Use the material. Sustain the assumptions.
Reduce uncertainty by making every assumption explicit.
Overview
PicoMats is an assumption-driven material ontology that sustains assumptions throughout your pipeline. It provides unit-informed material definitions with accompanying assumptions.
This is the demo/alpha release of PicoMats. The library and its ontology will be expanded.
- 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?
It is a computational abstraction for both reductionist and pragmatist applications.
The model is based on two categories:
Abstract: Defined by what it does (characteristics).
Fundamental: Defined by what it is (structure/state).
Why does it exist?
The ontology emerges from this simple series:
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 possible to model but computationally impractical for most applications.
Hence, the abstract section exists for empirical measurements.
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 a fundamental question, it's better to build from fundamental properties.
Hence the fundamental section.
Quick Start
A step-by-step introduction is available in example/.
from picomats import m
from picomats import Materials
# Pulls materials into the simulation
copper = Materials.abstract.pure.copper
density = copper.physical.density
volume = 0.1 * m ** 3
mass = volume * density
# > 893.0 (kg)
Installation
To install:
pip install PicoMats
Documentation
Full documentation is available in the docs/ folder,
including API reference, changelog, and contributors.
Metadata
Release files for picomats 0.1.3
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| picomats-0.1.3.tar.gz | 12.9 kB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| picomats-0.1.3-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 35.2 kB
Release files / picomats-0.1.3.tar.gz
| Download URL | picomats-0.1.3.tar.gz |
|---|---|
| Size | 12.9 kB |
| Tags | Source |
|
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Transparency logRelease files / picomats-0.1.3-py3-none-any.whl
| Download URL | picomats-0.1.3-py3-none-any.whl |
|---|---|
| Size | 22.3 kB |
| Tags | Python 3 |
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SHA-256 checksum How to use checksums |
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Uploaded using Trusted Publishing? What is trusted publishing? |
Yes |
| Uploaded via |
twine/7.0.0 CPython/3.13.14
|
Provenance
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PyPI Publish Attestation
PyPI verified that this artifact, at this checksum, originated from the publisher listed below.
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