Band structure quantification (fuller.metrics)
¶
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fuller.metrics.
abserror
(result, ref, keys, ofs=None, mask=1, **kwargs)¶ Calculate the averaged absolute approximation error per band.
Parameters
- result: dict
Dictionary containing the reconstruction results.
- ref: 3D array
Reference bands or band structure to compare against.
- keys: list/tuple
Dictionary keys.
- ofs: int | None
Pixel offset on each side.
- mask: 2D array | 1
Brillouin zone mask applied to the reconstruction results.
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fuller.metrics.
dcos
(a, b)¶ Cosine distance between vectors a and b.
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fuller.metrics.
demean
(arr, meanax=1, idx=0, **kwds)¶ Subtract the mean of an axial direction in an array (2D or higher) from all entries in that direciton.
Parameters
- arr: list/tuple/numpy array
Input array (at least 2D).
- meanax: int | 1
Axis along which to calculate the mean.
- idx: int | 0
Entry index in the axis specified previously.
- **kwds: keyword arguments
Additional arguments for the numpy.mean() function.
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fuller.metrics.
similarity_matrix
(feature_mat, axis=0, fmetric=<function dcos>, **kwds)¶ Calculation of the similarity matrix.
Parameters
- feature_mat: list/tuple/numpy array
Feature matrix (2D or higher dimenions).
- axis: int
Axis along which the features are aligned to.
- fmetric: function | dcos
Metric function for calculating the similarity between each pair of features.
- **kwds: keyword arguments
Extra arguments for the metric function
fmetric
.
Return
- smat: 2D numpy array
Calculated similarity matrix.