PyDASA#

PyPI Python Version License Documentation Status Coverage

PyDASA (Dimensional Analysis for Scientific Applications and Software Architecture) is an open-source Python library for dimensional analysis of complex phenomena across physical, chemical, computational, and software domains using the Buckingham Pi-theorem.

The Primary Need#

Epic User Story

As a researcher, engineer, or software architect analyzing complex systems,

I want a comprehensive dimensional analysis library implementing the Buckingham Pi theorem,

So that I can systematically discover dimensionless relationships, validate models, and understand system behavior across physical, computational, and software architecture domains.

Quick Navigation#

🚀 Getting Started

New to PyDASA? Check out the getting started guide for installation and quick start examples.

Installation
📖 User Guide

The user guide provides in-depth information on dimensional analysis concepts and PyDASA features.

User Guide
💡 Examples

Practical examples and tutorials demonstrating PyDASA capabilities in real-world scenarios.

Examples
📚 API Reference

Complete API documentation with detailed descriptions of all modules, classes, and functions.

API Reference

Acknowledgements#

The theoretical foundation of dimensional analysis in PyDASA draws upon the classical work:

Dimensionsanalyse: Theorie der physikalischen Dimensionen mit Anwendungen

Author:

Henry Görtler

Series:

Ingenieurwissenschaftliche Bibliothek (Engineering Science Library)

Publisher:

Springer-Verlag

Year:

1975

ISBN:

978-3642808739

Language:

German

This comprehensive treatise provides the rigorous mathematical foundation for the theory of physical dimensions and dimensional homogeneity that underlies modern dimensional analysis methods.

Also, PyDASA was inspired by the work of Mokbel Karam and Tony Saad in BuckinghamPy presented in:

BuckinghamPy: A Python software for dimensional analysis

Authors:

Mokbel Karam, Tony Saad

Journal:

SoftwareX

Volume:

16

Year:

2021

Article:

100851

DOI:

https://doi.org/10.1016/j.softx.2021.100851

License:

Creative Commons

We are grateful to the authors for their contribution into making dimensional analysis more accessible through computational tools, which motivated our development of PyDASA with expanded and costumizable capabilities for scientific and engineering applications.