Merge pull request #53 from ralfHielscher/master

mtex 4.0.18

EBSDAnalysis/@EBSD/plotAngleDistribution.m

@@ -67,3 +67,4 @@
 mtexFig.drawNow(varargin{:})
 
 if nargout==0, clear h;end
+hold off

Makefile

@@ -60,7 +60,7 @@ clean:
 
 
 # rule for making release
-RNAME = mtex-4.0.17
+RNAME = mtex-4.0.18
 RDIR = ../releases
 release:
 	rm -rf $(RDIR)/$(RNAME)*

PoleFigureAnalysis/@PoleFigure/calcError.m

@@ -44,3 +44,8 @@
   end
 end
 
+% TODO: implement a nice default output  
+%if nargout == 0
+%  disp('TODO')
+%  clear e;
+%end

VERSION

@@ -1 +1 @@
-MTEX 4.0.17
+MTEX 4.0.18

doc/GrainAnalysis/GrainSingleAnalysis.m

@@ -67,21 +67,20 @@
 % some process, we are interessed in quantifications.
 
 cs = ebsd(grain_selected).CS;
-plotPDF(ebsd(grain_selected).orientations,...
-  [Miller(0,0,1,cs),Miller(0,1,1,cs),Miller(1,1,1,cs)],'antipodal')
+ori = ebsd(grain_selected).orientations;
+plotPDF(ori,[Miller(0,0,1,cs),Miller(0,1,1,cs),Miller(1,1,1,cs)],'antipodal')
 
 
 %%
 % Testing on the distribution shows a gentle prolatness, nevertheless we
 % would reject the hypothesis for some level of significance, since the
 % distribution is highly concentrated and the numerical results vague.
 
-[qm,lambda,U,kappa] = mean(ebsd(grain_selected).orientations,'approximated');
-num2str(kappa')
+calcBinghamODF(ori,'approximated')
 
 %%
 %
-ori = ebsd(grain_selected).orientations;
+
 T_spherical = bingham_test(ori,'spherical','approximated');
 T_prolate   = bingham_test(ori,'prolate',  'approximated');
 T_oblate    = bingham_test(ori,'oblate',   'approximated');
@@ -137,4 +136,4 @@
 close, plotIPDF(ebsd_line.orientations,[xvector,yvector,zvector],...
   'property',ebsd_line.y,'markersize',3,'antipodal')
 
-colorbar
+colorbar

doc/PoleFigureAnalysis/PoleFigureTutorial.m

@@ -1,37 +1,126 @@
-%% Short Pole Figure Analysis Tutorial
-% How to estimate ODFs from diffraction data.
+%% Pole Figure Tutorial
+% Tutorial on x-ray and neutron diffraction data.
 
 %% Open in Editor
 %
 %% Import diffraction data
-% The following script is automatically generated by the import wizard.
+% Click on <matlab:import_wizard('PoleFigure') Import pole figure data> to
+% start the import wizard which is a GUI leading you throught the import of
+% pole figure data. After finishing the wizard you will end with a script
+% similar to the following one.
 
-% specify scrystal and specimen symmetry
-cs = crystalSymmetry('-3m',[1.4,1.4,1.5]);
+% This script was automatically created by the import wizard. You should
+% run the whoole script or parts of it in order to import your data. There
+% is no problem in making any changes to this script.
 
-% specify file names
+% *Specify Crystal and Specimen Symmetries*
+
+% crystal symmetry
+CS = crystalSymmetry('6/mmm', [2.633 2.633 4.8], 'X||a*', 'Y||b', 'Z||c');
+
+% specimen symmetry
+SS = specimenSymmetry('1');
+
+% plotting convention
+setMTEXpref('xAxisDirection','north');
+setMTEXpref('zAxisDirection','outOfPlane');
+
+% *Specify File Names*
+
+% path to files
+pname = '/home/hielscher/Downloads/mtexerrordefocusing';
+
+% which files to be imported
 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')};
+  [pname '/ZnCuTi_Wal_50_5x5_PF_002_R.UXD'],...
+  [pname '/ZnCuTi_Wal_50_5x5_PF_100_R.UXD'],...
+  [pname '/ZnCuTi_Wal_50_5x5_PF_101_R.UXD'],...
+  [pname '/ZnCuTi_Wal_50_5x5_PF_102_R.UXD'],...
+  };
 
-% specify crystal directions
-h = {Miller(1,0,-1,0,cs),[Miller(0,1,-1,1,cs),Miller(1,0,-1,1,cs)],Miller(1,1,-2,2,cs)};
+% defocusing
+pname = '/home/hielscher/Downloads/mtexerrordefocusing';
+fname_def = {...
+  [pname '/ZnCuTi_defocusing_PF_002_R.UXD'],...
+  [pname '/ZnCuTi_defocusing_PF_100_R.UXD'],...
+  [pname '/ZnCuTi_defocusing_PF_101_R.UXD'],...
+  [pname '/ZnCuTi_defocusing_PF_102_R.UXD'],...
+  };
 
-% specify structure coefficients
-c = {1,[0.52 ,1.23],1};
+% *Specify Miller Indice*
 
-% import pole figure data
-pf = loadPoleFigure(fname,h,cs,'superposition',c,...
-  'comment','Dubna Tutorial pole figures')
+h = { ...
+  Miller(0,0,2,CS),...
+  Miller(1,0,0,CS),...
+  Miller(1,0,1,CS),...
+  Miller(1,0,2,CS),...
+  };
 
-%% Plot pole figures
+% *Import the Data*
+
+% create a Pole Figure variable containing the data
+pf = loadPoleFigure(fname,h,CS,SS,'interface','uxd');
+
+% defocussing
+pf_def = loadPoleFigure(fname_def,h,CS,SS,'interface','uxd');
+
+% correct data
+pf = correct(pf,'def',pf_def);
+
+%% Plot Raw Data
+% You should run the sript section wise to see how MTEX imports the pole
+% figure data. Next you can plot your data
 
 plot(pf)
+annotate([xvector,yvector,zvector],'label',{'X','Y','Z'},'backgroundColor','w')
+
+%%
+% Make sure that the Miller indices are correctly assigned to the pole
+% figures and that the alignment of the specimen coordinate system, i.e.,
+% X, Y, Z is correct. In case of outliers or misanligned data you may want
+% to correct you raw data. See <ModifyPoleFigureData.html how to modify
+% pole figure data> for further information.
+%
+%% ODF Estimation
+%
+% Once your data are in a good shape, i.e. defocussing correction has been
+% done and only few outliers are left you can step to reconstruct an ODF
+% out of these data. This is done by the command <PoleFigure_calcODF.html
+% calcODF>. 
+
+odf = calcODF(pf,'silent')
+
+%%
+% Note that reconstructing an ODF from pole figure data is a severly ill
+% posed problem, i.e., it does *not* provide an unique solution. A more
+% troughout discussion on the ambiguity of ODF reconstruction from pole
+% figure data can be found <PF2ODFAmbiguity.html here>. As a rule of thumb:
+% as more pole figures you have and as more consistent you pole figure data
+% are as better you reconstructed ODF will be.
+%
+% To check how well your reconstructd ODF fitts the measured pole figure
+% data do
+
+plotPDF(odf,pf.h)
+
+%%
+% Compare the recalculated pole figures with the measured data. 
+% Quantitative measure for the fitting are the so called RP values. They
+% can be computed by
+
+calcError(odf,pf)
+
+%%
+% In case of a bad fitting you may want to tweak the reconstruction
+% algorithm. See <PoleFigure2odf.html here> for more information.
+
+%% Quantify the Reconstruction Error
+
+
+
 
-%% Estimate an ODF
-odf = calcODF(pf)
 
-%% Calculate c-axis pole figure from the ODF
-plotPDF(odf,Miller(0,0,1,cs),'antipodal')
+%% Visualize the ODF
 
+plotODF(odf)
+mtexColorMap LaboTeX

geometry/@Miller/Miller.m

@@ -59,7 +59,8 @@
 
       % check for symmetry
       m.CSprivate = getClass(varargin,'crystalSymmetry',[]);
-      assert(~isempty(m.CSprivate),['Starting with MTEX 4.0 Miller ' ...
+      assert(isa(varargin{1},'Miller') || ~isempty(m.CSprivate),...
+        ['Starting with MTEX 4.0 Miller ' ...
         'indices always require to specify a crystal symmetry!']);
 
       if nargin == 0 %empty constructor
@@ -69,7 +70,9 @@
       elseif isa(varargin{1},'Miller') % copy constructor
 
         if ~isempty(m.CSprivate), varargin{1}.CSprivate = m.CSprivate;end
-        m = varargin{1};        
+        m = varargin{1};
+        dispStyle = extract_option(varargin,{'uvw','UVTW','hkl','hkil','xyz'}); %#ok<*PROP>
+        if ~isempty(dispStyle), m.dispStyle = dispStyle{1}; end
 
         return;
 

geometry/@Miller/display.m

@@ -19,7 +19,7 @@ function display(m)
 
   eps = 1e4;
 
-  switch m.dispStyle
+  switch lower(m.dispStyle)
 
     case 'uvw'
 
@@ -28,7 +28,7 @@ function display(m)
 
       cprintf(uvtw.','-L','  ','-Lr',{'u' 'v' 'w'});
 
-    case 'UVTW'
+    case 'uvtw'
 
       uvtw = round(m.UVTW * eps)./eps;
       uvtw(uvtw==0) = 0;

geometry/@Miller/round.m

@@ -2,11 +2,11 @@
 % tries to round miller indizes to greatest common divisor
 
 
-switch h.dispStyle
+switch lower(h.dispStyle)
 
   case  'uvw'
     mOld = h.uvw;
-  case 'UVTW'
+  case 'uvtw'
     mOld = h.UVTW;  
   case 'hkl'
     mOld = h.hkl;
@@ -44,11 +44,11 @@
 h = h .* multiplier;
 
 % now round
-switch h.dispStyle
+switch lower(h.dispStyle)
 
-  case  'uvw'
+  case 'uvw'
     h.uvw = round(h.uvw);
-  case 'UVTW'
+  case 'uvtw'
     h.UVTW = round(h.UVTW);
   case 'hkl'
     h.hkl = round(h.hkl);

geometry/@orientation/angle.m

@@ -14,7 +14,8 @@
 if nargin == 1
 
   [~,omega] = project2FundamentalRegion(o1);
-
+  omega = reshape(omega,size(o1));
+
 else
 
   omega = real(2*acos(dot(o1,o2)));

geometry/@orientation/calcBinghamODF.m

@@ -21,4 +21,4 @@
 [~,~,ev,kappa] = mean(ori,varargin{:});
 
 % set up Bingham ODF
-odf = BinghamODF(kappa,ev,CS,SS);
+odf = BinghamODF(kappa,ev,ori.CS,ori.SS);

geometry/@orientation/calcKernelODF.m

@@ -3,35 +3,36 @@
 %
 % *calcKernelODF* is one of the core function of the MTEX toolbox.
 % It estimates an ODF from a set of individual crystal orientations by
-% [[EBSD2odf.html kernel,density estimation]].
+% [[EBSD2odf.html kernel density estimation]].
 %
-% The function *calcKernelODF* has several options to control the halfwidth of
-% the kernel functions, the resolution, etc. Most important the estimated
-% ODF is affected by the *halfwidth* of the kernel function.
+% The function *calcKernelODF* has several options to control the halfwidth
+% of the kernel functions, the resolution, etc. Most important the
+% estimated ODF is affected by the *halfwidth* of the kernel function.
 %
-% If the halfwidth is large the estimated ODF is smooth whereas a small halfwidth
-% results in a sharp ODF. It depends on your prior information about the
-% ODF to choose this parameter right. Look at this
+% If the halfwidth is large the estimated ODF is smooth whereas a small
+% halfwidth results in a sharp ODF. It depends on your prior information
+% about the ODF to choose this parameter right. Look at this
 % [[EBSDSimulation_demo.html, description]] for exhausive discussion.
 %
 % Syntax
-%   calcODF(ori,...,param,var,...)
-%   calcODF(ebsd,...,param,var,...)
+%   odf = calcKernelODF(ori)
+%   odf = calcKernelODF(grains.meanOrientation,'weigths',grains.area)
+%   odf = calcKernelODF(ebsd.orientations,'halfwidth',5*degree)
 %
 % Input
-%  ori  - @orientation
-%  ebsd - @EBSD
+%  ori - @orientation
 %
 % Output
 %  odf - @ODF
 %
 % Options
-%  HALFWIDTH        - halfwidth of the kernel function
-%  RESOLUTION       - resolution of the grid where the ODF is approximated
-%  KERNEL           - kernel function (default -- de la Valee Poussin kernel)
+%  halfwidth  - halfwidth of the kernel function
+%  resolution - resolution of the grid where the ODF is approximated
+%  kernel     - kernel function (default -- de la Valee Poussin kernel)
+%  weights    - list of weights for the orientations
 %
 % Flags
-%  EXACT            - no approximation to a corser grid
+%  exact      - no approximation to a corser grid
 %
 % See also
 % ebsd_demo EBSD2odf EBSDSimulation_demo loadEBSD ODF/calcEBSD EBSD/calcKernel kernel/kernel

geometry/@orientation/calcODF.m

@@ -13,6 +13,9 @@
 %   % use kernel density estimation with a 10 degree kernel
 %   odf = calcODF(ori,'halfwidth',10*degree) 
 %
+%   % use grain area as weights for the orientations
+%   odf = calcODF(grains.meanOrientation,'weights',grains.area)
+%
 %   % use a specific kernel
 %   psi = AbelPoissonKernel('halfwidth',10*degree)
 %   odf = calcODF(ori,'kernel',psi) 
@@ -27,6 +30,7 @@
 %  odf - @ODF
 %
 % Options
+%  weights    - list of weights for the orientations
 %  halfwidth  - halfwidth of the kernel function
 %  resolution - resolution of the grid where the ODF is approximated
 %  kernel     - kernel function (default -- de la Valee Poussin kernel)
@@ -63,4 +67,3 @@
   odf = calcKernelODF(ori,varargin{:},'kernel',psi);
 
 end
-