semvec/pypi-packages huggingface dataset:
e3x 1.0.2
JAX-Library for building E(3)-equivariant deep learning architectures based on Flax.
Requires Python: >=3.9
Keywords: equivariance, equivariant, invariance, invariant, E(3), SE(3), O(3), SO(3), Euclidean, Orthogonal, group, translation, translational, rotation, rotational, roto-translation, roto-translational, reflection, neural network, machine learning, deep learning

<div align="center">
<img src="https://raw.githubusercontent.com/google-research/e3x/main/docs/source/_static/logo.svg" alt="logo" width="200"></img>
</div>

# E3x: E(3)-Equivariant Deep Learning Made Easy

![Autopublish Workflow](https://github.com/google-research/e3x/actions/workflows/pytest_and_autopublish.yml/badge.svg)
![PyPI version](https://img.shields.io/pypi/v/e3x)
[![Documentation Status](https://readthedocs.org/projects/e3x/badge/?version=latest)](https://e3x.readthedocs.io/en/latest/?badge=latest)

[E3x](https://e3x.readthedocs.io) is a [JAX](https://jax.readthedocs.io) library
for constructing efficient E(3)-equivariant deep learning architectures built on
top of [Flax](https://flax.readthedocs.io).

The goal is to provide common neural network building blocks for
E(3)-equivariant architectures to make the development of models operating on
three-dimensional data (point clouds, polygon meshes, etc.) easier.

This is not an officially supported Google product.

## Installation

The easiest way to install E3x is via the Python Package Index (PyPI). Simply
run
```console
> python -m pip install --upgrade e3x
```
and you should be good to go.

Alternatively, you can clone this repository, enter the directory and run:
```console
> python -m pip install .
```

If you are a developer, you might want to also install the optional development
dependencies by running
```console
> python -m pip install .[dev]
```
instead.

## Documentation

Documentation for E3x, including usage examples and tutorials can be found
[here](https://e3x.readthedocs.io). For a more detailed overview over the
mathematical theory behind E3x, please refer to
[this paper](https://arxiv.org/abs/2401.07595).

## Citing E3x

If you find E3x useful and use it in your work, please cite:
```
@article{unke2024e3x,
  title={\texttt{E3x}: $\mathrm{E}(3)$-Equivariant Deep Learning Made Easy},
  author={Unke, Oliver T. and Maennel, Hartmut},
  journal={arXiv preprint arXiv:2401.07595},
  year={2024}
}
```
pyscal-rdf 0.2.5
Ontology based structural manipulation and quering

# pyscal_rdf

`pyscal_rdf` is a python tool for ontology-based creation, manipulation, and quering of structures. `pyscal_rdf` uses the [Computational Material Sample Ontology (CMSO)](https://github.com/Materials-Data-Science-and-Informatics/cmso-ontology). 

The package is currently under activate development and could be  unstable .

You can try `pyscal_rdf`  here:

| Jupyter notebook  | GUI |
|-------------------|-----|
| [![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/pyscal/pyscal_rdf/HEAD?labpath=example.ipynb)  | [![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/pyscal/pyscal_rdf/voila?urlpath=voila%2Frender%2Fexample_gui.ipynb)  |

## Installation

### Supported operating systems

`pyscal_rdf` can be installed on Linux and Mac OS based systems. On Windows systems, it is recommended to use  [Windows subsystem for Linux](https://docs.microsoft.com/en-us/windows/wsl/install).

### Using pip

```
pip install pyscal-rdf
```
### Using conda

```
conda install -c conda-forge pyscal-rdf
```


### Building from the repository

We strongly recommend creating a conda environment for the installation. To see how you can install conda see [here](https://docs.conda.io/projects/conda/en/latest/user-guide/install/).

Once a conda distribution is available, the following steps will help set up an environment to use `pyscal_rdf`. First step is to clone the repository.

```
git clone https://github.com/pyscal/pyscal_rdf.git
```

After cloning, an environment can be created from the included file-

```
cd pyscal_rdf
conda env create -f environment.yml
```

This will install the necessary packages and create an environment called rdf. It can be activated by,

```
conda activate rdf
```

then, install `pyscal_rdf` using,

```
pip install .
```

## Using `pyscal_rdf`

Coming soon..


## Acknowledgements
This work is supported by the [NFDI-Matwerk](https://nfdi-matwerk.de/) consortia.

Funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under the National Research Data Infrastructure – NFDI 38/1 – project number 460247524
GraphSTAM 1.2.10
Graph Based Spatio-Temporal Attention Models For Demand Forecasting

# GraphSTAM
Graph Based Spatio-Temporal Attention Models
###### Note: The current implementation works for GPU (CUDA) enabled machines. To run on CPU, install the following dependencies manually:

```
pip install torch --index-url https://download.pytorch.org/whl/cpu
pip install torch-geometric
# for latest PyG version, install from master: pip install git+https://github.com/pyg-team/pytorch_geometric.git
pip install torch_scatter torch_sparse -f https://data.pyg.org/whl/torch-2.0.0+cpu.html

```

#### For usage guide, run:
```
import graphstam
graphstam.usage()
```