NumPy for humans: a very good vector-geometry and linear-algebra toolbelt
Project description
vg
NumPy for humans – a very good toolbelt providing readable shortcuts for commonly used vector-geometry and linear-algebra functions.
The functions optionally can be vectorized, meaning they accept single inputs and stacks of inputs without the need to reshape. They return The Right Thing. With the power of NumPy, the vectorized functions are fast.
Features
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://vgpy.readthedocs.io/
Installation
pip install numpy vg
Usage
import numpy as np
import vg
projected = vg.sproj(np.array([5.0, -3.0, 1.0]), onto=vg.basis.neg_y)
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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