Explains how to manipulate pole figure data in MTEX.

Let us load some data and plot them.

mtexdata geesthacht % plot imported polefigure plot(pf)

loading data ... saving data to /home/hielscher/mtex/master/data/geesthacht.mat

As we can see the first and the third pole figure complete pole figures and the second and the fourth pole figures contain some values for background correction. Let us, therefore, split the pole figures into these two groups.

pf_complete = pf({1,3}) pf_background= pf({2,4})

pf_complete = PoleFigure crystal symmetry : m-3m specimen symmetry: -1 h = (104), r = 679 x 1 points h = (110), r = 679 x 1 points pf_background = PoleFigure crystal symmetry : m-3m specimen symmetry: -1 h = (104), r = 16 x 1 points h = (110), r = 16 x 1 points

Actually, it is possible to work with pole figures as with simple numbers. E.g. it is possible to add / subtract pole figures. A superposition of the first and the third pole figures can be written as

2*pf({1}) + 3*pf({3})

ans = PoleFigure crystal symmetry : m-3m specimen symmetry: -1 h = (104)(110), r = 679 x 1 points

In order to correct pole figures for background radiation and defocusing one can use the command correct. In our case the syntax is

```
pf = correct(pf_complete,'background',pf_background);
plot(pf)
```

Sometimes people want to have normalized pole figures. In the case of complete pole figures, this can be simply archived using the command normalize

pf_normalized = normalize(pf); plot(pf_normalized)

However, in the case of incomplete pole figures, it is well known, that the normalization can only by computed from an ODF. Therefore, one has to proceed as follows:

% compute an ODF from the pole figure data odf = calcODF(pf); % and use it for normalization pf_normalized = normalize(pf,odf); plot(pf_normalized)

As pole figures are usually experimental data they may contain outliers. In order to remove outliers from pole figure data one can use the function isOutlier. Here a simple example:

% Let us add 100 random outliers to the pole figure data % First we select 100 random positions within the pole figures ind = randperm(pf.length,100); % Next we multiply the intensity at these positions by a random value % between 3 and 4 factor = 3+rand(100,1); pf(ind).intensities = pf(ind).intensities(:) .* factor; % Let's check the result plot(pf)

check for outliers

condition = pf.isOutlier; % remove outliers pf(condition) = []; % plot the corrected pole figures plot(pf)

Sometimes applying the above correction is not suffcient. Then it can help to repeat the outlier detection ones again

pf(pf.isOutlier) = []; plot(pf)

In the same way, as we removed the outlier one can manipulate and delete pole figure data by any criteria. Lets, e.g. cap all values that are larger than 500.

% find those values condition = pf.intensities > 500; % cap the values in the pole figures pf(condition).intensities = 500; plot(pf)

Sometimes it is necessary to rotate the pole figures. In order to do this with MTEX one has first to define a rotation, e.e. by

```
% This defines a rotation around the x-axis about 100 degree
rot = rotation.byAxisAngle(xvector,100*degree);
```

Second, the command rotate can be used to rotate the pole figure data.

```
pf_rotated = rotate(pf,rot);
plot(pf_rotated,'antipodal')
```

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