Visualization of Merge sort using Matplotlib

Last Updated : 28 Jul, 2020

Prerequisites: Introduction to Matplotlib, Merge Sort

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. For this we will use matplotlib, to plot bar graphs to represent the elements of the array,

Approach:

We will generate an array with random elements.
The algorithm will be called on that array and yield statement will be used instead of a return statement for visualization purposes.
We will yield the current states of the array after comparing and swapping. Hence the algorithm will return a generator object.
Matplotlib animation will be used to visualize the comparing and swapping of the array.
The array will be stored in a matplotlib bar container object (‘bar_rects’), where the size of each bar will be equal to the corresponding value of the element in the array.
The inbuilt FuncAnimation method of matplotlib animation will pass the container and generator objects to the function used to create animation. Each frame of the animation corresponds to a single iteration of the generator.
The animation function is repeatedly called will set the height of the rectangle equal to the value of the elements.

Below is the implementation of the above approach.

Python3

   # import all the 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 
  
  
# function to recursively divide the arra 
def mergesort(A, start, end): 
    if end <= start: 
        return
  
    mid = start + ((end - start + 1) // 2) - 1
      
    # yield from statements have been used to yield  
    # the array from the functions  
    yield from mergesort(A, start, mid) 
    yield from mergesort(A, mid + 1, end) 
    yield from merge(A, start, mid, end) 
  
# function to merge the array 
def merge(A, start, mid, end): 
    merged = [] 
    leftIdx = start 
    rightIdx = mid + 1
  
    while leftIdx <= mid and rightIdx <= end: 
        if A[leftIdx] < A[rightIdx]: 
            merged.append(A[leftIdx]) 
            leftIdx += 1
        else: 
            merged.append(A[rightIdx]) 
            rightIdx += 1
  
    while leftIdx <= mid: 
        merged.append(A[leftIdx]) 
        leftIdx += 1
  
    while rightIdx <= end: 
        merged.append(A[rightIdx]) 
        rightIdx += 1
  
    for i in range(len(merged)): 
        A[start + i] = merged[i] 
        yield A 
  
# function to plot bars 
def showGraph(): 
      
    # for random unique values 
    n=20
    a=[i for i in range(1, n+1)] 
    random.shuffle(a) 
    datasetName='Random'
      
    # generator object returned by the function 
    generator = mergesort(a, 0, len(a)-1) 
    algoName='Merge Sort'
      
    # style of the chart 
    plt.style.use('fivethirtyeight') 
      
    # set colors of the bars 
    data_normalizer = mp.colors.Normalize() 
    color_map = mp.colors.LinearSegmentedColormap( 
        "my_map", 
        { 
            "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, ax = plt.subplots() 
      
    # bar container  
    bar_rects = ax.bar(range(len(a)), a, align="edge",  
                       color=color_map(data_normalizer(range(n)))) 
      
    # setting the limits of x and y axes 
    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'}) 
      
    # the text to be shown on the upper left 
    # indicating the number of iterations 
    # transform indicates the position with  
    # relevance to the axes coordinates. 
    text = ax.text(0.01, 0.95, "", transform=ax.transAxes,  
                   color="#E4365D") 
    iteration = [0] 
  
    def animate(A, rects, iteration): 
        for rect, val in zip(rects, A): 
              
            # setting the size of each bar equal  
            # to the value of the elements 
            rect.set_height(val) 
        iteration[0] += 1
        text.set_text("iterations : {}".format(iteration[0])) 
      
    # call animate function repeatedly 
    anim = FuncAnimation(fig, func=animate, 
        fargs=(bar_rects, iteration), frames=generator, interval=50, 
        repeat=False) 
    plt.show() 
  
showGraph()
 
  

Output:

3D Visualisation of Merge Sort using Matplotlib

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Visualization of Merge sort using Matplotlib

Python3

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