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3D Visualisation of Quick Sort using Matplotlib in Python
  • Last Updated : 20 Aug, 2020

Visualizing algorithms makes it easier to understand them by analyzing and comparing the number of operations that took place to compare and swap the elements. 3D visualization of algorithms is less common, for this we will use Matplotlib to plot bar graphs and animate them to represent the elements of the array.

Let’s see the 3D Visualizations 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.

Below is the implementation:






# 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 aftre 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'fivethirtyeight')
    # 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 posistions 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



For array size 20

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