Metadata-Version: 2.1
Name: stlpy
Version: 0.2.0
Summary: A Python library for control from Signal Temporal Logic (STL) specifications
Home-page: https://stlpy.readthedocs.io/en/latest/index.html
Author: Vince Kurtz
Author-email: vjkurtz@gmail.com
License: MIT
Description: [![Documentation Status](https://readthedocs.org/projects/stlpy/badge/?version=latest)](http://stlpy.readthedocs.io/?badge=latest)
        [![PyPi version](https://badgen.net/pypi/v/stlpy/)](https://pypi.org/project/stlpy)
        [![MIT license](https://img.shields.io/badge/License-MIT-blue.svg)](LICENSE)
        
        # stlpy
        
        A python library for control from Signal Temporal Logic (STL) specifications. 
        
        Includes implementations of several state-of-the-art [synthesis algorithms](https://stlpy.readthedocs.io/en/latest/solvers.html) and [benchmark specifications](https://stlpy.readthedocs.io/en/latest/benchmarks.html) (shown below). 
        
        | <img src="docs/source/images/either_or.png" alt="drawing" width="200"/> |<img src="docs/source/images/nonlinear_reach_avoid.png" alt="drawing" width="200"/> | <img src="docs/source/images/door_puzzle.png" alt="drawing" width="200"/> |<img src="docs/source/images/stepping_stones.png" alt="drawing" width="200"/> |
        | --- | --- | --- | --- | 
        
        ### Documentation
        
        Can be found online at [stlpy.readthedocs.io](https://stlpy.readthedocs.io/en/latest/index.html).
        
        ### Installation
        
        ```
        pip install stlpy
        ```
        
        The basic installation allows for defining and evaluating STL formulas. The more advanced solvers 
        require one or more of the following additional packages. See the 
        [documentation](https://stlpy.readthedocs.io/en/latest/solvers.html)
        for more details.
        
        - [scipy](https://scipy.org/install/) (for gradient-based optimization)
        - [Drake with Gurboi/Mosek](https://drake.mit.edu/from_source.html#building-the-python-bindings) enabled (for MICP-based optimization)
        - [Drake with SNOPT](https://drake.mit.edu/from_binary.html#binary-packages) enabled (for smooth optimization with sparse SQP)
        - [Gurobi](https://www.gurobi.com/documentation/9.5/quickstart_linux/cs_using_pip_to_install_gr.html) python bindings (version 9.0 or higher) for MICP-based optimization
        
        ### Usage
        
        See the [examples](examples) and the [documentation](https://stlpy.readthedocs.io/en/latest/getting_started.html#a-simple-example).
        
        ### Contributing
        
        If you have a new STL trajectory synthesis algorithm or benchmark scenario you would like to see included in this package, please open a [pull request](https://github.com/vincekurtz/stlpy/pulls). 
        
        
Platform: UNKNOWN
Requires-Python: >=3.8
Description-Content-Type: text/markdown
