core-labs/core
core-labs/core
1
0
Fork 0
Code Issues 1 Pull requests 1 Projects Releases Packages Wiki Activity

docs: document Cl(4,1) boost blade classification and float64 discipline

Browse source 
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
This commit is contained in:
Shay 2026-05-15 17:06:37 -07:00
parent c9dfad3017
commit 3b4fa242c6
1 changed files with 8 additions and 0 deletions
Show stats Download patch file Download diff file Expand all files Collapse all files

8
field/operators.py
View file

@ -29,6 +29,11 @@ class Operator(Protocol):
... ...
# Cl(4,1) bivector blade classification for the exponential map.
# Blades 9, 12, 14, 15 square to +1 (boost/hyperbolic planes involving e5).
# Blades 6-8, 10-11, 13 square to -1 (rotation planes).
# Use cosh/sinh for boosts, cos/sin for rotations — mixing them makes
# re-unitization diverge.
_BOOST_INDICES = frozenset({9, 12, 14, 15}) _BOOST_INDICES = frozenset({9, 12, 14, 15})
@ -37,6 +42,9 @@ def _unitize_f32(v: np.ndarray) -> np.ndarray:
Builds a proper rotor from the bivector content, ensuring Builds a proper rotor from the bivector content, ensuring
R·reverse(R) = 1 exactly in float64, then casts to float32. R·reverse(R) = 1 exactly in float64, then casts to float32.
Works in float64 throughout because algebra.backend's Rust
geometric_product silently returns float32 regardless of input dtype.
""" """
v64 = np.asarray(v, dtype=np.float64) v64 = np.asarray(v, dtype=np.float64)
norm = float(np.linalg.norm(v64)) norm = float(np.linalg.norm(v64))