# Cobweb plots

Cobweb plots are a way of visualizing iterations of a function.

For a function f and a starting point x, you plot (x, f(x)) as usual.

Then since f(x) will be the next value of x, you convert it to an x by drawing a horizontal line from (x, f(x)) to (f(x), f(x)).

In other words, you convert the previous y value to an x value by moving to where a horizontal line intersects the line y = x.

Then you go up from the new x to f applied to the new x.

The Python code below makes this all explicit.

Update: I made a couple changes after this post was first published.

I added the dotted line y = x to the plots, and I changed the aspect ratio from the default to 1 to make the horizontal and vertical scales the same.

import matplotlib.

pyplot as plt from scipy import cos, linspace def cobweb(f, x0, N, a=0, b=1): # plot the function being iterated t = linspace(a, b, N) plt.

plot(t, f(t), k) # plot the dotted line y = x plt.

plot(t, t, “k:”) # plot the iterates x, y = x0, f(x0) for _ in range(N): fy = f(y) plt.

plot([x, y], [y, y], b, linewidth=1) plt.

plot([y, y], [y, fy], b, linewidth=1) x, y = y, fy plt.

axes().

set_aspect(1) plt.

show() plt.

close() The plot above was made by calling cobweb(cos, 1, 20) to produce the cobweb plot for 20 iterations of cosine starting with x = 1.

There’s one fixed point, and the cobweb plot spirals into that fixed point.

Next let’s look at several iterations of the logistic map f(x) = rx(1 – x) for differing values of r.

# one fixed point cobweb(lambda x: 2.

9*x*(1-x), 0.

1, 100) # converging to two-point attractor cobweb(lambda x: 3.

1*x*(1-x), 0.

1, 100) # starting exactly on the attractor cobweb(lambda x: 3.

1*x*(1-x), 0.

558, 100) # in the chaotic region.

cobweb(lambda x: 4.

0*x*(1-x), 0.

1, 100) The logistic map also has one stable fixed point if r ≤ 3.

In the plot below, r = 2.

9.

Next we set r = 3.

1.

We start at x = 0.

1 and converge to the two attractor points.

If we start exactly on one of the attractor points the cobweb plot is simply a square.

Finally, when we set r = 4 we’re in the chaotic region.

More posts on iterated functions Logistic bifurcation diagram Logistic trajectories Variation on cosine fixed point.