79 lines
1.8 KiB
Markdown
79 lines
1.8 KiB
Markdown
# orx-hash-grid
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2D space partitioning for fast point queries.
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## Usage
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`orx-hash-grid` provides the classes `HashGrid` and `Cell`, in most cases only `HashGrid` is used.
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Create a hash grid for a given radius.
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```kotlin
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val grid = HashGrid(radius)
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```
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Check for a given query point if the grid is free, i.e. there is no point in the grid at distance less than `radius` away from the
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query point.
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```kotlin
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grid.isFree(query)
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```
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Add a point to the hash grid structure:
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```kotlin
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grid.insert(point)
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```
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Iterate over all points in the hash grid:
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```kotlin
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for (point in grid.points()) {
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// do something with point
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}
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```
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## Extensions to standard library
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`orx-hash-grid` provides short-hand extension functions to `List<Vector2>`
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<hr>
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```kotlin
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fun List<Vector2>.filter(radius: Double) : List<Vector2>
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```
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filters the points in the list such that only points with an inter-distance of `radius` remain.
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```kotlin
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val points = (0 until 10_000).map { drawer.bounds.uniform() }
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val filtered = points.filter(20.0)
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```
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<hr>
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```kotlin
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fun List<Vector2>.hashGrid(radius: Double) : HashGrid
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```
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constructs a (mutable) `HashGrid` containing all points in the list.
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```kotlin
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val points = (0 until 10_000).map { drawer.bounds.uniform() }
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val hashGrid = points.hashGrid(20.0)
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```
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<hr>
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## References
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* `orx-noise` uses `HashGrid` to generate Poisson distributed points. [Link](https://github.com/openrndr/orx/blob/master/orx-noise/src/commonMain/kotlin/PoissonDisk.kt)
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<!-- __demos__ -->
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## Demos
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### DemoFilter01
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[source code](src/jvmDemo/kotlin/DemoFilter01.kt)
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### DemoHashGrid01
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[source code](src/jvmDemo/kotlin/DemoHashGrid01.kt)
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