Merge pull request #2 from Elan456/feat/insert-rectangles

Feat/insert rectangles

Author: Elan456

README.md

@@ -134,7 +134,7 @@ Allowed. For k-nearest, duplicates are de-duplicated by id. For range queries yo
 Yes! Use `delete(id, xy)` to remove specific items. You must provide both the ID and exact location for precise deletion. This handles cases where multiple items exist at the same location. If you're using `track_objects=True`, you can also use `delete_by_object(obj)` for convenient object-based deletion with O(1) lookup. The tree automatically merges nodes when item counts drop below capacity.
 
 **Can I store rectangles or circles?**
-The core stores points. To index objects with extent, insert whatever representative point you choose. For rectangles you can insert centers or build an AABB tree separately.
+Yes, you can store rectangles using the `RectQuadTree` class. Circles can be approximated with bounding boxes. See the [RectQuadTree docs](https://elan456.github.io/fastquadtree/api/rect_quadtree/) for details.
 
 ## License
 

benchmarks/quadtree_bench/engines.py

@@ -104,7 +104,7 @@ def _create_fastquadtree_engine(
 
     def build(points):
         qt = RustQuadTree(bounds, max_points, max_depth=max_depth)
-        qt.insert_many_points(points)
+        qt.insert_many(points)
         return qt
 
     def query(qt, queries):

docs/api/item.md

@@ -0,0 +1,4 @@
+# fastquadtree.PointItem
+::: fastquadtree.PointItem
+    options:
+        inherited_members: true

docs/api/point_item.md

@@ -1,2 +1,4 @@
 # fastquadtree.QuadTree
 ::: fastquadtree.QuadTree
+    options:
+        inherited_members: true

docs/api/quadtree.md

@@ -0,0 +1,4 @@
+# fastquadtree.RectItem
+::: fastquadtree.RectItem
+    options:
+        inherited_members: true

docs/api/rect_item.md

@@ -0,0 +1,4 @@
+# fastquadtree.RectQuadTree
+::: fastquadtree.RectQuadTree
+    options:
+        inherited_members: true

docs/api/rect_quadtree.md

@@ -116,7 +116,7 @@ pts = [(random.random()*1000, random.random()*1000) for _ in range(N)]
 qt = QuadTree((0, 0, 1000, 1000), capacity=32)
 
 t0 = time.perf_counter()
-qt.insert_many_points(pts)
+qt.insert_many(pts)
 t1 = time.perf_counter()
 
 hits = qt.query((250, 250, 750, 750))
@@ -132,4 +132,4 @@ print(f"Build: {(t1-t0):.3f}s  Query: {(t2-t1):.3f}s  Hits: {len(hits)}")
 * **Use `capacity` 8 to 64** for most workloads
   If data is highly skewed, set a `max_depth` to avoid very deep trees.
 * **Use `clear()` to reset** when most points are moving rather than deleting and reinserting.
-* **Use `insert_many_points()`** to bulk load a large batch of points at once.
+* **Use `insert_many()`** to bulk load a large batch of points at once.

docs/quickstart.md

@@ -12,6 +12,14 @@ python interactive/interactive_v2.py
 
 ![Interactive_V2_Screenshot](https://raw.githubusercontent.com/Elan456/fastquadtree/main/assets/interactive_v2_screenshot.png)
 
+## 1.5 Interactive Demo with Rectangles
+- Similar to the above demo, but uses rectangles instead of points
+- If the rectangles intersect at all with the query area, they will be highlighted in red
+
+```bash
+pip install -r interactive/requirements.txt
+python interactive/interactive_v2_rect.py
+```
 
 ## 2. Ball Pit  
 - Spawn balls in a pit with physics-based collisions

docs/runnables.md

@@ -16,7 +16,7 @@
 
 
 def draw_quadtree(tree: QuadTree):
-    bboxs = tree.get_all_rectangles()
+    bboxs = tree.get_all_node_boundaries()
     for bbox in bboxs:
         rect = (
             bbox[0] - camera_x,