Skip to main content

The sumo-experiments library implements a python interface for the Simulation of Urban MObility (SUMO) software.

Project description

Contributors Forks Stargazers Issues Status Version MIT License LinkedIn


Logo

sumo-experiments

The sumo-experiments library implements a python interface for the Simulation of Urban MObility (SUMO) software.

Examples · Report Bug · Request Feature

Table of Contents
  1. About The Project
  2. Getting Started
  3. Usage
  4. Contributing
  5. License
  6. Contact
  7. Acknowledgments

About The Project

SUMO simulation


The sumo-experiments package aims to provide an ergonomic environment for creating and configuring highly reproducible SUMO simulations.

Creating a SUMO network for a simulation is very time-consuming. Infrastructure and flows have to be defined either using the netedit tool, or by manually creating all the numerous XML configuration files. This complexity also makes it difficult to reproduce experiments taken from scientific papers. The sumo-experiments package aims to solve this problem by deploying a set of tools to define SUMO networks, automatically generate configuration files and launch simulations, directly from Python.

For further information, please refer to the jupyter notebooks in the examples folder, which will guide you through the use of the package.

Getting Started

Prerequisites

This package only work with Debian distributions. Also, you must install SUMO. Please refer to the SUMO installation manual.

Installation

  1. Get the package from the Python Package Index.

    pip install sumo-experiments
    
  2. Check that the $SUMO_HOME environment variable is set. This command must return the value of $SUMO_HOME.

    printenv | grep 'SUMO_HOME'
    

    If the variable is not set, you can add it temporarily with the following command.

    export SUMO_HOME=your_path_to_sumo
    

    To set this variable permanently, write this in the .bashrc file.

Usage

This script is one of the more simple uses of the package. We first instanciate a preset network from preset_networks. This network contains only one intersection, making the junction between two two-way roads, with one lane for each way. Secondly, we instanciate an Experiment with three parameters : - The name of the experiment - A function that defines the infrastructures of the network (nodes, edges, connections, etc) from the preset network - A function that defines the flows of the simulation (vehicle types, density, etc) from the preset network Finally, we run the simulation with the SUMO GUI. We recommand you to use the clean files method to delete all configuration and data files.

from sumo_experiments import Experiment
from sumo_experiments.preset_networks import OneCrossroadNetwork

network = OneCrossroadNetwork()
exp = Experiment('Test', network.generate_infrastructures, network.generate_flows_all_directions)
exp.run(gui=True)
exp.clean_files()

For more examples, please refer to the examples folder

Contributing

Contributions are what make the open source community such an amazing place to learn, inspire, and create. Any contributions you make are greatly appreciated.

If you have a suggestion that would make this better, please fork the repo and create a pull request. You can also simply open an issue with the tag "enhancement". Don't forget to give the project a star! Thanks again!

  1. Fork the Project
  2. Create your Feature Branch (git checkout -b feature/AmazingFeature)
  3. Commit your Changes (git commit -m 'Add some AmazingFeature')
  4. Push to the Branch (git push origin feature/AmazingFeature)
  5. Open a Pull Request

License

Distributed under the LGPL-2.1 License. See LICENSE.txt for more information.

Contact

Jules Bompard - Linkedin - jules.bompard.etu@univ-lille.fr

Project Link: https://github.com/cristal-smac/sumo-experiments

Acknowledgments

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

sumo_experiments-3.4.5.tar.gz (2.7 MB view details)

Uploaded Source

Built Distribution

sumo_experiments-3.4.5-py3-none-any.whl (558.3 kB view details)

Uploaded Python 3

File details

Details for the file sumo_experiments-3.4.5.tar.gz.

File metadata

  • Download URL: sumo_experiments-3.4.5.tar.gz
  • Upload date:
  • Size: 2.7 MB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/5.1.1 CPython/3.12.3

File hashes

Hashes for sumo_experiments-3.4.5.tar.gz
Algorithm Hash digest
SHA256 ed1ede792b627b22fccbaa22112f53e9d123cb76022a260c9d0754bca7e7ba27
MD5 9423e4e8cee20d714cd3646036e66803
BLAKE2b-256 82da8cc11e274a4a14746901d81279fa74cc7f0963becd2774948db7725249ee

See more details on using hashes here.

File details

Details for the file sumo_experiments-3.4.5-py3-none-any.whl.

File metadata

File hashes

Hashes for sumo_experiments-3.4.5-py3-none-any.whl
Algorithm Hash digest
SHA256 01cd273de2e05d82e26494791e5a87655595a506d4953b05d49085417e8966b9
MD5 e5451dc8cb53aabc4998db7475cd7fdf
BLAKE2b-256 a876f52b833a61ec30e5b2bda4a20c793387398f117f80c7e229a58093f1b413

See more details on using hashes here.

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page