A vector-geometry toolbelt for dealing with 3D points and vectors
Project description
vg
A very good vector-geometry toolbelt for dealing with 3D points and vectors. These are simple NumPy operations made readable, built to scale from prototyping to production.
:book: See the complete documentation: https://vgpy.dev/
Examples
Normalize a stack of vectors:
# 😮
vs_norm = vs / np.linalg.norm(vs, axis=1)[:, np.newaxis]
# 😀
vs_norm = vg.normalize(vs)
Check for the zero vector:
# 😣
is_almost_zero = np.allclose(v, np.array([0.0, 0.0, 0.0]), rtol=0, atol=1e-05)
# 🤓
is_almost_zero = vg.almost_zero(v, atol=1e-05)
Find the major axis of variation (first principal component):
# 😩
mean = np.mean(coords, axis=0)
_, _, pcs = np.linalg.svd(coords - mean)
first_pc = pcs[0]
# 😍
first_pc = vg.major_axis(coords)
Compute pairwise angles between two stacks of vectors:
# 😭
dot_products = np.einsum("ij,ij->i", v1s.reshape(-1, 3), v2s.reshape(-1, 3))
cosines = dot_products / np.linalg.norm(v1s, axis=1) / np.linalg.norm(v1s, axis=1)
angles = np.arccos(np.clip(cosines, -1.0, 1.0))
# 🤯
angles = vg.angle(v1s, v2s)
Installation
pip install numpy vg
Usage
import numpy as np
import vg
projected = vg.scalar_projection(
np.array([5.0, -3.0, 1.0]),
onto=vg.basis.neg_y
)
Development
First, install Poetry.
After cloning the repo, run ./bootstrap.zsh to initialize a virtual
environment with the project's dependencies.
Subsequently, run ./dev.py install to update the dependencies.
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
the vx namespace was broken out into its own package. The project was renamed
to vg to resolve a name conflict.
License
The project is licensed under the two-clause BSD license.
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