hough_indexing_optimize_pc#

EBSD.hough_indexing_optimize_pc(pc0: np.ndarray | list | tuple, indexer: EBSDIndexer, batch: bool = False, method: str = 'Nelder-Mead', **kwargs) → EBSDDetector[source]#

Return a detector with one projection center (PC) per pattern optimized using Hough indexing from pyebsdindex.

See EBSDIndexer and pcopt for details.

Parameters:

pc0: A single initial guess of PC for all patterns in Bruker’s convention, (PCx, PCy, PCz).
indexer: PyEBSDIndex EBSD indexer instance to pass on to the optimization function. An indexer can be obtained with get_indexer().
batch: Whether the fit for the patterns should be optimized using the cumulative fit for all patterns (False, default), or if an optimization is run for each pattern individually.
method: Which optimization method to use, either "Nelder-Mead" from SciPy (default) or "PSO" (particle swarm).
**kwargs: Keyword arguments passed on to PyEBSDIndex’ optimization method (depending on the chosen method).

Returns:

new_detector: EBSD detector with one PC if batch=False or one PC per pattern if batch=True. The detector attributes are extracted from indexer.sampleTilt etc.

Notes

Requires pyebsdindex to be installed. See Dependencies for further details.