Simple Python interface for a traffic simulator: SUMO
Project description
sumo-tasks-pipeline
Run SUMO simulators as easy as possible!
The package sumo-docker-pipeline
enables you to run a traffic simulator SUMO efficiently
and to interact with Python easily.
Example
Just three lines to run a SUMO simulation.
from sumo_tasks_pipeline import LocalSumoController, SumoConfigObject
from pathlib import Path
path_config = Path().cwd().parent.joinpath('tests/resources/config_complete')
sumo_controller = LocalSumoController(sumo_command='/usr/local/bin/sumo')
sumo_config = SumoConfigObject(scenario_name='example', path_config_dir=path_config, config_name='grid.sumo.cfg')
sumo_result_obj = sumo_controller.start_job(sumo_config)
See examples
directory to know more.
Features
- Possible to resume your tasks. The feature is useful when you run simulators on Google Colab.
- Possible to save SUMO simulation result to Google Cloud Storage (GCS). No worries even when your local storage is small.
- Possible to run SUMO simulations with multiple machines if you use GCS as the storage backend.
Requirement
- python > 3.5
- docker
- docker-compose
Install
Pull the image (or build of a docker image with SUMO)
The existing image is on the Dockerhub.
docker pull kensukemi/sumo-ubuntu18
If you prefer to build with yourself, you run the following command.
docker-compose build
Install a python package
make install
For developer
pytest tests
license and credit
The source code is licensed MIT. The website content is licensed CC BY 4.0.
@misc{sumo-docker-pipeline,
author = {Kensuke Mitsuzawa},
title = {sumo-docker-pipeline},
year = {2021},
publisher = {GitHub},
journal = {GitHub repository},
howpublished = {\url{https://github.com/Kensuke-Mitsuzawa/sumo_docker_pipeline}},
}
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