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<img alt="Header image of SAIUnit." src="https://raw.githubusercontent.com/chaobrain/saiunit/main/docs/_static/logo.png" width=80%>
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## Motivation
[SAIUnit](https://github.com/chaobrain/saiunit) (/saɪ ˈjuːnɪt/) is designed to provide physical units and unit-aware mathematical systems tailored for **S**cientific **AI** within JAX. In this context, Scientific AI refers to the use of AI models or tools to advance scientific computations. SAIUnit evolves from our [BrainUnit](https://github.com/chaobrain/brainunit), a unit framework originally developed for brain dynamics modeling, extending its capabilities to support a broader range of scientific computing applications. SAIUnit is committed to providing rigorious and automatic physical unit conversion and analysis system for general AI-driven scientific computing.
## Features
Compared to existing unit libraries, such as [Quantities](https://github.com/python-quantities/python-quantities) and [Pint](https://github.com/hgrecco/pint), SAIUnit introduces a rigorous physical unit system specifically designed to support AI computations (e.g., automatic differentiation, just-in-time compilation, and parallelization). Its unique advantages include:
- Integration of over 2,000 commonly used physical units and constants
- Implementation of more than 500 unit-aware mathematical functions
- Deep integration with JAX, providing comprehensive support for modern AI framework features including automatic differentiation (autograd), just-in-time compilation (JIT), vectorization, and parallel computation
- Unit conversion and analysis are performed at compilation time, resulting in zero runtime overhead
- Strict physical unit type checking and dimensional inference system, detecting unit inconsistencies during compilation
```mermaid
graph TD
A[SAIUnit] --> B[Physical Units]
A --> C[Mathematical Functions]
A --> D[JAX Integration]
B --> B1[2000+ Units]
B --> B2[Physical Constants]
C --> C1[500+ Unit-aware Functions]
D --> D1[Autograd]
D --> D2[JIT Compilation]
D --> D3[Vectorization]
D --> D4[Parallelization]
```
We hope these features establish SAIUnit as a reliable physical unit handling solution for general AI-driven scientific computing scenarios.
A quick example:
```python
import saiunit as u
# Define a physical quantity
x = 3.0 * u.meter
x
# [out] 3. * meter
# autograd
f = lambda x: x ** 3
u.autograd.grad(f)(x)
# [out] 27. * meter2
# JIT
import jax
jax.jit(f)(x)
# [out] 27. * klitre
# vmap
jax.vmap(f)(u.math.arange(0. * u.mV, 10. * u.mV, 1. * u.mV))
# [out] ArrayImpl([ 0., 1., 8., 27., 64., 125., 216., 343., 512., 729.]) * mvolt3
```
## Installation
``saiunit`` has been well tested on ``python>=3.9`` + ``jax>=0.4.30`` environments, and can be installed on Windows, Linux, and MacOS.
You can install ``saiunit`` via pip:
```bash
pip install saiunit --upgrade
```
which should install in about 1 minute. If you want to install the latest version from the source, you can clone the repository and install it:
```bash
git clone https://github.com/chaobrain/saiunit.git
cd saiunit
pip install -e .
```
## Documentation
The official documentation is hosted on Read the Docs: [https://saiunit.readthedocs.io](https://saiunit.readthedocs.io)
## Ecosystem
`saiunit` has been deeply integrated into following diverse projects, such as:
- [``brainstate``](https://github.com/chaobrain/brainstate): A State-based Transformation System for Program Compilation and Augmentation
- [``braintaichi``](https://github.com/chaobrain/braintaichi): Leveraging Taichi Lang to customize brain dynamics operators
- [``braintools``](https://github.com/chaobrain/braintools): The Common Toolbox for Brain Dynamics Programming.
- [``dendritex``](https://github.com/chaobrain/dendritex): Dendritic Modeling in JAX
- [``pinnx``](https://github.com/chaobrain/pinnx): Physics-Informed Neural Networks for Scientific Machine Learning in JAX.
Other unofficial projects include:
- [``diffrax``](https://github.com/chaoming0625/diffrax): Numerical differential equation solvers in JAX.
- [``jax-md``](https://github.com/Routhleck/jax-md): Differentiable Molecular Dynamics in JAX
- [``Catalax``](https://github.com/Routhleck/Catalax): JAX-based framework to model biological systems
- ...
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In this context, Scientific AI refers to the use of AI models or tools to advance scientific computations. SAIUnit evolves from our [BrainUnit](https://github.com/chaobrain/brainunit), a unit framework originally developed for brain dynamics modeling, extending its capabilities to support a broader range of scientific computing applications. SAIUnit is committed to providing rigorious and automatic physical unit conversion and analysis system for general AI-driven scientific computing. \r\n\r\n\r\n\r\n## Features\r\n\r\nCompared to existing unit libraries, such as [Quantities](https://github.com/python-quantities/python-quantities) and [Pint](https://github.com/hgrecco/pint), SAIUnit introduces a rigorous physical unit system specifically designed to support AI computations (e.g., automatic differentiation, just-in-time compilation, and parallelization). Its unique advantages include:\r\n\r\n- Integration of over 2,000 commonly used physical units and constants\r\n- Implementation of more than 500 unit-aware mathematical functions\r\n- Deep integration with JAX, providing comprehensive support for modern AI framework features including automatic differentiation (autograd), just-in-time compilation (JIT), vectorization, and parallel computation\r\n- Unit conversion and analysis are performed at compilation time, resulting in zero runtime overhead\r\n- Strict physical unit type checking and dimensional inference system, detecting unit inconsistencies during compilation\r\n\r\n\r\n```mermaid\r\ngraph TD\r\n A[SAIUnit] --> B[Physical Units]\r\n A --> C[Mathematical Functions]\r\n A --> D[JAX Integration]\r\n B --> B1[2000+ Units]\r\n B --> B2[Physical Constants]\r\n C --> C1[500+ Unit-aware Functions]\r\n D --> D1[Autograd]\r\n D --> D2[JIT Compilation]\r\n D --> D3[Vectorization]\r\n D --> D4[Parallelization]\r\n```\r\n\r\nWe hope these features establish SAIUnit as a reliable physical unit handling solution for general AI-driven scientific computing scenarios.\r\n\r\nA quick example:\r\n\r\n```python\r\n\r\nimport saiunit as u\r\n\r\n# Define a physical quantity\r\nx = 3.0 * u.meter\r\nx\r\n# [out] 3. * meter\r\n\r\n# autograd\r\nf = lambda x: x ** 3\r\nu.autograd.grad(f)(x)\r\n# [out] 27. * meter2 \r\n\r\n\r\n# JIT\r\nimport jax\r\njax.jit(f)(x)\r\n# [out] 27. * klitre\r\n\r\n# vmap\r\njax.vmap(f)(u.math.arange(0. * u.mV, 10. * u.mV, 1. * u.mV))\r\n# [out] ArrayImpl([ 0., 1., 8., 27., 64., 125., 216., 343., 512., 729.]) * mvolt3\r\n```\r\n\r\n\r\n\r\n## Installation\r\n\r\n``saiunit`` has been well tested on ``python>=3.9`` + ``jax>=0.4.30`` environments, and can be installed on Windows, Linux, and MacOS.\r\n\r\nYou can install ``saiunit`` via pip:\r\n\r\n```bash\r\npip install saiunit --upgrade\r\n```\r\n\r\nwhich should install in about 1 minute. 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