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-1.0.8.tar.gz (102.6 MB view details)

Uploaded Source

Built Distribution

sumo_experiments-1.0.8-py3-none-any.whl (42.8 kB view details)

Uploaded Python 3

File details

Details for the file sumo_experiments-1.0.8.tar.gz.

File metadata

  • Download URL: sumo_experiments-1.0.8.tar.gz
  • Upload date:
  • Size: 102.6 MB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.2 CPython/3.10.12

File hashes

Hashes for sumo_experiments-1.0.8.tar.gz
Algorithm Hash digest
SHA256 7c8522c5065706d4598e106554bd0795a4b28641649c64dae05f5dce1b6db1b8
MD5 d6793fe4f1e0ffefdceb758847a2fbbe
BLAKE2b-256 19945bf8aca711ea578f80c1876eb34dc8c1528bffad3aff762f26e071d9721a

See more details on using hashes here.

File details

Details for the file sumo_experiments-1.0.8-py3-none-any.whl.

File metadata

File hashes

Hashes for sumo_experiments-1.0.8-py3-none-any.whl
Algorithm Hash digest
SHA256 2dd3e8aae0f0b1dfb47dea1155d4fe4602cd49d998aa8da9790b9f7980acb221
MD5 ca507286d896df28f115a54b7a57d4c3
BLAKE2b-256 e15c00ea2f0e1e3484adec0d7e490ae864b72ee85783c96a238dc8b8178b0ac0

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