lattice-dsp 0.1.1

Efficient stable IIR lattice filters, adaptive lattice-ladder DSP, and matrix/MIMO lattice experiments for Python

lattice-dsp

Stable IIR lattice filters, adaptive recursive DSP, and matrix/MIMO lattice experiments for Python.

lattice-dsp is a compact C++/Python toolkit for signal-processing workflows where stability-aware recursive filters matter. It focuses on reflection/PARCOR coefficients, lattice and lattice-ladder IIR filters, adaptive IIR experiments, AR spectral diagnostics, finite-Hankel model-reduction diagnostics, and experimental matrix/MIMO lattice tools.

Install with lattice-dsp; import with lattice_dsp.

python -m pip install lattice-dsp

import lattice_dsp as ld

Why lattice-dsp?

Direct-form IIR denominator updates can move poles outside the unit circle. Lattice parameterizations provide a safer coordinate system: bounded reflection coefficients correspond to stable recursive filters.

This package is intentionally narrower than SciPy or general audio/room-simulation libraries. It is for users who want stable recursive DSP building blocks, finite-section model-reduction diagnostics, and experimental MIMO/state-space lattice workflows in one tested Python package with a compiled backend.

What is included

• Stable scalar LatticeIIR and LatticeLadderIIR filters.
• Reflection/PARCOR coefficient conversion for stable IIR denominators.
• Adaptive lattice-ladder NLMS/RLS-style examples.
• Burg, Levinson-Durbin, AR, and spectral diagnostics.
• Batch processing for many independent filters/signals, with optional OpenMP.
• Finite-Hankel SISO/MIMO reduction helpers and Nehari/AAK-style finite-section diagnostics.
• MIMO Markov tensor and state-space processing utilities.
• Matrix-lattice/all-pass examples and causal online MIMO lattice prediction.
• Tangential Schur/Pick/J-inner diagnostic helpers.
• Sphinx tutorials, examples, benchmarks, and interoperability recipes.

Quick example: stable IIR from reflection coefficients

import numpy as np
import lattice_dsp as ld

rng = np.random.default_rng(0)
x = rng.normal(size=4096)

reflection = [0.35, -0.2, 0.1]
numerator = [0.25, 0.0, 0.15, 0.7]

print(ld.reflection_to_denominator(reflection))

filt = ld.LatticeIIR(reflection, numerator)
y = filt.process(x)

Quick example: adaptive stable IIR identification

import numpy as np
import lattice_dsp as ld

rng = np.random.default_rng(1)
x = rng.normal(size=8000)

target = ld.LatticeIIR([0.55, -0.32], [0.4, -0.1, 0.65])
desired = np.asarray(target.process(x), dtype=float)

adaptive = ld.AdaptiveLatticeLadderNLMS(
    initial_reflection=[0.0, 0.0],
    initial_taps=[0.0, 0.0, 0.0],
    mu_taps=0.06,
    mu_reflection=0.0015,
    margin=1e-4,
)

error = np.asarray(adaptive.adapt_block(x, desired), dtype=float)

print("learned reflection:", np.round(adaptive.reflection, 4))
print("final MSE:", np.mean(error[-1000:] ** 2))

MIMO and model-reduction scope

lattice-dsp includes MIMO/state-space and matrix-lattice tools, but the scope is deliberately conservative.

Implemented:

• finite-Hankel SISO reduction as a C++ finite-section Ho-Kalman-style baseline;
• MIMO block-Hankel reduction from Markov tensors;
• compiled batched MIMO state-space simulation;
• causal online MIMO lattice prediction from fitted matrix reflection coefficients;
• matrix-lattice/all-pass runtime examples and finite-record adjoint diagnostics;
• finite definite tangential Schur/Pick and J-inner diagnostic utilities.

Out of scope for the current alpha:

• full infinite-dimensional AAK/Nehari solvers;
• full matrix-valued AAK/Nehari optimal reduction;
• generalized indefinite matrix Schur synthesis;
• production acoustic echo cancellation;
• complete room-acoustics, wireless, or control-system frameworks.

The docs label offline estimation, causal runtime, inductive evaluation, and transductive/block diagnostics separately so examples are clear about data use.

Documentation

Start here:

• Documentation
• Learning path
• Interoperability recipes
• Algorithm-selection guide
• Hardy/Hankel/state-space guide
• API reference

Representative examples:

• examples/reflection_conversion.py
• examples/lattice_ladder_realization.py
• examples/adaptive_iir_system_identification.py
• examples/periodogram_vs_ar_spectrum.py
• examples/causal_mimo_lattice_prediction.py
• examples/mimo_coupled_model_reduction.py
• examples/matrix_lattice_allpass.py

Development

python -m pip install -e ".[dev,examples,benchmark,docs]"
pytest
ruff check .
ruff format .

Build docs locally:

./scripts/build_docs_with_results.sh
xdg-open docs/_build/html/index.html

The native extension is built with scikit-build-core, CMake, pybind11, NumPy, and optional OpenMP. If OpenMP is unavailable, the package falls back to serial batch processing.

Public validation

For a full release-style check:

python -m pip install -e ".[dev,examples,benchmark,docs]"
./scripts/release_validation.sh

For a quicker public-claim check:

PYTHONPATH="$PWD" pytest -q tests/test_release_trust_claims.py

License

Apache-2.0. See LICENSE and NOTICE.md.

Citation

If this package supports your research or teaching, please cite it using CITATION.cff.