Simplify Minimal Reproducible Example

debug.py

+import kdtree
+import numpy as np
+
+from sklearn import neighbors
+
+if __name__ == "__main__":
+    X = np.load("X.npy")
+    tree = kdtree.KDTree()
+
+    n_query = 10
+    k = 3
+    rng = np.random.RandomState(0)
+
+    Y = rng.rand(n_query, X.shape[1])
+
+    knn_indices = np.zeros((n_query, k), dtype=np.int32)
+    knn_distances = np.zeros((n_query, k), dtype=np.float64)
+    tree.query(Y, knn_indices, knn_distances)
+    # print(knn_distances)
+
+    sktree = neighbors.KDTree(X, leaf_size=256)
+
+    knn_distances, knn_indices = sktree.query(Y, k=k, return_distance=True)
+    # print(knn_distances)

runtime @ 4e6e7c2b

-Subproject commit 33678cf7a06ea219cfcfc9027c4d47ab4c519c13
+Subproject commit 4e6e7c2bb8a149984ccdbe72b851ab5975769a13