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ucon
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A lightweight, unit-aware computation library for Python — built on first-principles.
Documentation · Quickstart · API Reference
What is ucon?
ucon helps Python understand the physical meaning of your numbers. It treats units, dimensions, and scales as first-class objects — enforcing physics, not just labels.
from ucon import units
length = units.meter(5)
time = units.second(2)
speed = length / time # <2.5 m/s>
invalid = length + time # raises: incompatible dimensions
Installation
pip install ucon
With extras:
pip install ucon[pydantic] # Pydantic v2 integration
pip install ucon[mcp] # MCP server for AI agents
Quick Examples
Parse Quantities
from ucon import parse
velocity = parse("9.81 m/s^2") # <9.81 m/s²>
measurement = parse("1.234 ± 0.005 m") # <1.234 ± 0.005 m>
Unit Conversion
from ucon import units, Scale
km = Scale.kilo * units.meter
distance = km(5)
print(distance.to(units.mile)) # <3.107... mi>
Dimensional Safety
from ucon import Number, Dimension, enforce_dimensions
@enforce_dimensions
def speed(
distance: Number[Dimension.length],
time: Number[Dimension.time],
) -> Number:
return distance / time
speed(units.meter(100), units.second(10)) # <10.0 m/s>
speed(units.second(100), units.second(10)) # raises ValueError
Pydantic Integration
from pydantic import BaseModel
from ucon.pydantic import Number
class Measurement(BaseModel):
value: Number
m = Measurement(value={"quantity": 9.8, "unit": "m/s^2"})
print(m.model_dump_json())
# {"value": {"quantity": 9.8, "unit": "m/s^2", "uncertainty": null}}
MCP Server for AI Agents
Configure in Claude Desktop:
{
"mcpServers": {
"ucon": {
"command": "uvx",
"args": ["--from", "ucon[mcp]", "ucon-mcp"]
}
}
}
AI agents can then convert units, check dimensions, and perform factor-label calculations with dimensional validation at each step.
Features
- Physical constants — CODATA 2022 values with uncertainty propagation (
E = m * c**2) - String parsing —
parse("9.81 m/s^2")with uncertainty support (1.234 ± 0.005 m) - Dimensional algebra — Units combine through multiplication/division with automatic dimension tracking
- Scale prefixes — Full SI (kilo, milli, micro, etc.) and binary (kibi, mebi) prefix support
- Uncertainty propagation — Errors propagate through arithmetic and conversions
- Pseudo-dimensions — Semantically isolated handling of angles, ratios, and counts
- Pydantic v2 — Type-safe API validation and JSON serialization
- MCP server — AI agent integration with Claude, Cursor, and other MCP clients
- ConversionGraph — Extensible conversion registry with custom unit support
Roadmap Highlights
| Version | Theme | Status |
|---|---|---|
| 0.3.x | Dimensional Algebra | Complete |
| 0.4.x | Conversion System | Complete |
| 0.5.x | Dimensionless Units + Uncertainty | Complete |
| 0.6.x | Pydantic + MCP Server | Complete |
| 0.7.x | Compute Tool + Extension API | Complete |
| 0.8.x | Basis Abstraction + String Parsing | Complete |
| 0.9.x | Constants + Logarithmic Units | In Progress |
| 0.10.x | NumPy/Polars Integration | Planned |
| 1.0.0 | API Stability | Planned |
See full roadmap: ROADMAP.md
Documentation
| Section | Description |
|---|---|
| Getting Started | Why ucon, quickstart, installation |
| Guides | MCP server, Pydantic, custom units, dimensional analysis |
| Reference | API docs, unit tables, MCP tool schemas |
| Architecture | Design principles, ConversionGraph, comparison with Pint |
Contributing
make venv # Create virtual environment
source .ucon-3.12/bin/activate # Activate
make test # Run tests
make test-all # Run tests across all Python versions
When modifying ucon/dimension.py (adding/removing dimensions), regenerate the type stubs:
make stubs # Regenerate ucon/dimension.pyi
make stubs-check # Verify stubs are current (used in CI)
All pull requests must include a CHANGELOG.md entry under the [Unreleased] section:
## [Unreleased]
### Added
- Your new feature description (#PR_NUMBER)
Use the appropriate category: Added, Changed, Deprecated, Removed, Fixed, or Security.
License
Apache 2.0. See LICENSE.
1 maintainer
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