SciPy — Spatial Data

Distance calculations

from scipy.spatial.distance import euclidean, cityblock
import numpy as np

point1 = np.array([1, 2, 3])
point2 = np.array([4, 5, 6])

print(f"Euclidean: {euclidean(point1, point2):.2f}")
print(f"Manhattan: {cityblock(point1, point2):.2f}")

Distance matrix

from scipy.spatial.distance import cdist
import numpy as np

points1 = np.array([[0, 0], [1, 1]])
points2 = np.array([[2, 2], [3, 3]])

distances = cdist(points1, points2)
print(f"Distance matrix:\n{distances}")

Voronoi diagram

from scipy.spatial import Voronoi, voronoi_plot_2d
import numpy as np

points = np.array([[0, 0], [1, 0], [0, 1], [1, 1]])
vor = Voronoi(points)

Convex hull

from scipy.spatial import ConvexHull
import numpy as np

points = np.array([[0, 0], [1, 0], [0, 1], [1, 1], [0.5, 0.5]])
hull = ConvexHull(points)
print(f"Hull vertices: {hull.vertices}")

KD-Tree

from scipy.spatial import KDTree
import numpy as np

points = np.array([[0, 0], [1, 0], [0, 1], [1, 1]])
tree = KDTree(points)

# Find nearest neighbors
dist, idx = tree.query([0.5, 0.5], k=2)
print(f"Nearest: {idx}, distances: {dist}")

Delaunay triangulation

from scipy.spatial import Delaunay
import numpy as np

points = np.array([[0, 0], [1, 0], [0, 1], [1, 1]])
tri = Delaunay(points)
print(f"Triangles:\n{tri.simplices}")

Mini Practice

1. Calculate distances between points
2. Build a KD-Tree
3. Create a convex hull
4. Perform Delaunay triangulation

Up Next

Continue with Image Processing - Image manipulation.