https://github.com/predict-idlab/plotly-resampler-benchmarks

https://github.com/predict-idlab/plotly-resampler-benchmarks

Last synced: 10 months ago
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• Host: GitHub
• URL: https://github.com/predict-idlab/plotly-resampler-benchmarks
• Owner: predict-idlab
• Created: 2022-03-18T08:48:05.000Z (about 4 years ago)
• Default Branch: master
• Last Pushed: 2023-11-19T16:58:40.000Z (over 2 years ago)
• Last Synced: 2025-06-09T15:53:02.441Z (11 months ago)
• Language: Jupyter Notebook
• Size: 7.31 MB
• Stars: 2
• Watchers: 3
• Forks: 0
• Open Issues: 1
• Metadata Files:
  • Readme: README.md

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README

          
# Plotly-Resampler benchmarks

## 
 


![License: MIT](https://img.shields.io/badge/License-MIT-blue.svg?color=black)

[![PRs Welcome](https://img.shields.io/badge/PRs-welcome-brightgreen.svg?)](http://makeapullrequest.com) 

This repository withholds the [benchmark results](reports/benchmark_fig.png) and visualization code of the `plotly_resampler` paper and [toolkit](https://github.com/predict-idlab/plotly-resampler).

![bencmark_result](reports/benchmark_fig.png)

## Flow

The benchmark process follows these steps for each visualization-configuration:

1. Each toolkit-visualization configuration script is called 10 times to average out the memory usage and runtime. Remark that by re-calling the script in separate runs, no caching or memory is shared among executions.

2. Script execution:

   1. Construct the synthetic visualization data

   2. [VizTracer](https://github.com/gaogaotiantian/viztracer) starts logging

   3. Construct the visualization according to the configuration

   4. Wait till the graph is rendered in a selenium browser.

   5. VizTracer stops logging

   6. Write the VizTracer results to a JSON-file

The existing [benchmark JSONs](code/benchmark_jsons/) were collected on a desktop with an *AMD Ryzen 5 2600x @3.8Ghz* CPU and *48GB* RAM, with *Arch Linux* as operating system. Other running processes were limited to a minimum.

> more information about these outcomes can be found in the [**reports**](reports/README.md) readme.

## Instructions

To install the required dependencies, just run:

```bash

poetry install

```

If you want to **re-run the benchmarks**, use the [run_scripts](code/run_scripts.ipynb) notebook to generate new benchmark JSONs and then visualize them with the [benchmark visualization](code/benchmark_visualizations.ipynb) notebook.

## Contributing

We are open to new-benchmark use-cases via **pull-requests**!


> Examples of other interesting benchmarks are

>  * other data properties

>  * other eligible tools

>  * benchmarking graph-interaction response time.




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👤 Jonas Van Der Donckt