines[:,2])])
else:
baseline=zeros(3,'f')
aniso_logfile.write( "-I- No aTRM baseline for specimen %s\n"%specimen)
# sort measurements
M=zeros([6,3],'f')
for rec in atrmblock:
dec=float(rec['measurement_dec'])
inc=float(rec['measurement_inc'])
moment=float(rec['measurement_magn_moment'])
CART=array(pmag.dir2cart([dec,inc,moment]))-baseline
if float(rec['treatment_dc_field'])==0: # Ignore zero field steps
continue
if "LT-PTRM-I" in rec['magic_method_codes'].split(":"): # alteration check
Alteration_check=CART
Alteration_check_dc_field_phi=float(rec['treatment_dc_field_phi'])
Alteration_check_dc_field_theta=float(rec['treatment_dc_field_theta'])
if Alteration_check_dc_field_phi==0 and Alteration_check_dc_field_theta==0 :
Alteration_check_index=0
if Alteration_check_dc_field_phi==90 and Alteration_check_dc_field_theta==0 :
Alteration_check_index=1
if Alteration_check_dc_field_phi==0 and Alteration_check_dc_field_theta==90 :
Alteration_check_index=2
if Alteration_check_dc_field_phi==180 and Alteration_check_dc_field_theta==0 :
Alteration_check_index=3
if Alteration_check_dc_field_phi==270 and Alteration_check_dc_field_theta==0 :
Alteration_check_index=4
if Alteration_check_dc_field_phi==0 and Alteration_check_dc_field_theta==-90 :
Alteration_check_index=5
aniso_logfile.write( "-I- found alteration check for specimen %s\n"%specimen)
continue
treatment_dc_field_phi=float(rec['treatment_dc_field_phi'])
treatment_dc_field_theta=float(rec['treatment_dc_field_theta'])
treatment_dc_field=float(rec['treatment_dc_field'])
#+x, M[0]
if treatment_dc_field_phi==0 and treatment_dc_field_theta==0 :
M[0]=CART
#+Y , M[1]
if treatment_dc_field_phi==90 and treatment_dc_field_theta==0 :
M[1]=CART
#+Z , M[2]
if treatment_dc_field_phi==0 and treatment_dc_field_theta==90 :
M[2]=CART
#-x, M[3]
if treatment_dc_field_phi==180 and treatment_dc_field_theta==0 :
M[3]=CART
#-Y , M[4]
if treatment_dc_field_phi==270 and treatment_dc_field_theta==0 :
M[4]=CART
#-Z , M[5]
if treatment_dc_field_phi==0 and treatment_dc_field_theta==-90 :
M[5]=CART
# check if at least one measurement in missing
for i in range(len(M)):
if M[i][0]==0 and M[i][1]==0 and M[i][2]==0:
aniso_logfile.write( "-E- ERROR: missing atrm data for specimen %s\n"%(specimen))
Reject_specimen=True
# alteration check
anisotropy_alt=0
if Alteration_check!="":
for i in range(len(M)):
if Alteration_check_index==i:
M_1=sqrt(sum((array(M[i])**2)))
M_2=sqrt(sum(Alteration_check**2))
diff=abs(M_1-M_2)
diff_ratio=diff/mean([M_1,M_2])
diff_ratio_perc=100*diff_ratio
if diff_ratio_perc > anisotropy_alt:
anisotropy_alt=diff_ratio_perc
else:
aniso_logfile.write( "-W- Warning: no alteration check for specimen %s \n "%specimen )
# Check for maximum difference in anti parallel directions.
# if the difference between the two measurements is more than maximum_diff
# The specimen is rejected
# i.e. +x versus -x, +y versus -y, etc.s
for i in range(3):
M_1=sqrt(sum(array(M[i])**2))
M_2=sqrt(sum(array(M[i+3])**2))
diff=abs(M_1-M_2)
diff_ratio=diff/max(M_1,M_2)
diff_ratio_perc=100*diff_ratio
if diff_ratio_perc>anisotropy_alt:
anisotropy_alt=diff_ratio_perc
if not Reject_specimen:
# K vector (18 elements, M1[x], M1[y], M1[z], ... etc.)
K=zeros(18,'f')
K[0],K[1],K[2]=M[0][0],M[0][1],M[0][2]
K[3],K[4],K[5]=M[1][0],M[1][1],M[1][2]
K[6],K[7],K[8]=M[2][0],M[2][1],M[2][2]
K[9],K[10],K[11]=M[3][0],M[3][1],M[3][2]
K[12],K[13],K[14]=M[4][0],M[4][1],M[4][2]
K[15],K[16],K[17]=M[5][0],M[5][1],M[5][2]
if specimen not in Data_anisotropy.keys():
Data_anisotropy[specimen]={}
aniso_parameters=calculate_aniso_parameters(B,K)
Data_anisotropy[specimen]['ATRM']=aniso_parameters
Data_anisotropy[specimen]['ATRM']['anisotropy_alt']="%.2f"%anisotropy_alt
Data_anisotropy[specimen]['ATRM']['anisotropy_type']="ATRM"
Data_anisotropy[specimen]['ATRM']['er_sample_name']=atrmblock[0]['er_sample_name']
Data_anisotropy[specimen]['ATRM']['er_specimen_name']=specimen
Data_anisotropy[specimen]['ATRM']['er_site_name']=atrmblock[0]['er_site_name']
Data_anisotropy[specimen]['ATRM']['anisotropy_description']='Hext statistics adapted to ATRM'
Data_anisotropy[specimen]['ATRM']['magic_experiment_names']=specimen+";ATRM"
Data_anisotropy[specimen]['ATRM']['magic_method_codes']="LP-AN-TRM:AE-H"
#Data_anisotropy[specimen]['ATRM']['rmag_anisotropy_name']=specimen
if 'aarmblock' in Data[specimen].keys():
#-----------------------------------
# AARM - 6, 9 or 15 positions
#-----------------------------------
aniso_logfile.write( "-I- Start calculating AARM tensors specimen %s\n"%specimen)
aarmblock=Data[specimen]['aarmblock']
if len(aarmblock)<12:
aniso_logfile.write( "-W- WARNING: not enough aarm measurement for specimen %s\n"%specimen)
continue
elif len(aarmblock)==12:
n_pos=6
B=Matrices[6]['B']
M=zeros([6,3],'f')
elif len(aarmblock)==18:
n_pos=9
B=Matrices[9]['B']
M=zeros([9,3],'f')
# 15 positions
elif len(aarmblock)==30:
n_pos=15
B=Matrices[15]['B']
M=zeros([15,3],'f')
else:
aniso_logfile.write( "-E- ERROR: number of measurements in aarm block is incorrect sample %s\n"%specimen)
continue
Reject_specimen = False
for i in range(n_pos):
for rec in aarmblock:
if float(rec['measurement_number'])==i*2+1:
dec=float(rec['measurement_dec'])
inc=float(rec['measurement_inc'])
moment=float(rec['measurement_magn_moment'])
M_baseline=array(pmag.dir2cart([dec,inc,moment]))
if float(rec['measurement_number'])==i*2+2:
dec=float(rec['measurement_dec'])
inc=float(rec['measurement_inc'])
moment=float(rec['measurement_magn_moment'])
M_arm=array(pmag.dir2cart([dec,inc,moment]))
M[i]=M_arm-M_baseline
K=zeros(3*n_pos,'f')
for i in range(n_pos):
K[i*3]=M[i][0]
K[i*3+1]=M[i][1]
K[i*3+2]=M[i][2]
if specimen not in Data_anisotropy.keys():
Data_anisotropy[specimen]={}
aniso_parameters=calculate_aniso_parameters(B,K)
Data_anisotropy[specimen]['AARM']=aniso_parameters
Data_anisotropy[specimen]['AARM']['anisotropy_alt']=""
Data_anisotropy[specimen]['AARM']['anisotropy_type']="AARM"
Data_anisotropy[specimen]['AARM']['er_sample_name']=aarmblock[0]['er_sample_name']
Data_anisotropy[specimen]['AARM']['er_site_name']=aarmblock[0]['er_site_name']
Data_anisotropy[specimen]['AARM']['er_specimen_name']=specimen
Data_anisotropy[specimen]['AARM']['anisotropy_description']='Hext statistics adapted to AARM'
Data_anisotropy[specimen]['AARM']['magic_experiment_names']=specimen+";AARM"
Data_anisotropy[specimen]['AARM']['magic_method_codes']="LP-AN-ARM:AE-H"
#Data_anisotropy[specimen]['AARM']['rmag_anisotropy_name']=specimen
#-----------------------------------
specimens=Data_anisotropy.keys()
specimens.sort
# remove previous anistropy data, and replace with the new one:
s_list=Data.keys()
for sp in s_list:
if 'AniSpec' in Data[sp].keys():
del Data[sp]['AniSpec']
for specimen in specimens:
# if both AARM and ATRM axist prefer the AARM !!
if 'AARM' in Data_anisotropy[specimen].keys():
TYPES=['AARM']
if 'ATRM' in Data_anisotropy[specimen].keys():
TYPES=['ATRM']
if 'AARM' in Data_anisotropy[specimen].keys() and 'ATRM' in Data_anisotropy[specimen].keys():
TYPES=['ATRM','AARM']
aniso_logfile.write( "-W- WARNING: both aarm and atrm data exist for specimen %s. using AARM by default. If you prefer using one of them, delete the other!\n"%specimen)
for TYPE in TYPES:
String=""
for i in range (len(rmag_anistropy_header)):
try:
String=String+Data_anisotropy[specimen][TYPE][rmag_anistropy_header[i]]+'\t'
except:
String=String+"%f"%(Data_anisotropy[specimen][TYPE][rmag_anistropy_header[i]])+'\t'
rmag_anisotropy_file.write(String[:-1]+"\n")
String=""
Data_anisotropy[specimen][TYPE]['er_specimen_names']=Data_anisotropy[specimen][TYPE]['er_specimen_name']
Data_anisotropy[specimen][TYPE]['er_sample_names']=Data_anisotropy[specimen][TYPE]['er_sample_name']
Data_anisotropy[specimen][TYPE]['er_site_names']=Data_anisotropy[specimen][TYPE]['er_site_name']
for i in range (len(rmag_results_header)):
try:
String=String+Data_anisotropy[specimen][TYPE][rmag_results_header[i]]+'\t'
except:
String=String+"%f"%(Data_anisotropy[specimen][TYPE][rmag_results_header[i]])+'\t'
rmag_results_file.write(String[:-1]+"\n")
if 'AniSpec' not in Data[specimen]:
Data[specimen]['AniSpec']={}
Data[specimen]['AniSpec'][TYPE]=Data_anisotropy[specimen][TYPE]
aniso_logfile.write("------------------------\n")
aniso_logfile.write("-I- remanence_aniso_magic script finished sucsessfuly\n")
aniso_logfile.write( "------------------------\n")
rmag_anisotropy_file.close()
print "Anisotropy tensors elements are saved in rmag_anistropy.txt"
print "Other anisotropy statistics are saved in rmag_results.txt"
print "log file is in rmag_anisotropy.log"
def main():
"""
NAME
lowrie.py
DESCRIPTION
plots intensity decay curves for Lowrie experiments
SYNTAX
lowrie -h [command line options]
INPUT
takes SIO formatted input files
OPTIONS
-h prints help message and quits
-f FILE: specify input file
-N do not normalize by maximum magnetization
-fmt [svg, pdf, eps, png] specify fmt, default is svg
-sav save plots and quit
"""
fmt, plot = 'svg', 0
FIG = {} # plot dictionary
FIG['lowrie'] = 1 # demag is figure 1
pmagplotlib.plot_init(FIG['lowrie'], 6, 6)
norm = 1 # default is to normalize by maximum axis
if len(sys.argv) > 1:
if '-h' in sys.argv:
print(main.__doc__)
sys.exit()
if '-N' in sys.argv:
norm = 0 # don't normalize
if '-sav' in sys.argv:
plot = 1 # don't normalize
if '-fmt' in sys.argv: # sets input filename
ind = sys.argv.index("-fmt")
fmt = sys.argv[ind + 1]
if '-f' in sys.argv: # sets input filename
ind = sys.argv.index("-f")
in_file = sys.argv[ind + 1]
else:
print(main.__doc__)
print('you must supply a file name')
sys.exit()
else:
print(main.__doc__)
print('you must supply a file name')
sys.exit()
data = pmag.open_file(in_file)
PmagRecs = [] # set up a list for the results
keys = ['specimen', 'treatment', 'csd', 'M', 'dec', 'inc']
for line in data:
PmagRec = {}
rec = line.replace('\n', '').split()
for k in range(len(keys)):
PmagRec[keys[k]] = rec[k]
PmagRecs.append(PmagRec)
specs = pmag.get_dictkey(PmagRecs, 'specimen', '')
sids = []
for spec in specs:
if spec not in sids:
sids.append(spec) # get list of unique specimen names
for spc in sids: # step through the specimen names
print(spc)
specdata = pmag.get_dictitem(PmagRecs, 'specimen', spc,
'T') # get all this one's data
DIMs, Temps = [], []
for dat in specdata: # step through the data
DIMs.append(
[float(dat['dec']),
float(dat['inc']),
float(dat['M']) * 1e-3])
Temps.append(float(dat['treatment']))
carts = pmag.dir2cart(DIMs).transpose()
# if norm==1: # want to normalize
# nrm=max(max(abs(carts[0])),max(abs(carts[1])),max(abs(carts[2]))) # by maximum of x,y,z values
# ylab="M/M_max"
if norm == 1: # want to normalize
nrm = (DIMs[0][2]) # normalize by NRM
ylab = "M/M_o"
else:
nrm = 1. # don't normalize
ylab = "Magnetic moment (Am^2)"
xlab = "Temperature (C)"
pmagplotlib.plotXY(FIG['lowrie'],
Temps,
old_div(abs(carts[0]), nrm),
sym='r-')
pmagplotlib.plotXY(FIG['lowrie'],
Temps,
old_div(abs(carts[0]), nrm),
sym='ro') # X direction
pmagplotlib.plotXY(FIG['lowrie'],
Temps,
old_div(abs(carts[1]), nrm),
sym='c-')
pmagplotlib.plotXY(FIG['lowrie'],
Temps,
old_div(abs(carts[1]), nrm),
sym='cs') # Y direction
pmagplotlib.plotXY(FIG['lowrie'],
Temps,
old_div(abs(carts[2]), nrm),
sym='k-')
pmagplotlib.plotXY(FIG['lowrie'],
Temps,
old_div(abs(carts[2]), nrm),
sym='k^',
title=spc,
xlab=xlab,
ylab=ylab) # Z direction
files = {'lowrie': 'lowrie:_' + spc + '_.' + fmt}
if plot == 0:
pmagplotlib.drawFIGS(FIG)
ans = input('S[a]ve figure? [q]uit, <return> to continue ')
if ans == 'a':
pmagplotlib.saveP(FIG, files)
elif ans == 'q':
sys.exit()
else:
pmagplotlib.saveP(FIG, files)
pmagplotlib.clearFIG(FIG['lowrie'])
def main():
"""
NAME
lowrie_magic.py
DESCRIPTION
plots intensity decay curves for Lowrie experiments
SYNTAX
lowrie_magic.py -h [command line options]
INPUT
takes magic_measurements formatted input files
OPTIONS
-h prints help message and quits
-f FILE: specify input file, default is magic_measurements.txt
-N do not normalize by maximum magnetization
-fmt [svg, pdf, eps, png] specify fmt, default is svg
-sav saves plots and quits
"""
fmt,plot='svg',0
FIG={} # plot dictionary
FIG['lowrie']=1 # demag is figure 1
pmagplotlib.plot_init(FIG['lowrie'],6,6)
norm=1 # default is to normalize by maximum axis
in_file,dir_path='magic_measurements.txt','.'
if len(sys.argv)>1:
if '-WD' in sys.argv:
ind=sys.argv.index('-WD')
dir_path=sys.argv[ind+1]
if '-h' in sys.argv:
print main.__doc__
sys.exit()
if '-N' in sys.argv: norm=0 # don't normalize
if '-sav' in sys.argv: plot=1 # don't normalize
if '-fmt' in sys.argv: # sets input filename
ind=sys.argv.index("-fmt")
fmt=sys.argv[ind+1]
if '-f' in sys.argv: # sets input filename
ind=sys.argv.index("-f")
in_file=sys.argv[ind+1]
else:
print main.__doc__
print 'you must supply a file name'
sys.exit()
in_file=dir_path+'/'+in_file
print in_file
PmagRecs,file_type=pmag.magic_read(in_file)
if file_type!="magic_measurements":
print 'bad input file'
sys.exit()
PmagRecs=pmag.get_dictitem(PmagRecs,'magic_method_codes','LP-IRM-3D','has') # get all 3D IRM records
if len(PmagRecs)==0:
print 'no records found'
sys.exit()
specs=pmag.get_dictkey(PmagRecs,'er_specimen_name','')
sids=[]
for spec in specs:
if spec not in sids:sids.append(spec) # get list of unique specimen names
for spc in sids: # step through the specimen names
print spc
specdata=pmag.get_dictitem(PmagRecs,'er_specimen_name',spc,'T') # get all this one's data
DIMs,Temps=[],[]
for dat in specdata: # step through the data
DIMs.append([float(dat['measurement_dec']),float(dat['measurement_inc']),float(dat['measurement_magn_moment'])])
Temps.append(float(dat['treatment_temp'])-273.)
carts=pmag.dir2cart(DIMs).transpose()
if norm==1: # want to normalize
nrm=(DIMs[0][2]) # normalize by NRM
ylab="M/M_o"
else:
nrm=1. # don't normalize
ylab="Magnetic moment (Am^2)"
xlab="Temperature (C)"
pmagplotlib.plotXY(FIG['lowrie'],Temps,abs(carts[0])/nrm,sym='r-')
pmagplotlib.plotXY(FIG['lowrie'],Temps,abs(carts[0])/nrm,sym='ro') # X direction
pmagplotlib.plotXY(FIG['lowrie'],Temps,abs(carts[1])/nrm,sym='c-')
pmagplotlib.plotXY(FIG['lowrie'],Temps,abs(carts[1])/nrm,sym='cs') # Y direction
pmagplotlib.plotXY(FIG['lowrie'],Temps,abs(carts[2])/nrm,sym='k-')
pmagplotlib.plotXY(FIG['lowrie'],Temps,abs(carts[2])/nrm,sym='k^',title=spc,xlab=xlab,ylab=ylab) # Z direction
files={'lowrie':'lowrie:_'+spc+'_.'+fmt}
if plot==0:
pmagplotlib.drawFIGS(FIG)
ans=raw_input('S[a]ve figure? [q]uit, <return> to continue ')
if ans=='a':
pmagplotlib.saveP(FIG,files)
elif ans=='q':
sys.exit()
else:
pmagplotlib.saveP(FIG,files)
pmagplotlib.clearFIG(FIG['lowrie'])
def main():
"""
NAME
revtest_MM1990.py
DESCRIPTION
calculates Watson's V statistic from input files through Monte Carlo simulation in order to test whether normal and reversed populations could have been drawn from a common mean (equivalent to watsonV.py). Also provides the critical angle between the two sample mean directions and the corresponding McFadden and McElhinny (1990) classification.
INPUT FORMAT
takes dec/inc as first two columns in two space delimited files (one file for normal directions, one file for reversed directions).
SYNTAX
revtest_MM1990.py [command line options]
OPTIONS
-h prints help message and quits
-f FILE
-f2 FILE
-P (don't plot the Watson V cdf)
OUTPUT
Watson's V between the two populations and the Monte Carlo Critical Value Vc.
M&M1990 angle, critical angle and classification
Plot of Watson's V CDF from Monte Carlo simulation (red line), V is solid and Vc is dashed.
"""
D1,D2=[],[]
plot=1
Flip=1
if '-h' in sys.argv: # check if help is needed
print(main.__doc__)
sys.exit() # graceful quit
if '-P' in sys.argv: plot=0
if '-f' in sys.argv:
ind=sys.argv.index('-f')
file1=sys.argv[ind+1]
f1=open(file1,'r')
for line in f1.readlines():
rec=line.split()
Dec,Inc=float(rec[0]),float(rec[1])
D1.append([Dec,Inc,1.])
f1.close()
if '-f2' in sys.argv:
ind=sys.argv.index('-f2')
file2=sys.argv[ind+1]
f2=open(file2,'r')
print("be patient, your computer is doing 5000 simulations...")
for line in f2.readlines():
rec=line.split()
Dec,Inc=float(rec[0]),float(rec[1])
D2.append([Dec,Inc,1.])
f2.close()
#take the antipode for the directions in file 2
D2_flip=[]
for rec in D2:
d,i=(rec[0]-180.)%360.,-rec[1]
D2_flip.append([d,i,1.])
pars_1=pmag.fisher_mean(D1)
pars_2=pmag.fisher_mean(D2_flip)
cart_1=pmag.dir2cart([pars_1["dec"],pars_1["inc"],pars_1["r"]])
cart_2=pmag.dir2cart([pars_2['dec'],pars_2['inc'],pars_2["r"]])
Sw=pars_1['k']*pars_1['r']+pars_2['k']*pars_2['r'] # k1*r1+k2*r2
xhat_1=pars_1['k']*cart_1[0]+pars_2['k']*cart_2[0] # k1*x1+k2*x2
xhat_2=pars_1['k']*cart_1[1]+pars_2['k']*cart_2[1] # k1*y1+k2*y2
xhat_3=pars_1['k']*cart_1[2]+pars_2['k']*cart_2[2] # k1*z1+k2*z2
Rw=numpy.sqrt(xhat_1**2+xhat_2**2+xhat_3**2)
V=2*(Sw-Rw)
#
#keep weighted sum for later when determining the "critical angle" let's save it as Sr (notation of McFadden and McElhinny, 1990)
#
Sr=Sw
#
# do monte carlo simulation of datasets with same kappas, but common mean
#
counter,NumSims=0,5000
Vp=[] # set of Vs from simulations
for k in range(NumSims):
#
# get a set of N1 fisher distributed vectors with k1, calculate fisher stats
#
Dirp=[]
for i in range(pars_1["n"]):
Dirp.append(pmag.fshdev(pars_1["k"]))
pars_p1=pmag.fisher_mean(Dirp)
#
# get a set of N2 fisher distributed vectors with k2, calculate fisher stats
#
Dirp=[]
for i in range(pars_2["n"]):
Dirp.append(pmag.fshdev(pars_2["k"]))
pars_p2=pmag.fisher_mean(Dirp)
#
# get the V for these
#
Vk=pmag.vfunc(pars_p1,pars_p2)
Vp.append(Vk)
#
# sort the Vs, get Vcrit (95th percentile one)
#
Vp.sort()
k=int(.95*NumSims)
Vcrit=Vp[k]
#
# equation 18 of McFadden and McElhinny, 1990 calculates the critical value of R (Rwc)
#
Rwc=Sr-(old_div(Vcrit,2))
#
#following equation 19 of McFadden and McElhinny (1990) the critical angle is calculated.
#
k1=pars_1['k']
k2=pars_2['k']
R1=pars_1['r']
R2=pars_2['r']
critical_angle=numpy.degrees(numpy.arccos(old_div(((Rwc**2)-((k1*R1)**2)-((k2*R2)**2)),(2*k1*R1*k2*R2))))
D1_mean=(pars_1['dec'],pars_1['inc'])
D2_mean=(pars_2['dec'],pars_2['inc'])
angle=pmag.angle(D1_mean,D2_mean)
#
# print the results of the test
#
print("")
print("Results of Watson V test: ")
print("")
print("Watson's V: " '%.1f' %(V))
print("Critical value of V: " '%.1f' %(Vcrit))
if V<Vcrit:
print('"Pass": Since V is less than Vcrit, the null hypothesis that the two populations are drawn from distributions that share a common mean direction (antipodal to one another) cannot be rejected.')
elif V>Vcrit:
print('"Fail": Since V is greater than Vcrit, the two means can be distinguished at the 95% confidence level.')
print("")
print("M&M1990 classification:")
print("")
print("Angle between data set means: " '%.1f'%(angle))
print("Critical angle of M&M1990: " '%.1f'%(critical_angle))
if V>Vcrit:
print("")
elif V<Vcrit:
if critical_angle<5:
print("The McFadden and McElhinny (1990) classification for this test is: 'A'")
elif critical_angle<10:
print("The McFadden and McElhinny (1990) classification for this test is: 'B'")
elif critical_angle<20:
print("The McFadden and McElhinny (1990) classification for this test is: 'C'")
else:
print("The McFadden and McElhinny (1990) classification for this test is: 'INDETERMINATE;")
if plot==1:
CDF={'cdf':1}
pmagplotlib.plot_init(CDF['cdf'],5,5)
p1 = pmagplotlib.plotCDF(CDF['cdf'],Vp,"Watson's V",'r',"")
p2 = pmagplotlib.plotVs(CDF['cdf'],[V],'g','-')
p3 = pmagplotlib.plotVs(CDF['cdf'],[Vp[k]],'b','--')
pmagplotlib.drawFIGS(CDF)
files,fmt={},'svg'
if file2!="":
files['cdf']='WatsonsV_'+file1+'_'+file2+'.'+fmt
else:
files['cdf']='WatsonsV_'+file1+'.'+fmt
if pmagplotlib.isServer:
black = '#000000'
purple = '#800080'
titles={}
titles['cdf']='Cumulative Distribution'
CDF = pmagplotlib.addBorders(CDF,titles,black,purple)
pmagplotlib.saveP(CDF,files)
else:
ans=input(" S[a]ve to save plot, [q]uit without saving: ")
if ans=="a": pmagplotlib.saveP(CDF,files)
def main():
"""
NAME
dir_cart.py
DESCRIPTION
converts geomagnetic elements to cartesian coordinates
INPUT (COMMAND LINE ENTRY)
declination inclination [magnitude]
or
longitude latitude
if only two columns, assumes magnitude of unity
OUTPUT
x1 x2 x3
SYNTAX
dir_cart.py [command line options] [< filename]
OPTIONS
-h print help and quit
-i for interactive data entry
-f FILE, input file
-F FILE, output file
"""
out = ""
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')
if '-i' in sys.argv:
cont = 1
while cont == 1:
try:
dir = []
ans = input('Declination: [cntrl-D to quit] ')
dir.append(float(ans))
ans = input('Inclination: ')
dir.append(float(ans))
ans = input('Intensity [return for unity]: ')
if ans == '': ans = '1'
dir.append(float(ans))
cart = pmag.dir2cart(
dir) # send dir to dir2cart and spit out result
print('%8.4e %8.4e %8.4e' % (cart[0], cart[1], cart[2]))
except:
print('\n Good-bye \n')
sys.exit()
elif '-f' in sys.argv:
ind = sys.argv.index('-f')
file = sys.argv[ind + 1]
input = numpy.loadtxt(file)
else:
input = numpy.loadtxt(sys.stdin, dtype=numpy.float)
cart = pmag.dir2cart(input)
if len(cart.shape) == 1:
line = cart
print('%8.4e %8.4e %8.4e' % (line[0], line[1], line[2]))
if out != "":
out.write('%8.4e %8.4e %8.4e\n' % (line[0], line[1], line[2]))
else:
for line in cart:
print('%8.4e %8.4e %8.4e' % (line[0], line[1], line[2]))
if out != "":
out.write('%8.4e %8.4e %8.4e\n' % (line[0], line[1], line[2]))
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 er_samples.txt file
-fsa FILE: specify er_samples.txt file, default is er_samples.txt
-Fa FILE: specify anisotropy output file, default is arm_anisotropy.txt
-Fr FILE: specify results output file, default is aarm_results.txt
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
user = ""
meas_file = "aarm_measurements.txt"
samp_file = "er_samples.txt"
rmag_anis = "arm_anisotropy.txt"
rmag_res = "aarm_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]
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 "-fsa" in args:
ind = args.index("-fsa")
samp_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]
meas_file = dir_path + '/' + meas_file
samp_file = dir_path + '/' + samp_file
rmag_anis = dir_path + '/' + rmag_anis
rmag_res = dir_path + '/' + rmag_res
# read in data
meas_data, file_type = pmag.magic_read(meas_file)
meas_data = pmag.get_dictitem(meas_data, 'magic_method_codes', 'LP-AN-ARM',
'has')
if file_type != 'magic_measurements':
print file_type
print file_type, "This is not a valid magic_measurements file "
sys.exit()
if coord != '-1': # need to read in sample data
samp_data, file_type = pmag.magic_read(samp_file)
if file_type != 'er_samples':
print file_type
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 = [], []
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 = len(data) / 2
if npos == 9:
#
# get dec, inc, int and convert to x,y,z
#
B, H, tmpH = pmag.designAARM(
npos) # B matrix made from design matrix for positions
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] = 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 = 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(S / nf)
else:
sigma = 0
RmagSpecRec["rmag_anisotropy_name"] = data[0]["er_specimen_name"]
RmagSpecRec["er_location_name"] = data[0]["er_location_name"]
RmagSpecRec["er_specimen_name"] = data[0]["er_specimen_name"]
RmagSpecRec["er_sample_name"] = data[0]["er_sample_name"]
RmagSpecRec["er_site_name"] = data[0]["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]["er_location_name"]
RmagResRec["er_specimen_names"] = data[0]["er_specimen_name"]
RmagResRec["er_sample_names"] = data[0]["er_sample_name"]
RmagResRec["er_site_names"] = data[0]["er_site_name"]
RmagResRec["magic_experiment_names"] = RmagSpecRec[
"rmag_anisotropy_name"] + ":AARM"
RmagResRec["er_citation_names"] = "This study"
if "magic_instrument_codes" in 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(
PmagSpecRec["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':
sampe_bed_dir, sample_bed_dip = orient[
'sample_bed_dip_direction'], orient['sample_bed_dip']
s = pmag.dostilt(
s, bed_dir,
bed_dip) # rotate to geographic coordinates
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' % (trace / 3)
RmagSpecRec["anisotropy_sigma"] = '%8.6f' % (sigma)
RmagSpecRec["anisotropy_unit"] = "Am^2"
RmagSpecRec["anisotropy_n"] = '%i' % (npos)
RmagSpecRec["anisotropy_tilt_correction"] = coord
RmagSpecRec["anisotropy_F"] = '%7.1f ' % (
hpars["F"]
) # used by thellier_gui - must be taken out for uploading
RmagSpecRec["anisotropy_F_crit"] = hpars[
"F_crit"] # used by thellier_gui - must be taken out for uploading
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)
else:
print 'skipping specimen ', s, ' only 9 positions supported', '; this has ', npos
specimen += 1
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
remanence_aniso_magic.py
DESCRIPTION
This program is similar to aarm_magic.py and atrm_magic.py with minor modifications.
Converts magic measurement file with ATRM/AARM data to best-fit tensor (6 elements plus sigma)
following Hext (1963), and calculates F-test statistics.
Comments:
- infield steps are marked with method codes LT-T-I:LP-AN-TRM; LT-AF-I:LP-AN-ARM
- zerofield steps are marked with method codes LT-T-Z:LP-AN-TRM; LT-AF-Z:LP-AN-ARM
- alteration check is marked with method codes LT-PTRM-I:LP-AN-TRM
please notice;
- ATRM: The program uses treatment_dc_field_phi/treatment_dc_field_theta columns to infer the direction of the applied field
(this is a change from atrm_magic.py)
- ATRM: zerofield (baseline) magnetization is subtructed from all infield measurements
- AARM: The program uses measurement number (running number) to to infer the direction of the applied field
assuming the SIO protocol for 6,9,15 measurements scheme.
See cookbook for diagram and details.
- AARM: zerofield (baseline) are assumed to be before any infield, and the baseline is subtructed from the
subsequent infield magnetization.
SYNTAX
remanence_aniso_magic.py [-h] [command line options]
OPTIONS
-h prints help message and quits
-f FILE: specify input file, default is magic_measurements.txt
INPUT
magic measurement file with ATRM and/or AARM data.
if both types of measurements exist then the program calculates both.
OUTPUT
rmag_anisotropy.log
-I- information
-W- Warning
-E- Error
rmag_anistropy.txt:
This file contains in addition to some some magic information the following:
- anistropy tensor s1 to s6 normalized by the trace:
|Mx| |s1 s4 s6| |Bx|
|My| = |s4 s2 s5| . |By|
|Mz| |s6 s5 s3| |Bz|
- anisotropy_sigma (Hext, 1963)
- anisotropy_alt (altertion check for ATRM in units of %):
100* [abs(M_first-Mlast)/max(M_first,M_last)]
-
rmag_results.txt:
This file contains in addition to some magic information the follow(ing:
- anisotropy_t1,anisotropy_t2,anisotropy_t3 : eigenvalues
- anisotropy_v*_dec,anisotropy_v*_inc: declination/inclination of the eigenvectors
- anisotropy_ftest,anisotropy_ftest12,anisotropy_ftest13
- (the crtical F for 95% confidence level of anistropy is given in result_description column).
"""
#==================================================================================
meas_file = "magic_measurements.txt"
args = sys.argv
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 "-f" in args:
ind = args.index("-f")
meas_file = sys.argv[ind + 1]
else:
meas_file = dir_path + '/' + meas_file
WD = dir_path
#======================================
# functions
#======================================
def get_Data(magic_file):
#------------------------------------------------
# Read magic measurement file and sort to blocks
#------------------------------------------------
Data = {}
try:
meas_data, file_type = pmag.magic_read(magic_file)
except:
print "-E- ERROR: Cant read magic_measurement.txt file. File is corrupted."
return Data
# get list of unique specimen names
#sids=pmag.get_specs(meas_data) # samples ID's
for rec in meas_data:
s = rec["er_specimen_name"]
method_codes = rec["magic_method_codes"].strip('\n')
method_codes.replace(" ", "")
methods = method_codes.split(":")
if "LP-AN-TRM" in methods:
if s not in Data.keys():
Data[s] = {}
if 'atrmblock' not in Data[s].keys():
Data[s]['atrmblock'] = []
Data[s]['atrmblock'].append(rec)
if "LP-AN-ARM" in methods:
if s not in Data.keys():
Data[s] = {}
if 'aarmblock' not in Data[s].keys():
Data[s]['aarmblock'] = []
Data[s]['aarmblock'].append(rec)
return (Data)
#======================================
# better to put this one in pmagpy
#======================================
def calculate_aniso_parameters(B, K):
aniso_parameters = {}
S_bs = dot(B, K)
# normalize by trace
trace = S_bs[0] + S_bs[1] + S_bs[2]
S_bs = S_bs / trace
s1, s2, s3, s4, s5, s6 = S_bs[0], S_bs[1], S_bs[2], S_bs[3], S_bs[
4], S_bs[5]
s_matrix = [[s1, s4, s6], [s4, s2, s5], [s6, s5, s3]]
# calculate eigen vector,
t, evectors = eig(s_matrix)
# sort vectors
t = list(t)
t1 = max(t)
ix_1 = t.index(t1)
t3 = min(t)
ix_3 = t.index(t3)
for tt in range(3):
if t[tt] != t1 and t[tt] != t3:
t2 = t[tt]
ix_2 = t.index(t2)
v1 = [evectors[0][ix_1], evectors[1][ix_1], evectors[2][ix_1]]
v2 = [evectors[0][ix_2], evectors[1][ix_2], evectors[2][ix_2]]
v3 = [evectors[0][ix_3], evectors[1][ix_3], evectors[2][ix_3]]
DIR_v1 = pmag.cart2dir(v1)
DIR_v2 = pmag.cart2dir(v2)
DIR_v3 = pmag.cart2dir(v3)
aniso_parameters['anisotropy_s1'] = "%f" % s1
aniso_parameters['anisotropy_s2'] = "%f" % s2
aniso_parameters['anisotropy_s3'] = "%f" % s3
aniso_parameters['anisotropy_s4'] = "%f" % s4
aniso_parameters['anisotropy_s5'] = "%f" % s5
aniso_parameters['anisotropy_s6'] = "%f" % s6
aniso_parameters['anisotropy_degree'] = "%f" % (t1 / t3)
aniso_parameters['anisotropy_t1'] = "%f" % t1
aniso_parameters['anisotropy_t2'] = "%f" % t2
aniso_parameters['anisotropy_t3'] = "%f" % t3
aniso_parameters['anisotropy_v1_dec'] = "%.1f" % DIR_v1[0]
aniso_parameters['anisotropy_v1_inc'] = "%.1f" % DIR_v1[1]
aniso_parameters['anisotropy_v2_dec'] = "%.1f" % DIR_v2[0]
aniso_parameters['anisotropy_v2_inc'] = "%.1f" % DIR_v2[1]
aniso_parameters['anisotropy_v3_dec'] = "%.1f" % DIR_v3[0]
aniso_parameters['anisotropy_v3_inc'] = "%.1f" % DIR_v3[1]
# modified from pmagpy:
if len(K) / 3 == 9 or len(K) / 3 == 6 or len(K) / 3 == 15:
n_pos = len(K) / 3
tmpH = Matrices[n_pos]['tmpH']
a = s_matrix
S = 0.
comp = zeros((n_pos * 3), 'f')
for i in range(n_pos):
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(n_pos * 3):
d = K[i] / trace - comp[i] # del values
S += d * d
nf = float(n_pos * 3 - 6) # number of degrees of freedom
if S > 0:
sigma = math.sqrt(S / nf)
hpars = pmag.dohext(nf, sigma, [s1, s2, s3, s4, s5, s6])
aniso_parameters['anisotropy_sigma'] = "%f" % sigma
aniso_parameters['anisotropy_ftest'] = "%f" % hpars["F"]
aniso_parameters['anisotropy_ftest12'] = "%f" % hpars["F12"]
aniso_parameters['anisotropy_ftest23'] = "%f" % hpars["F23"]
aniso_parameters['result_description'] = "Critical F: %s" % (
hpars['F_crit'])
aniso_parameters['anisotropy_F_crit'] = "%f" % float(
hpars['F_crit'])
aniso_parameters['anisotropy_n'] = n_pos
return (aniso_parameters)
#======================================
# Main
#======================================
aniso_logfile = open(WD + "/rmag_anisotropy.log", 'w')
aniso_logfile.write("------------------------\n")
aniso_logfile.write("-I- Start rmag anisrotropy script\n")
aniso_logfile.write("------------------------\n")
Data = get_Data(meas_file)
#try:
# Data=get_Data(meas_file)
#except:
# aniso_logfile.write( "-E- Cant open measurement file %s\n" %meas_file)
# print "-E- Cant open measurement file %s\n exiting" %meas_file
# exit()
aniso_logfile.write("-I- Open measurement file %s\n" % meas_file)
Data_anisotropy = {}
specimens = Data.keys()
specimens.sort()
#-----------------------------------
# Prepare rmag_anisotropy.txt file for writing
#-----------------------------------
rmag_anisotropy_file = open(WD + "/rmag_anisotropy.txt", 'w')
rmag_anisotropy_file.write("tab\trmag_anisotropy\n")
rmag_results_file = open(WD + "/rmag_results.txt", 'w')
rmag_results_file.write("tab\trmag_results\n")
rmag_anistropy_header = [
'er_specimen_name', 'er_sample_name', 'er_site_name',
'anisotropy_type', 'anisotropy_n', 'anisotropy_description',
'anisotropy_s1', 'anisotropy_s2', 'anisotropy_s3', 'anisotropy_s4',
'anisotropy_s5', 'anisotropy_s6', 'anisotropy_sigma', 'anisotropy_alt',
'magic_experiment_names', 'magic_method_codes'
]
String = ""
for i in range(len(rmag_anistropy_header)):
String = String + rmag_anistropy_header[i] + '\t'
rmag_anisotropy_file.write(String[:-1] + "\n")
rmag_results_header=['er_specimen_names','er_sample_names','er_site_names','anisotropy_type','magic_method_codes','magic_experiment_names','result_description','anisotropy_t1','anisotropy_t2','anisotropy_t3','anisotropy_ftest','anisotropy_ftest12','anisotropy_ftest23',\
'anisotropy_v1_dec','anisotropy_v1_inc','anisotropy_v2_dec','anisotropy_v2_inc','anisotropy_v3_dec','anisotropy_v3_inc']
String = ""
for i in range(len(rmag_results_header)):
String = String + rmag_results_header[i] + '\t'
rmag_results_file.write(String[:-1] + "\n")
#-----------------------------------
# Matrices definitions:
# A design matrix
# B dot(inv(dot(A.transpose(),A)),A.transpose())
# tmpH is used for sigma calculation (9,15 measurements only)
#
# Anisotropy tensor:
#
# |Mx| |s1 s4 s6| |Bx|
# |My| = |s4 s2 s5| . |By|
# |Mz| |s6 s5 s3| |Bz|
#
# A matrix (measurement matrix):
# Each mesurement yields three lines in "A" matrix
#
# |Mi | |Bx 0 0 By 0 Bz| |s1|
# |Mi+1| = |0 By 0 Bx Bz 0 | . |s2|
# |Mi+2| |0 0 Bz 0 By Bx| |s3|
# |s4|
# |s5|
#
#-----------------------------------
Matrices = {}
for n_pos in [6, 9, 15]:
Matrices[n_pos] = {}
A = zeros((n_pos * 3, 6), 'f')
if n_pos == 6:
positions=[[0.,0.,1.],[90.,0.,1.],[0.,90.,1.],\
[180.,0.,1.],[270.,0.,1.],[0.,-90.,1.]]
if n_pos == 15:
positions=[[315.,0.,1.],[225.,0.,1.],[180.,0.,1.],[135.,0.,1.],[45.,0.,1.],\
[90.,-45.,1.],[270.,-45.,1.],[270.,0.,1.],[270.,45.,1.],[90.,45.,1.],\
[180.,45.,1.],[180.,-45.,1.],[0.,-90.,1.],[0,-45.,1.],[0,45.,1.]]
if n_pos == 9:
positions=[[315.,0.,1.],[225.,0.,1.],[180.,0.,1.],\
[90.,-45.,1.],[270.,-45.,1.],[270.,0.,1.],\
[180.,45.,1.],[180.,-45.,1.],[0.,-90.,1.]]
tmpH = zeros((n_pos, 3), 'f') # define tmpH
for i in range(len(positions)):
CART = pmag.dir2cart(positions[i])
a = CART[0]
b = CART[1]
c = CART[2]
A[3 * i][0] = a
A[3 * i][3] = b
A[3 * i][5] = c
A[3 * i + 1][1] = b
A[3 * i + 1][3] = a
A[3 * i + 1][4] = c
A[3 * i + 2][2] = c
A[3 * i + 2][4] = b
A[3 * i + 2][5] = a
tmpH[i][0] = CART[0]
tmpH[i][1] = CART[1]
tmpH[i][2] = CART[2]
B = dot(inv(dot(A.transpose(), A)), A.transpose())
Matrices[n_pos]['A'] = A
Matrices[n_pos]['B'] = B
Matrices[n_pos]['tmpH'] = tmpH
for specimen in specimens:
if 'atrmblock' in Data[specimen].keys():
#-----------------------------------
# aTRM 6 positions
#-----------------------------------
aniso_logfile.write(
"-I- Start calculating ATRM tensor for specimen %s\n " %
specimen)
atrmblock = Data[specimen]['atrmblock']
if len(atrmblock) < 6:
aniso_logfile.write(
"-W- specimen %s has not enough measurementf for ATRM calculation\n"
% specimen)
continue
B = Matrices[6]['B']
Reject_specimen = False
# The zero field step is a "baseline"
# Search the baseline in the ATRM measurement
baseline = ""
Alteration_check = ""
Alteration_check_index = ""
baselines = []
# search for baseline in atrm blocks
for rec in atrmblock:
dec = float(rec['measurement_dec'])
inc = float(rec['measurement_inc'])
moment = float(rec['measurement_magn_moment'])
# find the temperature of the atrm
if float(rec['treatment_dc_field']) != 0 and float(
rec['treatment_temp']) != 273:
atrm_temperature = float(rec['treatment_temp'])
# find baseline
if float(rec['treatment_dc_field']) == 0 and float(
rec['treatment_temp']) != 273:
baselines.append(array(pmag.dir2cart([dec, inc, moment])))
# Find alteration check
#print rec['measurement_number']
if len(baselines) != 0:
aniso_logfile.write(
"-I- found ATRM baseline for specimen %s\n" % specimen)
baselines = array(baselines)
baseline = array([
mean(baselines[:, 0]),
mean(baselines[:, 1]),
mean(baselines[:, 2])
])
else:
baseline = zeros(3, 'f')
aniso_logfile.write("-I- No aTRM baseline for specimen %s\n" %
specimen)
# sort measurements
M = zeros([6, 3], 'f')
for rec in atrmblock:
dec = float(rec['measurement_dec'])
inc = float(rec['measurement_inc'])
moment = float(rec['measurement_magn_moment'])
CART = array(pmag.dir2cart([dec, inc, moment])) - baseline
if float(rec['treatment_dc_field']
) == 0: # Ignore zero field steps
continue
if "LT-PTRM-I" in rec['magic_method_codes'].split(
":"): # alteration check
Alteration_check = CART
Alteration_check_dc_field_phi = float(
rec['treatment_dc_field_phi'])
Alteration_check_dc_field_theta = float(
rec['treatment_dc_field_theta'])
if Alteration_check_dc_field_phi == 0 and Alteration_check_dc_field_theta == 0:
Alteration_check_index = 0
if Alteration_check_dc_field_phi == 90 and Alteration_check_dc_field_theta == 0:
Alteration_check_index = 1
if Alteration_check_dc_field_phi == 0 and Alteration_check_dc_field_theta == 90:
Alteration_check_index = 2
if Alteration_check_dc_field_phi == 180 and Alteration_check_dc_field_theta == 0:
Alteration_check_index = 3
if Alteration_check_dc_field_phi == 270 and Alteration_check_dc_field_theta == 0:
Alteration_check_index = 4
if Alteration_check_dc_field_phi == 0 and Alteration_check_dc_field_theta == -90:
Alteration_check_index = 5
aniso_logfile.write(
"-I- found alteration check for specimen %s\n" %
specimen)
continue
treatment_dc_field_phi = float(rec['treatment_dc_field_phi'])
treatment_dc_field_theta = float(
rec['treatment_dc_field_theta'])
treatment_dc_field = float(rec['treatment_dc_field'])
#+x, M[0]
if treatment_dc_field_phi == 0 and treatment_dc_field_theta == 0:
M[0] = CART
#+Y , M[1]
if treatment_dc_field_phi == 90 and treatment_dc_field_theta == 0:
M[1] = CART
#+Z , M[2]
if treatment_dc_field_phi == 0 and treatment_dc_field_theta == 90:
M[2] = CART
#-x, M[3]
if treatment_dc_field_phi == 180 and treatment_dc_field_theta == 0:
M[3] = CART
#-Y , M[4]
if treatment_dc_field_phi == 270 and treatment_dc_field_theta == 0:
M[4] = CART
#-Z , M[5]
if treatment_dc_field_phi == 0 and treatment_dc_field_theta == -90:
M[5] = CART
# check if at least one measurement in missing
for i in range(len(M)):
if M[i][0] == 0 and M[i][1] == 0 and M[i][2] == 0:
aniso_logfile.write(
"-E- ERROR: missing atrm data for specimen %s\n" %
(specimen))
Reject_specimen = True
# alteration check
anisotropy_alt = 0
if Alteration_check != "":
for i in range(len(M)):
if Alteration_check_index == i:
M_1 = sqrt(sum((array(M[i])**2)))
M_2 = sqrt(sum(Alteration_check**2))
diff = abs(M_1 - M_2)
diff_ratio = diff / mean([M_1, M_2])
diff_ratio_perc = 100 * diff_ratio
if diff_ratio_perc > anisotropy_alt:
anisotropy_alt = diff_ratio_perc
else:
aniso_logfile.write(
"-W- Warning: no alteration check for specimen %s \n " %
specimen)
# Check for maximum difference in anti parallel directions.
# if the difference between the two measurements is more than maximum_diff
# The specimen is rejected
# i.e. +x versus -x, +y versus -y, etc.s
for i in range(3):
M_1 = sqrt(sum(array(M[i])**2))
M_2 = sqrt(sum(array(M[i + 3])**2))
diff = abs(M_1 - M_2)
diff_ratio = diff / max(M_1, M_2)
diff_ratio_perc = 100 * diff_ratio
if diff_ratio_perc > anisotropy_alt:
anisotropy_alt = diff_ratio_perc
if not Reject_specimen:
# K vector (18 elements, M1[x], M1[y], M1[z], ... etc.)
K = zeros(18, 'f')
K[0], K[1], K[2] = M[0][0], M[0][1], M[0][2]
K[3], K[4], K[5] = M[1][0], M[1][1], M[1][2]
K[6], K[7], K[8] = M[2][0], M[2][1], M[2][2]
K[9], K[10], K[11] = M[3][0], M[3][1], M[3][2]
K[12], K[13], K[14] = M[4][0], M[4][1], M[4][2]
K[15], K[16], K[17] = M[5][0], M[5][1], M[5][2]
if specimen not in Data_anisotropy.keys():
Data_anisotropy[specimen] = {}
aniso_parameters = calculate_aniso_parameters(B, K)
Data_anisotropy[specimen]['ATRM'] = aniso_parameters
Data_anisotropy[specimen]['ATRM'][
'anisotropy_alt'] = "%.2f" % anisotropy_alt
Data_anisotropy[specimen]['ATRM']['anisotropy_type'] = "ATRM"
Data_anisotropy[specimen]['ATRM'][
'er_sample_name'] = atrmblock[0]['er_sample_name']
Data_anisotropy[specimen]['ATRM'][
'er_specimen_name'] = specimen
Data_anisotropy[specimen]['ATRM']['er_site_name'] = atrmblock[
0]['er_site_name']
Data_anisotropy[specimen]['ATRM'][
'anisotropy_description'] = 'Hext statistics adapted to ATRM'
Data_anisotropy[specimen]['ATRM'][
'magic_experiment_names'] = specimen + ";ATRM"
Data_anisotropy[specimen]['ATRM'][
'magic_method_codes'] = "LP-AN-TRM:AE-H"
#Data_anisotropy[specimen]['ATRM']['rmag_anisotropy_name']=specimen
if 'aarmblock' in Data[specimen].keys():
#-----------------------------------
# AARM - 6, 9 or 15 positions
#-----------------------------------
aniso_logfile.write(
"-I- Start calculating AARM tensors specimen %s\n" % specimen)
aarmblock = Data[specimen]['aarmblock']
if len(aarmblock) < 12:
aniso_logfile.write(
"-W- WARNING: not enough aarm measurement for specimen %s\n"
% specimen)
continue
elif len(aarmblock) == 12:
n_pos = 6
B = Matrices[6]['B']
M = zeros([6, 3], 'f')
elif len(aarmblock) == 18:
n_pos = 9
B = Matrices[9]['B']
M = zeros([9, 3], 'f')
# 15 positions
elif len(aarmblock) == 30:
n_pos = 15
B = Matrices[15]['B']
M = zeros([15, 3], 'f')
else:
aniso_logfile.write(
"-E- ERROR: number of measurements in aarm block is incorrect sample %s\n"
% specimen)
continue
Reject_specimen = False
for i in range(n_pos):
for rec in aarmblock:
if float(rec['measurement_number']) == i * 2 + 1:
dec = float(rec['measurement_dec'])
inc = float(rec['measurement_inc'])
moment = float(rec['measurement_magn_moment'])
M_baseline = array(pmag.dir2cart([dec, inc, moment]))
if float(rec['measurement_number']) == i * 2 + 2:
dec = float(rec['measurement_dec'])
inc = float(rec['measurement_inc'])
moment = float(rec['measurement_magn_moment'])
M_arm = array(pmag.dir2cart([dec, inc, moment]))
M[i] = M_arm - M_baseline
K = zeros(3 * n_pos, 'f')
for i in range(n_pos):
K[i * 3] = M[i][0]
K[i * 3 + 1] = M[i][1]
K[i * 3 + 2] = M[i][2]
if specimen not in Data_anisotropy.keys():
Data_anisotropy[specimen] = {}
aniso_parameters = calculate_aniso_parameters(B, K)
Data_anisotropy[specimen]['AARM'] = aniso_parameters
Data_anisotropy[specimen]['AARM']['anisotropy_alt'] = ""
Data_anisotropy[specimen]['AARM']['anisotropy_type'] = "AARM"
Data_anisotropy[specimen]['AARM']['er_sample_name'] = aarmblock[0][
'er_sample_name']
Data_anisotropy[specimen]['AARM']['er_site_name'] = aarmblock[0][
'er_site_name']
Data_anisotropy[specimen]['AARM']['er_specimen_name'] = specimen
Data_anisotropy[specimen]['AARM'][
'anisotropy_description'] = 'Hext statistics adapted to AARM'
Data_anisotropy[specimen]['AARM'][
'magic_experiment_names'] = specimen + ";AARM"
Data_anisotropy[specimen]['AARM'][
'magic_method_codes'] = "LP-AN-ARM:AE-H"
#Data_anisotropy[specimen]['AARM']['rmag_anisotropy_name']=specimen
#-----------------------------------
specimens = Data_anisotropy.keys()
specimens.sort
# remove previous anistropy data, and replace with the new one:
s_list = Data.keys()
for sp in s_list:
if 'AniSpec' in Data[sp].keys():
del Data[sp]['AniSpec']
for specimen in specimens:
# if both AARM and ATRM axist prefer the AARM !!
if 'AARM' in Data_anisotropy[specimen].keys():
TYPES = ['AARM']
if 'ATRM' in Data_anisotropy[specimen].keys():
TYPES = ['ATRM']
if 'AARM' in Data_anisotropy[specimen].keys(
) and 'ATRM' in Data_anisotropy[specimen].keys():
TYPES = ['ATRM', 'AARM']
aniso_logfile.write(
"-W- WARNING: both aarm and atrm data exist for specimen %s. using AARM by default. If you prefer using one of them, delete the other!\n"
% specimen)
for TYPE in TYPES:
String = ""
for i in range(len(rmag_anistropy_header)):
try:
String = String + Data_anisotropy[specimen][TYPE][
rmag_anistropy_header[i]] + '\t'
except:
String = String + "%f" % (Data_anisotropy[specimen][TYPE][
rmag_anistropy_header[i]]) + '\t'
rmag_anisotropy_file.write(String[:-1] + "\n")
String = ""
Data_anisotropy[specimen][TYPE][
'er_specimen_names'] = Data_anisotropy[specimen][TYPE][
'er_specimen_name']
Data_anisotropy[specimen][TYPE][
'er_sample_names'] = Data_anisotropy[specimen][TYPE][
'er_sample_name']
Data_anisotropy[specimen][TYPE]['er_site_names'] = Data_anisotropy[
specimen][TYPE]['er_site_name']
for i in range(len(rmag_results_header)):
try:
String = String + Data_anisotropy[specimen][TYPE][
rmag_results_header[i]] + '\t'
except:
String = String + "%f" % (Data_anisotropy[specimen][TYPE][
rmag_results_header[i]]) + '\t'
rmag_results_file.write(String[:-1] + "\n")
if 'AniSpec' not in Data[specimen]:
Data[specimen]['AniSpec'] = {}
Data[specimen]['AniSpec'][TYPE] = Data_anisotropy[specimen][TYPE]
aniso_logfile.write("------------------------\n")
aniso_logfile.write(
"-I- remanence_aniso_magic script finished sucsessfuly\n")
aniso_logfile.write("------------------------\n")
rmag_anisotropy_file.close()
print "Anisotropy tensors elements are saved in rmag_anistropy.txt"
print "Other anisotropy statistics are saved in rmag_results.txt"
print "log file is in rmag_anisotropy.log"
def main():
"""
NAME
revtest_MM1990.py
DESCRIPTION
calculates Watson's V statistic from input files through Monte Carlo simulation in order to test whether normal and reversed populations could have been drawn from a common mean (equivalent to watsonV.py). Also provides the critical angle between the two sample mean directions and the corresponding McFadden and McElhinny (1990) classification.
INPUT FORMAT
takes dec/inc as first two columns in two space delimited files (one file for normal directions, one file for reversed directions).
SYNTAX
revtest_MM1990.py [command line options]
OPTIONS
-h prints help message and quits
-f FILE
-f2 FILE
-P (don't plot the Watson V cdf)
OUTPUT
Watson's V between the two populations and the Monte Carlo Critical Value Vc.
M&M1990 angle, critical angle and classification
Plot of Watson's V CDF from Monte Carlo simulation (red line), V is solid and Vc is dashed.
"""
D1, D2 = [], []
plot = 1
Flip = 1
if '-h' in sys.argv: # check if help is needed
print(main.__doc__)
sys.exit() # graceful quit
if '-P' in sys.argv: plot = 0
if '-f' in sys.argv:
ind = sys.argv.index('-f')
file1 = sys.argv[ind + 1]
f1 = open(file1, 'r')
for line in f1.readlines():
rec = line.split()
Dec, Inc = float(rec[0]), float(rec[1])
D1.append([Dec, Inc, 1.])
f1.close()
if '-f2' in sys.argv:
ind = sys.argv.index('-f2')
file2 = sys.argv[ind + 1]
f2 = open(file2, 'r')
print("be patient, your computer is doing 5000 simulations...")
for line in f2.readlines():
rec = line.split()
Dec, Inc = float(rec[0]), float(rec[1])
D2.append([Dec, Inc, 1.])
f2.close()
#take the antipode for the directions in file 2
D2_flip = []
for rec in D2:
d, i = (rec[0] - 180.) % 360., -rec[1]
D2_flip.append([d, i, 1.])
pars_1 = pmag.fisher_mean(D1)
pars_2 = pmag.fisher_mean(D2_flip)
cart_1 = pmag.dir2cart([pars_1["dec"], pars_1["inc"], pars_1["r"]])
cart_2 = pmag.dir2cart([pars_2['dec'], pars_2['inc'], pars_2["r"]])
Sw = pars_1['k'] * pars_1['r'] + pars_2['k'] * pars_2['r'] # k1*r1+k2*r2
xhat_1 = pars_1['k'] * cart_1[0] + pars_2['k'] * cart_2[0] # k1*x1+k2*x2
xhat_2 = pars_1['k'] * cart_1[1] + pars_2['k'] * cart_2[1] # k1*y1+k2*y2
xhat_3 = pars_1['k'] * cart_1[2] + pars_2['k'] * cart_2[2] # k1*z1+k2*z2
Rw = numpy.sqrt(xhat_1**2 + xhat_2**2 + xhat_3**2)
V = 2 * (Sw - Rw)
#
#keep weighted sum for later when determining the "critical angle" let's save it as Sr (notation of McFadden and McElhinny, 1990)
#
Sr = Sw
#
# do monte carlo simulation of datasets with same kappas, but common mean
#
counter, NumSims = 0, 5000
Vp = [] # set of Vs from simulations
for k in range(NumSims):
#
# get a set of N1 fisher distributed vectors with k1, calculate fisher stats
#
Dirp = []
for i in range(pars_1["n"]):
Dirp.append(pmag.fshdev(pars_1["k"]))
pars_p1 = pmag.fisher_mean(Dirp)
#
# get a set of N2 fisher distributed vectors with k2, calculate fisher stats
#
Dirp = []
for i in range(pars_2["n"]):
Dirp.append(pmag.fshdev(pars_2["k"]))
pars_p2 = pmag.fisher_mean(Dirp)
#
# get the V for these
#
Vk = pmag.vfunc(pars_p1, pars_p2)
Vp.append(Vk)
#
# sort the Vs, get Vcrit (95th percentile one)
#
Vp.sort()
k = int(.95 * NumSims)
Vcrit = Vp[k]
#
# equation 18 of McFadden and McElhinny, 1990 calculates the critical value of R (Rwc)
#
Rwc = Sr - (old_div(Vcrit, 2))
#
#following equation 19 of McFadden and McElhinny (1990) the critical angle is calculated.
#
k1 = pars_1['k']
k2 = pars_2['k']
R1 = pars_1['r']
R2 = pars_2['r']
critical_angle = numpy.degrees(
numpy.arccos(
old_div(((Rwc**2) - ((k1 * R1)**2) - ((k2 * R2)**2)),
(2 * k1 * R1 * k2 * R2))))
D1_mean = (pars_1['dec'], pars_1['inc'])
D2_mean = (pars_2['dec'], pars_2['inc'])
angle = pmag.angle(D1_mean, D2_mean)
#
# print the results of the test
#
print("")
print("Results of Watson V test: ")
print("")
print("Watson's V: " '%.1f' % (V))
print("Critical value of V: " '%.1f' % (Vcrit))
if V < Vcrit:
print(
'"Pass": Since V is less than Vcrit, the null hypothesis that the two populations are drawn from distributions that share a common mean direction (antipodal to one another) cannot be rejected.'
)
elif V > Vcrit:
print(
'"Fail": Since V is greater than Vcrit, the two means can be distinguished at the 95% confidence level.'
)
print("")
print("M&M1990 classification:")
print("")
print("Angle between data set means: " '%.1f' % (angle))
print("Critical angle of M&M1990: " '%.1f' % (critical_angle))
if V > Vcrit:
print("")
elif V < Vcrit:
if critical_angle < 5:
print(
"The McFadden and McElhinny (1990) classification for this test is: 'A'"
)
elif critical_angle < 10:
print(
"The McFadden and McElhinny (1990) classification for this test is: 'B'"
)
elif critical_angle < 20:
print(
"The McFadden and McElhinny (1990) classification for this test is: 'C'"
)
else:
print(
"The McFadden and McElhinny (1990) classification for this test is: 'INDETERMINATE;"
)
if plot == 1:
CDF = {'cdf': 1}
pmagplotlib.plot_init(CDF['cdf'], 5, 5)
p1 = pmagplotlib.plot_cdf(CDF['cdf'], Vp, "Watson's V", 'r', "")
p2 = pmagplotlib.plot_vs(CDF['cdf'], [V], 'g', '-')
p3 = pmagplotlib.plot_vs(CDF['cdf'], [Vp[k]], 'b', '--')
pmagplotlib.draw_figs(CDF)
files, fmt = {}, 'svg'
if file2 != "":
files['cdf'] = 'WatsonsV_' + file1 + '_' + file2 + '.' + fmt
else:
files['cdf'] = 'WatsonsV_' + file1 + '.' + fmt
if pmagplotlib.isServer:
black = '#000000'
purple = '#800080'
titles = {}
titles['cdf'] = 'Cumulative Distribution'
CDF = pmagplotlib.add_borders(CDF, titles, black, purple)
pmagplotlib.save_plots(CDF, files)
else:
ans = input(" S[a]ve to save plot, [q]uit without saving: ")
if ans == "a": pmagplotlib.save_plots(CDF, files)
def plotZ(datablock, angle, s, norm):
"""
function to make Zijderveld diagrams
"""
amin, amax = 0., -100.
fact = 1. / (datablock[0][3]) # normalize to NRM=1
if norm == 0: fact = 1.
x, y, z = [], [], []
xb, yb, zb = [], [], []
forVDS = []
labels = []
# convert to cartesian
recnum, delta = 0, ""
for plotrec in datablock:
forVDS.append([plotrec[1], plotrec[2], plotrec[3] / datablock[0][3]])
rec = pmag.dir2cart([(plotrec[1] - angle), plotrec[2],
plotrec[3] * fact])
if len(plotrec) == 4:
plotrec.append('0') # fake the ZI,IZ step for old data
if len(plotrec) == 5:
plotrec.append('g') # assume good measurement if not specified
if plotrec[5] == 'g':
# z.append(-rec[2])
z.append(rec[2])
x.append(rec[0])
# y.append(-rec[1])
y.append(rec[1])
if x[-1] > amax: amax = x[-1]
if y[-1] > amax: amax = y[-1]
if z[-1] > amax: amax = z[-1]
if x[-1] < amin: amin = x[-1]
if y[-1] < amin: amin = y[-1]
if z[-1] < amin: amin = z[-1]
if delta == "": delta = .02 * x[-1]
if recnum % 2 == 0 and len(x) > 0:
# labels.append(' '*3 +str(int(datablock[recnum][0] - 273))+'$\degree$C')
plt.text(x[-1] - delta,
z[-1] + delta,
' ' * 3 + str(int(datablock[recnum][0] - 273)) +
'$\degree$C',
fontsize=9)
recnum += 1
elif len(plotrec) >= 6 and plotrec[5] == 'b':
# zb.append(-rec[2])
zb.append(rec[2])
xb.append(rec[0])
# yb.append(-rec[1])
yb.append(rec[1])
if xb[-1] > amax: amax = xb[-1]
if yb[-1] > amax: amax = yb[-1]
if zb[-1] > amax: amax = zb[-1]
if xb[-1] < amin: amin = xb[-1]
if yb[-1] < amin: amin = yb[-1]
if zb[-1] < amin: amin = zb[-1]
if delta == "": delta = .02 * xb[-1]
# labels.append(' '*3 +str(int(datablock[recnum][0] - 273))+'$\degree$C')
plt.text(xb[-1] - delta,
zb[-1] + delta,
' ' * 3 + str(int(datablock[recnum][0] - 273)) +
'$\degree$C',
fontsize=9)
recnum += 1
# plotting stuff
if angle != 0: tempstr = "\n Declination rotated by: " + str(angle) + '\n'
# globals.text.insert(globals.END,tempstr)
Zlist = x
Zlisty = y
Zlistz = z
if len(xb) > 0:
plt.scatter(xb, yb, marker='d', c='w', s=30)
plt.scatter(xb, zb, marker='d', c='w', s=30)
plt.plot(x, y, 'r')
plt.plot(x, z, 'b')
dec_points = plt.scatter(x, y, marker='o', c='r')
inc_points = plt.scatter(x, z, marker='s', c='w')
xline = [amin, amax]
# yline=[-amax,-amin]
yline = [amax, amin]
zline = [0, 0]
plt.plot(xline, zline, c='k')
plt.plot(zline, xline, c='k')
# if angle!=0:xlab="X: rotated to Dec = "+'%7.1f'%(angle)
# if angle==0:xlab="X: rotated to Dec = "+'%7.1f'%(angle)
# plt.xlabel(xlab)
# plt.ylabel("Circles: Y; Squares: Z")
tstring = s + ': NRM = ' + '%9.2e' % (datablock[0][3])
plt.axis([amin, amax, amax, amin])
plt.axis("equal")
plt.axis('off')
plt.tight_layout()
plt.title(tstring)