- EBSD.dictionary_indexing(dictionary: EBSD, metric: SimilarityMetric | str = 'ncc', keep_n: int = 20, n_per_iteration: int | None = None, navigation_mask: ndarray | None = None, signal_mask: ndarray | None = None, rechunk: bool = False, dtype: str | dtype | type | None = None) CrystalMap #
One EBSD signal with dictionary patterns. The signal must have a 1D navigation axis, an
xmapproperty with crystal orientations set, and equal detector shape.
Similarity metric, by default
"ndp"(normalized dot product) is also available. A valid user-defined similarity metric may be used instead. The metric must be a class implementing the
SimilarityMetricabstract class methods. See
Number of best matches to keep, by default 20 or the number of dictionary patterns if fewer than 20 are available.
Number of dictionary patterns to compare to all experimental patterns in each indexing iteration. If not given, and the dictionary is a
LazyEBSDsignal, it is equal to the chunk size of the first pattern array axis, while if if is an
EBSDsignal, it is set equal to the number of dictionary patterns, yielding only one iteration. This parameter can be increased to use less memory during indexing, but this will increase the computation time.
A boolean mask equal to the signal’s navigation (map) shape, where only patterns equal to
Falseare indexed. This can be used by
prepare_experimental(). If not given, all patterns are indexed.
A boolean mask equal to the experimental patterns’ detector shape, where only pixels equal to
Falseare matched. This can be used by
prepare_experimental(). If not given, all pixels are used.
metricis allowed to rechunk experimental and dictionary patterns before matching. Default is
False. Rechunking usually makes indexing faster, but uses more memory. If a custom
metricis passed, whatever
rechunkis set to will be used.
Which data type
metricshall cast the patterns to before matching. If not given,
"float32"will be used unless a custom
metricis passed and it has set the
dtype, which will then be used instead.
"float64"are allowed for the available
A crystal map with
keep_nrotations per point with the sorted best matching orientations in the dictionary. The corresponding best scores and indices into the dictionary are stored in the
Merge multiple single phase crystal maps into one multi phase map.
Calculate an orientation similarity map.