Python Matplotlib Mandelbrot Visualization

Introduction

This lab will guide you through generating a shaded and power-normalized rendering of the Mandelbrot set using Python's Matplotlib library.

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Import Required Libraries

First, we need to import the libraries we will be using: NumPy, Matplotlib, and Colors.

import numpy as np
import matplotlib.pyplot as plt
from matplotlib import colors

Define the Mandelbrot Set Function

Next, we will define a function that generates the Mandelbrot set. The function takes in several parameters:

• xmin, xmax, ymin, ymax: the minimum and maximum values for the x and y axes
• xn and yn: the number of points to generate along each axis
• maxiter: the maximum number of iterations to perform for each point
• horizon: the maximum value at which to consider a point to be part of the set

def mandelbrot_set(xmin, xmax, ymin, ymax, xn, yn, maxiter, horizon=2.0):
    X = np.linspace(xmin, xmax, xn).astype(np.float32)
    Y = np.linspace(ymin, ymax, yn).astype(np.float32)
    C = X + Y[:, None] * 1j
    N = np.zeros_like(C, dtype=int)
    Z = np.zeros_like(C)
    for n in range(maxiter):
        I = abs(Z) < horizon
        N[I] = n
        Z[I] = Z[I]**2 + C[I]
    N[N == maxiter-1] = 0
    return Z, N

Generate the Mandelbrot Set

Now we will generate the Mandelbrot set by calling the mandelbrot_set function with our desired parameters. This will give us two arrays:

• Z: the final values of the complex numbers we iterated over
• N: the number of iterations performed for each point before it was determined to be part of the set

xmin, xmax, xn = -2.25, +0.75, 3000 // 2
ymin, ymax, yn = -1.25, +1.25, 2500 // 2
maxiter = 200
horizon = 2.0 ** 40
log_horizon = np.log2(np.log(horizon))
Z, N = mandelbrot_set(xmin, xmax, ymin, ymax, xn, yn, maxiter, horizon)

Normalize the Data

In order to create a shaded and power-normalized rendering of the Mandelbrot set, we need to normalize our data. We will do this using the following formula:

M = N + 1 - np.log2(np.log(abs(Z))) + log_horizon

with np.errstate(invalid='ignore'):
    M = np.nan_to_num(N + 1 - np.log2(np.log(abs(Z))) + log_horizon)

Create the Plot

Now that we have our normalized data, we can create the plot. We will use the imshow function to display the data as an image, and we will also add some text to the plot to indicate what we are looking at.

dpi = 72
width = 10
height = 10*yn/xn
fig = plt.figure(figsize=(width, height), dpi=dpi)
ax = fig.add_axes([0, 0, 1, 1], frameon=False, aspect=1)

light = colors.LightSource(azdeg=315, altdeg=10)
M = light.shade(M, cmap=plt.cm.hot, vert_exag=1.5,
                norm=colors.PowerNorm(0.3), blend_mode='hsv')
ax.imshow(M, extent=[xmin, xmax, ymin, ymax], interpolation="bicubic")
ax.set_xticks([])
ax.set_yticks([])

year = time.strftime("%Y")
text = ("The Mandelbrot fractal set\n"
        "Rendered with matplotlib %s, %s - https://matplotlib.org"
        % (matplotlib.__version__, year))
ax.text(xmin+.025, ymin+.025, text, color="white", fontsize=12, alpha=0.5)

plt.show()

Summary

In this lab, we learned how to generate a shaded and power-normalized rendering of the Mandelbrot set using Python's Matplotlib library. We accomplished this by defining a function to generate the set, normalizing the data, and creating a plot using the normalized data. This technique can be applied to other data sets to create visually appealing and informative images.