https://github.com/otvam/scatter_simplify_matlab

MATLAB Code for Simplifying Scatter Plots
https://github.com/otvam/scatter_simplify_matlab

big-data matlab pareto-front reduction scatter-plot simplify

Last synced: 6 months ago
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MATLAB Code for Simplifying Scatter Plots

• Host: GitHub
• URL: https://github.com/otvam/scatter_simplify_matlab
• Owner: otvam
• License: bsd-2-clause
• Created: 2021-02-04T10:52:23.000Z (over 4 years ago)
• Default Branch: main
• Last Pushed: 2024-12-18T04:44:22.000Z (10 months ago)
• Last Synced: 2025-03-28T00:41:25.655Z (6 months ago)
• Topics: big-data, matlab, pareto-front, reduction, scatter-plot, simplify
• Language: MATLAB
• Homepage:
• Size: 1.21 MB
• Stars: 3
• Watchers: 1
• Forks: 1
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE.md

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README

          
# MATLAB Code for Simplifying Scatter Plots

![license - BSD](https://img.shields.io/badge/license-BSD-green)

![language - MATLAB](https://img.shields.io/badge/language-MATLAB-blue)

![category - science](https://img.shields.io/badge/category-science-lightgrey)

![status - unmaintained](https://img.shields.io/badge/status-unmaintained-red)

**MATLAB scatter plots** with **millions of points** are slow and resource intensive.

However, most of the points are not visible since they are hidden by other points.

This code detects which points are **hidden** and **remove** them.

The used **algorithm** is particularly **efficient** and can handle millions of points:

* a pixel matrix is generated

* the points are circle occupying a given number of pixels

* the indices of the points are placed (in order) in the pixel matrix

* the points that do not appear in the pixel matrix will be invisible in the plot

* the invisible points are removed

In other words, this algorithm work as a virtual graphic buffer.

The plot is precomputed and invisible elements are deleted.

This algorithm (o(n) complexity) features several advantages:

* no need to compute the distance between all the points

* the memory requirement is linearly proportional to the number of pixels

* the memory requirement is linearly proportional to the number of scatter points

* computational cost is linearly proportional to the number of scatter points 

This code has been successfully tested with **large datasets**:

* this algorithm is vectorized and many points are treated together.

* the number of points (chunk size) processed in a step can be selected.

* 100'000'000 points can be simplified in several minutes

## Example

Look at the examples [run_example.m](run_example.m).

A dataset with random points is successfully simplified (by a factor of 40) without changing the scatter plot result.



    

    



## Compatibility

* Tested with MATLAB R2018b.

* No toolboxes are required.

* Compatibility with GNU Octave not tested but probably easy to achieve.

## Author

**Thomas Guillod** - [GitHub Profile](https://github.com/otvam)

## License

This project is licensed under the **BSD License**, see [LICENSE.md](LICENSE.md).