3D Visualisation of Sorting using Matplotlib

Author - Neelima Mohanty
30/01/2022 | 10:30 PM

Note 1:This Tutorial was first written using reStructuredText and then converted to HTML

Note 2:The terms with * are explained in the Refference table

Matplotlib is a plotting library for the Python programming language and its numerical mathematics extension NumPy. A Sorting Algorithm is used to rearrange a given array or list elements according to a comparison operator on the elements. The comparison operator is used to decide the new order of element in the respective data structure. Through this tutorial we will see the 3D Visualizations of Quick Sort*.


Before following this tutorial, you need the following:

3D Visualisation of Quick Sort


1.We will generate an array* with random elements.
2.The algorithm will be called on that array and yield statement will be used instead of return statement for visualization purposes.
3.We will yield the current states of the array after comparing and swapping. Hence, the algorithm will return a generator object.
4.Matplotlib animation will be used to visualize the comparing and swapping of the array.
5.We will then plot the graph, which will return an object of Poly3dCollection* using which further animation will be done.


Create a file named main.py and type the following code:

# importing all required modules
import matplotlib.pyplot as plt
from matplotlib.animation import FuncAnimation
from mpl_toolkits.mplot3d import axes3d
import matplotlib as mp
import numpy as np
import random
# quicksort function
def quicksort(a, l, r):
if l>=r:
for i in range(l+1, r+1):
if a[i]<=x:
a[j], a[i] = a[i], a[j]
yield a
a[l], a[j]=a[j], a[l]
yield a
# yield from statement used to yield
# the array after dividing
yield from quicksort(a, l, j-1)
yield from quicksort(a, j+1, r)
# function to plot bars
def showGraph():
# for random unique values
n=int(input("enter array size\n"))
a=[i for i in range(1, n+1)]
# generator object returned
# by the function
generator = quicksort(a, 0, n-1)
algoName='Quick Sort'
# style of the chart
# set colors of the bars
data_normalizer = mp.colors.Normalize()
color_map = mp.colors.LinearSegmentedColormap(
"red": [(0, 1.0, 1.0),
(1.0, .5, .5)],
"green": [(0, 0.5, 0.5),
(1.0, 0, 0)],
"blue": [(0, 0.50, 0.5),
(1.0, 0, 0)]
fig = plt.figure()
ax = fig.add_subplot(projection='3d')
# z values and positions of the bars
z = np.zeros(n)
dx = np.ones(n)
dy = np.ones(n)
dz = [i for i in range(len(a))]
# Poly3dCollection returned
# into variable rects
rects = ax.bar3d(range(len(a)), a, z, dx,
dy, dz,
color = color_map(data_normalizer(range(n))))
# setting and x and y limits
# equal to the length of the array
ax.set_xlim(0, len(a))
ax.set_ylim(0, int(1.1*len(a)))
ax.set_title("ALGORITHM : "+algoName+"\n"+"DATA SET : "+datasetName,
fontdict={'fontsize': 13, 'fontweight': 'medium',
'color' : '#E4365D'})
# text to plot on the chart
text = ax.text2D(0.1,0.95, "", horizontalalignment = 'center',
verticalalignment = 'center',
color = "#E4365D")
iteration = [0]
# animation function to be
# repeatedly called
def animate(A, rects, iteration):
# to clear the bars from
# the Poly3DCollection object
ax.bar3d(range(len(a)), A, z, dx,
dy, dz,
color = color_map(data_normalizer(range(n))))
iteration[0] += 1
text.set_text("iterations : {}".format(iteration[0]))
# animate function is called here
# and the generator object is passed
anim = FuncAnimation(fig, func=animate,
fargs = (rects, iteration),
frames = generator, interval=50,
# show the plot
# function call


Output: For array size 20


Term used Description
Quick Sort Quicksort is the widely used sorting algorithm that makes n log n comparisons in average case for sorting an array of n elements. It is a faster and highly efficient sorting algorithm
Array List of elements
Poly3dCollection A collection of 3D polygons

Happy Learning!

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