# -----------------------------------------------------------------------------
# From Numpy to Python
# Copyright (2017) Nicolas P. Rougier - BSD license
# More information at https://github.com/rougier/numpy-book
# -----------------------------------------------------------------------------
import math
import random
import matplotlib.pyplot as plt
def DART_sampling_python(width=1.0, height=1.0, radius=0.025, k=100):
def squared_distance(p0, p1):
dx, dy = p0[0]-p1[0], p0[1]-p1[1]
return dx*dx+dy*dy
points = []
i = 0
last_success = 0
while True:
x = random.uniform(0, width)
y = random.uniform(0, height)
accept = True
for p in points:
if squared_distance(p, (x, y)) < radius*radius:
accept = False
break
if accept is True:
points.append((x, y))
if i-last_success > k:
break
last_success = i
i += 1
return points
if __name__ == '__main__':
plt.figure()
plt.subplot(1, 1, 1, aspect=1)
points = DART_sampling_python()
X = [x for (x, y) in points]
Y = [y for (x, y) in points]
plt.scatter(X, Y, s=10)
plt.xlim(0, 1)
plt.ylim(0, 1)
plt.show()