Metadata-Version: 2.5
Name: PicoMats
Version: 0.0.0
Summary: A unit-informed, assumption-driven material ontology.
Project-URL: Homepage, https://github.com/Bowley-Systems/PicoMats
Project-URL: Bug Tracker, https://github.com/Bowley-Systems/PicoMats/issues
Author-email: William Bowley <wgrantbowley@gmail.com>
Maintainer-email: William Bowley <wgrantbowley@gmail.com>
License: MIT License
        
        Copyright (c) 2026 William Bowley
        
        Permission is hereby granted, free of charge, to any person obtaining a copy
        of this software and associated documentation files (the "Software"), to deal
        in the Software without restriction, including without limitation the rights
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        furnished to do so, subject to the following conditions:
        
        The above copyright notice and this permission notice shall be included in all
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        THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
        IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
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License-File: LICENSE
Keywords: assumptions,materials,physics,units
Classifier: Intended Audience :: Developers
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python :: 3
Classifier: Topic :: Scientific/Engineering
Requires-Python: >=3.10
Requires-Dist: numpy
Requires-Dist: picounits
Description-Content-Type: text/markdown

<!-- 
Color palette: 
#006d77ff -> deep, muted teal-green 
#d92c2aff -> bold, warm crimson-red 

It might seem risky to define a new ontology for materials,
but imagine for a second you're trying to do work across
electromagnetic, mechanical, and chemical systems.

And imagine your goal is to have a material backend that
has the same routing from v0.1 to vn.n. It needs to be
made general-purpose.

— William Bowley, 12th of August, 2026
-->

<!-- Update this image before its on pypi -->

<p align="center"><img src="https://raw.githubusercontent.com/Bowley-Systems/PicoMats/refs/heads/main/media/logo.png" alt="PicoMats logo" style="width:100%; max-width:100%; display:block;"></p>
<h4 align="center">A unit-informed, assumption-driven material ontology.</h4>
<p align="center">
    Use the material. Sustain the assumptions. <br>
    Reduce uncertainty by making every assumption explicit.
</p>

# Overview

![License](https://img.shields.io/badge/License-MIT-E14F4C?style=flat-square)
![Python Version](https://img.shields.io/badge/Python-3.10%2B-006D77?style=flat-square)

PicoMats is an assumption-driven material ontology that sustains assumptions throughout your pipeline. It provides unit-informed material definitions with accompanying assumptions.  

> [!important]
>
> ### Features:
> - Follows a computational ontology called `Abstract-Fundamental Ontology`.
> - Uses `UnitValues` and `PicoUnits` for encoding typed numerical definitions.
> - Tracks assumptions alongside material definitions to reduce model uncertainty.

## What is the Abstract-Fundamental Ontology?

Abstract-Fundamental Ontology is a computational abstraction for both reductionist and pragmatist applications.

The model is based on two categories:

```
Abstract:       Defined by what it does     (measured properties).
Fundamental:    Defined by what it is       (atomic structure).
```

---

The ontology emerges from this simple thought experiment:

```
Let's model a ball rolling down a ramp.
        ↓   
What forces act on the ball?
        ↓
Gravity, electromagnetic repulsion, and friction.
        ↓
How do we model friction?
        ↓
A coefficient? Isn't that arbitrary?
        ↓
Why not just model it?
        ↓
What exactly is friction?
        ↓
Oh, random microscopic interactions...
```

It's a mistake to assume it's impossible to model, but computational cost is prohibitive for most applications. Hence, you `abstract` it via empirical methods to measure friction, losing all but the necessary information.

But `abstract` isn't always the right model...

```
I want to research superconductors.
        ↓
Oh, my temperature range is 0 K to 200 K.
        ↓
Where do I get material definitions for that range?
        ↓
I'll just interpolate the standard Niobium definition.
        ↓
Actually, how were these measurements obtained?
```

When asking such a fundamental question, wouldn't it be best to derive the relations from what the material fundamentally is? Hence, you use `fundamental`, which describes what the material is, and build the necessary properties without measurement-implicit assumptions. 

> [!IMPORTANT]
>
> PicoMats does not calculate material properties from the fundamental characteristics of the elements. It is up to the user to compute them via custom modelling or external frameworks.

---

> [!NOTE]
>
> Both `abstract` and `fundamental` also make a good semantic boundary for model development. For example, `material.abstract.copper` and `material.fundamental.copper` explicitly denote the reality of their origin.

## Proposed workflow

PicoMats is still under development for `v0.1-alpha`. Implementation details and some abstractions may change.

```py
from picomats import mm
from picomats import Materials

# Pulls materials into the simulation
copper = Materials.abstract.pure.copper

density = copper.density
volume = 100 * mm ** 3

mass = volume * density
# > Output: 0.896 m(kg)

copper.assumptions.density
# > Output:
# >└── Method
# >    ├── Hydrostatic Balance (Archimedes' Principle)
# >    └── Media Type: Distilled Water
# >└── Assumptions
# >    ├── Lack of air bubbles within media
# >    └── Media temperature stability
```

## Installation 
 
Until release, this only installs the overview page and related files:
```bash
pip install PicoMats
```

### Documentation

All internal documentation can be found within this repo's [issues](https://github.com/wgbowley/PicoMaterials/issues).
