Colahall SPARK: Signal Processing Algorithms, Routines, and Kernels

SPARK is a lightweight DSP library that combines high-level math in
Python with SIMD-optimized C kernels. It’s designed for developers who
need real-time speed without the overhead of heavy scientific libraries.

⚠️ Work in progress: SPARK is still in early alpha. APIs may change.

Why SPARK?

Prototype DSP algorithms quickly in Python.
Run them at near-native speed with optimized C backends.
Bridge research code and production kernels without rewriting everything.

Key Features

High Performance: SIMD-optimized C implementations for speed and memory efficiency.
Pythonic API: Simple, intuitive bindings for everyday use.
Lightweight: Minimal dependencies—easy to embed in larger projects.
Cross-Platform: Linux and macOS supported out of the box.
Modern Tooling: Meson + meson-python build system for reliable integration.

Installation

Requirements:

Python ≥ 3.10
C compiler (GCC or Clang)
meson + ninja

Early releases will be published soon. For now, build from source.

Developer Setup

# 1. Clone the repository
git clone https://github.com/Colahall/SPARK.git
cd SPARK

# 2. Create a virtual environment
uv venv
source .venv/bin/activate

# 3. Install in editable mode with test deps
uv pip install -e ".[test]" --no-build-isolation

License

Python package (spark): GPL-3.0-or-later
C core (libspark): MIT License

This dual-licensing model ensures the core C kernels can be embedded widely,
while the Python package remains open under GPL.

Roadmap

SIMD-optimized EQ and filter kernels
Cross-platform CI and packaging
Expanded DSP primitives (convolution, FFT wrappers, etc.)

Colahall/SPARK