Do DPC

The purpose of this code is to provide a Framework for Data-Driven Predictive Control (DPC) with illustrative examples.

Please visit the extensive documentation, kindly hosted on readthedocs.

Citing do-dpc

Please follow the instructions if you want to use do-dpc for published work.

Getting Started

This library utilizes the Mosek Solver. While it is possible to use any solver compatible with CVXPY, it is recommended to use Mosek for optimal performance. Please follow the installation instructions on the Mosek website to set it up.

To ensure a clean and isolated development environment, it is recommended to use Python's virtual environment (venv).

Linux / macOS

sh python3.12 -m venv venv source venv/bin/activate pip install -r requirements.txt

Windows

sh py -m venv venv .\venv\Scripts\activate pip install -r requirements.txt

Additional Tools: SWIG

The library boxd2 requires SWIG, the Simplified Wrapper and Interface Generator.
SWIG is a tool that connects programs written in C or C++ with high-level languages such as Python. It is necessary for building Python bindings to C/C++ code, which is how boxd2 exposes its functionality to Python.

Installing SWIG

Windows

• Via Chocolatey (recommended): sh choco install swig
• Manual download:
  Download the installer from swig.org/download.html and follow the installation instructions.

macOS

• Via Homebrew: sh brew install swig

Linux (Debian/Ubuntu)

• Via apt: sh sudo apt-get update sudo apt-get install swig

• For other distributions, use the appropriate package manager (dnf, yum, pacman, etc.).

Example Usage

Once your environment is set up and dependencies installed, you can use boxd2 in your Python code:

```python import boxd2

Example: create a box and compute its area

box = boxd2.Box(2, 3) print("Area:", box.area()) ```

Note:
If you encounter build errors related to SWIG when installing or using boxd2, ensure that SWIG is correctly installed and available in your system's PATH.

Building the documentation

Navigate to the docs Folder:

shell cd docs

Create and activate a virtual environment:

sh python3.12 -m venv venv source venv/bin/activate pip install -r requirements.txt

Generate the documentation:

shell make html

Once the HTML files are created, you can serve them locally:

shell python -m http.server --directory build/html 8000

Open your browser and visit:

http://localhost:8000

Folder structure

The code contains the following files and folders:

do-dpc/ ├── do_dpc/ # Core Python source code for the DPC library │ ├── control_utils/ # Utilities for control systems (PID, noise generators, etc.) │ ├── dpc/ # Core DPC algorithms and implementations │ │ ├── dpc.py # Main DPC implementation │ │ ├── dpc_structs.py # Data structures for DPC │ ├── utils/ # General utility modules (logging, path management, etc.) ├── docs/ # Documentation files (Sphinx) │ ├── build/ # Build artifacts for generated documentation │ ├── source/ # Source files for documentation │ │ ├── _static/ # Static assets (images, CSS, etc.) │ │ ├── _templates/ # Templates for documentation structure │ │ ├── dpc_methods/ # Documentation for different DPC methods │ │ ├── getting_started/ # Guides and introductory documentation │ │ ├── conf.py # Sphinx configuration file │ │ ├── index.rst # Main entry point for documentation │ ├── .readthedocs.yaml # Configuration for Read the Docs │ ├── Makefile # Makefile for building the documentation │ ├── make.bat # Windows batch script for building documentation ├── tests/ # Test suite for the DPC library │ ├── fixtures/ # Pytest fixtures for setting up test cases │ ├── system_tests/ # System-level integration tests │ ├── unit_tests/ # Unit tests for individual modules ├── .gitlab-ci.yml # CI/CD pipeline configuration for GitLab ├── .pylintrc # Linter configuration for Python code ├── CITATION.cff # Citation file for academic references ├── confest.py # Pytest configuration file ├── LICENSE.txt # Licensing information ├── README.md ├── requirements.txt └── setup.py # Setup script for packaging and installation