Open Matlab File in the Editor MTEX

Orientation Density Functions

This example demonstrates the most important MTEX tools for analysing Pole Figure Data.

Import Pole Figures

% specify crystal and specimen symmetry
CS = crystalSymmetry('-3m',[1.4 1.4 1.5]);
SS = specimenSymmetry;

% specify file names
fname = {...
  fullfile(mtexDataPath,'PoleFigure','dubna','Q(10-10)_amp.cnv'),...
  fullfile(mtexDataPath,'PoleFigure','dubna','Q(10-11)(01-11)_amp.cnv'),...
  fullfile(mtexDataPath,'PoleFigure','dubna','Q(11-22)_amp.cnv')};

% specify crystal directions
h = {Miller(1,0,-1,0,CS),...
     [Miller(0,1,-1,1,CS),Miller(1,0,-1,1,CS)],... % superposed pole figures
     Miller(1,1,-2,2,CS)};

% specify structure coefficients
c = {1,[0.52 ,1.23],1};

% import data
pf = loadPoleFigure(fname,h,CS,SS,'interface','dubna','superposition',c);

plot(pf)
mtexColorbar

Extract information from imported pole figure data

get raw data Data stored in a PoleFigure variable can be extracted by

I = pf.intensities; % intensities
h = pf.h;            % Miller indice
r = pf.r;            % specimen directions

basic statistics There are also some basic statics on pole figure intensities

min(pf)
max(pf)
isOutlier(pf);
ans =
     0     0     0
ans =
   1.0e+03 *
    0.0898    1.3600    0.9620

Manipulate pole figure data

pf_modified = pf(pf.r.theta < 70*degree | pf.r.theta > 75*degree)

plot(pf_modified)
 
pf_modified = PoleFigure  
  crystal symmetry : -3m1, X||a*, Y||b, Z||c*
  specimen symmetry: 1
 
  h = (10-10), r = 1 x 1224 points
  h = (01-11)(10-11), r = 1 x 1224 points
  h = (11-22), r = 1 x 1224 points
rot = rotation.byAxisAngle(xvector-yvector,25*degree);
pf_modified = rotate(pf,rot)

plot(pf_modified)
 
pf_modified = PoleFigure  
  crystal symmetry : -3m1, X||a*, Y||b, Z||c*
  specimen symmetry: 1
 
  h = (10-10), r = 72 x 19 points
  h = (01-11)(10-11), r = 72 x 19 points
  h = (11-22), r = 72 x 19 points

PDF - to - ODF Reconstruction

rec = calcODF(pf,'RESOLUTION',10*degree,'iter_max',6)

plotPDF(rec,h)
mtexColorbar
 
rec = ODF  
  crystal symmetry : -3m1, X||a*, Y||b, Z||c*
  specimen symmetry: 1
 
  Radially symmetric portion:
    kernel: de la Vallee Poussin, halfwidth 10°
    center: 2427 orientations, resolution: 10°
    weight: 1
 
odf = SantaFe

% define specimen directions
r = regularS2Grid('antipodal')
 
odf = ODF  
  crystal symmetry : m-3m
  specimen symmetry: 222
 
  Uniform portion:
    weight: 0.73
 
  Radially symmetric portion:
    kernel: van Mises Fisher, halfwidth 10°
    center: (297°,48°,27°)
    weight: 0.27
 
 
r = S2Grid  
 size: 72 x 19

define crystal directions

h = [Miller(1,0,0,odf.CS),Miller(1,1,0,odf.CS),Miller(1,1,1,odf.CS)];

simulate pole figure data

pf_SantaFe = calcPoleFigure(SantaFe,h,r);

estimate an ODF with ghost correction

rec = calcODF(pf_SantaFe,'RESOLUTION',10*degree,'background',10)

plot(rec,'sections',6)
 
rec = ODF  
  crystal symmetry : m-3m
  specimen symmetry: 222
 
  Uniform portion:
    weight: 0.73238
 
  Radially symmetric portion:
    kernel: de la Vallee Poussin, halfwidth 10°
    center: 128 orientations, resolution: 10°
    weight: 0.26762
 
Warning: Plot properties not compatible to previous plot! I'going
to create a new figure. 

without ghost correction

rec_ng = calcODF(pf_SantaFe,'RESOLUTION',10*degree,'background',10,'NoGhostCorrection')

plot(rec_ng,'sections',6)
 
rec_ng = ODF  
  crystal symmetry : m-3m
  specimen symmetry: 222
 
  Radially symmetric portion:
    kernel: de la Vallee Poussin, halfwidth 10°
    center: 150 orientations, resolution: 10°
    weight: 1
 
Warning: Plot properties not compatible to previous plot! I'going
to create a new figure. 

Error Analysis

calcError(pf_SantaFe,rec)
calcError(pf_SantaFe,rec_ng)
progress: 100%
ans =
    0.0202    0.0261    0.0238
progress: 100%
ans =
    0.0358    0.0283    0.0250

Difference plot

plotDiff(pf_SantaFe,rec)
progress: 100%

ODF error

calcError(SantaFe,rec)
calcError(SantaFe,rec_ng)
ans =
    0.0312
ans =
    0.0893

Exercises

3)

a) Load the pole figure data of a quartz specimen from: data/dubna!

b) Inspect the raw data. Are there noticeable problems?

c) Compute an ODF from the pole figure data.

d) Plot some pole figures of that ODF and compare them to the measured pole figures.

e) Compute the RP errors for each pole figure.

f) Plot the difference between the raw data and the calculated pole figures. What do you observe?

g) Remove the erroneous values from the pole figure data and repeat the ODF calculation. How do the RP error change?

h) Vary the number of pole figures used for the ODF calculation. What is the minimum set of pole figures needed to obtain a meaningful ODF?