PoleFigureDubna.m

%% Orientation Density Functions
%
%%
% This example demonstrates the most important MTEX tools for analysing
% Pole Figure Data.

% specify crystal and specimen symmetry
CS = crystalSymmetry('-3m',[4.9 4.9 5.4]);
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 = PoleFigure.load(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.PoleFigure.html 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);

%% Manipulate pole figure data
%
%%

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

plot(pf_modified)

%%
rot = rotation.byAxisAngle(xvector-yvector,25*degree);
pf_modified = rotate(pf,rot)

plot(pf_modified)

%% PDF - to - ODF Reconstruction


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

plotPDF(rec,h)
mtexColorbar

%%
%
odf = SantaFe

% define specimen directions
r = regularS2Grid('antipodal')

%%
% 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)

%%
% without ghost correction
rec_ng = calcODF(pf_SantaFe,'RESOLUTION',10*degree,'background',10,'NoGhostCorrection')

plot(rec_ng,'sections',6)


%% Error Analysis


calcError(pf_SantaFe,rec)
calcError(pf_SantaFe,rec_ng)

%%
% *Difference plot*

plotDiff(pf_SantaFe,rec)


%%
% *ODF error*

calcError(SantaFe,rec)
calcError(SantaFe,rec_ng)

%% 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?

%#ok<*NASGU>