NormalVectorScore

class NormalVectorScore(target_coordinates, template_coordinates, target_weights, template_weights, template_mask_coordinates=None, target_mask_coordinates=None, negate_score=True, **kwargs)

Bases: _MatchCoordinatesToCoordinates

The Normal Vector Score (NVS) between the target’s \(f\) and the template \(g\)’s normal vectors is calculated as:

\[\text{d(f,g)} = \frac{1}{N} \sum_{i=1}^{N} \frac{ {\vec{f}_i} \cdot {\vec{g}_i} }{ ||\vec{f}_i|| \, ||\vec{g}_i|| }\]

Parameters:

targetNDArray

A d-dimensional target to match the template coordinate set to.

template_coordinatesNDArray

Template coordinate array with shape (d,n).

template_weightsNDArray

Template weight array with shape (n,).

template_mask_coordinatesNDArray, optional

Template mask coordinates with shape (d,n).

target_maskNDArray, optional

A d-dimensional mask to be applied to the target.

negate_scorebool, optional

Whether the final score should be multiplied by negative one. Default is True.

return_gradientbool, optional

Invoking __call_ returns a tuple of score and parameter gradient. Default is False.

**kwargsDict, optional

Keyword arguments propagated to downstream functions.

References

[1]

Daven Vasishtan and Maya Topf, “Scoring functions for cryoEM density fitting”, Journal of Structural Biology, vol. 174, no. 2, pp. 333–343, 2011. DOI: https://doi.org/10.1016/j.jsb.2011.01.012

Methods

NormalVectorScore.rotate_array(arr, ...[, ...])
NormalVectorScore.score(x)	Compute the matching score for the given transformation parameters.
NormalVectorScore.score_angles(x)	Computes the score after a given rotation.
NormalVectorScore.score_translation(x)	Computes the score after a given translation.