slicot 1.0.5

Python bindings for SLICOT - Subroutine Library in Control Theory

SLICOT

Python bindings for SLICOT (Subroutine Library In COntrol Theory) - numerical routines for control systems analysis and design. This is a low-level API primarily targeting AI agents and higher-level libraries.

Installation

pip install slicot

Features

• 600+ routines for control systems
• State-space methods: Riccati, Lyapunov, pole placement
• Model reduction: Balance & Truncate, Hankel-norm
• System identification: MOESP, N4SID
• NumPy integration: Column-major arrays

Usage

While you can use this library directly, it's recommended to access it through AI coding agents that understand control theory conventions and SLICOT's API patterns.

Compatible agents: Claude Code, Codex, GitHub Copilot, Cursor, and other Agent Skills-compatible tools.

This package includes agent skills for common control theory tasks like PID loop tuning and system analysis.

Quick Start

import numpy as np
import slicot

# Controllability analysis
A = np.array([[1, 2], [3, 4]], order='F')
B = np.array([[1], [0]], order='F')

a_out, b_out, ncont, z, tau, info = slicot.ab01md('I', A, B.flatten(), 0.0)
print(f"Controllable dimension: {ncont}")

Column-Major Arrays

SLICOT uses Fortran conventions:

A = np.array([[1, 2], [3, 4]], order='F')  # Required!

Contributions

I don't accept direct contributions. Issues and PRs are welcome for illustration, but won't be merged directly. An AI agent reviews submissions and independently decides whether/how to address them. Bug reports appreciated.

License

BSD-3-Clause. See LICENSE.