Metadata-Version: 2.5
Name: axiomize
Version: 1.12.3
Summary: Rigorous scientific modeling engine and agent skill pack with native multi-family simulation, validation, causal/Bayesian inference and reproducible export
Project-URL: Homepage, https://github.com/Furox-Art/axiomize
Project-URL: Repository, https://github.com/Furox-Art/axiomize
Project-URL: Issues, https://github.com/Furox-Art/axiomize/issues
Project-URL: Documentation, https://furox-art.github.io/axiomize/
Author-email: Furox-Art <177975472+Furox-Art@users.noreply.github.com>
License-Expression: MIT
License-File: LICENSE
Keywords: agent-skills,bayesian,calibration,causal-inference,differential-equations,finite-elements,first-principles,mathematical-modeling,modeling,optimization,parameter-estimation,reproducibility,scientific-computing,sensitivity-analysis,simulation,system-identification,uncertainty-quantification
Classifier: Development Status :: 4 - Beta
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Science/Research
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.11
Classifier: Programming Language :: Python :: 3.12
Classifier: Programming Language :: Python :: 3.13
Classifier: Topic :: Scientific/Engineering
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Classifier: Topic :: Scientific/Engineering :: Mathematics
Classifier: Topic :: Software Development :: Libraries :: Python Modules
Requires-Python: >=3.10
Requires-Dist: casadi>=3.6
Requires-Dist: control>=0.10
Requires-Dist: cvxpy>=1.5
Requires-Dist: matplotlib>=3.7
Requires-Dist: networkx>=3.0
Requires-Dist: numpy>=1.24
Requires-Dist: scipy>=1.10
Requires-Dist: statsmodels>=0.14
Requires-Dist: sympy>=1.12
Requires-Dist: z3-solver<5,>=4.13; sys_platform == 'darwin'
Requires-Dist: z3-solver>=4.12; sys_platform != 'darwin'
Provides-Extra: dev
Requires-Dist: mypy>=1.10; extra == 'dev'
Requires-Dist: pytest>=8.0; extra == 'dev'
Requires-Dist: ruff>=0.4; extra == 'dev'
Provides-Extra: full
Requires-Dist: jax>=0.4; extra == 'full'
Requires-Dist: pymc>=5.0; extra == 'full'
Provides-Extra: playground
Requires-Dist: gradio>=4.0; extra == 'playground'
Requires-Dist: pandas>=1.5; extra == 'playground'
Description-Content-Type: text/markdown

# Axiomize  
  
Current package line: **1.12.3**  
  
![License](https://img.shields.io/badge/license-MIT-blue)  
![Python](https://img.shields.io/badge/python-3.10%2B-informational)  
![CI](https://github.com/Furox-Art/axiomize/actions/workflows/ci.yml/badge.svg)  
[![PyPI](https://img.shields.io/pypi/v/axiomize)](https://pypi.org/project/axiomize/)  
[![npm](https://img.shields.io/npm/v/axiomize)](https://www.npmjs.com/package/axiomize)  
[![Downloads](https://img.shields.io/pypi/dm/axiomize)](https://pypi.org/project/axiomize/)  
  
I got tired of scientific models that live in Jupyter notebooks and die there.  
  
You know the pattern: someone writes a beautiful simulation, it works on their machine, they graduate or change jobs, and six months later nobody can run it. The dependencies are broken, the data is missing, and the "documentation" is a 47-cell notebook with no explanation.  
  
Axiomize is my attempt to fix that. It forces you to write models as explicit, versioned, testable code-not as exploratory spaghetti. Every assumption is written down. Every parameter has units. Every result can be reproduced by someone else, on a different machine, years later.  
  
## What it actually does  
  
- Turns vague ideas into explicit mathematical models with real constraints  
- Validates dimensional consistency (no more adding meters to seconds)  
- Runs sensitivity analysis so you know which parameters actually matter  
- Exports to LaTeX, PDF, and portable formats that don't require Python  
- Keeps a full audit trail so you can prove what you did and why  
  
## Quick start  
  
```bash  
pip install axiomize  
# or  
npx axiomize  
```  
  
## Why I built this  
  
I was reviewing a paper last year and the authors claimed their model predicted some chemical reaction yield within 2%. I spent three days trying to reproduce it. The code was a mess of global variables, the data wasn't available, and when I finally got it running, the answer was off by 40%.  
  
That shouldn't be normal. Science should be checkable.  
  
## License  
  
MIT. Use it, break it, fix it. 

