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48 def test_different_points_1(self): 49 assert math.isclose(distance(1, 1, 2, 2), math.sqrt(2))

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30 def test_order_of_points(self): 31 assert distance(-3, 3, 1, 6) == distance(1, 6, -3, 3)

203 def distances(points1, points2): 204 # Much faster than any manual calculations 205 return scipy.spatial.distance.cdist(points1, points2, metric='euclidean')

27 def test_the_order_of_point_does_not_matter(self): 28 self.assertEqual(distance(9, 5, 5, 2), distance(5, 2, 9, 5))

7 def pathlength(x, y): 8 L = 0 9 for i in range(1, len(x)): 10 dL_squared = (x[i] - x[i - 1]) ** 2 + (y[i] - y[i - 1]) ** 2 11 L += sqrt(dL_squared) 12 return L

708 def _closest_point_1_ ( line , point ) : 709 """Find the point on line closest to the given point 710 >>> line = ... 711 >>> point = ... 712 >>> ClosestPoint = line.closestPoint ( point ) 713 """ 714 return _GeomFun.closestPoint ( point , line )

299 def distance_between_points(point_a, point_b): 300 """ 301 302 Vectorized Euclidean Distance 303 304 :param point_a: 305 :param point_b: 306 :return: 307 """ 308 return np.sqrt(np.sum((np.array(point_a) - np.array(point_b))**2))

7 @vectorize([float64(float64,float64,float64,float64,float64)]) 8 def line_to_point_distance(a,b,c,x,y): 9 return abs(a*x + b*y + c) / sqrt(a**2 + b**2)

46 def distance(x1, y1, x2, y2): 47 """Get the distance between two points.""" 48 return ((x2 - x1) ** 2 + (y2 - y1) ** 2) ** 0.5

30 def modified_distance(p1, p2): 31 """ Hack which modifies the distance between two points to be +inf 32 when these two points are in the same cluster. 33 """ 34 if union_find.find(p1) == union_find.find(p2): 35 dist = float('inf') 36 else: 37 dist = distance(p1, p2) 38 return dist