Metadata-Version: 2.1
Name: xitorch
Version: 0.5.1
Summary: Differentiable scientific computing library
Home-page: https://xitorch.readthedocs.io/
License: MIT
Keywords: project,library,linear-algebra,autograd,functionals
Author: mfkasim1
Author-email: firman.kasim@gmail.com
Requires-Python: >=3.8.1,<3.12
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.9
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.11
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Classifier: Topic :: Scientific/Engineering
Classifier: Topic :: Scientific/Engineering :: Mathematics
Classifier: Topic :: Scientific/Engineering :: Physics
Requires-Dist: numpy (>=1.24.1)
Requires-Dist: scipy (>=1.10.0)
Requires-Dist: torch (>=1.13.1)
Project-URL: Repository, https://github.com/xitorch/xitorch/tree/master/
Description-Content-Type: text/markdown

# `xitorch`: differentiable scientific computing library

![Build](https://img.shields.io/github/workflow/status/xitorch/xitorch/CI)
[![Docs](https://img.shields.io/readthedocs/xitorch)](https://xitorch.readthedocs.io/)
[![Code coverage](https://img.shields.io/codecov/c/github/xitorch/xitorch)](https://codecov.io/gh/xitorch/xitorch)

`xitorch` is a PyTorch-based library of differentiable functions and functionals that
can be widely used in scientific computing applications as well as deep learning.

The documentation can be found at: https://xitorch.readthedocs.io/

## Example

Finding root of a function:

```python
import torch
from xitorch.optimize import rootfinder

def func1(y, A):  # example function
    return torch.tanh(A @ y + 0.1) + y / 2.0

# set up the parameters and the initial guess
A = torch.tensor([[1.1, 0.4], [0.3, 0.8]]).requires_grad_()
y0 = torch.zeros((2, 1))  # zeros as the initial guess

# finding a root
yroot = rootfinder(func1, y0, params=(A,))

# calculate the derivatives
dydA, = torch.autograd.grad(yroot.sum(), (A,), create_graph=True)
grad2A, = torch.autograd.grad(dydA.sum(), (A,), create_graph=True)
```

## Modules

* [`linalg`](xitorch/linalg/): Linear algebra and sparse linear algebra module
* [`optimize`](xitorch/optimize/): Optimization and root finder module
* [`integrate`](xitorch/integrate/): Quadrature and integration module
* [`interpolate`](xitorch/interpolate/): Interpolation

## Requirements

* python >=3.8.1,<3.12
* pytorch 1.13.1 or higher (install [here](https://pytorch.org/))

## Getting started

After fulfilling all the requirements, type the commands below to install `xitorch`

    python -m pip install xitorch

Or to install from GitHub:

    python -m pip install git+https://github.com/xitorch/xitorch.git

Finally, if you want to make an editable install from source:

    git clone https://github.com/xitorch/xitorch.git
    cd xitorch
    python -m pip install -e .

Note that the last option is only available per [PEP 660](https://peps.python.org/pep-0660/), so you will require [pip >= 23.1](https://pip.pypa.io/en/stable/news/#v21-3)
    
## Used in

* Differentiable Quantum Chemistry (DQC): https://dqc.readthedocs.io/

## Gallery

Neural mirror design ([example 01](examples/01-mirror-design/)):

![neural mirror design](examples/01-mirror-design/images/mirror.gif)

Initial velocity optimization in molecular dynamics ([example 02](examples/02-molecular-dynamics/)):

![molecular dynamics](examples/02-molecular-dynamics/images/md.gif)

