Exploring the Lorenz System of Differential Equations

In this Notebook we explore the Lorenz system of differential equations:

$$ \begin{aligned} \dot{x} & = \sigma(y-x) \\ \dot{y} & = \rho x - y - xz \\ \dot{z} & = -\beta z + xy \end{aligned} $$

This is one of the classic systems in non-linear differential equations. It exhibits a range of different behaviors as the parameters ($\sigma$, $\beta$, $\rho$) are varied.


First, we import the needed things from IPython, NumPy, Matplotlib and SciPy.

In [1]:
%matplotlib inline
In [2]:
from IPython.html.widgets import interact, interactive
from IPython.display import clear_output, display, HTML
In [3]:
import numpy as np
from scipy import integrate

from matplotlib import pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
from matplotlib.colors import cnames
from matplotlib import animation

Computing the trajectories and plotting the result

We define a function that can integrate the differential equations numerically and then plot the solutions. This function has arguments that control the parameters of the differential equation ($\sigma$, $\beta$, $\rho$), the numerical integration (N, max_time) and the visualization (angle).

In [4]:
def solve_lorenz(N=10, angle=0.0, max_time=4.0, sigma=10.0, beta=8./3, rho=28.0):

    fig = plt.figure()
    ax = fig.add_axes([0, 0, 1, 1], projection='3d')

    # prepare the axes limits
    ax.set_xlim((-25, 25))
    ax.set_ylim((-35, 35))
    ax.set_zlim((5, 55))
    def lorenz_deriv((x, y, z), t0, sigma=sigma, beta=beta, rho=rho):
        """Compute the time-derivative of a Lorentz system."""
        return [sigma * (y - x), x * (rho - z) - y, x * y - beta * z]

    # Choose random starting points, uniformly distributed from -15 to 15
    x0 = -15 + 30 * np.random.random((N, 3))

    # Solve for the trajectories
    t = np.linspace(0, max_time, int(250*max_time))
    x_t = np.asarray([integrate.odeint(lorenz_deriv, x0i, t)
                      for x0i in x0])
    # choose a different color for each trajectory
    colors = plt.cm.jet(np.linspace(0, 1, N))

    for i in range(N):
        x, y, z = x_t[i,:,:].T
        lines = ax.plot(x, y, z, '-', c=colors[i])
        plt.setp(lines, linewidth=2)

    ax.view_init(30, angle)

    return t, x_t

Let's call the function once to view the solutions. For this set of parameters, we see the trajectories swirling around two points, called attractors.

In [5]:
t, x_t = solve_lorenz(angle=0, N=10)

Using IPython's interactive function, we can explore how the trajectories behave as we change the various parameters.

In [6]:
w = interactive(solve_lorenz, angle=(0.,360.), N=(0,50), sigma=(0.0,50.0), rho=(0.0,50.0))

The object returned by interactive is a Widget object and it has attributes that contain the current result and arguments:

In [7]:
t, x_t = w.result
In [8]:
{u'N': 25,
 u'angle': 180.0,
 u'beta': 2.6666666666666665,
 u'max_time': 4.0,
 u'rho': 25.0,
 u'sigma': 25.0}

After interacting with the system, we can take the result and perform further computations. In this case, we compute the average positions in $x$, $y$ and $z$.

In [9]:
xyz_avg = x_t.mean(axis=1)
In [10]:
(25, 3)

Creating histograms of the average positions (across different trajectories) show that on average the trajectories swirl about the attractors.

In [11]:
plt.title('Average $x(t)$')
<matplotlib.text.Text at 0x10c0387d0>
In [12]:
plt.title('Average $y(t)$')
<matplotlib.text.Text at 0x10cadb610>