galaga_matrix

Matrix representations for galaga Clifford algebras.

Status: published to PyPI alongside galaga. Released as part of the monorepo.

What it does

Every multivector in a Clifford algebra can be represented as a matrix. This package provides to_matrix / from_matrix conversions, spinor-column conversions for supported even subalgebras, quaternion-block output for selected quaternionic signatures, and a MatrixRepr wrapper with LaTeX rendering for use in marimo notebooks and Jupyter.

The canonical spinor-column API is to_spinor_column / from_spinor_column. to_spinor_matrix / from_spinor_matrix are compatibility aliases.

Two modes

Quick start

from galaga import Algebra
from galaga_matrix import to_matrix, from_matrix, MatrixRepr

# Pauli matrices from Cl(3,0)
cl3 = Algebra(3)
e1, e2, e3 = cl3.basis_vectors()

mat = to_matrix(e1, mode="compact")   # returns MatrixRepr (2×2 Pauli σ₁)
mat @ mat                              # MatrixRepr: σ₁² = I
mat.inv()                              # MatrixRepr: σ₁⁻¹ = σ₁
mat.trace()                            # 0 (traceless)
mat.mv                                 # back to Multivector

# Named MV gets automatic ρ(name) symbolic name
R = (e1 * e2).name(latex=r"\hat{B}")
M = to_matrix(R, mode="compact")
M.latex()                              # "\\rho(\\hat{B}) = \\begin{pmatrix}..."
M.expr.latex()                         # "\\rho(\\hat{B})"

# from_matrix can infer algebra and mode from MatrixRepr
from_matrix(M)                         # MV named ρ⁻¹(ρ(B̂))
from_matrix(cl3, M.mat, mode="compact")  # unnamed MV (raw array)

# Dirac matrices from Cl(1,3)
sta = Algebra(1, 3)
g0 = sta.basis_vectors()[0]
to_matrix(g0, mode="compact")  # 4×4 Dirac γ⁰

# Left-regular works for everything, including PGA
pga = Algebra(2, 0, 1)
v = pga.basis_vectors()[0]
to_matrix(v)  # 8×8 real matrix

MatrixRepr

MatrixRepr is a transparent numpy proxy that wraps a matrix with:

• All arithmetic operations — @, +, -, *, /, ** return MatrixRepr
• Linear algebra — .T, .H, .conj(), .trace(), .det(), .inv()
• Numpy interop — np.add(M, N), np.conj(M) etc. return MatrixRepr via __array_ufunc__
• Symbolic naming — .name() gives matrices the same naming and expression-tree behavior as multivectors
• Metadata propagation — algebra, mode, basis, and kind pass through operations
• Indexing — M[i,j] for elements, M[0:2, 0:2] for submatrices
• Rendering — .latex(), ._repr_latex_() for notebooks
• Escape hatch — .mat gives the raw numpy array
• Roundtrip — .mv converts back to a Multivector (requires algebra=)
• Factories — MatrixRepr.identity(k), MatrixRepr.zeros((m,n)), .kron(other)

Auto-naming

to_matrix(named_mv) names the result as ρ(name) and gives it a symbolic representation-map expression. from_matrix(named_matrix) can infer the algebra and mode from MatrixRepr and names the recovered MV as ρ⁻¹(name-or-expression). Raw arrays still need from_matrix(alg, array, mode=...). Unnamed inputs pass through without new names. Quaternion-block mode uses ρ_{\mathbb{H}}(name).

Works in galaga_marimo t-strings:

gm.md(t"""
The Pauli matrix: {to_matrix(e1, mode="compact"):block}
""")

Named special cases

The compact representation produces the standard textbook matrices:

• Cl(3,0): 2×2 Pauli matrices (σ₁, σ₂, σ₃)
• Cl(0,3): 2×2 with iσ₁, iσ₂, iσ₃
• Cl(1,3): 4×4 Dirac matrices (γ⁰, γ¹, γ², γ³) in the Dirac representation
• Cl(3,1): 4×4 Dirac matrices in the mostly-plus convention

All other non-degenerate signatures are handled by the general periodicity recursion.

Limitations

• Degenerate algebras (r > 0): only left-regular mode works. compact raises NotImplementedError.
• Double algebras (Cl(p,q) where (q−p) mod 8 ∈ {3, 7}): to_matrix compact works, but from_matrix compact raises if the selected compact representation is not injective. Use left-regular for exact inverse conversion. See Double Clifford Algebras.
• Quaternion output: to_quaternion_matrix and quaternion spinor conversions use explicit quaternion-block bases. They currently support Cl(0,2) and Cl(1,3), and reject double algebras such as Cl(0,3).
• Spinor roundtrip: spinor conversions are rank-checked for the actual reference-column map. Signatures whose even subalgebra is not injective under that map raise TypeError.
• No caching: blade matrices are rebuilt on every call. Fine for interactive use, not for hot loops.

Architecture decisions

See docs/adrs/.

Tests

PYTHONPATH=.:packages/galaga_matrix uv run pytest packages/galaga_matrix/tests/