Vector and linear algebra toolbelt for NumPy
Project description
vx
Vector and linear algebra toolbelt for NumPy.
Features
normalize
normalizes a vector.sproj
computes the scalar projection of one vector onto another.proj
computes the vector projection of one vector onto another.reject
computes the vector rejection of one vector from another.reject_axis
zeros or squashes one component of a vector.magnitude
computes the magnitude of a vector.angle
computes the unsigned angle between two vectors.signed_angle
computes the signed angle between two vectors.almost_zero
tests if a vector is almost the zero vector.almost_collinear
tests if two vectors are almost collinear.pad_with_ones
adds a column of ones.unpad
strips off a column (e.g. of ones).apply_homogeneous
applies a transformation matrix using homogeneous coordinates.- Complete documentation: http://vx.readthedocs.io/
Installation
pip install numpy vector_shortcuts
Usage
import numpy as np
import vx
projected = vx.sproj(np.array([5.0, -3.0, 1.0]), onto=vx.basis.neg_y)
Contribute
- Issue Tracker: https://github.com/metabolize/vx/issues
- Source Code: https://github.com/metabolize/vx
Pull requests welcome!
Support
If you are having issues, please let us know.
Acknowledgements
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.
License
The project is licensed under the two-clause BSD license.
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