## PySpart
[](https://github.com/habedi/spart/tree/main/pyspart/LICENSE)
[](https://github.com/habedi/spart/tree/main/pyspart)
[](https://pypi.org/project/pyspart)
Python bindings for the [Spart](https://github.com/habedi/spart) library.
### Installation
```bash
pip install pyspart
````
### Examples
Below are some examples of how to use the different trees in PySpart.
#### Quadtree (2D)
```python
from pyspart import Quadtree, Point2D
# Define the bounding area for the Quadtree.
boundary = {"x": 0.0, "y": 0.0, "width": 10.0, "height": 10.0}
# Create a new Quadtree with a maximum capacity of 3 points per node.
tree = Quadtree(boundary, 3)
# Define some 2D points.
point1 = Point2D(1.0, 2.0, "Point1")
point2 = Point2D(3.0, 4.0, "Point2")
point3 = Point2D(5.0, 6.0, "Point3")
point4 = Point2D(7.0, 8.0, "Point4")
point5 = Point2D(2.0, 3.0, "Point5")
# Insert points into the Quadtree.
tree.insert(point1)
tree.insert(point2)
tree.insert(point3)
tree.insert(point4)
tree.insert(point5)
# Perform a k-nearest neighbor (kNN) search.
neighbors = tree.knn_search(point1, 2)
print(f"kNN search results for {point1}: {neighbors}")
# Perform a range search with a radius of 5.0.
range_points = tree.range_search(point1, 5.0)
print(f"Range search results for {point1}: {range_points}")
# Remove a point from the tree.
tree.delete(point1)
```
#### Octree (3D)
```python
from pyspart import Octree, Point3D
# Define the bounding area for the Octree.
boundary = {"x": 0.0, "y": 0.0, "z": 0.0, "width": 10.0, "height": 10.0, "depth": 10.0}
# Create a new Octree with a maximum capacity of 3 points per node.
tree = Octree(boundary, 3)
# Define some 3D points.
point1 = Point3D(1.0, 2.0, 3.0, "Point1")
point2 = Point3D(3.0, 4.0, 5.0, "Point2")
point3 = Point3D(5.0, 6.0, 7.0, "Point3")
point4 = Point3D(7.0, 8.0, 9.0, "Point4")
point5 = Point3D(2.0, 3.0, 4.0, "Point5")
# Insert points into the Octree.
tree.insert(point1)
tree.insert(point2)
tree.insert(point3)
tree.insert(point4)
tree.insert(point5)
# Perform a kNN search.
neighbors = tree.knn_search(point1, 2)
print(f"kNN search results for {point1}: {neighbors}")
# Perform a range search with a radius of 5.0.
range_points = tree.range_search(point1, 5.0)
print(f"Range search results for {point1}: {range_points}")
# Remove a point from the tree.
tree.delete(point1)
```
#### Kd-tree (3D)
```python
from pyspart import KdTree3D, Point3D
# Create a new Kd-tree for 3D points.
tree = KdTree3D()
# Define some 3D points.
point1 = Point3D(1.0, 2.0, 3.0, "Point1")
point2 = Point3D(3.0, 4.0, 5.0, "Point2")
point3 = Point3D(5.0, 6.0, 7.0, "Point3")
point4 = Point3D(7.0, 8.0, 9.0, "Point4")
point5 = Point3D(2.0, 3.0, 4.0, "Point5")
# Insert points into the Kd-tree.
tree.insert(point1)
tree.insert(point2)
tree.insert(point3)
tree.insert(point4)
tree.insert(point5)
# Perform a kNN search.
neighbors = tree.knn_search(point1, 2)
print(f"kNN search results for {point1}: {neighbors}")
# Perform a range search with a radius of 5.0.
range_points = tree.range_search(point1, 5.0)
print(f"Range search results for {point1}: {range_points}")
# Remove a point from the tree.
tree.delete(point1)
```
#### R-tree (3D)
```python
from pyspart import RTree3D, Point3D
# Create a new R-tree with a maximum capacity of 4 points per node.
tree = RTree3D(4)
# Define some 3D points.
point1 = Point3D(1.0, 2.0, 3.0, "Point1")
point2 = Point3D(3.0, 4.0, 5.0, "Point2")
point3 = Point3D(5.0, 6.0, 7.0, "Point3")
point4 = Point3D(7.0, 8.0, 9.0, "Point4")
point5 = Point3D(2.0, 3.0, 4.0, "Point5")
# Insert points into the R-tree.
tree.insert(point1)
tree.insert(point2)
tree.insert(point3)
tree.insert(point4)
tree.insert(point5)
# Perform a kNN search.
neighbors = tree.knn_search(point1, 2)
print(f"kNN search results for {point1}: {neighbors}")
# Perform a range search with a radius of 5.0.
range_points = tree.range_search(point1, 5.0)
print(f"Range search results for {point1}: {range_points}")
# Remove a point from the tree.
tree.delete(point1)
```
#### R*-tree (3D)
```python
from pyspart import RStarTree3D, Point3D
# Create a new R*-tree with a maximum capacity of 4 points per node.
tree = RStarTree3D(4)
# Define some 3D points.
point1 = Point3D(1.0, 2.0, 3.0, "Point1")
point2 = Point3D(3.0, 4.0, 5.0, "Point2")
point3 = Point3D(5.0, 6.0, 7.0, "Point3")
point4 = Point3D(7.0, 8.0, 9.0, "Point4")
point5 = Point3D(2.0, 3.0, 4.0, "Point5")
# Insert points into the R*-tree.
tree.insert(point1)
tree.insert(point2)
tree.insert(point3)
tree.insert(point4)
tree.insert(point5)
# Perform a kNN search.
neighbors = tree.knn_search(point1, 2)
print(f"kNN search results for {point1}: {neighbors}")
# Perform a range search with a radius of 5.0.
range_points = tree.range_search(point1, 5.0)
print(f"Range search results for {point1}: {range_points}")
# Remove a point from the tree.
tree.delete(point1)
```
Check out the [examples](https://github.com/habedi/spart/tree/main/pyspart/examples) directory for more examples.
### Serialization
In Python, you can use the `save` and `load` methods to serialize and deserialize the tree to and from a file:
```python
from pyspart import Quadtree, Point2D
# Create a Quadtree and insert some points
boundary = {"x": 0.0, "y": 0.0, "width": 100.0, "height": 100.0}
qt = Quadtree(boundary, 4)
qt.insert(Point2D(10.0, 20.0, "point1"))
qt.insert(Point2D(50.0, 50.0, "point2"))
# Save the tree to a file
qt.save("quadtree.spart")
# Load the tree from the file
loaded_qt = Quadtree.load("quadtree.spart")
```
### License
PySpart is licensed under the [MIT License](https://github.com/habedi/spart/tree/main/pyspart/LICENSE).
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"description": "## PySpart\n\n[](https://github.com/habedi/spart/tree/main/pyspart/LICENSE)\n[](https://github.com/habedi/spart/tree/main/pyspart)\n[](https://pypi.org/project/pyspart)\n\nPython bindings for the [Spart](https://github.com/habedi/spart) library.\n\n### Installation\n\n```bash\npip install pyspart\n````\n\n### Examples\n\nBelow are some examples of how to use the different trees in PySpart.\n\n#### Quadtree (2D)\n\n```python\nfrom pyspart import Quadtree, Point2D\n\n# Define the bounding area for the Quadtree.\nboundary = {\"x\": 0.0, \"y\": 0.0, \"width\": 10.0, \"height\": 10.0}\n\n# Create a new Quadtree with a maximum capacity of 3 points per node.\ntree = Quadtree(boundary, 3)\n\n# Define some 2D points.\npoint1 = Point2D(1.0, 2.0, \"Point1\")\npoint2 = Point2D(3.0, 4.0, \"Point2\")\npoint3 = Point2D(5.0, 6.0, \"Point3\")\npoint4 = Point2D(7.0, 8.0, \"Point4\")\npoint5 = Point2D(2.0, 3.0, \"Point5\")\n\n# Insert points into the Quadtree.\ntree.insert(point1)\ntree.insert(point2)\ntree.insert(point3)\ntree.insert(point4)\ntree.insert(point5)\n\n# Perform a k-nearest neighbor (kNN) search.\nneighbors = tree.knn_search(point1, 2)\nprint(f\"kNN search results for {point1}: {neighbors}\")\n\n# Perform a range search with a radius of 5.0.\nrange_points = tree.range_search(point1, 5.0)\nprint(f\"Range search results for {point1}: {range_points}\")\n\n# Remove a point from the tree.\ntree.delete(point1)\n```\n\n#### Octree (3D)\n\n```python\nfrom pyspart import Octree, Point3D\n\n# Define the bounding area for the Octree.\nboundary = {\"x\": 0.0, \"y\": 0.0, \"z\": 0.0, \"width\": 10.0, \"height\": 10.0, \"depth\": 10.0}\n\n# Create a new Octree with a maximum capacity of 3 points per node.\ntree = Octree(boundary, 3)\n\n# Define some 3D points.\npoint1 = Point3D(1.0, 2.0, 3.0, \"Point1\")\npoint2 = Point3D(3.0, 4.0, 5.0, \"Point2\")\npoint3 = Point3D(5.0, 6.0, 7.0, \"Point3\")\npoint4 = Point3D(7.0, 8.0, 9.0, \"Point4\")\npoint5 = Point3D(2.0, 3.0, 4.0, \"Point5\")\n\n# Insert points into the Octree.\ntree.insert(point1)\ntree.insert(point2)\ntree.insert(point3)\ntree.insert(point4)\ntree.insert(point5)\n\n# Perform a kNN search.\nneighbors = tree.knn_search(point1, 2)\nprint(f\"kNN search results for {point1}: {neighbors}\")\n\n# Perform a range search with a radius of 5.0.\nrange_points = tree.range_search(point1, 5.0)\nprint(f\"Range search results for {point1}: {range_points}\")\n\n# Remove a point from the tree.\ntree.delete(point1)\n```\n\n#### Kd-tree (3D)\n\n```python\nfrom pyspart import KdTree3D, Point3D\n\n# Create a new Kd-tree for 3D points.\ntree = KdTree3D()\n\n# Define some 3D points.\npoint1 = Point3D(1.0, 2.0, 3.0, \"Point1\")\npoint2 = Point3D(3.0, 4.0, 5.0, \"Point2\")\npoint3 = Point3D(5.0, 6.0, 7.0, \"Point3\")\npoint4 = Point3D(7.0, 8.0, 9.0, \"Point4\")\npoint5 = Point3D(2.0, 3.0, 4.0, \"Point5\")\n\n# Insert points into the Kd-tree.\ntree.insert(point1)\ntree.insert(point2)\ntree.insert(point3)\ntree.insert(point4)\ntree.insert(point5)\n\n# Perform a kNN search.\nneighbors = tree.knn_search(point1, 2)\nprint(f\"kNN search results for {point1}: {neighbors}\")\n\n# Perform a range search with a radius of 5.0.\nrange_points = tree.range_search(point1, 5.0)\nprint(f\"Range search results for {point1}: {range_points}\")\n\n# Remove a point from the tree.\ntree.delete(point1)\n```\n\n#### R-tree (3D)\n\n```python\nfrom pyspart import RTree3D, Point3D\n\n# Create a new R-tree with a maximum capacity of 4 points per node.\ntree = RTree3D(4)\n\n# Define some 3D points.\npoint1 = Point3D(1.0, 2.0, 3.0, \"Point1\")\npoint2 = Point3D(3.0, 4.0, 5.0, \"Point2\")\npoint3 = Point3D(5.0, 6.0, 7.0, \"Point3\")\npoint4 = Point3D(7.0, 8.0, 9.0, \"Point4\")\npoint5 = Point3D(2.0, 3.0, 4.0, \"Point5\")\n\n# Insert points into the R-tree.\ntree.insert(point1)\ntree.insert(point2)\ntree.insert(point3)\ntree.insert(point4)\ntree.insert(point5)\n\n# Perform a kNN search.\nneighbors = tree.knn_search(point1, 2)\nprint(f\"kNN search results for {point1}: {neighbors}\")\n\n# Perform a range search with a radius of 5.0.\nrange_points = tree.range_search(point1, 5.0)\nprint(f\"Range search results for {point1}: {range_points}\")\n\n# Remove a point from the tree.\ntree.delete(point1)\n```\n\n#### R*-tree (3D)\n\n```python\nfrom pyspart import RStarTree3D, Point3D\n\n# Create a new R*-tree with a maximum capacity of 4 points per node.\ntree = RStarTree3D(4)\n\n# Define some 3D points.\npoint1 = Point3D(1.0, 2.0, 3.0, \"Point1\")\npoint2 = Point3D(3.0, 4.0, 5.0, \"Point2\")\npoint3 = Point3D(5.0, 6.0, 7.0, \"Point3\")\npoint4 = Point3D(7.0, 8.0, 9.0, \"Point4\")\npoint5 = Point3D(2.0, 3.0, 4.0, \"Point5\")\n\n# Insert points into the R*-tree.\ntree.insert(point1)\ntree.insert(point2)\ntree.insert(point3)\ntree.insert(point4)\ntree.insert(point5)\n\n# Perform a kNN search.\nneighbors = tree.knn_search(point1, 2)\nprint(f\"kNN search results for {point1}: {neighbors}\")\n\n# Perform a range search with a radius of 5.0.\nrange_points = tree.range_search(point1, 5.0)\nprint(f\"Range search results for {point1}: {range_points}\")\n\n# Remove a point from the tree.\ntree.delete(point1)\n```\n\nCheck out the [examples](https://github.com/habedi/spart/tree/main/pyspart/examples) directory for more examples.\n\n### Serialization\n\nIn Python, you can use the `save` and `load` methods to serialize and deserialize the tree to and from a file:\n\n```python\nfrom pyspart import Quadtree, Point2D\n\n# Create a Quadtree and insert some points\nboundary = {\"x\": 0.0, \"y\": 0.0, \"width\": 100.0, \"height\": 100.0}\nqt = Quadtree(boundary, 4)\nqt.insert(Point2D(10.0, 20.0, \"point1\"))\nqt.insert(Point2D(50.0, 50.0, 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