Metadata-Version: 2.1
Name: e3nn
Version: 0.0.2
Summary: Equivariant convolutional neural networks for the group E(3) of 3 dimensional rotations, translations, and mirrors.
Home-page: https://e3nn.org
License: MIT
Platform: UNKNOWN
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3 :: Only
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: POSIX
Classifier: Operating System :: MacOS
Requires-Python: >=3.7
Description-Content-Type: text/markdown
Requires-Dist: lie-learn
Requires-Dist: scipy
Requires-Dist: sympy
Requires-Dist: torch (>=1.4.0)
Requires-Dist: torch-scatter
Requires-Dist: torch-sparse
Requires-Dist: torch-cluster
Requires-Dist: torch-spline-conv
Requires-Dist: torch-geometric
Requires-Dist: ase
Requires-Dist: pymatgen

# e3nn
[![Coverage Status](https://coveralls.io/repos/github/e3nn/e3nn/badge.svg?branch=master)](https://coveralls.io/github/e3nn/e3nn?branch=master)

E(3) is the [Euclidean group](https://en.wikipedia.org/wiki/Euclidean_group) in dimension 3. That is the group of rotations, translations and mirror.
`e3nn` is a [pytorch](https://pytorch.org) library that aims to create **E**(**3**) equivariant **n**eural **n**etworks.

![](https://user-images.githubusercontent.com/333780/79220728-dbe82c00-7e54-11ea-82c7-b3acbd9b2246.gif)

## Installation

After having installed [pytorch_geometric](https://github.com/rusty1s/pytorch_geometric#installation) run the command:

`pip install e3nn`

To get the CUDA kernels read the instructions in `INSTALL.md`.

## Example
```python
from functools import partial

import torch

from e3nn import Kernel, rs
from e3nn.non_linearities.norm import Norm
from e3nn.non_linearities.rescaled_act import swish
from e3nn.point.operations import Convolution
from e3nn.radial import GaussianRadialModel

# Define the input and output representations
Rs_in = [(1, 0), (2, 1)]  # Input = One scalar plus two vectors
Rs_out = [(1, 1)]  # Output = One single vector

# Radial model:  R+ -> R^d
RadialModel = partial(GaussianRadialModel, max_radius=3.0, number_of_basis=3, h=100, L=1, act=swish)

# kernel: composed on a radial part that contains the learned parameters
#  and an angular part given by the spherical hamonics and the Clebsch-Gordan coefficients
K = partial(Kernel, RadialModel=RadialModel)

# Create the convolution module
conv = Convolution(K(Rs_in, Rs_out))

# Module to compute the norm of each irreducible component
norm = Norm(Rs_out)


n = 5  # number of input points
features = rs.randn(1, n, Rs_in, requires_grad=True)
in_geometry = torch.randn(1, n, 3)
out_geometry = torch.zeros(1, 1, 3)  # One point at the origin


out = norm(conv(features, in_geometry, out_geometry))
out.backward()

print(out)
print(features.grad)
```

## Hierarchy

- `e3nn` contains the library
  - `e3nn/o3.py` O(3) irreducible representations
  - `e3nn/rsh.py` real spherical harmonics
  - `e3nn/rs.py` geometrical tensor representations
  - `e3nn/image` contains voxels linear operations
  - `e3nn/point` contains points linear operations
  - `e3nn/non_linearities` non linearities operations
- `examples` simple scripts and experiments

## Citing
[![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.3723557.svg)](https://doi.org/10.5281/zenodo.3723557)

```
@software{e3nn_2020_3723557,
  author       = {Mario Geiger and
                  Tess Smidt and
                  Benjamin K. Miller and
                  Wouter Boomsma and
                  Kostiantyn Lapchevskyi and
                  Maurice Weiler and
                  Michał Tyszkiewicz and
                  Jes Frellsen},
  title        = {github.com/e3nn/e3nn},
  month        = may,
  year         = 2020,
  publisher    = {Zenodo},
  version      = {0.0.0},
  doi          = {10.5281/zenodo.3723557},
  url          = {https://doi.org/10.5281/zenodo.3723557}
}
```


