https://github.com/makieorg/topoplots.jl

Makie topo plot recipes, for neuro-science, geo plots and anyone needing surface plots from unstructured data
https://github.com/makieorg/topoplots.jl

Last synced: about 1 month ago
JSON representation

Makie topo plot recipes, for neuro-science, geo plots and anyone needing surface plots from unstructured data

• Host: GitHub
• URL: https://github.com/makieorg/topoplots.jl
• Owner: MakieOrg
• License: mit
• Created: 2022-05-11T14:49:29.000Z (over 2 years ago)
• Default Branch: master
• Last Pushed: 2024-10-24T17:15:40.000Z (about 2 months ago)
• Last Synced: 2024-10-25T10:18:17.388Z (about 2 months ago)
• Language: Julia
• Size: 138 MB
• Stars: 26
• Watchers: 4
• Forks: 8
• Open Issues: 13
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

Awesome Lists containing this project

README

        
# TopoPlots

[![Stable](https://img.shields.io/badge/docs-stable-blue.svg)](https://MakieOrg.github.io/TopoPlots.jl/stable)

[![Dev](https://img.shields.io/badge/docs-dev-blue.svg)](https://MakieOrg.github.io/TopoPlots.jl/dev)

[![Build Status](https://github.com/MakieOrg/TopoPlots.jl/actions/workflows/CI.yml/badge.svg?branch=master)](https://github.com/MakieOrg/TopoPlots.jl/actions/workflows/CI.yml?query=branch%3Amaster)

[![Coverage](https://codecov.io/gh/MakieOrg/TopoPlots.jl/branch/master/graph/badge.svg)](https://codecov.io/gh/MakieOrg/TopoPlots.jl)

A package for creating topoplots from data that were measured on arbitrarily positioned sensors.

Quickstart:

```julia

using TopoPlots, CairoMakie

topoplot(rand(10), rand(Point2f, 10); contours=(color=:white, linewidth=2), label_scatter=true, bounding_geometry=Rect)

```

![](https://user-images.githubusercontent.com/1010467/176476327-4d29d1db-456d-4148-89f1-fa84796dc14b.png)

```julia

using GLMakie

data, positions = TopoPlots.example_data()

eeg_topoplot(data[:, 340, 1]; positions=positions)

```

![](https://user-images.githubusercontent.com/1010467/175339668-56646201-1a1d-4484-bc44-2f0a41df98c1.png)

### Funding

Work by Benedikt Ehinger was funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) – Project-ID 251654672 – TRR 161