arrayType #244 with tests

README.md

@@ -63,16 +63,17 @@ Returns the zoom on which the cluster expands into several children (useful for
 
 ## Options
 
-| Option     | Default | Description                                                       |
-|------------|---------|-------------------------------------------------------------------|
-| minZoom    | 0       | Minimum zoom level at which clusters are generated.               |
-| maxZoom    | 16      | Maximum zoom level at which clusters are generated.               |
-| minPoints  | 2       | Minimum number of points to form a cluster.                       |
-| radius     | 40      | Cluster radius, in pixels.                                        |
-| extent     | 512     | (Tiles) Tile extent. Radius is calculated relative to this value. |
-| nodeSize   | 64      | Size of the KD-tree leaf node. Affects performance.               |
-| log        | false   | Whether timing info should be logged.                             |
-| generateId | false   | Whether to generate ids for input features in vector tiles.       |
+| Option     | Default      | Description                                                                         |
+|------------|--------------|-------------------------------------------------------------------------------------|
+| minZoom    | 0            | Minimum zoom level at which clusters are generated.                                 |
+| maxZoom    | 16           | Maximum zoom level at which clusters are generated.                                 |
+| minPoints  | 2            | Minimum number of points to form a cluster.                                         |
+| radius     | 40           | Cluster radius, in pixels.                                                          |
+| extent     | 512          | (Tiles) Tile extent. Radius is calculated relative to this value.                   |
+| nodeSize   | 64           | Size of the KD-tree leaf node. Affects performance.                                 |
+| arrayType  | Float32Array | TypedArray to use for storing coordinate values. Affects accuracy for small radius. |
+| log        | false        | Whether timing info should be logged.                                               |
+| generateId | false        | Whether to generate ids for input features in vector tiles.                         |
 
 ### Property map/reduce options
 

index.js

@@ -8,6 +8,7 @@ const defaultOptions = {
     radius: 40,   // cluster radius in pixels
     extent: 512,  // tile extent (radius is calculated relative to it)
     nodeSize: 64, // size of the KD-tree leaf node, affects performance
+    arrayType: Float32Array, // TypedArray to use for storing coordinate values, affects accuracy for small radius
     log: false,   // whether to log timing info
 
     // whether to generate numeric ids for input features (in vector tiles)
@@ -34,6 +35,7 @@ export default class Supercluster {
         this.trees = new Array(this.options.maxZoom + 1);
         this.stride = this.options.reduce ? 7 : 6;
         this.clusterProps = [];
+        this.coordinateRoundFn = this.options.arrayType === Float32Array ? fround : (x => x);
     }
 
     load(points) {
@@ -54,8 +56,8 @@ export default class Supercluster {
             if (!p.geometry) continue;
 
             const [lng, lat] = p.geometry.coordinates;
-            const x = fround(lngX(lng));
-            const y = fround(latY(lat));
+            const x = this.coordinateRoundFn(lngX(lng));
+            const y = this.coordinateRoundFn(latY(lat));
             // store internal point/cluster data in flat numeric arrays for performance
             data.push(
                 x, y, // projected point coordinates
@@ -220,7 +222,7 @@ export default class Supercluster {
     }
 
     _createTree(data) {
-        const tree = new KDBush(data.length / this.stride | 0, this.options.nodeSize, Float32Array);
+        const tree = new KDBush(data.length / this.stride | 0, this.options.nodeSize, this.options.arrayType);
         for (let i = 0; i < data.length; i += this.stride) tree.add(data[i], data[i + 1]);
         tree.finish();
         tree.data = data;

test/test.js

@@ -179,3 +179,31 @@ test('does not throw on zero items', () => {
         assert.deepEqual(index.getClusters([-180, -85, 180, 85], 0), []);
     });
 });
+
+test('very close points which shouldn\'t cluster, are clustered when using Float32Array', () => {
+    const index = new Supercluster({
+        maxZoom: 22,
+        extent: 8192,
+        radius: 4,
+        arrayType: Float32Array
+    }).load([
+        {type: 'Feature', geometry: {type: 'Point', coordinates: [-1.426798, 53.943034]}},
+        {type: 'Feature', geometry: {type: 'Point', coordinates: [-1.426799, 53.943034]}}
+    ]);
+
+    assert.equal(index.trees[22].ids.length, 1);
+});
+
+test('very close points which shouldn\'t cluster, are not clustered when using Float64Array', () => {
+    const index = new Supercluster({
+        maxZoom: 22,
+        extent: 8192,
+        radius: 4,
+        arrayType: Float64Array
+    }).load([
+        {type: 'Feature', geometry: {type: 'Point', coordinates: [-1.426798, 53.943034]}},
+        {type: 'Feature', geometry: {type: 'Point', coordinates: [-1.426799, 53.943034]}}
+    ]);
+
+    assert.equal(index.trees[22].ids.length, 2);
+});