sysbench-loader 0.0.6

Downloads and prepares various system identification benchmark datasets

Sysbench Loader

The sysbench_loader package provides a collection of standardized data loaders for common system identification benchmark datasets. It downloads, prepares, and converts various benchmark datasets into a unified HDF5 format, making them readily available for machine learning and system identification applications.

Install

pip install sysbench_loader

Features

• Downloads benchmark datasets from various sources
• Converts data to standardized HDF5 format
• Splits data into train/validation/test sets
• Provides consistent interface across different benchmarks
• Handles setup and cleanup of downloaded files

Available Benchmarks

The package includes loaders for the following benchmark datasets:

Nonlinear Systemidentification Workshop Benchmarks

• Wiener-Hammerstein: Electronic nonlinear system
• Silverbox: Electronic circuit with nonlinear feedback
• Cascaded Tanks: Fluid dynamics system
• EMPS: Electro-Mechanical Positioning System
• Noisy Wiener-Hammerstein: WH system with process noise

Robotic Systems

• Industrial Robot: Forward and inverse identification models
• Quad Pelican: Quadrotor UAV system
• Quad Pi: Raspberry Pi-based quadrotor system

Other Systems

• Ship: Ship propulsion and steering dynamics
• Broad: Broad spectrum system identification dataset

# Basic usage
import sysbench_loader
from pathlib import Path

# Example: Download a single dataset
# Note: Always use a Path object, not a string
save_path = Path('./tmp/wh')
sysbench_loader.workshop.wiener_hammerstein(save_path)

# List all available dataset loaders
sysbench_loader.all_dataset_loader

[<function sysbench_loader.workshop.wiener_hammerstein(save_path: pathlib.Path)>,
 <function sysbench_loader.workshop.silverbox(save_path: pathlib.Path)>,
 <function sysbench_loader.workshop.cascaded_tanks(save_path: pathlib.Path)>,
 <function sysbench_loader.workshop.emps(save_path: pathlib.Path)>,
 <function sysbench_loader.workshop.noisy_wh(save_path: pathlib.Path)>,
 <function sysbench_loader.industrial_robot.robot_forward(save_path: pathlib.Path)>,
 <function sysbench_loader.industrial_robot.robot_inverse(save_path: pathlib.Path)>,
 <function sysbench_loader.ship.ship(save_path: pathlib.Path, remove_download=True)>,
 <function sysbench_loader.quad_pelican.quad_pelican(save_path: pathlib.Path, remove_download=False)>,
 <function sysbench_loader.quad_pi.quad_pi(save_path: pathlib.Path, remove_download=False)>,
 <function sysbench_loader.broad.broad(save_path: pathlib.Path)>]

HDF5 Data Format

Each dataset is converted to a standard HDF5 format with the following structure: - Train/valid/test split in separate directories - Input data stored as ‘u0’, ‘u1’, etc. (one per input dimension) - Output data stored as ‘y0’, ‘y1’, etc. (one per output dimension) - Data converted to 32-bit float (f4) for consistency

This standardized format makes it easy to use these datasets with any machine learning framework that supports HDF5 files.