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.
📖 See the complete documentation: https://vgpy.dev/
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(v2s, axis=1)
angles = np.arccos(np.clip(cosines, -1.0, 1.0))
# 🤯
angles = vg.angle(v1s, v2s)
pip install numpy vg
import numpy as np
import vg
projected = vg.scalar_projection(
np.array([5.0, -3.0, 1.0]),
onto=vg.basis.neg_y
)
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.
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.
The project is licensed under the two-clause BSD license.