galaga 1.6.5

Numeric geometric algebra for Python — named functions, no ambiguity, unicode pretty-printing

galaga — Geometric Algebra for Python

A numeric geometric algebra library with a stable, programmer-first API.

• Single dependency — only NumPy. No framework lock-in, installs in seconds
• Named functions are the contract — gp, op, grade, reverse, dual, inverse never change meaning
• Operators are sugar — *, ^, |, ~ are convenience only
• No ambiguity — every inner product variant has its own name
• Unicode pretty-printing — 3 + 2e₁ - e₃, γ₀γ₁, σₓσᵧ
• Symbolic expression trees — write grade(R * v * ~R, 1) and see ⟨RvR̃⟩₁
• Naming and evaluation — .name("B"), .anon(), .symbolic(), .numeric() mutate in-place; .eval() returns a copy
• LaTeX-driven naming — .name(latex=r"\theta") auto-derives unicode (θ) and ASCII (theta)

Install

pip install galaga

Quick Start

from galaga import *

# Create a 3D Euclidean algebra
alg = Algebra((1, 1, 1))
e1, e2, e3 = alg.basis_vectors()

# Build multivectors naturally
v = 3*e1 + 4*e2
B = e1 ^ e2            # bivector
mv = 1 + 2*e1 + 3*B    # mixed grade

print(v)    # 3e₁ + 4e₂
print(B)    # e₁₂
print(mv)   # 1 + 2e₁ + 3e₁₂

Algebra Construction

The signature tuple defines each basis vector's square: +1, -1, or 0.

cl3 = Algebra((1, 1, 1))           # Cl(3,0) — 3D Euclidean
sta = Algebra((1, -1, -1, -1))     # Cl(1,3) — Spacetime Algebra
pga = Algebra((1, 1, 1, 0))        # Cl(3,0,1) — Projective GA

Constructors

alg.basis_vectors()              # (e₁, e₂, e₃) — named + numeric
alg.basis_vectors(symbolic=True) # (e₁, e₂, e₃) — named + symbolic
alg.basis_blades(2)              # (e₁₂, e₁₃, e₂₃) — all grade-k blades
alg.locals()                     # {"e1": e₁, "e12": e₁₂, …} for locals().update()
alg.pseudoscalar()               # e₁₂₃ (also alg.I)
alg.identity            # scalar 1 (𝟙)
alg.scalar(5.0)         # 5
alg.vector([1, 2, 3])   # e₁ + 2e₂ + 3e₃
alg.blade("e12")        # e₁₂
alg.rotor(B, radians=θ) # rotor for rotation by θ in plane B

Bulk Blade Access

# Unpack specific grades
e1, e2, e3 = alg.basis_blades(1)          # same as basis_vectors()
e12, e13, e23 = alg.basis_blades(2)        # all bivectors

# Alternatively, inject all blades into local scope (notebooks / top-level scripts)
d = alg.locals(grades=[1, 2], symbolic=True)
locals().update(d)
print("Injected:", ", ".join(d))           # Injected: e1, e2, e3, e12, e13, e23

Products

Every product has a definitive named function. Operators are optional shorthand.

Operation	Function	Operator	Unicode
Geometric product	gp(a, b)	a * b	juxtaposition
Outer (wedge) product	op(a, b)	a ^ b	∧
Left contraction	left_contraction(a, b)		⌋
Right contraction	right_contraction(a, b)		⌊
Doran–Lasenby inner	doran_lasenby_inner(a, b)	a | b	·
Hestenes inner	hestenes_inner(a, b)		·
Scalar product	scalar_product(a, b)		∗
Commutator	commutator(a, b)		[a, b]
Anticommutator	anticommutator(a, b)		{a, b}
Lie bracket	lie_bracket(a, b)		½[a, b]
Jordan product	jordan_product(a, b)		½{a, b}

e1, e2, e3 = alg.basis_vectors()

gp(e1, e2)                  # e₁₂
op(e1, e2)                  # e₁₂
left_contraction(e1, e1^e2) # e₂
scalar_product(e1, e1)      # 1

# Operator shorthand
e1 * e2     # geometric product
e1 ^ e2     # outer product
e1 | (e1^e2)  # Doran–Lasenby inner product

Unified Inner Product

The ip function dispatches to the specific inner product you want — no ambiguity.

ip(e1, e1)                            # Doran–Lasenby (default)
ip(e1, e1, mode="hestenes")           # Hestenes
ip(e1, e1 ^ e2, mode="left")          # left contraction
ip(e1 ^ e2, e2, mode="right")         # right contraction
ip(e1, e2, mode="scalar")             # scalar product

When Do the Inner Products Differ?

For vector-on-vector they all agree. The differences show up with mixed grades:

Expression	Left contraction	Right contraction	Hestenes	Doran–Lasenby
vector, bivector	e₂ (grade 2−1=1)	0 (1−2 < 0)	e₂ (|1−2|=1)	e₂ (|1−2|=1)
bivector, vector	0 (1−2 < 0)	e₂ (grade 2−1=1)	-e₂ (|2−1|=1)	-e₂ (|2−1|=1)
scalar, vector	3e₁ (passes through)	0 (0−1 < 0)	0 (kills scalars)	3e₁ (|0−1|=1)
vector, scalar	0 (1−0 < 0)	3e₁ (passes through)	0 (kills scalars)	3e₁ (|1−0|=1)

e12 = e1 ^ e2

left_contraction(e1, e12)      # e₂  — vector "removes" from bivector
left_contraction(e12, e1)      # 0   — can't remove higher from lower

right_contraction(e12, e1)     # e₂  — mirror of left contraction
right_contraction(e1, e12)     # 0

hestenes_inner(e1, e12)        # e₂  — uses |grade difference|
hestenes_inner(e12, e1)        # -e₂ — nonzero both ways (unlike left/right)
hestenes_inner(cl3.scalar(3), e1)  # 0 — always zero if either is scalar

doran_lasenby_inner(e1, e12)        # e₂  — same as Hestenes for non-scalars
doran_lasenby_inner(cl3.scalar(3), e1)  # 3e₁ — includes scalars (unlike Hestenes)

Rule of thumb: Doran–Lasenby (the | operator) is the most general — it includes scalars. Hestenes is the same but kills scalars. Left contraction is the most common in GA literature.

Unary Operations

Operation	Function	Operator	Unicode
Reverse	reverse(x)	~x	x̃
Grade involution	involute(x)		x̂
Clifford conjugate	conjugate(x)		x̄

B = e1 ^ e2

reverse(B)      # -e₁₂  (or ~B)
involute(B)     #  e₁₂  (even grade unchanged)
conjugate(B)    # -e₁₂  (reverse ∘ involute)

Grade Operations

mv = 3 + 2*e1 + (e1 ^ e2)

grade(mv, 0)    # 3
grade(mv, 1)    # 2e₁
grade(mv, 2)    # e₁₂
mv[0]           # 3       (shorthand for grade)
mv[1]           # 2e₁
mv[2]           # e₁₂
grades(mv, [0, 2])  # 3 + e₁₂

even_grades(mv)     # 3 + e₁₂  (grades 0, 2, ...)
odd_grades(mv)      # 2e₁      (grades 1, 3, ...)

mv.scalar_part   # 3.0 (float)

You can also use grade() directly:

grade(mv, "even")   # same as even_grades(mv)
grade(mv, "odd")    # same as odd_grades(mv)

Norm, Unit, Inverse

v = 3*e1 + 4*e2

norm(v)         # 5.0
norm2(v)        # 25.0
unit(v)         # 0.6e₁ + 0.8e₂
normalize(v)    # same as unit(v)
inverse(v)      # v⁻¹ such that v * inverse(v) = 1

Convenience properties on multivectors:

v.inv           # inverse(v)
v.dag           # reverse(v)  — the "dagger"
v.sq            # gp(v, v)    — squared (also v**2)
v.scalar_part   # grade-0 coefficient as float
v.vector_part   # grade-1 coefficients as np.ndarray

Extract components for use with numpy, matplotlib, etc:

v = 3*e1 + 4*e2 + 5*e3
v.vector_part           # np.array([3., 4., 5.])
v.scalar_part           # 0.0

mv = 7 + v + (e1^e2)
mv.vector_part          # np.array([3., 4., 5.])
mv.scalar_part          # 7.0

Dual

dual(e1 ^ e2)    # e₃  (in 3D Euclidean)
undual(e3)       # e₁₂

Predicates

is_scalar(alg.scalar(5))    # True
is_vector(e1 + e2)          # True
is_bivector(e1 ^ e2)        # True
is_even(1 + (e1 ^ e2))      # True
is_rotor(R)                 # True (even-graded and R*~R = 1)

Rotation Example

Rotate e₁ by 90° in the e₁e₂ plane:

import numpy as np

theta = np.pi / 2
B = e1 ^ e2
R = alg.rotor(B, radians=theta)

v_rotated = R * e1 * ~R    # sandwich product
print(v_rotated)            # e₂

Or manually:

R = alg.scalar(np.cos(theta/2)) - np.sin(theta/2) * B

Exponential & Logarithm

exp(B) builds a rotor from a bivector directly — no manual cos/sin:

B = (np.pi / 4) * (e1 ^ e2)
R = exp(B)                      # cos(π/4) + sin(π/4) e₁₂
print(R * e1 * ~R)              # e₂ (90° rotation)

exp handles all signatures automatically:

• Euclidean bivector (B² < 0): uses cos/sin
• Timelike bivector (B² > 0): uses cosh/sinh (Lorentz boosts)
• Null bivector (B² = 0): returns 1 + B (translations in PGA)

log(R) is the inverse — extract the bivector from a rotor:

B_back = log(R)                 # recovers the bivector
R_back = exp(log(R))            # roundtrip: R_back == R

Note: alg.rotor(B, radians=θ) computes exp(-θ/2 * B) for a unit bivector B.

Projection, Rejection, Reflection

v = 3*e1 + 4*e2 + 5*e3
plane = e1 ^ e2

project(v, plane)    # 3e₁ + 4e₂   (component in the plane)
reject(v, plane)     # 5e₃         (component perpendicular)

Projection and rejection always sum back to the original:

project(v, plane) + reject(v, plane) == v   # True

Reflection flips the component parallel to a normal vector:

reflect(e1 + e2, e1)    # -e₁ + e₂   (flip the e₁ part)
reflect(e2, e1)         #  e₂        (perpendicular: unchanged)
reflect(e1, e1)         # -e₁        (parallel: negated)

Double reflection is always identity: reflect(reflect(v, n), n) == v.

Spacetime Algebra

sta = Algebra(1, 3, blades=b_sta())
g0, g1, g2, g3 = sta.basis_vectors()

print(g0 * g0)      #  1   (timelike)
print(g1 * g1)      # -1   (spacelike)
print(g0 * g1)      # γ₀γ₁ (bivector)
print(sta.I)        # 𝑰

Basis Naming

Control how basis blades display via the blades= parameter and convention factories.

Convention Factories

from galaga import Algebra, b_default, b_gamma, b_sigma, b_sigma_xyz, b_pga, b_sta, b_cga, b_complex, b_quaternion

# Default: e₁, e₂, e₃ — compact subscripts (e₁₂ for bivectors)
alg = Algebra(3)

# Gamma: γ₀, γ₁, γ₂, γ₃
sta = Algebra(1, 3, blades=b_gamma())

# STA with sigma bivector aliases, pseudoscalar → I
sta = Algebra(1, 3, blades=b_sta(sigmas=True, pss="I"))

# Sigma (numbered): σ₁, σ₂, σ₃
pauli = Algebra(3, blades=b_sigma())

# Sigma (xyz): σₓ, σᵧ, σz
pauli = Algebra(3, blades=b_sigma_xyz())

# PGA: 0-based, compact, pseudoscalar → I
pga = Algebra(3, 0, 1, blades=b_pga())

# CGA: e₁…e₃, eₒ, e∞
cga = Algebra(4, 1, blades=b_cga())

# Complex numbers: Cl(2,0) with i = e₁₂
alg_c = Algebra(2, blades=b_complex())

# Quaternions: Cl(3,0) with i, j, k bivectors
alg_q = Algebra(3, blades=b_quaternion())

All factories accept pss= to name the pseudoscalar, style= to choose blade display, and overrides= for per-blade customisation.

Blade Styles

Algebra(3, blades=b_default(style="compact"))      # e₁₂   (default)
Algebra(3, blades=b_default(style="juxtapose"))     # e₁e₂
Algebra(3, blades=b_default(style="wedge"))         # e₁∧e₂

Custom Names and Overrides

# Custom vector names
alg = Algebra((1, 1, 1), blades=BladeConvention(
    vector_names=[("a", "𝐚", "𝐚"), ("b", "𝐛", "𝐛"), ("c", "𝐜", "𝐜")]
))

# Override specific blades using metric-role keys
alg = Algebra(3, blades=b_default(overrides={
    "+1+2": "B",     # e₁₂ → B
    "pss":  "I",     # pseudoscalar → I
}))

Blade Lookup

alg.blade("e12")         # prefix + digits
alg.blade("+1+2")        # metric-role key
alg.blade("pss")         # pseudoscalar shorthand
alg.blade("I")           # display name match (if overridden)

Post-hoc Renaming

alg.get_basis_blade("+1+2").rename("B")                    # all formats
alg.get_basis_blade("+1+2").rename(("B12", "B₁₂", r"B_{12}"))  # per-format

Display

str() and repr() both use unicode by default:

mv = 3 + 2*e1 - e3

str(mv)     # '3 + 2e₁ - e₃'
repr(mv)    # '3 + 2e₁ - e₃'

The pseudoscalar always displays as I / 𝑰:

print(alg.I)        # 𝑰
print(repr(alg.I))  # 𝑰  (repr_unicode=True by default)

Coefficients of ±1 are suppressed: e₁₂ not 1e₁₂, -e₃ not -1e₃.

Symbolic Expressions

Name multivectors and build expression trees that render as mathematical notation.

Naming and Evaluation

Any multivector can be named and made symbolic with .name(). All configuration methods (.name(), .anon(), .symbolic(), .numeric()) mutate in-place and return self. Only .eval() returns a new copy.

alg = Algebra((1, 1, 1))
e1, e2, e3 = alg.basis_vectors()

B = (e1 ^ e2).name("B")   # mutates, sets lazy
print(B)              # B
print(B.anon())       # e₁₂  (name removed in-place)

B.name("B")           # re-name it
print(B.eval())       # e₁₂  (new copy, B unchanged)

LaTeX-driven naming

Pass latex= and unicode/ASCII are derived automatically:

theta = alg.scalar(0.5).name(latex=r"\theta")
# unicode: θ, ascii: theta, latex: \theta — all auto-derived

F = (e1 ^ e2).name(latex=r"\mathbf{F}")
# unicode: 𝐅, ascii: F — derived from \mathbf{F}

n = e1.name(latex=r"\hat{n}")
# unicode: n̂, ascii: hat_n — combining accent

The label parameter is optional when latex is provided. User-supplied unicode= and ascii= always take precedence over auto-derivation.

Naming and evaluation are orthogonal axes:

	Anonymous	Named
Eager	e1 + e2 → e₁ + e₂	e1 → e₁ (basis blades)
Lazy	B.anon() → expr tree	B = (e1^e2).name("B") → B

Lazy Propagation

When a lazy value participates in an operation, the result is lazy:

B = (e1 ^ e2).name("B")
x = B + e3
print(x)              # B + e₃  (symbolic)
print(x.eval())       # e₁₂ + e₃  (concrete)

Names don't propagate — the result is anonymous but named operands appear by name in the expression tree.

Symbolic Basis Blades

For fully symbolic workflows, use symbolic=True — every operation builds an expression tree automatically:

e1, e2, e3 = alg.basis_vectors(symbolic=True)

e1 ^ e2              # e₁∧e₂  (not e₁₂)
e1 * e2              # e₁e₂
3 * e1 + e2          # 3e₁ + e₂
(e1 ^ e2).eval()     # e₁₂  (concrete when you need it)

Division renders as fractions, exp() renders symbolically:

theta = alg.scalar(0.5).name("θ")
B = (e1 ^ e2).name("B")
print(-B * theta / 2)     # -Bθ/2
print(exp(-B * theta / 2))  # exp(-Bθ/2)

sym() Compatibility

sym() still works as a convenience alias. Unlike .name(), it copies the original (does not mutate):

from galaga import sym, grade, reverse, simplify

R = sym(e1 * e2, "R")   # copy of e1*e2, named "R"
v = sym(e1, "v")         # copy of e1, named "v" — e1 unchanged
v = sym(e1 + 2*e2, "v")

Rendering

R = (e1 * e2).name("R")
v = e1.name("v")

print(R * v * ~R)                     # RvR̃
print(grade(R * v * ~R, 1))           # ⟨RvR̃⟩₁

Full rendering table:

Expression	Code	Renders as
Geometric product	R * v * ~R	RvR̃
Wedge	a ^ b	a∧b
Left contraction	left_contraction(a, b)	a⌋b
Right contraction	right_contraction(a, b)	a⌊b
Doran–Lasenby inner	a | b	A·B
Hestenes inner	hestenes_inner(a, b)	A·B
Scalar product	scalar_product(a, b)	A∗B
Reverse	~R	R̃
Involute	involute(v)	v̂
Conjugate	conjugate(v)	v̄
Dual	dual(v)	v⋆
Undual	undual(v)	v⋆⁻¹
Norm	norm(v)	‖v‖
Unit	unit(v)	v̂
Inverse	v.inv	v⁻¹
Grade projection	grade(A * B, 2)	⟨AB⟩₂
Even grades	even_grades(A)	⟨A⟩₊
Odd grades	odd_grades(A)	⟨A⟩₋
Squared	squared(R) or R.sq	R²
Addition	a + b	a + b
Scalar multiply	3 * a	3a

Evaluation

Every lazy multivector can be evaluated to its concrete form:

expr = grade(R * v * ~R, 1)
print(expr)          # ⟨RvR̃⟩₁
print(expr.eval())   # concrete Multivector result

.eval() returns a new anonymous numeric copy (non-mutating). .numeric() mutates in-place.

LaTeX Output

Every expression has a .latex() method for use in documents, notebooks, and markdown:

expr = grade(R * v * ~R, 1)
expr.latex()            # \langle R v \tilde{R} \rangle_{1}
expr.latex(wrap='$')    # $\langle R v \tilde{R} \rangle_{1}$
expr.latex(wrap='$$')   # $$\n...\n$$  (display block)

The wrap parameter is handy in f-strings for marimo/Jupyter markdown cells:

mo.md(f"{expr.latex(wrap='$')} = {expr.eval().latex(wrap='$')}")

In Jupyter notebooks, expressions render automatically via _repr_latex_().

Concrete Multivector objects also have .latex():

v = 3*e1 + 4*e2
v.latex()  # 3 e_{1} + 4 e_{2}

Full LaTeX rendering table:

Expression	Code	Unicode	LaTeX
Geometric product	R * v * ~R	RvR̃	R v \tilde{R}
Wedge	a ^ b	a∧b	a \wedge b
Left contraction	left_contraction(a, b)	a⌋b	a \;\lrcorner\; b
Right contraction	right_contraction(a, b)	a⌊b	a \;\llcorner\; b
Doran–Lasenby inner	a | b	A·B	A \cdot B
Hestenes inner	hestenes_inner(a, b)	A·B	A \cdot B
Scalar product	scalar_product(a, b)	A∗B	A * B
Reverse	~R	R̃	\tilde{R}
Involute	involute(v)	v̂	v^\dagger
Conjugate	conjugate(v)	v̄	\bar{v}
Dual	dual(v)	v⋆	v^*
Undual	undual(v)	v⋆⁻¹	v^{*^{-1}}
Norm	norm(v)	‖v‖	\lVert v \rVert
Unit	unit(v)	v̂	\hat{v}
Inverse	v.inv	v⁻¹	v^{-1}
Grade projection	grade(A * B, 2)	⟨AB⟩₂	\langle A B \rangle_{2}
Even grades	even_grades(A)	⟨A⟩₊	\langle A \rangle_{\text{even}}
Odd grades	odd_grades(A)	⟨A⟩₋	\langle A \rangle_{\text{odd}}
Squared	squared(R) or R.sq	R²	R^2
Addition	a + b	a + b	a + b
Scalar multiply	3 * a	3a	3 a

Drop-in Functions

The symbolic module provides drop-in replacements for all galaga functions. They detect lazy Multivector or Expr arguments and build trees; with plain eager Multivector arguments they delegate to the numeric core:

from galaga import gp, grade, reverse

# With lazy/named MV → builds expression tree
R = (e1 * e2).name("R")
v = e1.name("v")
grade(R * v * ~R, 1)   # returns lazy Multivector with expr tree

# With eager MV → returns Multivector directly (zero overhead)
grade(e1 + e2, 1)      # returns eager Multivector

Notation

Override how operations render — per-algebra:

from galaga.notation import Notation, NotationRule

# Use Hestenes convention (reverse as dagger)
alg = Algebra((1, 1, 1), notation=Notation.hestenes())
e1, e2, _ = alg.basis_vectors(symbolic=True)
v = e1.name("v")
print(reverse(v))   # v†

# Or override individual rules
alg.notation.set("Reverse", "unicode", NotationRule(kind="postfix", symbol="†"))
alg.notation.set("Dual", "unicode", NotationRule(kind="prefix", symbol="*"))

Built-in presets: Notation.default(), Notation.hestenes(), Notation.doran_lasenby().

Simplification

simplify() applies algebraic rewrite rules to expression trees:

from galaga import sym, simplify, grade, norm, unit, inverse, op

alg = Algebra((1, 1, 1))
e1, e2, e3 = alg.basis_vectors()

v = sym(e1, "v")
R = sym(alg.rotor(e1^e2, radians=0.5), "R")
a = sym(e1, "a")
B = sym(e1^e2, "B")

simplify(~~v)              # v         (double reverse)
simplify(inverse(inverse(v)))  # v     (double inverse)
simplify(a ^ a)            # 0         (wedge self = 0)
simplify(a + a)            # 2a        (collection)
simplify(3 * (2 * v))      # 6v        (scalar collapse)
simplify(R * ~R)           # 1         (rotor normalization)
simplify(norm(unit(v)))    # 1         (unit has norm 1)
simplify(grade(v, 1))      # v         (v is known grade-1)
simplify(grade(v, 2))      # 0         (v has no grade-2)

simplify() accepts both Expr objects and lazy Multivector objects.

Grade is auto-detected from the multivector data, so sym(e1, "v") knows it's grade-1 and sym(e1^e2, "B") knows it's grade-2. Simplification runs to a fixed point, so cascading rules like a - (-a) → a + a → 2a resolve fully.

Sandwich Product

The sandwich product R x R̃ is common enough to deserve a shortcut:

sandwich(R, e1)     # R * e1 * ~R
sw(R, e1)           # same thing, short alias

Works in the symbolic layer too:

from galaga import sandwich

R = (alg.rotor(e1^e2, radians=np.pi/2)).name("R")
v = e1.name("v")
print(sandwich(R, v))        # RvR̃
print(sandwich(R, v).eval()) # e₂

Aliases

Short names for experienced users, long names for readability:

Short	Long
gp	geometric_product
op	wedge, outer_product
ip	inner_product
rev	reverse
unit	normalize, normalise
sw	sandwich
alg.rotor	alg.rotor_from_bivector, alg.rotor_from_plane_angle

Recipes

Things you can build from the primitives — no extra functions needed.

Angle Between Vectors

angle = np.arctan2(norm(a ^ b), (a | b).scalar_part)

Uses atan2 for numerical stability (works even when vectors are nearly parallel or perpendicular). The wedge magnitude is |a||b|sin θ, the inner product is |a||b|cos θ.

Check Parallel / Perpendicular

parallel      = np.isclose(norm(a ^ b), 0)    # wedge vanishes
perpendicular = np.isclose((a | b).scalar_part, 0)  # inner product vanishes

Compose Rotations

Rotors compose by geometric product — apply R1 first, then R2:

R_final = R2 * R1
v_rotated = R_final * v * ~R_final

Order matters: R2 * R1 means "do R1, then R2" (right-to-left, like matrix multiplication).

Interpolate a Rotation (SLERP)

R_half = exp(0.5 * log(R))       # 50% of the rotation
R_t    = exp(t * log(R))         # fraction t ∈ [0, 1]

log extracts the bivector, scaling it interpolates the angle, exp rebuilds the rotor.

Gram–Schmidt (Orthogonalize)

Make b orthogonal to a by removing the parallel component:

b_orth = reject(b, a)            # component of b perpendicular to a

For a full basis, chain rejections:

u1 = unit(a)
u2 = unit(reject(b, a))
u3 = unit(reject(reject(c, a), u2))

Rotate 90° Within a Plane

B = e1 ^ e2
R90 = alg.rotor(B, degrees=90)
perp = R90 * v * ~R90            # v rotated 90° in the e₁e₂ plane

Useful for finding the perpendicular direction within a subspace.

Area and Volume

The wedge product directly gives oriented area and volume:

area = norm(u ^ v)               # parallelogram area
vol  = norm(u ^ v ^ w)           # parallelepiped volume

These work in any dimension — norm(a ^ b) is the area of the parallelogram spanned by a and b, regardless of the ambient space.

Cross Product (3D Only)

cross = dual(a ^ b)              # a × b as a vector

The wedge gives a bivector (oriented plane); the dual converts it to the normal vector. Only meaningful in 3D where bivectors and vectors are dual.

Hodge Star (Poincaré Dual)

The Hodge star ⋆x = xI⁻¹ uses the full geometric product instead of left contraction. It agrees with dual() on blades but differs on mixed-grade multivectors:

def hodge_star(x):
    return x * inverse(x.algebra.pseudoscalar())

Like dual(), this requires an invertible pseudoscalar — it does not work in degenerate algebras (PGA). Use complement() there instead.

Bivector Commutator Algebra

Bivectors form a Lie algebra under the commutator product:

commutator(e1^e2, e2^e3)         # 2e₁₃ — unnormalised (ab - ba)
lie_bracket(e1^e2, e2^e3)        # e₁₃  — normalised ½(ab - ba)

The library provides both conventions:

Function	Definition	Use case
commutator(a, b)	ab - ba	Raw commutator
anticommutator(a, b)	ab + ba	Raw anticommutator
lie_bracket(a, b)	½(ab - ba)	Lie algebra with clean structure constants
jordan_product(a, b)	½(ab + ba)	Symmetric product (equals inner product for vectors)

In 3D Euclidean space, the Lie bracket of bivectors is isomorphic to the vector cross product. In Cl(1,3), it gives the Lorentz algebra.

API Reference

Algebra(p_or_signature, q=0, r=0, *, blades=None, repr_unicode=True, notation=None, display=False)

Method / Property	Description
basis_vectors(symbolic=False)	Tuple of basis 1-vectors (symbolic=True for expression trees)
pseudoscalar()	Unit pseudoscalar
I	Unit pseudoscalar (property)
identity	Scalar 1
scalar(value)	Scalar multivector
vector(coeffs)	1-vector from list
blade(name)	Basis blade by name
rotor(B, radians=, degrees=)	Rotor for rotation in plane B

Multivector

Property / Method	Description
[k]	Grade-k projection (x[2] = grade(x, 2))
.name(label, *, latex=, unicode=, ascii=)	Set display name, set symbolic (mutates). label optional if latex given.
.anon()	Remove display name (mutates)
.symbolic()	Set symbolic mode (mutates)
.numeric()	Force numeric, strip name (mutates)
.numeric("B")	Force numeric, keep/set name (mutates)
.eval()	Return a new anonymous numeric copy (non-mutating)
.inv	Inverse
.dag	Reverse (dagger)
.sq	Squared (geometric product with self)
.scalar_part	Grade-0 coefficient as float
.vector_part	Grade-1 coefficients as np.ndarray
.algebra	Parent algebra
.data	NumPy coefficient array

Functions

Function	Description
gp(a, b)	Geometric product
op(a, b)	Outer (wedge) product
left_contraction(a, b)	Left contraction a ⌋ b
right_contraction(a, b)	Right contraction a ⌊ b
hestenes_inner(a, b)	Hestenes inner product
scalar_product(a, b)	Scalar product
ip(a, b, mode=...)	Unified inner product dispatcher
commutator(a, b)	ab - ba
anticommutator(a, b)	ab + ba
reverse(x)	Reverse x̃
involute(x)	Grade involution x̂
conjugate(x)	Clifford conjugate x̄
grade(x, k)	Grade-k projection
grades(x, ks)	Multi-grade projection
dual(x)	Dual
undual(x)	Undual
norm(x)	√|x x̃|
norm2(x)	⟨x x̃⟩₀
unit(x)	Normalize to unit
inverse(x)	Versor inverse
squared(x)	x² — geometric product with self
sandwich(r, x)	Sandwich product r x r̃
exp(B)	Bivector exponential → rotor
log(R)	Rotor logarithm → bivector
project(v, B)	Component of v in subspace B
reject(v, B)	Component of v perpendicular to B
reflect(v, n)	Reflect v in hyperplane orthogonal to n
even_grades(x)	Even-grade components
odd_grades(x)	Odd-grade components
is_scalar(x)	True if pure scalar
is_vector(x)	True if pure 1-vector
is_bivector(x)	True if pure 2-vector
is_even(x)	True if even-graded
is_rotor(x)	True if even and x*~x = 1

Examples

Interactive marimo notebooks in examples/:

basics/ — Library Features

• naming_demo.py — .name(), .anon(), .symbolic(), .numeric(), real-world examples
• lazy_blades_demo.py — basis_vectors(symbolic=True) for fully symbolic workflows
• latex_naming_demo.py — LaTeX-driven naming across physics domains
• rendering_gallery.py — Visual gallery of all expression types with LaTeX rendering
• symbolic_demo.py — Expression trees, rendering, simplification, LaTeX output
• notation_demo.py — Notation presets and overrides
• dynamic_notation.py — Runtime notation switching
• latex_rewrites_demo.py — LaTeX rewrite pipeline demo
• galaga_marimo_demo.py — galaga-marimo t-string rendering demo
• blade_renaming.py — Basis blade renaming API
• blade_conventions.py — Blade convention gallery
• complex_and_quaternions.py — Complex numbers and quaternions via b_complex() and b_quaternion()

algebra/ — Core GA Operations

• exterior_algebra_intuition.py — Wedge-product intuition for oriented length, area, and volume
• inner_product_family.py — Comparison of the library's inner-product family
• commutator_lie_jordan.py — Symmetric and antisymmetric product splits
• involutions_and_grade_ops.py — Reverse, involute, conjugate, grade projection, even/odd
• norms_units_inverses.py — Norms, unitization, and inverses
• duality_and_complements.py — Metric duality vs combinatorial complements
• duality_and_subspaces.py — Duality with project/reject/reflect
• projectors_ga.py — Projection and rejection onto lines and planes
• exp_log_rotors.py — Rotor exponentials and logarithms
• rotor_demo.py — Rotor construction and rotation
• rotors_from_reflections.py — How rotors arise from reflection composition
• rotations_from_bivectors.py — Rotations from bivectors
• sandwich_products.py — Reflections and rotor sandwiches
• meets_joins_pga.py — Projective joins using PGA complement patterns

physics/ — Classical Physics

• special_relativity_lazy.py — Boosts, Minkowski diagrams, rapidity addition
• twin_paradox.py — Worldline and proper-time twin paradox
• relativistic_rocket_equation.py — Rapidity-first rocket equation with burn and coast
• one_g_travel_calculator.py — Relativistic 1g travel calculator
• thomas_precession.py — Non-collinear boost composition and Thomas-Wigner rotation
• kepler_orbits_ga.py — Kepler orbit and hodograph with orbital bivectors
• coupled_oscillators_modes.py — Normal modes in configuration-space GA
• planar_kinematics_lazy.py — Rotor-based planar rigid-body kinematics
• robot_kinematics_pga.py — Two-link planar robot with PGA-style motors
• polarisation.py — Polarisation states and Jones calculus in GA
• optics_polarisation_lazy.py — Polariser projectors and wave-plate rotors
• fresnel_polarisation_ga.py — Fresnel reflection with GA polarisation decomposition

quantum/ — Quantum Mechanics

• quantum_physics.py — Spin-½: Bloch sphere, measurement, Stern–Gerlach, Larmor precession
• qubits_and_superposition_ga.py — Qubits and superposition via rotors and Bloch vectors
• quantum_gates_ga.py — Single-qubit gates as Bloch-sphere rotations
• single_qubit_circuits_ga.py — Single-qubit circuit builder using rotor composition
• quantum_spin_lazy.py — Lazy Pauli-blade spin states and Stern-Gerlach probabilities
• measurement_and_interference_ga.py — Phase, recombination, and interference
• pauli_equation_toy.py — Spin precession as rotor evolution
• aharonov_bohm.py — Holonomy-first Aharonov-Bohm with internal phase rotors
• bell_states_and_correlations.py — Bell singlet correlations with geometric measurement axes
• quantum_teleportation_ga.py — Teleportation as Bloch-vector correction
• grover_search_ga.py — Grover search as repeated rotations in a search plane
• deutsch_jozsa_ga.py — Deutsch-Jozsa interference with oracle phases
• phase_estimation_geometry.py — Phase estimation as repeated rotor-angle accumulation

pga/ — Projective Geometric Algebra

• pga_demo.py — Cl(3,0,1): translations, reflections, motors, screw interpolation
• pga_geometry_constructions.py — Point-line-triangle constructions with lazy joins
• camera_geometry_pga.py — Pinhole camera geometry and image-plane intersections
• thin_lens_and_rays_pga.py — Thin-lens image construction with projective optics
• screw_motion_pga.py — Screw-motion combining rotation and axial drift

spacetime/ — Spacetime Algebra

• spacetime_algebra.py — Cl(1,3): boosts, rotations, EM field, Thomas–Wigner rotation
• pauli_matrices_vs_ga.py — Pauli matrices/spinors vs Cl(3,0) rotors/vectors
• dirac_matrices_vs_sta.py — Dirac gamma matrices vs STA boosts/vectors
• electromagnetism_lazy.py — Faraday bivector, invariants, boosted field components
• em_waves_sta.py — Plane-wave EM fields and null invariants
• maxwell_equations_sta.py — Maxwell's equations in compact STA form
• lorentz_force_sta.py — Lorentz-force motion in the STA field picture
• null_geometry_sta.py — Null vectors, light cones, and observer geometry

uv run marimo edit examples/quantum/quantum_physics.py

Internals

• Basis blades are represented as bitmasks — e₁ = 0b001, e₂ = 0b010, e₁₂ = 0b011
• Multiplication tables are precomputed at algebra creation time
• Multivector coefficients are dense NumPy arrays of length 2ⁿ
• The Algebra object is immutable after creation
• Multivector is lightweight: just an algebra reference + data array

Testing

uv run pytest packages/galaga/tests/ -v                                    # run all tests
uv run pytest packages/galaga/tests/ --cov=galaga --cov-report=term        # with coverage

1600+ tests. Tests include:

• Algebraic identities (associativity, distributivity, reverse-of-product)
• Golden tests for Cl(2,0), Cl(3,0), Cl(1,3)
• All five inner products with mixed-grade cases where they diverge
• Exponential/logarithm roundtrips (Euclidean, hyperbolic, null)
• Projection/rejection complement property, reflection involution
• Symbolic rendering, evaluation, simplification rules
• LaTeX output for both Multivector and Expr
• Naming/evaluation semantics: .name(), .anon(), .symbolic(), .numeric()
• Symbolic propagation through all operators
• Symbolic basis blades: basis_vectors(symbolic=True)
• MV / MV division, ScalarDiv/Div/Exp expression nodes
• Precedence-aware rendering (93 dedicated tests)
• LaTeX symbol mapping (101 tests) and auto-derivation
• Small value display with explicit format specs
• All 10 spec use cases from the symbolic redesign
• Edge cases and error handling

Documentation

Architecture Decision Records (ADRs)

Design decisions are documented in docs/adrs/. Each ADR records the context, decision, and consequences for a significant design choice. 59 ADRs covering the symbolic layer, rendering pipeline, notation system, linting, packaging, and more.

ls docs/adrs/

Specifications

Formal rendering and formatting specs live in docs/specs/. Each spec defines authoritative rules with decision tables and examples that map to tests.

Spec	Description
SPEC-001	LaTeX coefficient formatting and scientific notation
SPEC-002	Precedence and parenthesisation rules
SPEC-003	Notation system: kinds, dispatch, and override semantics
SPEC-004	Display method: name/reveal/eval deduplication
SPEC-005	Accent width selection (narrow vs wide)
SPEC-006	Postfix wrapping: compound names, superscripts, fractions
SPEC-007	Unicode coefficient formatting
SPEC-008	Lazy/eager propagation rules
SPEC-009	Expression tree rendering (SlashFrac, Frac, Sup interactions)
SPEC-010	Blade naming and display system
SPEC-011	Custom basis blade display ordering

Other Documentation

• docs/RELEASE_PROCESS.md — release workflow, PyPI publishing, custodianship
• docs/PYREFLY_STATUS.md — type checking status and remaining work
• docs/DESIGN_DECISIONS.md — high-level design summary