From Werner KRAUTH
import random, math
def event(a, b, dirc, sigma):
d_perp = abs(b[not dirc] - a[not dirc]) % 1.0
d_perp = min(d_perp, 1.0 - d_perp)
if d_perp > 2.0 * sigma:
return float("inf")
else:
d_para = math.sqrt(4.0 * sigma ** 2 - d_perp ** 2)
return (b[dirc] - a[dirc] - d_para + 1.0) % 1.0
L = [[0.25, 0.25], [0.25, 0.75], [0.75, 0.25], [0.75, 0.75]]
ltilde = 0.819284; sigma = 0.15
for iter in xrange(20000):
dirc = random.randint(0, 1)
print iter, dirc, L
distance_to_go = ltilde
next_a = random.choice(L)
while distance_to_go > 0.0:
a = next_a
event_min = distance_to_go
for b in [x for x in L if x != a]:
event_b = event(a, b, dirc, sigma)
if event_b < event_min:
next_a = b
event_min = event_b
a[dirc] = (a[dirc] + event_min) % 1.0
distance_to_go -= event_min
