def spitout(line):
dir=[] # initialize list for dec,inc,intensity
dat=line.split() # split the data on a space into columns
for element in dat: # step through dec,inc, int
dir.append(float(element)) # append floating point variable to "dir"
if len(dir)==2:dir.append(1.)
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])
return cart
def Rotate_zijderveld(Zdata, rot_declination):
if len(Zdata) == 0:
return ([])
CART_rot = []
for i in range(0, len(Zdata)):
DIR = cart2dir(Zdata[i])
DIR[0] = (DIR[0] - rot_declination) % 360.
CART_rot.append(array(dir2cart(DIR)))
CART_rot = array(CART_rot)
return (CART_rot)
def Rotate_zijderveld(Zdata,rot_declination):
if len(Zdata)==0:
return([])
CART_rot=[]
for i in range(0,len(Zdata)):
DIR=cart2dir(Zdata[i])
DIR[0]=(DIR[0]-rot_declination)%360.
CART_rot.append(array(dir2cart(DIR)))
CART_rot=array(CART_rot)
return(CART_rot)
def get_bounds(DIs):
#2sigma bounds
bounds=[]
# convert to cartesian coordinates
cart=pmag.dir2cart(DIs).transpose()
min=int(0.025*len(cart[0]))
max=int(0.975*len(cart[0]))
for i in range(3):
comp=cart[i]
comp.sort()
bounds.append([comp[min],comp[max]])
return bounds
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, "rU")
for line in f1.readlines():
rec = line.split()
Dec, Inc = float(rec[0]), float(rec[1])
D1.append([Dec, Inc, 1.0])
f1.close()
if "-f2" in sys.argv:
ind = sys.argv.index("-f2")
file2 = sys.argv[ind + 1]
f2 = open(file2, "rU")
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, 1.0])
f2.close()
# take the antipode for the directions in file 2
D2_flip = []
for rec in D2:
d, i = (rec[0] - 180.0) % 360.0, -rec[1]
D2_flip.append([d, i, 1.0])
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(0.95 * NumSims)
Vcrit = Vp[k]
#
# equation 18 of McFadden and McElhinny, 1990 calculates the critical value of R (Rwc)
#
Rwc = Sr - (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(((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 = raw_input(" S[a]ve to save plot, [q]uit without saving: ")
if ans == "a":
pmagplotlib.saveP(CDF, files)
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
"""
fmt='svg'
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 '-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. # 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
pmagplotlib.drawFIGS(FIG)
ans=raw_input('S[a]ve figure? [q]uit, <return> to continue ')
if ans=='a':
files={'lowrie':'lowrie:_'+spc+'_.'+fmt}
pmagplotlib.saveP(FIG,files)
elif ans=='q':
sys.exit()
pmagplotlib.clearFIG(FIG['lowrie'])
#!/usr/bin/env python
import pmag
dat=open('princ.di','rU').readlines()
x,y,z=[],[],[]
Dirs=[]
for line in dat:
rec=line.split()
Dirs=[float(rec[0]),float(rec[1]),float(rec[2])*1e-4]
cart=pmag.dir2cart(Dirs)
x.append(cart[0])
y.append(cart[1]*1.1)
z.append(cart[2])
from enthought.mayavi.mlab import points3d,savefig,show,outline,plot3d
from numpy import array,sum,transpose
from numpy.linalg import eig
X,Y,Z=array(x),array(y),array(z)
points3d(X,Y,Z,color=(0,0,0),scale_factor=0.25,opacity=.5)
outline(color=(.7,0,0))
T=array([[sum(X*X),sum(X*Y),sum(X*Z)],
[sum(Y*X),sum(Y*Y),sum(Y*Z)],
[sum(Z*X),sum(Z*Y),sum(Z*Z)]])
vals,vects=eig(T)
pv=transpose(vects)[0]*3.
plot3d([pv[0],-pv[0]],[pv[1],-pv[1]],[pv[2],-pv[2]],tube_radius=0.1,color=(0,1,0))
show()
def plotELL(pars, col, lower, plot):
"""
function to calculate points on an ellipse about Pdec,Pdip with angle beta,gamma
"""
rad = np.pi / 180.
Pdec, Pinc, beta, Bdec, Binc, gamma, Gdec, Ginc = pars[0], pars[1], pars[
2], pars[3], pars[4], pars[5], pars[6], pars[7]
if beta > 90. or gamma > 90:
beta = 180. - beta
gamma = 180. - beta
Pdec = Pdec - 180.
Pinc = -Pinc
beta, gamma = beta * rad, gamma * rad # convert to radians
X_ell, Y_ell, X_up, Y_up, PTS = [], [], [], [], []
nums = 201
xnum = float(nums - 1.) / 2.
# set up t matrix
t = [[0, 0, 0], [0, 0, 0], [0, 0, 0]]
X = pmag.dir2cart((Pdec, Pinc, 1.0)) # convert to cartesian coordintes
if lower == 1 and X[2] < 0:
for i in range(3):
X[i] = -X[i]
# set up rotation matrix t
t[0][2] = X[0]
t[1][2] = X[1]
t[2][2] = X[2]
X = pmag.dir2cart((Bdec, Binc, 1.0))
if lower == 1 and X[2] < 0:
for i in range(3):
X[i] = -X[i]
t[0][0] = X[0]
t[1][0] = X[1]
t[2][0] = X[2]
X = pmag.dir2cart((Gdec, Ginc, 1.0))
if lower == 1 and X[2] < 0:
for i in range(3):
X[i] = -X[i]
t[0][1] = X[0]
t[1][1] = X[1]
t[2][1] = X[2]
# set up v matrix
v = [0, 0, 0]
for i in range(nums): # incremental point along ellipse
psi = float(i) * np.pi / xnum
v[0] = np.sin(beta) * np.cos(psi)
v[1] = np.sin(gamma) * np.sin(psi)
v[2] = np.sqrt(1. - v[0]**2 - v[1]**2)
elli = [0, 0, 0]
# calculate points on the ellipse
for j in range(3):
for k in range(3):
elli[j] = elli[j] + t[j][k] * v[
k] # cartesian coordinate j of ellipse
PTS.append(pmag.cart2dir(elli))
R = np.sqrt(1. - abs(elli[2])) / (np.sqrt(elli[0]**2 + elli[1]**2)
) # put on an equal area projection
if elli[2] < 0:
# for i in range(3): elli[i]=-elli[i]
X_up.append(elli[1] * R)
Y_up.append(elli[0] * R)
# Adding None values stops plotting of an additional straight line
# between the points where the ellipse crosses the edge of the stereonet
X_ell.append(None)
Y_ell.append(None)
else:
X_ell.append(elli[1] * R)
Y_ell.append(elli[0] * R)
if plot == 1:
if X_ell != []:
plt.plot(X_ell, Y_ell, color=col, linewidth=2, zorder=6)
if X_up != []:
plt.plot(X_up, Y_up, color=col, linewidth=2, linestyle=':')
else:
return PTS
def plotELL(pars,col,lower,plot): #Modified from PmagPy (Tauxe et al., 2016)
"""
function to calculate points on an ellipse about Pdec,Pdip with angle beta,gamma
"""
#pylab.figure(num=fignum)
Pdec,Pinc,beta,Bdec,Binc,gamma,Gdec,Ginc=pars[0],pars[1],pars[2],pars[3],pars[4],pars[5],pars[6],pars[7]
if beta > 90. or gamma>90:
beta=180.-beta
gamma=180.-beta
Pdec=Pdec-180.
Pinc=-Pinc
beta,gamma=beta*rad,gamma*rad # convert to radians
X_ell,Y_ell,X_up,Y_up,PTS=[],[],[],[],[]
nums=201
xnum=float(nums-1.)/2.
# set up t matrix
t=[[0,0,0],[0,0,0],[0,0,0]]
X=pmag.dir2cart((Pdec,Pinc,1.0)) # convert to cartesian coordintes
if lower==1 and X[2]<0:
for i in range(3):
X[i]=-X[i]
# set up rotation matrix t
t[0][2]=X[0]
t[1][2]=X[1]
t[2][2]=X[2]
X=pmag.dir2cart((Bdec,Binc,1.0))
if lower==1 and X[2]<0:
for i in range(3):
X[i]=-X[i]
t[0][0]=X[0]
t[1][0]=X[1]
t[2][0]=X[2]
X=pmag.dir2cart((Gdec,Ginc,1.0))
if lower==1 and X[2]<0:
for i in range(3):
X[i]=-X[i]
t[0][1]=X[0]
t[1][1]=X[1]
t[2][1]=X[2]
# set up v matrix
v=[0,0,0]
for i in range(nums): # incremental point along ellipse
psi=float(i)*np.pi/xnum
v[0]=np.sin(beta)*np.cos(psi)
v[1]=np.sin(gamma)*np.sin(psi)
v[2]=np.sqrt(1.-v[0]**2 - v[1]**2)
elli=[0,0,0]
# calculate points on the ellipse
for j in range(3):
for k in range(3):
elli[j]=elli[j] + t[j][k]*v[k] # cartesian coordinate j of ellipse
PTS.append(pmag.cart2dir(elli))
R=np.sqrt( 1.-abs(elli[2]))/(np.sqrt(elli[0]**2+elli[1]**2)) # put on an equal area projection
if elli[2]<0:
# for i in range(3): elli[i]=-elli[i]
X_up.append(elli[1]*R)
Y_up.append(elli[0]*R)
else:
X_ell.append(elli[1]*R)
Y_ell.append(elli[0]*R)
if plot==1:
if X_ell!=[]:plt.plot(X_ell,Y_ell,col,linewidth=2,zorder=5)#pylab.plot(X_ell,Y_ell,col,zorder=3)
if X_up!=[]:plt.plot(X_up,Y_up,col,linewidth=2,zorder=5)#pylab.plot(X_up,Y_up,'g-',zorder=3)
#pylab.draw()
else:
return PTS
def bootstrap_common_mean(Data1,Data2,NumSims=1000):
"""
Conduct a bootstrap test (Tauxe, 2010) for a common mean on two declination,
inclination data sets
This function modifies code from PmagPy for use calculating and plotting
bootstrap statistics. Three plots are generated (one for x, one for y and
one for z). If the 95 percent confidence bounds for each component overlap
each other, the two directions are not significantly different.
Arguments
----------
Data1 : a list of directional data [dec,inc]
Data2 : a list of directional data [dec,inc]
NumSims : number of bootstrap samples (default is 1000)
"""
counter=0
BDI1=pmag.di_boot(Data1)
BDI2=pmag.di_boot(Data2)
cart1= pmag.dir2cart(BDI1).transpose()
X1,Y1,Z1=cart1[0],cart1[1],cart1[2]
cart2= pmag.dir2cart(BDI2).transpose()
X2,Y2,Z2=cart2[0],cart2[1],cart2[2]
print "Here are the results of the bootstrap test for a common mean:"
fignum = 1
fig = plt.figure(figsize=(9,3))
fig = plt.subplot(1,3,1)
minimum = int(0.025*len(X1))
maximum = int(0.975*len(X1))
X1,y=pmagplotlib.plotCDF(fignum,X1,"X component",'r',"")
bounds1=[X1[minimum],X1[maximum]]
pmagplotlib.plotVs(fignum,bounds1,'r','-')
X2,y=pmagplotlib.plotCDF(fignum,X2,"X component",'b',"")
bounds2=[X2[minimum],X2[maximum]]
pmagplotlib.plotVs(fignum,bounds2,'b','--')
plt.ylim(0,1)
plt.subplot(1,3,2)
Y1,y=pmagplotlib.plotCDF(fignum,Y1,"Y component",'r',"")
bounds1=[Y1[minimum],Y1[maximum]]
pmagplotlib.plotVs(fignum,bounds1,'r','-')
Y2,y=pmagplotlib.plotCDF(fignum,Y2,"Y component",'b',"")
bounds2=[Y2[minimum],Y2[maximum]]
pmagplotlib.plotVs(fignum,bounds2,'b','--')
plt.ylim(0,1)
plt.subplot(1,3,3)
Z1,y=pmagplotlib.plotCDF(fignum,Z1,"Z component",'r',"")
bounds1=[Z1[minimum],Z1[maximum]]
pmagplotlib.plotVs(fignum,bounds1,'r','-')
Z2,y=pmagplotlib.plotCDF(fignum,Z2,"Z component",'b',"")
bounds2=[Z2[minimum],Z2[maximum]]
pmagplotlib.plotVs(fignum,bounds2,'b','--')
plt.ylim(0,1)
plt.tight_layout()
plt.show()
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','rmag_anisotropy_name']
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/max(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 dokent(data, NN): #From PmagPy (Tauxe et al., 2016)
"""
gets Kent parameters for data ([D,I],N)
"""
X, kpars = [], {}
N = len(data)
if N < 2:
return kpars
#
# get fisher mean and convert to co-inclination (theta)/dec (phi) in radians
#
fpars = pmag.fisher_mean(data)
pbar = fpars["dec"] * np.pi / 180.
tbar = (90. - fpars["inc"]) * np.pi / 180.
#
# initialize matrices
#
H = [[0., 0., 0.], [0., 0., 0.], [0., 0., 0.]]
w = [[0., 0., 0.], [0., 0., 0.], [0., 0., 0.]]
b = [[0., 0., 0.], [0., 0., 0.], [0., 0., 0.]]
gam = [[0., 0., 0.], [0., 0., 0.], [0., 0., 0.]]
xg = []
#
# set up rotation matrix H
#
H = [[
np.cos(tbar) * np.cos(pbar), -np.sin(pbar),
np.sin(tbar) * np.cos(pbar)
], [
np.cos(tbar) * np.sin(pbar),
np.cos(pbar),
np.sin(pbar) * np.sin(tbar)
], [-np.sin(tbar), 0., np.cos(tbar)]]
#
# get cartesian coordinates of data
#
for rec in data:
X.append(pmag.dir2cart([rec[0], rec[1], 1.]))
#
# put in T matrix
#
T = pmag.Tmatrix(X)
for i in range(3):
for j in range(3):
T[i][j] = old_div(T[i][j], float(NN))
#
# compute B=H'TH
#
for i in range(3):
for j in range(3):
for k in range(3):
w[i][j] += T[i][k] * H[k][j]
for i in range(3):
for j in range(3):
for k in range(3):
b[i][j] += H[k][i] * w[k][j]
#
# choose a rotation w about North pole to diagonalize upper part of B
#
psi = 0.5 * np.arctan(2. * b[0][1] / (b[0][0] - b[1][1]))
w = [[np.cos(psi), -np.sin(psi), 0], [np.sin(psi),
np.cos(psi), 0], [0., 0., 1.]]
for i in range(3):
for j in range(3):
gamtmp = 0.
for k in range(3):
gamtmp += H[i][k] * w[k][j]
gam[i][j] = gamtmp
for i in range(N):
xg.append([0., 0., 0.])
for k in range(3):
xgtmp = 0.
for j in range(3):
xgtmp += gam[j][k] * X[i][j]
xg[i][k] = xgtmp
# compute asymptotic ellipse parameters
#
xmu, sigma1, sigma2 = 0., 0., 0.
for i in range(N):
xmu += xg[i][2]
sigma1 = sigma1 + xg[i][0]**2
sigma2 = sigma2 + xg[i][1]**2
xmu = old_div(xmu, float(N))
sigma1 = old_div(sigma1, float(N))
sigma2 = old_div(sigma2, float(N))
g = -2.0 * np.log(0.05) / (float(NN) * xmu**2)
if np.sqrt(sigma1 * g) < 1:
eta = np.arcsin(np.sqrt(sigma1 * g))
if np.sqrt(sigma2 * g) < 1:
zeta = np.arcsin(np.sqrt(sigma2 * g))
if np.sqrt(sigma1 * g) >= 1.:
eta = old_div(np.pi, 2.)
if np.sqrt(sigma2 * g) >= 1.:
zeta = old_div(np.pi, 2.)
#
# convert Kent parameters to directions,angles
#
kpars["dec"] = fpars["dec"]
kpars["inc"] = fpars["inc"]
kpars["n"] = NN
ZDir = pmag.cart2dir([gam[0][1], gam[1][1], gam[2][1]])
EDir = pmag.cart2dir([gam[0][0], gam[1][0], gam[2][0]])
kpars["Zdec"] = ZDir[0]
kpars["Zinc"] = ZDir[1]
kpars["Edec"] = EDir[0]
kpars["Einc"] = EDir[1]
if kpars["Zinc"] < 0:
kpars["Zinc"] = -kpars["Zinc"]
kpars["Zdec"] = (kpars["Zdec"] + 180.) % 360.
if kpars["Einc"] < 0:
kpars["Einc"] = -kpars["Einc"]
kpars["Edec"] = (kpars["Edec"] + 180.) % 360.
kpars["Zeta"] = zeta * 180. / np.pi
kpars["Eta"] = eta * 180. / np.pi
return kpars
Ps.append(P*4*math.pi*rr**2)
#pylab.plot(Rs,Ps)
Xs,Ys,Zs=[],[],[]
for k in range(len(Rs)):
N=int(Ps[k]*20)
for n in range(N):
ran=random.random()
random.jumpahead(int(ran*1000))
ran2=random.random()
theta=2.*math.pi*ran
phi=2.*math.pi*ran2
if n%2==0:
sign=10.
else:
sign=-10.
offset=pmag.dir2cart([45.,45.,sign])
Xs.append(offset[0]+math.cos(theta)*math.cos(phi)*Rs[k])
Ys.append(offset[1]+math.cos(theta)*math.sin(phi)*Rs[k])
Zs.append(offset[2]+math.sin(theta)*Rs[k])
X=numpy.array(Xs)
Y=numpy.array(Ys)
Z=numpy.array(Zs)
points3d(X,Y,Z,color=(1.,0.,0.))
line=[-15.,15.]
zline=[0.,0.]
Line=numpy.array(line)
ZLine=numpy.array(zline)
plot3d(Line,ZLine,ZLine,tube_radius=.1)
plot3d(ZLine,Line,ZLine,tube_radius=.1)
plot3d(ZLine,ZLine,Line,tube_radius=.1)
view(azimuth=125., elevation=0.)
def common_dir_MM90(dir1,dir2,NumSims=5000,plot='no'):
dir1['r']=get_R(dir1)
dir2['r']=get_R(dir2)
#largely based on iWatsonV routine of Swanson-Hyell in IPMag
cart_1=pmag.dir2cart([dir1["dec"],dir1["inc"],dir1["r"]])
cart_2=pmag.dir2cart([dir2['dec'],dir2['inc'],dir2["r"]])
Sw=dir1['k']*dir1['r']+dir2['k']*dir2['r'] # k1*r1+k2*r2
xhat_1=dir1['k']*cart_1[0]+dir2['k']*cart_2[0] # k1*x1+k2*x2
xhat_2=dir1['k']*cart_1[1]+dir2['k']*cart_2[1] # k1*y1+k2*y2
xhat_3=dir1['k']*cart_1[2]+dir2['k']*cart_2[2] # k1*z1+k2*z2
Rw=np.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 as data,
# but a common mean
counter=0
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(int(dir1["n"])):
Dirp.append(pmag.fshdev(dir1["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(int(dir2["n"])):
Dirp.append(pmag.fshdev(dir2["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-(Vcrit/2)
# following equation 19 of McFadden and McElhinny (1990) the critical
# angle is calculated. If the observed angle (also calculated below)
# between the data set means exceeds the critical angle the hypothesis
# of a common mean direction may be rejected at the 95% confidence
# level. The critical angle is simply a different way to present
# Watson's V parameter so it makes sense to use the Watson V parameter
# in comparison with the critical value of V for considering the test
# results. What calculating the critical angle allows for is the
# classification of McFadden and McElhinny (1990) to be made
# for data sets that are consistent with sharing a common mean.
k1=dir1['k']
k2=dir2['k']
R1=dir1['r']
R2=dir2['r']
critical_angle=np.degrees(np.arccos(((Rwc**2)-((k1*R1)**2)
-((k2*R2)**2))/
(2*k1*R1*k2*R2)))
D1=(dir1['dec'],dir1['inc'])
D2=(dir2['dec'],dir2['inc'])
angle=pmag.angle(D1,D2)
if V<Vcrit:
outcome='Pass'
if critical_angle<5: MM90class='A'
elif critical_angle<10: MM90class='B'
elif critical_angle<20: MM90class='C'
else: MM90class='INDETERMINATE'
else:
outcome='Fail'
MM90class='FAIL'
result=pd.Series([outcome,V,Vcrit,angle[0],critical_angle,MM90class], index=['Outcome','VWatson','Vcrit','angle','critangle','MM90class'])
if plot=='yes':
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)
return result
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['magic_method_codes']="LP-PI-TRM" # thellier Method
import SPD
import SPD.spd 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
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) #
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["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 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 Data[s].keys() and len(Data[s]['tail_check_temperatures'])>0:
pars['magic_method_codes']+=":LP-PI-BT-MD"
if 'additivity_check_temperatures' in Data[s].keys() and len(Data[s]['additivity_check_temperatures'])>0:
pars['magic_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]['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 Data[s].keys() and TYPE in Data[s]['AniSpec'].keys()\
and 'anisotropy_sigma' in 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['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]['anisotropy_sigma']=""
Data[s]['AniSpec'][TYPE]['ftest']=99999
if 'anisotropy_alt' in Data[s]['AniSpec'][TYPE].keys() and Data[s]['AniSpec'][TYPE]['anisotropy_alt']!="":
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 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 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 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['magic_method_codes']:
pars['magic_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
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
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
-Fa FILE: specify anisotropy output file, default is rmag_anisotropy.txt
-Fr FILE: specify results output file, default is rmag_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"
rmag_anis="rmag_anisotropy.txt"
rmag_res="rmag_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)
if file_type != 'magic_measurements':
print file_type
print file_type,"This is not a valid magic_measurements file "
sys.exit()
#
# sort the specimen names
#
ssort=[]
for rec in meas_data:
spec=rec["er_specimen_name"]
ssort.append(spec)
ssort.sort()
bak=ssort[0]
#
# get list of unique specimen names
#
sids=[bak]
for s in ssort:
if s != bak:
sids.append(s)
bak=s
#
# 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
#
for rec in meas_data:
if rec["er_specimen_name"]==s:
data.append(rec)
#
# find out the number of measurements (9, 12 or 15)
#
npos=len(data)/2
if npos==9:
print 'Processing: ',s, ' Number of positions: ',npos
#
# 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=math.sqrt(S/nf)
else: sigma=0
hpars=pmag.dohext(nf,sigma,s)
#
# prepare for output
#
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"]
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"
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'
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"])
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 npos
print 'skipping specimen ',s,' only 9 positions supported'
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 watson_common_mean(Data1,Data2,NumSims=5000,plot='no'):
"""
Conduct a Watson V test for a common mean on two declination, inclination data sets
This function calculates Watson's V statistic from input files through Monte Carlo
simulation in order to test whether two populations of directional data could have
been drawn from a common mean. The critical angle between the two sample mean
directions and the corresponding McFadden and McElhinny (1990) classification is printed.
Required Arguments
----------
Data1 : a list of directional data [dec,inc]
Data2 : a list of directional data [dec,inc]
Optional Arguments
----------
NumSims : number of Monte Carlo simulations (default is 5000)
plot : the default is no plot ('no'). Putting 'yes' will the plot the CDF from
the Monte Carlo simulations.
"""
pars_1=pmag.fisher_mean(Data1)
pars_2=pmag.fisher_mean(Data2)
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=np.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 as data,
# but a common mean
counter=0
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-(Vcrit/2)
# following equation 19 of McFadden and McElhinny (1990) the critical
# angle is calculated. If the observed angle (also calculated below)
# between the data set means exceeds the critical angle the hypothesis
# of a common mean direction may be rejected at the 95% confidence
# level. The critical angle is simply a different way to present
# Watson's V parameter so it makes sense to use the Watson V parameter
# in comparison with the critical value of V for considering the test
# results. What calculating the critical angle allows for is the
# classification of McFadden and McElhinny (1990) to be made
# for data sets that are consistent with sharing a common mean.
k1=pars_1['k']
k2=pars_2['k']
R1=pars_1['r']
R2=pars_2['r']
critical_angle=np.degrees(np.arccos(((Rwc**2)-((k1*R1)**2)
-((k2*R2)**2))/
(2*k1*R1*k2*R2)))
D1=(pars_1['dec'],pars_1['inc'])
D2=(pars_2['dec'],pars_2['inc'])
angle=pmag.angle(D1,D2)
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'
print 'that the two populations are drawn from distributions'
print 'that share a common mean direction can not be rejected.'
elif V>Vcrit:
print '"Fail": Since V is greater than Vcrit, the two means can'
print 'be distinguished at the 95% confidence level.'
print ""
print "M&M1990 classification:"
print ""
print "Angle between data set means: " '%.1f'%(angle)
print "Critical angle for M&M1990: " '%.1f'%(critical_angle)
if V>Vcrit:
print ""
elif V<Vcrit:
if critical_angle<5:
print "The McFadden and McElhinny (1990) classification for"
print "this test is: 'A'"
elif critical_angle<10:
print "The McFadden and McElhinny (1990) classification for"
print "this test is: 'B'"
elif critical_angle<20:
print "The McFadden and McElhinny (1990) classification for"
print "this test is: 'C'"
else:
print "The McFadden and McElhinny (1990) classification for"
print "this test is: 'INDETERMINATE;"
if plot=='yes':
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)
def main():
"""
NAME
lowrie_magic.py
DESCRIPTION
plots intensity decay curves for Lowrie experiments
SYNTAX
lowrie -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
"""
fmt='svg'
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 '-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
pmagplotlib.drawFIGS(FIG)
ans=raw_input('S[a]ve figure? [q]uit, <return> to continue ')
if ans=='a':
files={'lowrie':'lowrie:_'+spc+'_.'+fmt}
pmagplotlib.saveP(FIG,files)
elif ans=='q':
sys.exit()
pmagplotlib.clearFIG(FIG['lowrie'])
def plotELL(pars,col,lower,plot):
"""
function to calculate points on an ellipse about Pdec,Pdip with angle beta,gamma
"""
rad=np.pi/180.
Pdec,Pinc,beta,Bdec,Binc,gamma,Gdec,Ginc=pars[0],pars[1],pars[2],pars[3],pars[4],pars[5],pars[6],pars[7]
if beta > 90. or gamma>90:
beta=180.-beta
gamma=180.-beta
Pdec=Pdec-180.
Pinc=-Pinc
beta,gamma=beta*rad,gamma*rad # convert to radians
X_ell,Y_ell,X_up,Y_up,PTS=[],[],[],[],[]
nums=201
xnum=float(nums-1.)/2.
# set up t matrix
t=[[0,0,0],[0,0,0],[0,0,0]]
X=pmag.dir2cart((Pdec,Pinc,1.0)) # convert to cartesian coordintes
if lower==1 and X[2]<0:
for i in range(3):
X[i]=-X[i]
# set up rotation matrix t
t[0][2]=X[0]
t[1][2]=X[1]
t[2][2]=X[2]
X=pmag.dir2cart((Bdec,Binc,1.0))
if lower==1 and X[2]<0:
for i in range(3):
X[i]=-X[i]
t[0][0]=X[0]
t[1][0]=X[1]
t[2][0]=X[2]
X=pmag.dir2cart((Gdec,Ginc,1.0))
if lower==1 and X[2]<0:
for i in range(3):
X[i]=-X[i]
t[0][1]=X[0]
t[1][1]=X[1]
t[2][1]=X[2]
# set up v matrix
v=[0,0,0]
for i in range(nums): # incremental point along ellipse
psi=float(i)*np.pi/xnum
v[0]=np.sin(beta)*np.cos(psi)
v[1]=np.sin(gamma)*np.sin(psi)
v[2]=np.sqrt(1.-v[0]**2 - v[1]**2)
elli=[0,0,0]
# calculate points on the ellipse
for j in range(3):
for k in range(3):
elli[j]=elli[j] + t[j][k]*v[k] # cartesian coordinate j of ellipse
PTS.append(pmag.cart2dir(elli))
R=np.sqrt( 1.-abs(elli[2]))/(np.sqrt(elli[0]**2+elli[1]**2)) # put on an equal area projection
if elli[2]<0:
# for i in range(3): elli[i]=-elli[i]
X_up.append(elli[1]*R)
Y_up.append(elli[0]*R)
# Adding None values stops plotting of an additional straight line
# between the points where the ellipse crosses the edge of the stereonet
X_ell.append(None)
Y_ell.append(None)
else:
X_ell.append(elli[1]*R)
Y_ell.append(elli[0]*R)
if plot==1:
if X_ell!=[]:plt.plot(X_ell,Y_ell,color=col, linewidth=2, zorder=6)
if X_up!=[]:plt.plot(X_up,Y_up,color=col,linewidth=2,linestyle=':')
else:
return PTS
def bootstrap_common_mean(Data1, Data2, NumSims=1000):
"""
Conduct a bootstrap test (Tauxe, 2010) for a common mean on two declination,
inclination data sets
This function modifies code from PmagPy for use calculating and plotting
bootstrap statistics. Three plots are generated (one for x, one for y and
one for z). If the 95 percent confidence bounds for each component overlap
each other, the two directions are not significantly different.
Arguments
----------
Data1 : a list of directional data [dec,inc]
Data2 : a list of directional data [dec,inc]
NumSims : number of bootstrap samples (default is 1000)
"""
counter = 0
BDI1 = pmag.di_boot(Data1)
BDI2 = pmag.di_boot(Data2)
cart1 = pmag.dir2cart(BDI1).transpose()
X1, Y1, Z1 = cart1[0], cart1[1], cart1[2]
cart2 = pmag.dir2cart(BDI2).transpose()
X2, Y2, Z2 = cart2[0], cart2[1], cart2[2]
print "Here are the results of the bootstrap test for a common mean:"
fignum = 1
fig = plt.figure(figsize=(9, 3))
fig = plt.subplot(1, 3, 1)
minimum = int(0.025 * len(X1))
maximum = int(0.975 * len(X1))
X1, y = pmagplotlib.plotCDF(fignum, X1, "X component", 'r', "")
bounds1 = [X1[minimum], X1[maximum]]
pmagplotlib.plotVs(fignum, bounds1, 'r', '-')
X2, y = pmagplotlib.plotCDF(fignum, X2, "X component", 'b', "")
bounds2 = [X2[minimum], X2[maximum]]
pmagplotlib.plotVs(fignum, bounds2, 'b', '--')
plt.ylim(0, 1)
plt.subplot(1, 3, 2)
Y1, y = pmagplotlib.plotCDF(fignum, Y1, "Y component", 'r', "")
bounds1 = [Y1[minimum], Y1[maximum]]
pmagplotlib.plotVs(fignum, bounds1, 'r', '-')
Y2, y = pmagplotlib.plotCDF(fignum, Y2, "Y component", 'b', "")
bounds2 = [Y2[minimum], Y2[maximum]]
pmagplotlib.plotVs(fignum, bounds2, 'b', '--')
plt.ylim(0, 1)
plt.subplot(1, 3, 3)
Z1, y = pmagplotlib.plotCDF(fignum, Z1, "Z component", 'r', "")
bounds1 = [Z1[minimum], Z1[maximum]]
pmagplotlib.plotVs(fignum, bounds1, 'r', '-')
Z2, y = pmagplotlib.plotCDF(fignum, Z2, "Z component", 'b', "")
bounds2 = [Z2[minimum], Z2[maximum]]
pmagplotlib.plotVs(fignum, bounds2, 'b', '--')
plt.ylim(0, 1)
plt.tight_layout()
plt.show()
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 rec.keys() and 'inc_s' in 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 rec.keys() and 'inc_g' in 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 rec.keys() and 'inc_t' in 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 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 watson_common_mean(Data1, Data2, NumSims=5000, plot='no'):
"""
Conduct a Watson V test for a common mean on two declination, inclination data sets
This function calculates Watson's V statistic from input files through Monte Carlo
simulation in order to test whether two populations of directional data could have
been drawn from a common mean. The critical angle between the two sample mean
directions and the corresponding McFadden and McElhinny (1990) classification is printed.
Required Arguments
----------
Data1 : a list of directional data [dec,inc]
Data2 : a list of directional data [dec,inc]
Optional Arguments
----------
NumSims : number of Monte Carlo simulations (default is 5000)
plot : the default is no plot ('no'). Putting 'yes' will the plot the CDF from
the Monte Carlo simulations.
"""
pars_1 = pmag.fisher_mean(Data1)
pars_2 = pmag.fisher_mean(Data2)
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 = np.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 as data,
# but a common mean
counter = 0
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 - (Vcrit / 2)
# following equation 19 of McFadden and McElhinny (1990) the critical
# angle is calculated. If the observed angle (also calculated below)
# between the data set means exceeds the critical angle the hypothesis
# of a common mean direction may be rejected at the 95% confidence
# level. The critical angle is simply a different way to present
# Watson's V parameter so it makes sense to use the Watson V parameter
# in comparison with the critical value of V for considering the test
# results. What calculating the critical angle allows for is the
# classification of McFadden and McElhinny (1990) to be made
# for data sets that are consistent with sharing a common mean.
k1 = pars_1['k']
k2 = pars_2['k']
R1 = pars_1['r']
R2 = pars_2['r']
critical_angle = np.degrees(
np.arccos(((Rwc**2) - ((k1 * R1)**2) - ((k2 * R2)**2)) /
(2 * k1 * R1 * k2 * R2)))
D1 = (pars_1['dec'], pars_1['inc'])
D2 = (pars_2['dec'], pars_2['inc'])
angle = pmag.angle(D1, D2)
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'
print 'that the two populations are drawn from distributions'
print 'that share a common mean direction can not be rejected.'
elif V > Vcrit:
print '"Fail": Since V is greater than Vcrit, the two means can'
print 'be distinguished at the 95% confidence level.'
print ""
print "M&M1990 classification:"
print ""
print "Angle between data set means: " '%.1f' % (angle)
print "Critical angle for M&M1990: " '%.1f' % (critical_angle)
if V > Vcrit:
print ""
elif V < Vcrit:
if critical_angle < 5:
print "The McFadden and McElhinny (1990) classification for"
print "this test is: 'A'"
elif critical_angle < 10:
print "The McFadden and McElhinny (1990) classification for"
print "this test is: 'B'"
elif critical_angle < 20:
print "The McFadden and McElhinny (1990) classification for"
print "this test is: 'C'"
else:
print "The McFadden and McElhinny (1990) classification for"
print "this test is: 'INDETERMINATE;"
if plot == 'yes':
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)
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
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
