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.
Before you start using tsl, please review the documentation to get an understanding of the library and its capabilities.
You can also explore the examples provided in the examples directory to see how train deep learning models working with spatiotemporal data.
Before installing tsl, make sure you have installed
PyTorch (>=1.9.0) and
PyG (>=2.0.3) in your virtual environment (see PyG installation guidelines).
tsl is available for Python>=3.8. We recommend installation from github to be up-to-date with the latest version:
pip install git+https://github.com/TorchSpatiotemporal/tsl.gitAlternatively, you can install the library from the pypi repository:
pip install torch-spatiotemporalTo avoid dependencies issues, we recommend using Anaconda and the provided environment configuration by running the command:
conda env create -f conda_env.ymlThe best way to start using tsl is by following the tutorial notebook in
examples/notebooks/a_gentle_introduction_to_tsl.ipynb.
The documentation is hosted on readthedocs. For local access, you can build it from the docs directory.
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.
Thanks to all contributors! Check the Contributing guidelines and help us build a better tsl.