https://github.com/juliageometry/regiontrees.jl

Quadtrees, Octrees, and more in Julia
https://github.com/juliageometry/regiontrees.jl

Last synced: 6 months ago
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Quadtrees, Octrees, and more in Julia

• Host: GitHub
• URL: https://github.com/juliageometry/regiontrees.jl
• Owner: JuliaGeometry
• License: other
• Created: 2016-12-03T02:58:49.000Z (over 9 years ago)
• Default Branch: master
• Last Pushed: 2022-08-26T16:54:11.000Z (almost 4 years ago)
• Last Synced: 2025-03-22T09:11:26.325Z (about 1 year ago)
• Language: Julia
• Homepage:
• Size: 1.21 MB
• Stars: 112
• Watchers: 5
• Forks: 22
• Open Issues: 6
• Metadata Files:
  • Readme: Readme.md
  • License: LICENSE.md

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README

          
# RegionTrees.jl: Quadtrees, Octrees, and their N-Dimensional Cousins

[![Build Status](https://github.com/rdeits/RegionTrees.jl/workflows/CI/badge.svg)](https://github.com/rdeits/RegionTrees.jl/actions?query=workflow%3ACI)

[![codecov](https://codecov.io/gh/rdeits/RegionTrees.jl/branch/master/graph/badge.svg)](https://codecov.io/gh/rdeits/RegionTrees.jl)

This Julia package is a lightweight framework for defining N-Dimensional region trees. In 2D, these are called *region quadtrees*, and in 3D they are typically referred to as *octrees*. A region tree is a simple data structure used to describe some kind of spatial data with varying resolution. Each element in the tree can be a leaf, representing an N-dimensional rectangle of space, or a node which is divided exactly in half along each axis into 2^N children. In addition, each element in a `RegionTrees.jl` tree can carry an arbitrary data payload. This makes it easy to use `RegionTrees` to approximate functions or describe other interesting spatial data.

## Features

* Lightweight code with few dependencies (only `StaticArrays.jl` and `Iterators.jl` are required)

* Optimized for speed and for few memory allocations

    * Liberal use of `@generated` functions lets us unroll most loops and prevent allocating temporary arrays

* Built-in support for general adaptive sampling techniques

## Usage

See [examples/demo/demo.ipynb](examples/demo/demo.ipynb) for a tour through the API. You can also check out:

* [examples/adaptive_distance_fields/adaptive_distances.ipynb](examples/adaptive_distance_fields/adaptive_distances.ipynb) for an adaptively-sampled distance field, or [AdaptiveDistanceFields.jl](https://github.com/rdeits/AdaptiveDistanceFields.jl) for a more complete example [1]

* [examples/adaptive_mpc/adaptive_mpc.ipynb](examples/adaptive_mpc/adaptive_mpc.ipynb) for an adaptive approximation of a model-predictive controller

[1] Frisken et al. "Adaptively Sampled Distance Fields: A General Representation of Shape for Computer Graphics". SIGGRAPH 2000.

## Gallery

An adaptively sampled distance field, from `examples/adaptive_distances.ipynb`:

![](http://rdeits.github.io/RegionTrees.jl/img/distance.svg)

An adaptively sampled model-predictive control problem, from `examples/adaptive_mpc.ipynb`:

![](http://rdeits.github.io/RegionTrees.jl/img/mpc.svg)

An adaptive distance field in 3D, from [AdaptiveDistanceFields.jl](https://github.com/rdeits/AdaptiveDistanceFields.jl):

![](http://rdeits.github.io/RegionTrees.jl/img/distance_3d.png)

## Related Packages

* [Morton.jl](https://github.com/JaneliaSciComp/Morton.jl)

* [Interpolations.jl](https://github.com/JuliaMath/Interpolations.jl)