pysib 0.2.2

System Identification Toolbox (Slow is Better)

pysib — System Identification Toolbox

pysib is a System Identification Toolbox for identifying discrete-time SISO dynamic systems from input/output data.

The package implements classical polynomial model structures used in system identification, with an emphasis on reliable model estimation rather than fast black-box routines.

Documentation: https://pysib.net/

Install

pip install pysib

Requirements: Python >= 3.9, NumPy, SciPy, Matplotlib, and LAPACK (Accelerate on macOS, liblapack on Linux).

For development from a local checkout:

pip install -e .

Quick Start

import numpy as np
import pysib
from scipy.signal import lfilter

N = 1000
u = np.sin(np.arange(N) * 2 * np.pi / 100)
y = lfilter([0, 1], [1, -0.9], u) + 0.01 * np.random.randn(N)

# Estimate an ARX model by least squares
theta, model = pysib.arx(u, y, na=1, nb=1, nz=1)

# Estimate an Output Error model with the nonlinear optimizer
theta, model = pysib.oe(u, y, nb=1, nf=1, nz=1)

# Use the estimated model
yp = pysib.predict(u, y, model)
ys = pysib.simulate(u, model)

All estimators return (theta, model), where theta is the estimated parameter vector and model is a dictionary with polynomial arrays A, B, C, D, and F.

Included API

• Estimators: arx, iv, correlation, sm, oe, armax, bj, oe_filtered, armax_filtered, bj_filtered.
• Model use: predict, simulate.
• Utility: plota.

See https://pysib.net/ for the complete documentation.

Publications

• Documentation site: https://pysib.net/
• Software archive: https://doi.org/10.5281/zenodo.20572074
• User Manual: https://pysib.net/manual/main.pdf
• ACM TOMS Paper: https://pysib.net/paper/main.pdf

Test

python3 -m pytest tests/ -v