maclariz

@harripj Here you go: ```python [ 0.5101 -0.0107 0.0421 -0.859 ] [ 0.5114 -0.0107 0.0421 -0.8582] [ 0.2451 -0.0222 0.0373 -0.9685] [ 0.2455 -0.016 0.0368 -0.9686] [ 0.2442 -0.0223 0.0373...

@harripj m3m (FCC)

The following sorted out my data and resulted in a consistent set of orientations that give consistent misorientation angles: ```python # Take the data into a pure numpy array ori5...

@pc494 I don't know what this really adds, but I'll give you some output from this. This is a distance matrix of 6 points in this area with the orientation...

@pc494 Here is the result of .to_matrix() on the same 6 data points: ```python array([[[ 0.87929659, -0.4723087 , -0.06133516], [ 0.47562573, 0.8775068 , 0.06133494], [ 0.024853 , -0.08310418, 0.99623091]], [[...

@hakonanes Yes, these are results from cluster analysis. Actually slightly more clusters found than really exist (it split some areas of very similar misorientation). And then I get all the...

I already have a think in there for the point symmetry for HCP, and this is enough for the lattice vectors. Why do I need to import the full cell?...

I get the following error: DimensionError: Miller requires data of dimension 3 but received dimension 1. The matrix I am putting in looks like this: Vector3d (11,) [[ 0.4437 0.884...

I also get an error with: rot_axes.round() ValueError: The Miller-Bravais indices convention U + V + T = 0 is not satisfied This suggests that the calculation is having rounding...

Normalisation is odd on conversion to UVTW format too. All my vectors had length 0.27664721 after doing so (as calculated manually by me using my own knowledge of 4-vector maths).