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ucon

Pronounced: yoo · cahn

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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)
  • Custom constants — Define domain-specific constants with uncertainty propagation via MCP or Python API
  • String parsingparse("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
  • Natural units — Custom dimensional bases where c=ℏ=k_B=1 for particle physics
  • Logarithmic units — dB, pH, and neper conversions with uncertainty propagation
  • 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 + Natural Units Complete
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.

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