Trieste Lectures 2015

From Werner KRAUTH

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import random, math import random, math
- +
def event(a, b, dirc, sigma): def event(a, b, dirc, sigma):
d_perp = abs(b[not dirc] - a[not dirc]) % 1.0 d_perp = abs(b[not dirc] - a[not dirc]) % 1.0

Revision as of 11:47, 15 September 2015

!Lecture 1: Programs

!Lecture 2: Programs

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
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