dimtensor 0.1.1

Unit-aware tensors for physics and scientific machine learning

dimtensor

Unit-aware tensors for physics and scientific machine learning.

dimtensor wraps NumPy arrays with physical unit tracking, catching dimensional errors at operation time rather than after hours of computation.

Installation

pip install dimtensor

Quick Start

from dimtensor import DimArray, units

# Create dimension-aware arrays
velocity = DimArray([10, 20, 30], units.m / units.s)
time = DimArray([1, 2, 3], units.s)

# Operations preserve/check dimensions
distance = velocity * time
print(distance)  # [10 40 90] m

# Catch errors early
acceleration = DimArray([9.8], units.m / units.s**2)
velocity + acceleration  # DimensionError: cannot add m/s to m/s^2

Features

• Dimensional Safety: Operations between incompatible dimensions raise DimensionError immediately
• Unit Conversion: Convert between compatible units with .to()
• SI Units: Full support for SI base and derived units
• NumPy Integration: Works with NumPy arrays and supports common operations
• Lightweight: Minimal overhead, just metadata tracking

Examples

Kinematics

from dimtensor import DimArray, units

v = DimArray([10], units.m / units.s)  # velocity
t = DimArray([5], units.s)              # time
d = v * t                               # distance = 50 m

Force and Energy

m = DimArray([2], units.kg)              # mass
g = DimArray([9.8], units.m / units.s**2)  # gravity
h = DimArray([10], units.m)              # height

# Potential energy
PE = m * g * h  # 196 J

# Force
F = m * g  # 19.6 N

Unit Conversion

distance = DimArray([1], units.km)
in_meters = distance.to(units.m)  # 1000 m
in_miles = distance.to(units.mile)  # ~0.621 mi

Why dimtensor?

1. Catch errors early: Don't waste compute on dimensionally invalid calculations
2. Self-documenting code: Units make physics code clearer
3. Designed for ML: Built with PyTorch/JAX integration in mind (coming soon)
4. Lightweight: Just metadata tracking, minimal overhead

License

MIT