""" Andrew's Monotone Chain Convex Hull Algorithm. Reference: https://en.wikipedia.org/wiki/Convex_hull_algorithms#Andrew's_monotone_chain_algorithm Reference: Andrew, A. M. (1979). "Another efficient algorithm for convex hulls in two dimensions". Information Processing Letters, 9(5), 216-219. Andrew's monotone chain algorithm computes the convex hull of a set of 2D points in O(n log n) time. It first sorts the points lexicographically (by x-coordinate, and in case of a tie, by y-coordinate) and then constructs the lower and upper hulls in two separate O(n) passes. """ from __future__ import annotations from typing import NamedTuple class Point(NamedTuple): """ A 2D point with real-valued coordinates. >>> Point(0.0, 0.0) Point(x=0.0, y=0.0) >>> Point(1.5, -2.0) Point(x=1.5, y=-2.0) """ x: float y: float def cross_product_direction(origin: Point, point_a: Point, point_b: Point) -> float: """ Compute the 2D cross product of vectors (origin -> point_a) and (origin -> point_b). The return value encodes the orientation of the ordered triplet (origin, point_a, point_b): > 0 : Counter-clockwise turn (left turn) < 0 : Clockwise turn (right turn) = 0 : Collinear points Parameters: origin: The reference pivot point. point_a: The first endpoint. point_b: The second endpoint. Returns: The signed magnitude of the 2D cross product. >>> cross_product_direction(Point(0, 0), Point(1, 0), Point(1, 1)) 1 >>> cross_product_direction(Point(0, 0), Point(1, 1), Point(1, 0)) -1 >>> cross_product_direction(Point(0, 0), Point(1, 1), Point(2, 2)) 0 """ return (point_a.x - origin.x) * (point_b.y - origin.y) - (point_a.y - origin.y) * ( point_b.x - origin.x ) def monotone_chain(points: list[Point]) -> list[Point]: """ Compute the convex hull of a set of 2D points in counter-clockwise order using Andrew's Monotone Chain algorithm. Parameters: points: A list of 2D points. Returns: A list of vertices forming the convex hull in counter-clockwise order. Time Complexity: O(n log n) where n is the number of points. Space Complexity: O(n) Examples: >>> monotone_chain([]) [] >>> monotone_chain([Point(1, 1)]) [Point(x=1, y=1)] >>> monotone_chain([Point(0, 0), Point(1, 1)]) [Point(x=0, y=0), Point(x=1, y=1)] >>> square_points = [ ... Point(0, 0), Point(2, 0), Point(2, 2), Point(0, 2), ... Point(1, 1), Point(1, 0.5) ... ] >>> monotone_chain(square_points) [Point(x=0, y=0), Point(x=2, y=0), Point(x=2, y=2), Point(x=0, y=2)] >>> triangle_with_duplicates = [ ... Point(0, 0), Point(4, 0), Point(2, 3), ... Point(0, 0), Point(4, 0), Point(2, 1) ... ] >>> monotone_chain(triangle_with_duplicates) [Point(x=0, y=0), Point(x=4, y=0), Point(x=2, y=3)] >>> collinear_points = [Point(0, 0), Point(1, 1), Point(2, 2), Point(3, 3)] >>> monotone_chain(collinear_points) [Point(x=0, y=0), Point(x=3, y=3)] """ unique_sorted_points = sorted(set(points)) if len(unique_sorted_points) <= 1: return unique_sorted_points # Build the lower hull: only keep counter-clockwise turns lower_hull: list[Point] = [] for candidate_point in unique_sorted_points: while ( len(lower_hull) >= 2 and cross_product_direction(lower_hull[-2], lower_hull[-1], candidate_point) <= 0 ): lower_hull.pop() lower_hull.append(candidate_point) # Build the upper hull: only keep counter-clockwise turns upper_hull: list[Point] = [] for candidate_point in reversed(unique_sorted_points): while ( len(upper_hull) >= 2 and cross_product_direction(upper_hull[-2], upper_hull[-1], candidate_point) <= 0 ): upper_hull.pop() upper_hull.append(candidate_point) # Omit the last point of each half because it is repeated at the ends return lower_hull[:-1] + upper_hull[:-1] if __name__ == "__main__": import doctest doctest.testmod()