gacalc

A small, readable Geometric (Clifford) Algebra library in Python, built as a companion to Hestenes & Sobczyk, Clifford Algebra to Geometric Calculus. It runs both numerically and fully symbolically (coefficients may be plain numbers or sympy expressions) — and numeric stays numeric: a float vector's magnitude() is a Python float, not a sympy object, while int and symbolic inputs stay exact.

The algebra of n-dimensional Euclidean space is written 𝒢ₙ (Hestenes' notation). This package gives you:

• Gn — the general, dimension-agnostic representation (any n), and
• G — specialized, much faster representations of 𝒢₁ / 𝒢₂ / 𝒢₃ whose geometric product is a closed form generated from Gn so it is provably consistent with the reference.

Terminology: 𝒢ₙ denotes the algebra; an instance of a class is an element of that algebra (a multivector). The classes are named after their algebra.

Layout

src/gacalc/
  base.py          MultiVectorBase (the abstract base) + type aliases + display symbols
  gn.py            Gn (general 𝒢ₙ) + e_1.. constants + transforms + `MultiVector` alias
  g1.py g2.py g3.py   one specialized class each (generated, not in git -- run `make generate`)
  frame.py         frames + Gram–Schmidt / Hestenes orthogonalization
  measure.py       area / volume / content, signed and unsigned
  vectorcalc.py    the cross product (the dual of the wedge, 𝒢₃)
  transforms.py    translate / scale / rotation factories (composable, invertible)

All representations interoperate through one interchange format: the blade coefficient dictionary ({(1, 2): 4} means 4·e₁e₂; () keys the scalar), read/written by to_blade_dict() / from_blade_dict(). Its full contract is documented at BladeCoef in base.py.

Installing from a git checkout (not from PyPI)? Run make generate once first — the specialized g*.py modules aren't committed; they're generated from Gn (and baked into the published wheel, so pip install gacalc needs no generator).

Import just the algebra you need:

from gacalc.g2 import G, e_1, e_2

a = 3 * e_1 + 4 * e_2
a.magnitude_squared()  # 25  (a vector squared is its magnitude squared)
a * a == G.from_scalar(25)  # True
e_1 * e_2  # the unit bivector e_12
a.dual()  # the dual; n defaults to this algebra's dimension (2)
a.coefficient(e_1)  # 3   (the stored coefficient on a unit blade — a thin
#      reader over to_blade_dict; any grade, e.g.
#      B.coefficient(e_1 ^ e_2))

Each g* module exports its own basis constants (zero, one, e_1, …, and the pseudoscalar e_12 / e_123) at their graded type — g2.e_1 is a Vector, g2.e_12 a Bivector, zero / one a Scalar — so unqualified code keeps the precise graded type (3*e_1 + 4*e_2 is a Vector, and g2.e_1 * g2.e_2 is a Rotor valued as the unit bivector e_12). To build the full G concisely, use the class's own constant instead (G.e_1, so 3*G.e_1 + 4*G.e_2 is a G). 2D vs 3D e_1 are simply in different modules.

Graded subtypes (Vector, Bivector, Rotor, …)

Besides the full multivector classes, each algebra has graded subtypes that hold only one grade's components — the way mathematicians usually work:

There is one grade-pure type per grade up to the pseudoscalar, named by the grade_name(k) scheme — Scalar/Vector/Bivector/Trivector for grades 0–3, then the number-word FourVector, FiveVector, … (𝒢₄/𝒢₅ are release-only; see "Generating the algebras").

The grade-0 ScalarN is per algebra (not one shared type), so its dual is precise: Scalar.dual() → Vector, Scalar.dual() → Bivector, Scalar.dual() → Trivector (grade 0 → the pseudoscalar).

The product decides the return type — resolved when the classes are generated, so it never depends on (float-fuzzy) coefficient values. It is also precise for a type checker, not just at runtime: the operators and products carry @typing.overload signatures, so a static checker knows a * b is a Rotor and a ^ b a Bivector (and 2 + 3*(a^b) a Rotor) — the type(...) calls below print the same types the checker infers:

from gacalc.g2 import Vector

a, b = 3 * Vector.e_1 + 4 * Vector.e_2, 1 * Vector.e_1 + 2 * Vector.e_2

type(a * b)  # Rotor     (a·b scalar  +  a∧b bivector)
type(a ^ b)  # Bivector  (the wedge — ask for a blade with ^)
type(a.inner_product(b))  # Scalar
type(
    a < (a ^ b)
)  # Vector   left contraction  a ⌋ B  (grade m−k; a.left_contraction(B))
type(
    (a ^ b) > a
)  # Vector   right contraction B ⌊ a  (grade k−m; B.right_contraction(a))

The contractions follow M.D. Taylor, An Introduction to Geometric Algebra and Geometric Calculus (2021), p. 103; unlike the Hestenes inner_product/dot they include grade 0 (a scalar has a contraction but no Hestenes dot).

Each class exposes its basis blades as class constants of its own type — Vector.e_1 / Vector.e_2 (vectors), Bivector.e_12, G.e_123, etc. — equivalent to cls.basis_vector(n) but named. They live on the class (Vector.e_1); because the stored coefficient fields are named coeff_e_1 … (not e_1), an instance v.e_1 resolves to the same basis constant, while v.coeff_e_1 is that component's value. Read a coefficient back out with v.coefficient(Vector.e_1) (a thin reader over to_blade_dict()). (Gn, being dimension-agnostic, has no fixed class constants — use the module-level gn.e_1 … or Gn.basis_vector(n).)

The value types are immutable (@dataclass(frozen=True, slots=True), and @typing.final — not subclassable). Coefficient fields and the x/y/z coordinate properties are read-only: to "change a coordinate," rebind a new value (v = Vector(-v.x, v.y)) rather than mutating in place. This makes the basis constants (Vector.e_1, …) safe to share, and a multivector held in a shared location can't be mutated out from under you.

Iterating a value yields its coefficient values in blade order — so list(v) / tuple(v) / np.array([list(v), …]) give the components (a vector reads as its coordinate tuple). To decompose into one single-blade multivector per term instead, iterate v.to_blade_dict().

Return-type table for the geometric product * (𝒢₂ shown):

A result that spans grades no single type covers widens to the full G_n — this doesn't arise in the 𝒢₂ table above (every product is covered), but in 𝒢₃ a Vector * Bivector spans grades 1 and 3, so it widens to G. Build values by linear combination of the basis (3*e_1 + 4*e_2; a bivector via e_1 ^ e_2; a rotor via scalar + bivector — +/- also narrow to the tightest type). Rotors carry plane_of_rotation(), and rotor_from_vectors(from, to) builds the rotor whose sandwich R v R.inverse() equals projection_rotation(from, to)(v) (a free function in gacalc.transforms). To separate the plane from the angle, plane_rotation(a, b) (new in 0.0.8) wedge-normalizes the two vectors into a unit bivector once and returns a factory: each θ yields an InvertibleFunction doing the half-angle rotor sandwich (numeric θ stays float — no sympy in the result). Rotors can also be built the exp-map way the textbooks write them: exp of a bivector is a rotor — B.exp() returns a Rotor (unit by construction), and exp(-(θ/2) * i) for a unit bivector i equals plane_rotation's half-angle rotor (exp is defined only when A² < 0 — a bivector or the 𝒢₃ pseudoscalar; a vector, whose square is positive, raises ValueError). A full walkthrough is in notebooks/displaygraded.py; the exp-map section lives in notebooks/displayrotations.py.

The cross product (𝒢₃, new in 0.0.18) is the dual of the wedge — a × b = (a ∧ b) I₃⁻¹, standard right-handed sign:

from gacalc.g3 import e_1, e_2, e_3
from gacalc.vectorcalc import cross

cross(1 * e_1, 1 * e_2) == 1 * e_3  # True; method form: (1 * e_1).cross(1 * e_2)

On g3.Vector the method is a generated closed form typed Vector -> Vector. Dot and the scalar triple product need no new names: dot is scalar_product, and a · (b × c) is measure.signed_volume(a, b, c).

Custom blade display symbols (0.0.18): in a notebook setup cell, set_blade_symbols({(1,): r"\mathbf{i}", (2,): r"\mathbf{j}", (3,): r"\mathbf{k}"}) renders every later LaTeX display with i/j/k instead of e₁/e₂/e₃ (display only — values and repr unchanged; pass {} to reset). Demo: notebooks/displayvectorcalc.py.

Because the specialized/graded classes don't eagerly simplify, a symbolic result can carry un-reduced coefficients (e.g. terms that should cancel). v.simplified() / v.expanded() return the same value with each coefficient sympy.simplify'd / sympy.expand'd for a clean view.

Generating the algebras (and which ones ship)

The specialized classes are generated from Gn — no new math by hand. 𝒢₁–𝒢₅ are already declared in ALL_ALGEBRAS in tools/gen_specialized.py; which are generated on a given run is chosen by the GACALC_DIMS env var (default 1,2,3), because generation cost grows fast (below).

make generate          # dev default: g1/g2/g3 only (~23 s)
make generate-all      # ALL dims incl. g4/g5  (SLOW — g5 ~87 min)
GACALC_DIMS=1,2,3,4 python tools/gen_specialized.py   # a custom subset

𝒢₄ and 𝒢₅ are release-only. make shell / make generate build only g1–g3, so dev never pays their cost; make dist / make release set GACALC_DIMS=1,2,3,4,5 so g4/g5 are generated once at publish and baked into the sdist/wheel — a pip install gacalc then gives you from gacalc.g4 import G, e_1, e_2 with no generation needed. To exercise the full set locally (e.g. before a release), use make test-all-dims (the full-dim gate) — it generates g1–g5 and runs the suite.

To add a brand-new dimension (say 𝒢₆), append one entry to ALL_ALGEBRAS:

ALL_ALGEBRAS = [
    (1, "G", "g1.py"),
    ...(6, "G", "g6.py"),  # <-- (dimension, class name (always "G"), output module)
]

then generate it with GACALC_DIMS=…,6. The docstring, DIMENSION, basis constants, and all dimension-fixed methods (dual(), unit_pseudoscalar(), …) are generated automatically; you do not touch base.py or gn.py. The conformance suite (tests/test_conformance.py) picks up any of g4/g5 that are present automatically.

Heads-up — generation cost grows superlinearly, and the factor accelerates. The generator runs the general symbolic geometric/inner/outer products in Gn (2ⁿ basis blades, 4ⁿ term pairs, eager simplify). Measured: 𝒢₁/𝒢₂ < 1 s, 𝒢₃ ≈ 23 s, 𝒢₄ ≈ 5 min, 𝒢₅ ≈ 87 min (𝒢₆ would be many hours). This cost is paid once, at generation time — the generated code itself is fast. Details: tasks/reference/generated-algebra-generation-cost.md.

Benchmarks

python tools/bench.py compares Gn against the specialized classes. The specialized geometric product is ~15–34× faster numerically and thousands of times faster symbolically (the general Gn eagerly sympy.simplifys every intermediate; the closed form does a single simplify-free pass).

Contributing

Coding standards (naming, idioms, the mutate-vs-return rule, type-annotation policy, function shape) live in CLAUDE.md › "Coding standard (Python)" — the canonical source. Most of PEP 8 is enforced mechanically by ruff (see pyproject.toml); that section covers the judgment calls ruff can't. Run make format (ruff + ty) and make test before sending a change.

License

LGPL v2.1 (SPDX: LGPL-2.1-only). See LICENSE.