https://github.com/kolaru/particlepcarepresentation.jl

https://github.com/kolaru/particlepcarepresentation.jl

Last synced: 24 days ago
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• Host: GitHub
• URL: https://github.com/kolaru/particlepcarepresentation.jl
• Owner: Kolaru
• License: mit
• Created: 2022-06-16T18:30:52.000Z (over 2 years ago)
• Default Branch: master
• Last Pushed: 2024-02-22T15:09:07.000Z (11 months ago)
• Last Synced: 2024-10-23T06:57:53.549Z (2 months ago)
• Language: Julia
• Size: 111 KB
• Stars: 2
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# ParticlePCARepresentation

This is a simple utility package to apply PCA on data representing

particles and to visualize it.

The visualization is done by describing the "effect" of a component on

a particle by an arrow on the particle.

The direction and relative length of the arrows thus inform on the shape

of the associated collective motion.

## Example (random data)

![An example with 3 particles](simple2d.png)

## Usage

### Installation

Once you have [Julia](https://julialang.org),

open the REPL in package mode (using `]`) and type

```

add GLMakie https://github.com/Kolaru/ParticlePCARepresentation.jl

```

Then you will be able to run the examples in the `example` folder.

### Functions

Firstly we provide the `ParticlePCA` type that compute PCA for data shaped

in a `(ndims x nparticles x nobs)` array.

It also allows to retrieve the components (using the `projection`) function

as an `(ndims x nparticles x ncomps)` array.

Internally the PCA is performed by concatenating the first two dimensions,

that is by erasing the information that the data represents particles.

We also provide the functions `plot_component2D` and `plot_component3D`

to plot the PCA using the above mentioned representation in 2D

(projected on two axes) or 3D.

The example use `GLMakie.jl` but any of the `Makie.jl` backend should work.