[pre-commit.ci] auto fixes from pre-commit.com hooks

for more information, see https://pre-commit.ci

Author: pre-commit-ci[bot]

data_structures/kd_tree/build_kdtree.py

@@ -1,8 +1,7 @@
 from data_structures.kd_tree.kd_node import KDNode
 
-def build_kdtree(
-        points: list[list[float]], depth: int = 0
-) -> KDNode | None:
+
+def build_kdtree(points: list[list[float]], depth: int = 0) -> KDNode | None:
     """
     Builds a KD-Tree from a list of points.
 
@@ -25,7 +24,7 @@ def build_kdtree(
 
     # Create node and construct subtrees
     left_points = points[:median_idx]
-    right_points = points[median_idx + 1:]
+    right_points = points[median_idx + 1 :]
 
     return KDNode(
         point=points[median_idx],

data_structures/kd_tree/example/hypercube_points.py

@@ -1,7 +1,8 @@
 import numpy as np
 
+
 def hypercube_points(
-        num_points: int, hypercube_size: float, num_dimensions: int
+    num_points: int, hypercube_size: float, num_dimensions: int
 ) -> np.ndarray:
     """
     Generates random points uniformly distributed within an n-dimensional hypercube.

data_structures/kd_tree/kd_node.py

@@ -1,5 +1,6 @@
 from typing import Optional
 
+
 class KDNode:
     """
     Represents a node in a KD-Tree.

data_structures/kd_tree/nearest_neighbour_search.py

@@ -1,8 +1,8 @@
 from data_structures.kd_tree.kd_node import KDNode
 
+
 def nearest_neighbour_search(
-        root: KDNode | None,
-        query_point: list[float]
+    root: KDNode | None, query_point: list[float]
 ) -> tuple[list[float] | None, float, int]:
     """
     Performs a nearest neighbor search in a KD-Tree for a given query point.
@@ -21,10 +21,7 @@ def nearest_neighbour_search(
     nearest_dist: float = float("inf")
     nodes_visited: int = 0
 
-    def search(
-            node: KDNode | None,
-            depth: int = 0
-    ) -> None:
+    def search(node: KDNode | None, depth: int = 0) -> None:
         """
         Recursively searches for the nearest neighbor in the KD-Tree.
 

data_structures/kd_tree/tests/test_kdtree.py

@@ -4,6 +4,7 @@
 from data_structures.kd_tree.kd_node import KDNode
 from data_structures.kd_tree.example.hypercube_points import hypercube_points
 
+
 def test_build_kdtree():
     """
     Test that KD-Tree is built correctly.
@@ -23,6 +24,7 @@ def test_build_kdtree():
     # Check that the tree is balanced to some extent (simplistic check)
     assert isinstance(kdtree, KDNode)
 
+
 def test_nearest_neighbour_search():
     """
     Test the nearest neighbor search function.
@@ -49,6 +51,7 @@ def test_nearest_neighbour_search():
     # Check that nodes visited is a non-negative integer
     assert nodes_visited >= 0
 
+
 def test_edge_cases():
     """
     Test edge cases such as an empty KD-Tree.
@@ -65,6 +68,8 @@ def test_edge_cases():
     assert nearest_dist == float("inf")
     assert nodes_visited == 0
 
+
 if __name__ == "__main__":
     import pytest
+
     pytest.main()