A toolbox to compute the robustness of STL formulas using computation graphs. This is the PyTorch version of the STLCG++, an updated version of the original STLCG toolbox originally implemented in PyTorch.
A JAX version of STLCG++ can be found at STLJAX
Requires Python 3.10+
Install the repo:
pip install stlcgpp
Alternatively, if you want to develop on the code base, please fork this repo and make pull requests as needed.
pip install -e .
Please take a look at README at STLJAX for more details about the latest changes and updates to this toolbox.
We aim to have the Jax and PyTorch libraries to essentially mirror each other in terms of functionality and usage, aside from specific Jax/PyTorch syntaxes and conventions.
In the future, we will have documentation in a single location. But for the meantime, please refer to the README at STLJAX.
demo.ipynb is an IPython jupyter notebook that showcases the basic functionality of the toolbox:
- Setting up signals for the formulas, including the use of Expressions and Predicates
- Defining STL formulas and visualizing them
- Evaluating STL robustness, and robustness trace
If there are any issues with the code, please make file an issue, or make a pull request.
