# Lorenz Attractor¶

by Paulo Marques, 2013/09/21

This notebook implements the beautiful Lorenz Attractor in Python. The Lorenz Attractor is probably the most ilustrative example of a system that exibits cahotic behaviour. Slightly changing the initial conditions of the system leads to completely different solutions. The system itself corresponds to the movement of a point particle in a 3D space over time.

![Lorenz Attractor](http://upload.wikimedia.org/wikipedia/commons/e/e0/Lorenz.png)

The system is formally described by three different differential equations. These equations represent the movement of a point $(x, y, z)$ in space over time. In the following equations, $t$ represents time, $\sigma$, $\rho$, $\beta$ are constants.

$$\frac{dx}{dt} = \sigma (y - x)$$$$\frac{dy}{dt} = x (\rho - z) - y$$$$\frac{dz}{dt} = x y - \beta z$$

Let's implement it in python.

Let's start by importing some basic libraries.

In [1]:
%pylab inline
from scipy.integrate import odeint
from mpl_toolkits.mplot3d.axes3d import Axes3D

Populating the interactive namespace from numpy and matplotlib


We need to define the system of differential equations as an equation of the form: ${\bf r}' = {\bf f}({\bf r},t)$ where ${\bf r} = (x, y, z)$ and ${\bf f}({\bf r},t)$ is the mapping function.

In [2]:
def f(r, t):
(x, y, z) = r

# The Lorenz equations
dx_dt = sigma*(y - x)
dy_dt = x*(rho - z) - y
dz_dt = x*y - beta*z

return [dx_dt, dy_dt, dz_dt]


Let's define the initial conditions of the system ${\bf r}_0 = (x_0, y_0, z_0)$, the constants $\sigma$, $\rho$ and $\beta$ and a time grid.

In [3]:
# Initial position in space
r0 = [0.1, 0.0, 0.0]

# Constants sigma, rho and beta
sigma = 10.0
rho   = 28.0
beta  = 8.0/3.0

# Time grid
tf = 100.0
t = linspace(0, tf, tf*100)


Now let's solve the differencial equations numericaly and extract the corresponding $(x, y, z)$:

In [4]:
pos = odeint(f, r0, t)

x = pos[:, 0]
y = pos[:, 1]
z = pos[:, 2]


Let's see how it looks in 3D.

In [5]:
fig = figure(figsize=(16,10))
ax = fig.gca(projection='3d')
ax.plot(x, y, z)

Out[5]:
[<mpl_toolkits.mplot3d.art3d.Line3D at 0x111a61bd0>]

Let's see different cuts around the axes:

In [6]:
fig, ax = subplots(1, 3, sharex=True, sharey=True, figsize=(16,8))

ax[0].plot(x, y)
ax[0].set_title('X-Y cut')

ax[1].plot(x, z)
ax[1].set_title('X-Z cut')

ax[2].plot(y, z)
ax[2].set_title('Y-Z cut')

Out[6]:
<matplotlib.text.Text at 0x112197a10>

# MIT LICENSE¶

Copyright (C) 2013 Paulo Marques ([email protected])

Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.