vidigi (Visual Interactive Dynamics and Integrated Graphical Insights)

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Welcome to vidigi - a package for visualising real or simulated pathways.

vidigi is the Esperanto for 'to show'

(or it's the backronym 'Visual Interactive Dynamics and Integrated Graphical Insights' - whichever floats your boat)

2023-11-23.10-14-16.mp4

Primarily developed for healthcare simulation and intended to allow easy integration with tools like Streamlit so users can see the impact of changes to simulation parameters in real-time, vidigi handles the conversion of your simulation event logs into engaging and flexible animations.

With just a minimal set of logs - with helper functions provided to make that easier than ever to integrate into existing SimPy or Ciw simulations - you can start generating and customising your visualisations in minutes.

Getting started

Head to the documentation to find out how to create an animated version of your model.

You can install vidigi from PyPi with the command pip install vidigi.

Introduction

Visual display of the outputs of discrete event simulations in simpy have been identified as one of the limitations of simpy, potentially hindering adoption of FOSS simulation in comparison to commercial modelling offerings or GUI FOSS alternatives such as JaamSim.

When compared to commercial DES software packages that are commonly used in health research, such as Simul8, or AnyLogic, a limitation of our approach is that we do not display a dynamic patient pathway or queuing network that updates as the model runs a single replication. This is termed Visual Interactive Simulation (VIS) and can help users understand where process problems and delays occur in a patient pathway; albeit with the caveat that single replications can be outliers. A potential FOSS solution compatible with a browser-based app could use a Python package that can represent a queuing network, such as NetworkX, and displaying results via matplotlib. If sophisticated VIS is essential for a FOSS model then researchers may need to look outside of web apps; for example, salabim provides a powerful FOSS solution for custom animation of DES models.

• Monks T and Harper A. Improving the usability of open health service delivery simulation models using Python and web apps [version 2; peer review: 3 approved]. NIHR Open Res 2023, 3:48 (https://doi.org/10.3310/nihropenres.13467.2)

This package allows visually appealing, flexible visualisations of the movement of entities through some kind of pathway.

It is primarily tested with discrete event simulations to be created from SimPy and Ciw models, though could be used with other simulation libraries or real-world data.

Plotly is leveraged to create the final animation, meaning that users can benefit from the ability to further customise or extend the plotly plot, as well as easily integrating with web frameworks such as Streamlit, Dash or Shiny for Python.

Examples

To develop and demonstrate the concept, it has so far been used to incorporate visualisation into several existing simpy models that were not initially designed with this sort of visualisation in mind:

• a minor injuries unit, showing the utility of the model at high resolutions with branching pathways and the ability to add in a custom background to clearly demarcate process steps

2023-11-23.10-14-16.mp4

• an elective surgical pathway (with a focus on cancelled theatre slots due to bed unavailability in recovery areas), with length of stay displayed as well as additional text and graphical data

2024-01-08.09-50-00.mp4

• a community mental health assessment pathway, showing the wait to an appointment as well as highlighting 'urgent' patients with a different icon and showing the time from referral to appointment below the patient icons when they attend the appointment.

2024-01-08.09-51-13.mp4

• a community mental health assessment pathway with pooling of clinics, showing the 'home' clinic for clients via icon so the balance between 'home' and 'other' clients can be explored.

2024-02-01.15-11-23.mp4

• a community mental health assessment and treatment pathway, showing the movement of clients between a wait list, a booking list, and returning for repeat appointments over a period of time while sitting on a caseload in between.

2024-02-01.15-16-38.mp4

Test Coverage

Vidigi has an extensive test suite, which runs in CI across Python 3.10-3.14 and both Plotly 5 and Plotly 6, plus a minimum-dependency and a latest-dependency configuration.

The badges below are the maintainers' judgement of how well each part of the package is tested - they are not a measured coverage percentage. They were last reviewed for vidigi 2.0.0. Even where coverage is good, please continue to sense-check your outputs!

Fancy helping out? Consider submitting a pull request with some tests, particularly for anything marked as partially or not covered! It's a great way to get to know the codebase better.

Animation Functions

Resource Classes and Helper Functions

Logging Classes and Helpers

Event Positioning Helpers

Analysis and Plotting Functions

Process Mapping

Citation

If you use vidigi in your work, please cite the paper:

Rosser, S., & Chalk, D. (2026). Vidigi: A Python library for interactive-animated visualisations of discrete event simulation models. Journal of Simulation. Advance online publication. https://doi.org/10.1080/17477778.2026.2663849

BibTeX

@article{rosser2026vidigi,
  title     = {Vidigi: a Python library for interactive-animated visualisations of discrete event simulation models},
  author    = {Rosser, Sammi and Chalk, Daniel},
  journal   = {Journal of Simulation},
  year      = {2026},
  publisher = {Taylor \& Francis},
  doi       = {10.1080/17477778.2026.2663849}
}

To cite a specific version of the software, use its Zenodo archive - the all-versions DOI always resolves to the latest release. GitHub's Cite this repository button generates both citations from CITATION.cff.

Acknowledgements

Thanks are due to

• Dr Daniel Chalk for support and simpy training on the HSMA programme
• Professor Tom Monks for his extensive materials and teaching on the use of simpy in healthcare and his material on converting code into packages
• Helena Robinson for testing and bugfinding

Models used as examples

Emergency department (Treatment Centre) model

Monks.T, Harper.A, Anagnoustou. A, Allen.M, Taylor.S. (2022) Open Science for Computer Simulation

https://github.com/TomMonks/treatment-centre-sim

The layout code for the emergency department model: https://github.com/hsma-programme/Teaching_DES_Concepts_Streamlit

The hospital efficiency project model

Harper, A., & Monks, T. Hospital Efficiency Project Orthopaedic Planning Model Discrete-Event Simulation [Computer software]. https://doi.org/10.5281/zenodo.7951080

https://github.com/AliHarp/HEP/tree/main

Simulation model with scheduling example

Monks, T.

https://github.com/health-data-science-OR/stochastic_systems

https://github.com/health-data-science-OR/stochastic_systems/tree/master/labs/simulation/lab5

Contributors

Thanks goes to all of the following people (emoji key).

Amy Heather 🐛 📖 💡 🤔 🚇 🚧 ⚠️ ✅

Sammi Rosser 💻 📖 ⚠️ 🐛 🖋 🎨 💡 🤔 🚇 🚧 📆 📣 🔬 📢 ✅

Helena Robinson 🐛 🤔 📓

Dr Daniel Chalk 🧑‍🏫

Tom Monks 🧑‍🏫

ReyTan8 🐛 🤔 📓

Contributors of any kind - not just code - are welcome! Please see CONTRIBUTING.md for guidance.

Generative AI use disclosure and policy

A note about AI usage from Sammi (primary library author and maintainer):

I've been coding for over ten years, love the act of coding, and have significant concerns about the ethics and environmental impact of AI. However, I can't deny its utility as a solo maintainer when it comes to making all the features I want to include in my projects a reality. I hope you find these projects useful enough to help offset some of the downsides of AI, and will use them to help do some good in the world, or at least use them to claw back some of your time so you can spend more of it enjoying being human. For my part, I will continue to use AI critically and carefully, and I will try to make environmentally-conscious choices in my personal life to help balance the scales.

The fundamentals of this library were written by hand, with occasionaly use of the web version of ChatGPT, Gemini and Claude for debugging, debating architecture, and exploring possible approaches. Vidigi was born from a real gap that I saw in simulation visualisation for teaching, debugging and communication, and throughout the development process, I have used vidigi myself in real projects to help steer its development and test it thoroughly beyond the borders of the notebooks and tests in the library.

Claude usage via Claude Code was not introduced until 17 August 2026. The project's CLAUDE.md is provided for transparency, and for consistency if contributors wish to use it. CHAT-GPT usage via Codex was introduced on 15 September 2026, and the AGENTS.md is similarly provided for transparency.

All AI-generated code or prose will always be thoroughly reviewed, tweaked and tested before inclusion.

We are happy to accept AI-supported contributions to the library, but reserve the right to reject wholly AI generated pull requests which are not felt to add value to the project.