From f5b745e5c53b7ae2db5c0912332e7beb5cdcc5f9 Mon Sep 17 00:00:00 2001 From: Zach Burnett Date: Tue, 3 Jan 2023 16:17:14 -0500 Subject: [PATCH] vectorized and documentation --- src/stcal/jump/circle.py | 139 +++++++++++++++++++++------------------ 1 file changed, 75 insertions(+), 64 deletions(-) diff --git a/src/stcal/jump/circle.py b/src/stcal/jump/circle.py index 18e2c096e..2a66c90bc 100644 --- a/src/stcal/jump/circle.py +++ b/src/stcal/jump/circle.py @@ -1,15 +1,15 @@ -import math import random from typing import Union, Tuple, List import numpy +RELATIVE_TOLERANCE = 1 + 1e-14 + class Circle: - RELATIVE_TOLERANCE = 1 + 1e-14 def __init__(self, center: Tuple[float, float], radius: float): - self.center = center + self.center = numpy.array(center) self.radius = radius @classmethod @@ -31,15 +31,13 @@ def from_points(cls, points: List[Tuple[float, float]]) -> 'Circle': if len(points) == 0: return None elif len(points) == 1: - return Circle(points[0], 0.0) + return cls(center=points[0], radius=0.0) elif len(points) == 2: - a, b = points + points = numpy.array(points) + center = numpy.mean(points, axis=0) + radius = numpy.max(numpy.hypot(*(center - points).T)) - cx = (a[0] + b[0]) / 2 - cy = (a[1] + b[1]) / 2 - r0 = math.hypot(cx - a[0], cy - a[1]) - r1 = math.hypot(cx - b[0], cy - b[1]) - return cls((cx, cy), max(r0, r1)) + return cls(center=center, radius=radius) else: # Convert to float and randomize order shuffled_points = [(float(point[0]), float(point[1])) for point in points] @@ -55,7 +53,7 @@ def from_points(cls, points: List[Tuple[float, float]]) -> 'Circle': def __getitem__(self, index: int) -> Union[tuple, float]: if index == 0: - return self.center + return tuple(self.center) elif index == 1: return self.radius else: @@ -63,82 +61,90 @@ def __getitem__(self, index: int) -> Union[tuple, float]: def __add__(self, delta: Tuple[float, float]) -> 'Circle': if isinstance(delta, float): - delta = [delta, delta] - return self.__class__((self.center[0] + delta[0], self.center[1] + delta[1]), self.radius) + delta = (delta, delta) + return self.__class__(center=self.center + numpy.array(delta), radius=self.radius) def __mul__(self, factor: float) -> 'Circle': - return self.__class__(self.center, self.radius + factor) + return self.__class__(center=self.center, radius=self.radius + factor) - def __contains__(self, point: Tuple[float, float]): - return math.hypot(point[0] - self.center[0], point[1] - self.center[1]) <= self.radius * self.RELATIVE_TOLERANCE + def __contains__(self, point: Tuple[float, float]) -> bool: + return numpy.hypot(*(numpy.array(point) - self.center)) <= self.radius * RELATIVE_TOLERANCE def __eq__(self, other: 'Circle') -> bool: - return self.center[0] == other.center[0] and self.center[1] == other.center[1] and self.radius == other.radius + return numpy.all(self.center == other.center) and self.radius == other.radius def almost_equals(self, other: 'Circle', delta: float = None) -> bool: if delta is None: - delta = self.RELATIVE_TOLERANCE - return numpy.allclose([self.center[0], self.center[1], self.radius], - [other.center[0], other.center[1], other.radius], atol=delta) + delta = RELATIVE_TOLERANCE + return numpy.allclose(self.center, other.center, atol=delta) and \ + numpy.allclose(self.radius, other.radius, atol=delta) def __repr__(self) -> str: return f'{self.__class__.__name__}({self.center}, {self.radius})' def _expand_circle_from_one_point( - a: Tuple[float, float], + known_boundary_point: Tuple[float, float], points: List[Tuple[float, float]], ) -> Circle: """ - One boundary point known + iteratively expand a circle from one known boundary point to enclose the given set of points from https://www.nayuki.io/page/smallest-enclosing-circle """ - circle = Circle(a, 0.0) - for (b_index, b) in enumerate(points): - if b not in circle: + circle = Circle(known_boundary_point, 0.0) + for point_index, point in enumerate(points): + if point not in circle: if circle.radius == 0.0: - circle = Circle.from_points([a, b]) + circle = Circle.from_points([known_boundary_point, point]) else: - circle = _expand_circle_from_two_points(a, b, points[: b_index + 1]) + circle = _expand_circle_from_two_points(known_boundary_point, point, points[: point_index + 1]) return circle def _expand_circle_from_two_points( - a: Tuple[float, float], - b: Tuple[float, float], + known_boundary_point_a: Tuple[float, float], + known_boundary_point_b: Tuple[float, float], points: List[Tuple[float, float]], ) -> Circle: """ - Two boundary points known + iteratively expand a circle from two known boundary points to enclose the given set of points from https://www.nayuki.io/page/smallest-enclosing-circle """ - circ = Circle.from_points([a, b]) + known_boundary_points = numpy.array([known_boundary_point_a, known_boundary_point_b]) + + circle = Circle.from_points(known_boundary_points) left = None right = None - px, py = a - qx, qy = b # For each point not in the two-point circle - for r in points: - if r in circ: + for point in points: + if point in circle: continue # Form a circumcircle and classify it on left or right side - cross = _triangle_cross_product(((px, py), (qx, qy), (r[0], r[1]))) - c = circumcircle(a, b, r) - cross_2 = _triangle_cross_product(((px, py), (qx, qy), (c.center[0], c.center[1]))) - if c is None: + circumcircle_cross_product = _triangle_cross_product((*known_boundary_points, point)) + circumcircle = circumcircle_from_points(known_boundary_point_a, known_boundary_point_b, point) + circumcenter_cross_product = _triangle_cross_product((*known_boundary_points, circumcircle.center)) + if circumcircle is None: continue - elif cross > 0.0 and (left is None or cross_2 > _triangle_cross_product(((px, py), (qx, qy), left.center))): - left = c - elif cross < 0.0 and (right is None or cross_2 < _triangle_cross_product(((px, py), (qx, qy), right.center))): - right = c + elif circumcircle_cross_product > 0.0 and \ + ( + left is None or + circumcenter_cross_product > _triangle_cross_product((*known_boundary_points, left.center)) + ): + left = circumcircle + elif circumcircle_cross_product < 0.0 and \ + ( + right is None or + circumcenter_cross_product < _triangle_cross_product((*known_boundary_points, right.center)) + ): + right = circumcircle # Select which circle to return if left is None and right is None: - return circ + return circle elif left is None: return right elif right is None: @@ -147,39 +153,44 @@ def _expand_circle_from_two_points( return left if (left.radius <= right.radius) else right -def circumcircle( +def circumcircle_from_points( a: Tuple[float, float], b: Tuple[float, float], c: Tuple[float, float], ) -> Circle: """ + build a circumcircle from three points, using the algorithm from https://en.wikipedia.org/wiki/Circumscribed_circle from https://www.nayuki.io/page/smallest-enclosing-circle """ - # Mathematical algorithm from Wikipedia: Circumscribed circle - ox = (min(a[0], b[0], c[0]) + max(a[0], b[0], c[0])) / 2 - oy = (min(a[1], b[1], c[1]) + max(a[1], b[1], c[1])) / 2 - ax = a[0] - ox - ay = a[1] - oy - bx = b[0] - ox - by = b[1] - oy - cx = c[0] - ox - cy = c[1] - oy - d = (ax * (by - cy) + bx * (cy - ay) + cx * (ay - by)) * 2.0 - if d == 0.0: + points = numpy.array([a, b, c]) + incenter = ((numpy.min(points, axis=0) + numpy.max(points, axis=0)) / 2) + + relative_points = points - incenter + a, b, c = relative_points + + intermediate = 2 * (a[0] * (b[1] - c[1]) + b[0] * (c[1] - a[1]) + c[0] * (a[1] - b[1])) + if intermediate == 0: return None - x = ox + ((ax * ax + ay * ay) * (by - cy) + (bx * bx + by * by) * (cy - ay) + (cx * cx + cy * cy) * ( - ay - by)) / d - y = oy + ((ax * ax + ay * ay) * (cx - bx) + (bx * bx + by * by) * (ax - cx) + (cx * cx + cy * cy) * ( - bx - ax)) / d - ra = math.hypot(x - a[0], y - a[1]) - rb = math.hypot(x - b[0], y - b[1]) - rc = math.hypot(x - c[0], y - c[1]) - return Circle((x, y), max(ra, rb, rc)) + + relative_circumcenter = numpy.array([ + (a[0] ** 2 + a[1] ** 2) * (b[1] - c[1]) + + (b[0] ** 2 + b[1] ** 2) * (c[1] - a[1]) + + (c[0] ** 2 + c[1] ** 2) * (a[1] - b[1]), + (a[0] ** 2 + a[1] ** 2) * (c[0] - b[0]) + + (b[0] ** 2 + b[1] ** 2) * (a[0] - c[0]) + + (c[0] ** 2 + c[1] ** 2) * (b[0] - a[0]), + ]) / intermediate + + return Circle( + center=relative_circumcenter + incenter, + radius=numpy.max(numpy.hypot(*(relative_circumcenter - relative_points).T)), + ) def _triangle_cross_product(triangle: Tuple[Tuple[float, float], Tuple[float, float], Tuple[float, float]]) -> float: """ + calculates twice the signed area of the provided triangle from https://www.nayuki.io/page/smallest-enclosing-circle :param triangle: three points defining a triangle