Skip to main content

PedPy is a Python module for pedestrian movement analysis.

Project description

PedPy Logo

PyPI Latest Release PyPI - Python Version DOI License ci workflow Ruff Documentation Status OpenSSF Best Practices fair-software.eu

PedPy: Analysis of pedestrian dynamics based on trajectory files.

PedPy is a python module for pedestrian movement analysis. It implements different measurement methods for density, velocity and flow.

If you use PedPy in your work, please cite it using the following information from zenodo:

DOI

Getting started

Setup Python

For setting up your Python Environment a Python version >= 3.10 is recommended (our code is tested with 3.10, 3.11, and 3.12). To avoid conflicts with other libraries/applications the usage of virtual environments is recommended, see Python Documentation for more detail.

Installing PedPy

To install the latest stable version of PedPy and its dependencies from PyPI:

python3 -m pip install pedpy

You can also install the latest version of PedPy directly from the repository, by following these steps:

  1. Uninstall an installed version of PedPy:
python3 -m pip uninstall pedpy
  1. Install latest version of PedPy from repository:
python3 -m pip install git+https://github.com/PedestrianDynamics/PedPy.git

Usage

For first time users, have a look at the getting started notebook, as it shows the first steps to start an analysis with PedPy. A more detailed overview of PedPy is demonstrated in the user guide notebook. The fundamental diagram notebook shows how to use PedPy for computing the fundamental diagram of a series of experiments.

Interactive online session

If you want to try out PedPy for the first time, you can find an interactive online environments for both notebooks here:

  • Getting started: Binder
  • User guide: Binder
  • Fundamental diagram: Binder

Note: The execution might be slower compared to a local usage, as only limited resources are available. It is possible to also upload different trajectory files and run the analysis completely online, but this might not be advisable for long computations.

Local usage of the notebooks

For local usage of the notebooks, you can either download the notebooks and demo files from the GitHub repository or clone the whole repository with:

git clone https://github.com/PedestrianDynamics/pedpy.git

For using either of the notebook some additional libraries need to be installed, mainly for plotting. You can install the needed libraries with:

python3 -m pip install jupyter matplotlib

Afterward, you can start a jupyter server with:

jupyter notebook

After navigating to one of the notebooks, you can see how the library can be used for different kinds of analysis.

Some examples how the computed values can be visualized are also shown in the notebooks, e.g., density/velocity profiles, fundamental diagrams, N-T-diagrams, etc.

voronoi

density

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

pedpy-1.2.0.tar.gz (47.0 kB view details)

Uploaded Source

Built Distribution

PedPy-1.2.0-py3-none-any.whl (53.0 kB view details)

Uploaded Python 3

File details

Details for the file pedpy-1.2.0.tar.gz.

File metadata

  • Download URL: pedpy-1.2.0.tar.gz
  • Upload date:
  • Size: 47.0 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/5.1.1 CPython/3.12.6

File hashes

Hashes for pedpy-1.2.0.tar.gz
Algorithm Hash digest
SHA256 0ba40ec9413c5a475133022c2f81363d0d3254d500e30cc239801eaae15ff621
MD5 3af9c09fcfde8a09ec7c6637811c9236
BLAKE2b-256 7ebdefdc166f2aebffdfeedfaee85832fd90091d317e37fa4ac4e48fcc3ff777

See more details on using hashes here.

File details

Details for the file PedPy-1.2.0-py3-none-any.whl.

File metadata

  • Download URL: PedPy-1.2.0-py3-none-any.whl
  • Upload date:
  • Size: 53.0 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/5.1.1 CPython/3.12.6

File hashes

Hashes for PedPy-1.2.0-py3-none-any.whl
Algorithm Hash digest
SHA256 683cd8e46fd2d416b9d53a279ad475ad73913313a932d83097a76a12e90a37d4
MD5 d68fc7bf3a928a4d2fddced0ac98461f
BLAKE2b-256 c395a8810b9baea5d23c6e37c5f34de72c5f3927d9e2bb01fdb3eb7f921856f8

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