Vector and linear algebra toolbelt for NumPy
Vector and linear algebra toolbelt for NumPy.
normalizenormalizes a vector.
sprojcomputes the scalar projection of one vector onto another.
projcomputes the vector projection of one vector onto another.
rejectcomputes the vector rejection of one vector from another.
reject_axiszeros or squashes one component of a vector.
magnitudecomputes the magnitude of a vector.
anglecomputes the unsigned angle between two vectors.
signed_anglecomputes the signed angle between two vectors.
almost_zerotests if a vector is almost the zero vector.
almost_collineartests if two vectors are almost collinear.
pad_with_onesadds a column of ones.
unpadstrips off a column (e.g. of ones).
apply_homogeneousapplies a transformation matrix using homogeneous coordinates.
- Complete documentation: http://vx.readthedocs.io/
pip install numpy vector_shortcuts
import numpy as np import vx projected = vx.sproj(np.array([5.0, -3.0, 1.0]), onto=vx.basis.neg_y)
- Issue Tracker: https://github.com/metabolize/vx/issues
- Source Code: https://github.com/metabolize/vx
Pull requests welcome!
If you are having issues, please let us know.
This collection was developed at Body Labs by Paul Melnikow and extracted from the Body Labs codebase and open-sourced as part of blmath by Alex Weiss. blmath was subsequently forked by Paul Melnikow and later this namespace was broken out into its own package.
The project is licensed under the two-clause BSD license.
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