# Brownian Motion: A Numerical Experiment¶

Suppose we want to describe the motion of a particle in a gas:

• We could model all those little particles and their collisions.

• Model the motion of the big particle as random.

• Assume the particle gets a 'bump' in X and Y that is uniformly distributed between $-0.1$ and $0.1$.

Draw 10,000 random numbers modeling the bumps, print them, store them in the array bumps.

In [2]:
from random import uniform

bumps = []
for i in range(10000):
b = uniform(-0.1, 0.1)
bumps.append(b)


Plot a histogram to confirm the distribution of the bumps:

In [3]:
import matplotlib.pyplot as pt
pt.hist(bumps)

Out[3]:
(array([ 1006.,  1021.,   960.,   996.,  1056.,  1017.,   976.,   986.,
1000.,   982.]),
array([ -9.99919044e-02,  -7.99948511e-02,  -5.99977978e-02,
-4.00007445e-02,  -2.00036913e-02,  -6.63797718e-06,
1.99904153e-02,   3.99874686e-02,   5.99845219e-02,
7.99815751e-02,   9.99786284e-02]),
<a list of 10 Patch objects>)

Question 1: Where does the particle end up, on average?

Question 2: How does the average distance from the origin depend on the time?

Approach: Sum the bumps.

In [4]:
sum(bumps)

Out[4]:
-3.0897944581411325

How did it get there?

Plot the path.

In [5]:
pos = 0
path = []
for bump in bumps:
path.append(pos)
pos += bump
path.append(pos)

pt.plot(path)

Out[5]:
[<matplotlib.lines.Line2D at 0x7fbf595a14e0>]

Do 1000 of these. Store the endpoints in endpoints.

In [9]:
from random import normalvariate

endpoints = []

for realization in range(1000):
bumps = []
for i in range(10000):
b = normalvariate(0, 0.1)
bumps.append(b)

endpoint = sum(bumps)
endpoints.append(endpoint)


Plot a histogram of the endpoints:

In [7]:
pt.hist(endpoints)

Out[7]:
(array([   3.,   22.,   59.,  152.,  254.,  261.,  157.,   67.,   21.,    4.]),
array([-34.97767545, -28.09727797, -21.21688049, -14.33648301,
-7.45608553,  -0.57568805,   6.30470943,  13.18510691,
20.06550439,  26.94590187,  33.82629935]),
<a list of 10 Patch objects>)

To answer our question 1: What is the average endpoint?

In [35]:
sum(endpoints)/len(endpoints)

Out[35]:
0.03684222340619225

• It looks like the endpoints follow a Normal distribution.
• Is this result robust with respect to distribution choice? (We somewhat arbitrarily chose 'uniform'.)

Let's compute the average distance from the origin based on the length of the simulation.

In [24]:
from random import normalvariate

avg_dists = []
for nsteps in range(0, 1500, 100):
print(nsteps, "steps")

endpoints = []
for realization in range(1000):
bumps = []
for i in range(nsteps):
b = normalvariate(0, 0.1)
bumps.append(b)

endpoint = sum(bumps)
endpoints.append(endpoint)

avg_dists.append(sum(ep**2 for ep in endpoints) / len(endpoints))

0 steps
100 steps
200 steps
300 steps
400 steps
500 steps
600 steps
700 steps
800 steps
900 steps
1000 steps
1100 steps
1200 steps
1300 steps
1400 steps

In [25]:
pt.plot(avg_dists)

Out[25]:
[<matplotlib.lines.Line2D at 0x7fbf562128d0>]

Got a hypothesis?