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Executor-Tyrant-Framework

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//! NuWave Lenia Engine — Rust Core
//!
//! Applies Lenia dynamics to transformer weight matrices.
//! The hot path: 196 weight matrices × (conv2d + growth + conservation) per step.
//! Python/PyTorch: ~65s. Rust target: <5s.
//!
//! The growth function IS the learning rule. No backprop. No loss function.
//! Each weight neighborhood evolves based on its neighbors' states
//! and the activation flow through it.
//!
//! PyO3 bindings: Python calls step() with flat f32 arrays.
//! Rust does the math. Returns delta arrays Python applies to tensors.

mod engine;
mod kernel;

use pyo3::prelude::*;

#[pymodule]
fn nuwave_lenia(m: &Bound<'_, PyModule>) -> PyResult<()> {
    m.add_class::<engine::RustLeniaEngine>()?;
    m.add_class::<engine::LeniaStepResult>()?;
    Ok(())
}

	//! NuWave Lenia Engine — Rust Core
	//!
	//! Applies Lenia dynamics to transformer weight matrices.
	//! The hot path: 196 weight matrices × (conv2d + growth + conservation) per step.
	//! Python/PyTorch: ~65s. Rust target: <5s.
	//!
	//! The growth function IS the learning rule. No backprop. No loss function.
	//! Each weight neighborhood evolves based on its neighbors' states
	//! and the activation flow through it.
	//!
	//! PyO3 bindings: Python calls step() with flat f32 arrays.
	//! Rust does the math. Returns delta arrays Python applies to tensors.

	mod engine;
	mod kernel;

	use pyo3::prelude::*;

	#[pymodule]
	fn nuwave_lenia(m: &Bound<'_, PyModule>) -> PyResult<()> {
	m.add_class::<engine::RustLeniaEngine>()?;
	m.add_class::<engine::LeniaStepResult>()?;
	Ok(())
	}