# Gauss test.py

Revision as of 21:41, 22 September 2015; view current revision

This page presents the program markov_disks_box.py, a Markov-chain algorithm for four disks in a square box of sides 1.

# Program

```import random

L = [[0.25, 0.25], [0.75, 0.25], [0.25, 0.75], [0.75, 0.75]]
sigma = 0.15
sigma_sq = sigma ** 2
delta = 0.1
n_steps = 1000
for steps in range(n_steps):
a = random.choice(L)
b = [a[0] + random.uniform(-delta, delta), a[1] + random.uniform(-delta, delta)]
min_dist = min((b[0] - c[0]) ** 2 + (b[1] - c[1]) ** 2 for c in L if c != a)
box_cond = min(b[0], b[1]) < sigma or max(b[0], b[1]) > 1.0 - sigma
if not (box_cond or min_dist < 4.0 * sigma ** 2):
a[:] = b
print L
```

# Version

See history for version information.

```import random, math

def gauss_test(sigma):
phi = random.uniform(0.0, 2.0 * math.pi)
Upsilon = random.uniform(0.0, 1.0)
Psi = - math.log(Upsilon)
r = sigma * math.sqrt(2.0 * Psi)
x = r * math.cos(phi)
y = r * math.sin(phi)
return [x, y]

nsamples = 50
for sample in range(nsamples):
[x, y] = gauss_test(1.0)
print x, y
```