bispectrum 0.3.5

Bispectrum analysis for machine learning

bispectrum

Bispectrum analysis for machine learning.

Installation

pip install bispectrum

Or with uv:

uv pip install bispectrum

Development

git clone https://github.com/geometric-intelligence/bispectrum.git
cd bispectrum
uv pip install -e ".[dev]"
pre-commit install

Pre-commit Hooks

This project uses pre-commit for code quality checks. After installing dev dependencies, the hooks run automatically on each commit.

# Run hooks on all files (useful for first-time setup or CI)
pre-commit run --all-files

# Run a specific hook
pre-commit run ruff --all-files

# Update hooks to latest versions
pre-commit autoupdate

Usage

Cyclic group on itself

from bispectrum import CnonCn

bsp = CnonCn(n=8)
f = torch.randn(4, 8)       # signal on Z/8Z
beta = bsp(f)                # shape (4, 8), complex64
f_rec = bsp.invert(beta)     # reconstructed up to cyclic shift

SO(3) on the 2-sphere

from bispectrum import SO3onS2

# Selective bispectrum: O(L²) entries with generic completeness
bsp = SO3onS2(lmax=5, nlat=64, nlon=128, selective=True)
f = torch.randn(1, 64, 128)  # signal on S²
beta = bsp(f)                 # shape (1, 35), complex64

# Full bispectrum: O(L³) entries
bsp_full = SO3onS2(lmax=5, nlat=64, nlon=128, selective=False)
beta_full = bsp_full(f)       # shape (1, 69), complex64

Octahedral group

from bispectrum import OctaonOcta

bsp = OctaonOcta()
f = torch.randn(4, 24)       # signal on O (|O| = 24)
beta = bsp(f)                 # shape (4, 172), complex64
f_rec = bsp.invert(beta)      # reconstructed up to group action

See DESIGN.md for the full API and all supported groups.

License

MIT