indexing

Tools for indexing of EBSD patterns by matching to a dictionary of simulated patterns.

Some of these tools are used in dictionary_indexing().

Functions

compute_refine_orientation_projection_center_results(...)	Compute the results from refine_orientation_projection_center() and return the CrystalMap and EBSDDetector.
compute_refine_orientation_results(results, ...)	Compute the results from refine_orientation() and return the CrystalMap.
compute_refine_projection_center_results(...)	Compute the results from refine_projection_center() and return the score array, EBSDDetector and number of function evaluations per pattern.
merge_crystal_maps(crystal_maps[, ...])	Return a multi phase CrystalMap by merging maps of 1D or 2D navigation shape based on scores.
orientation_similarity_map(xmap[, n_best, ...])	Compute an orientation similarity map (OSM) where the ranked list of the dictionary indices of the best matching simulated patterns in one point is compared to the corresponding lists in the nearest neighbour points [Marquardt et al., 2017].
xmap_from_hough_indexing_data(data, phase_list)	Convert Hough indexing result array from pyebsdindex to a CrystalMap.

Classes

NormalizedCrossCorrelationMetric([...])	Similarity metric implementing the normalized cross-correlation, or Pearson Correlation Coefficient [Gonzalez and Woods, 2017].
NormalizedDotProductMetric([...])	Similarity metric implementing the normalized dot product [Chen et al., 2015].
SimilarityMetric([n_experimental_patterns, ...])	Abstract class implementing a similarity metric to match experimental and simulated EBSD patterns in a dictionary.