torch-spatiotemporal 0.9.0

A PyTorch library for spatiotemporal data processing

tsl (Torch Spatiotemporal) is a library built to accelerate research on neural spatiotemporal data processing methods, with a focus on Graph Neural Networks.

tsl is built on several libraries of the Python scientific computing ecosystem, with the final objective of providing a straightforward process that goes from data preprocessing to model prototyping. In particular, tsl offers a wide range of utilities to develop neural networks in PyTorch for processing spatiotemporal data signals.

Installation

tsl is compatible with Python>=3.7. We recommend installation from source to be up-to-date with the latest version:

git clone https://github.com/TorchSpatiotemporal/tsl.git
cd tsl
python setup.py install  # Or 'pip install .'

To solve all dependencies, we recommend using Anaconda and the provided environment configuration by running the command:

conda env create -f tsl_env.yml

Alternatively, you can install the library from pip:

pip install torch-spatiotemporal

Please refer to PyG installation guidelines for installation of PyG ecosystem without conda.

Tutorial

The best way to start using tsl is by following the tutorial notebook in examples/notebooks/a_gentle_introduction_to_tsl.ipynb.

Documentation

The documentation is hosted on readthedocs. For local access, you can build it from the docs directory.

Citing

If you use Torch Spatiotemporal for your research, please consider citing the library

@software{Cini_Torch_Spatiotemporal_2022,
    author = {Cini, Andrea and Marisca, Ivan},
    license = {MIT},
    month = {3},
    title = {{Torch Spatiotemporal}},
    url = {https://github.com/TorchSpatiotemporal/tsl},
    year = {2022}
}

By Andrea Cini and Ivan Marisca.