## What will we cover in this tutorial?

- Understand what the Mandelbrot set it and why it is so fascinating.
- Master how to make images in multiple colors of the Mandelbrot set.
- How to implement it using NumPy vectorization.

## Step 1: What is Mandelbrot?

Mandelbrot is a set of complex numbers for which the function **f(z) = z^2 + c** does not converge when iterated from **z=0** (from wikipedia).

Take a complex number, c, then you calculate the sequence for N iterations:

**z_(n+1) = z_n + c** for **n = 0, 1, …, N-1**

If **absolute(z_(N-1)) < 2**, then it is said not to diverge and is part of the Mandelbrot set.

The Mandelbrot set is part of the complex plane, which is colored by numbers part of the Mandelbrot set and not.

This only gives a block and white colored image of the complex plane, hence often the images are made more colorful by giving it colors by the iteration number it diverged. That is if z_4 diverged for a point in the complex plane, then it will be given the color 4. That is how you end up with colorful maps like this.

## Step 2: Understand the code of the non-vectorized approach to compute the Mandelbrot set

To better understand the images from the Mandelbrot set, think of the complex numbers as a diagram, where the real part of the complex number is x-axis and the imaginary part is y-axis (also called the Argand diagram).

Then each point is a complex number **c**. That complex number will be given a color depending on which iteration it diverges (if it is not part of the Mandelbrot set).

Now the pseudocode for that should be easy to digest.

```
for x in [-2, 2] do:
for y in [-1.5, 1.5] do:
c = x + i*y
z = 0
N = 0
while absolute(z) < 2 and N < MAX_ITERATIONS:
z = z^2 + c
set color for x,y to N
```

Simple enough to understand. That is some of the beauty of it. The simplicity.

## Step 3: Make a vectorized version of the computations

Now we understand the concepts behind we should translate that into to a vectorized version. If you are new to vectorization we can recommend you read this tutorial first.

What do we achieve with vectorization? That we compute all the complex numbers simultaneously. To understand that inspect the initialization of all the points here.

```
import numpy as np
def mandelbrot(height, width, x_from=-2, x_to=1, y_from=-1.5, y_to=1.5, max_iterations=100):
x = np.linspace(x_from, x_to, width).reshape((1, width))
y = np.linspace(y_from, y_to, height).reshape((height, 1))
c = x + 1j * y
```

You see that we initialize all the * x*-coordinates at once using the linespace. It will create an array with numbers from

**x_from**to

**x_to**in

**width**points. The reshape is to fit the plane.

The same happens for **y**.

Then all the complex numbers are created in **c = x + 1j*y**, where **1j** is the imaginary part of the complex number.

This leaves us to the full implementation.

There are two things we need to keep track of in order to make a colorful Mandelbrot set. First, in which iteration the point diverged. Second, to achieve that, we need to remember when a point diverged.

```
import numpy as np
import matplotlib.pyplot as plt
def mandelbrot(height, width, x=-0.5, y=0, zoom=1, max_iterations=100):
# To make navigation easier we calculate these values
x_width = 1.5
y_height = 1.5*height/width
x_from = x - x_width/zoom
x_to = x + x_width/zoom
y_from = y - y_height/zoom
y_to = y + y_height/zoom
# Here the actual algorithm starts
x = np.linspace(x_from, x_to, width).reshape((1, width))
y = np.linspace(y_from, y_to, height).reshape((height, 1))
c = x + 1j * y
# Initialize z to all zero
z = np.zeros(c.shape, dtype=np.complex128)
# To keep track in which iteration the point diverged
div_time = np.zeros(z.shape, dtype=int)
# To keep track on which points did not converge so far
m = np.full(c.shape, True, dtype=bool)
for i in range(max_iterations):
z[m] = z[m]**2 + c[m]
diverged = np.greater(np.abs(z), 2, out=np.full(c.shape, False), where=m) # Find diverging
div_time[diverged] = i # set the value of the diverged iteration number
m[np.abs(z) > 2] = False # to remember which have diverged
return div_time
# Default image of Mandelbrot set
plt.imshow(mandelbrot(800, 1000), cmap='magma')
# The image below of Mandelbrot set
# plt.imshow(mandelbrot(800, 1000, -0.75, 0.0, 2, 200), cmap='magma')
# The image below of below of Mandelbrot set
# plt.imshow(mandelbrot(800, 1000, -1, 0.3, 20, 500), cmap='magma')
plt.show()
```

Notice that **z[m] = z[m]**2 + c[m]** only computes updates on values that are still not diverged.

I have added the following two images from above (the one not commented out is above in previous step.

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Thanks for the great post, Helped me to understand better numpy’s vectorization.

In working through the code I noticed an improvement that could be made, so here it is:

The last line of the inner loop is `m[np.abs(z) > 2] = False`. That’s doing quite a lot of calculation.

You can simply replace it with `m[diverged] = False`.

The boolean array `diverged` already holds `True` values for each cell/pixel/z-sequence that diverged

on the current iteration.

This cut the inner loop computation time to about 61% of the original on my simple tests.

Hi Bill,

First sorry for the late reply.

Thank you for sharing your insights. I appreciate it.

Rune