Visualizing Quick Sort using Tkinter in Python

Last Updated : 30 Nov, 2021

Prerequisite: QuickSort

Tkinter is a very easy-to-use and beginner-friendly GUI library that can be used to visualize the sorting algorithms. Here Quick Sort Algorithm is visualized which is a divide and conquer algorithm. It first considers a pivot element then, creates two subarrays to hold elements less than the pivot value and elements greater than the pivot value, and then recursively sort the sub-arrays. There are two fundamental operations in the algorithm, swapping items in place and partitioning a section of the array. The process is repeated by recursion until the sub-arrays are small enough to be sorted easily. Ultimately, the smaller sub-arrays can be placed one on top of the other to produce a fully sorted and ordered set of elements.

In this article, we will use the Python GUI Library Tkinter to visualize the QuickSort algorithm.

Algorithm:

Selecting any element as a pivot
Elements lesser than the pivot are placed before it and the ones which are greater are placed after it. Two sub-arrays are created on either side of the pivot.
The same process is applied recursively on the right and left sub-arrays to sort them.

Time Complexity :

Best case: The best-case occurs when the pivot always separates the array into two equal halves. In the best case, the result will be log(N) levels of partitions, with the top-level having one array of size N, the next having an array of size N/2, and so on. The best-case complexity of the quick sort algorithm is O(log N)
Worst case: The worst case will occur when the pivot does a poor job of breaking the array, i.e. when there are no elements in one partition and N-1 elements in the other. The worst-case time complexity of Quick Sort would be O(N^2).

Extension Code for Quick Sort :

This is the extension code for the quick sort algorithm which is imported in the main Tkinter visualizer code to implement the quick sort algorithm and return the sorted result.

Python3

# Extension Quick Sort Code
# importing time module
import time

# to implement divide and conquer
def partition(data, head, tail, drawData, timeTick):
    border = head
    pivot = data[tail]

    drawData(data, getColorArray(len(data), head, 
                                 tail, border, border))
    time.sleep(timeTick)

    for j in range(head, tail):
        if data[j] < pivot:
            drawData(data, getColorArray(
                len(data), head, tail, border, j, True))
            time.sleep(timeTick)

            data[border], data[j] = data[j], data[border]
            border += 1

        drawData(data, getColorArray(len(data), head, 
                                     tail, border, j))
        time.sleep(timeTick)

    # swapping pivot with border value
    drawData(data, getColorArray(len(data), head, 
                                 tail, border, tail, True))
    time.sleep(timeTick)

    data[border], data[tail] = data[tail], data[border]

    return border


# head  --> Starting index,
# tail  --> Ending index
def quick_sort(data, head, tail, 
               drawData, timeTick):
    if head < tail:
        partitionIdx = partition(data, head, 
                                 tail, drawData, 
                                 timeTick)

        # left partition
        quick_sort(data, head, partitionIdx-1, 
                   drawData, timeTick)

        # right partition
        quick_sort(data, partitionIdx+1, 
                   tail, drawData, timeTick)

# Function to apply colors to bars while sorting:
# Grey - Unsorted elements
# Blue - Pivot point element
# White - Sorted half/partition
# Red - Starting pointer
# Yellow - Ending pointer
# Green - after all elements are sorted

# assign color representation to elements


def getColorArray(dataLen, head, tail, border,
                  currIdx, isSwaping=False):
    colorArray = []
    for i in range(dataLen):
        # base coloring
        if i >= head and i <= tail:
            colorArray.append('Grey')
        else:
            colorArray.append('White')

        if i == tail:
            colorArray[i] = 'Blue'
        elif i == border:
            colorArray[i] = 'Red'
        elif i == currIdx:
            colorArray[i] = 'Yellow'

        if isSwaping:
            if i == border or i == currIdx:
                colorArray[i] = 'Green'

    return colorArray

Tkinter Implementation:

In this code, we are generating the data values as bars of different lengths and a particular color. The basic layout is designed in a Tkinter ‘Frame’ and the portion when the bars are generated and the quick sort algorithm is visualized is designed in a Tkinter ‘Canvas’.

The code essentially has the following components:

Mainframe: a Tkinter frame to arrange all the necessary components(labels, buttons, speed bar, etc.) in an organized manner
Canvas: A Tkinter canvas used as the space where the generated data bars are drawn and the sorting process is visualized
generate(): Method to generate the data values by accepting a range and then passing that as a parameter to the drawData() function
drawData(): Method to generate bars to normalized data values(within the given range) of a particular color on the canvas
start_algorithm(): This function is called when the “START” button is pressed. It initiates the sorting process by calling the quick_sort() function from the Quick Sort Extension Code.

Python3

# code for Quick Sort Visualizer 
# using Python and Tkinter
# import modules
from tkinter import *
from tkinter import ttk
import random
from quick import quick_sort

# initialising root class for Tkinter
root = Tk()
root.title("Quick Sort Visualizer")

# maximum window size
root.maxsize(900, 600)
root.config(bg="Black")

select_alg = StringVar()
data = []

# function to generate the data values 
# by accepting a given range
def generate():

    global data

    # minval : minimum value of the range
    minval = int(minEntry.get())

    # maxval : maximum value of the range
    maxval = int(maxEntry.get())

    # sizeval : number of data 
    # values/bars to be generated
    sizeval = int(sizeEntry.get())

    # creating a blank data list which will 
    # be further filled with random data values
    # within the entered range
    data = []
    for _ in range(sizeval):
        data.append(random.randrange(minval, maxval+1))

    drawData(data, ['Red' for x in range(len(data))])

# function to create the data bars 
# by creating a canvas in Tkinter
def drawData(data, colorlist):
    canvas.delete("all")
    can_height = 380
    can_width = 550
    x_width = can_width/(len(data) + 1)
    offset = 30
    spacing = 10
    # normalizing data for rescaling real-valued 
    # numeric data within the
    # given range
    normalized_data = [i / max(data) for i in data]

    for i, height in enumerate(normalized_data):
        # top left corner
        x0 = i*x_width + offset + spacing
        y0 = can_height - height*340

        # bottom right corner
        x1 = ((i+1)*x_width) + offset
        y1 = can_height

        # data bars are generated as Red 
        # colored vertical rectangles
        canvas.create_rectangle(x0, y0, x1, y1, 
                                fill=colorlist[i])
        canvas.create_text(x0+2, y0, anchor=SE, 
                           text=str(data[i]))
    root.update_idletasks()

# function to initiate the sorting 
# process by calling the extension code
def start_algorithm():
    global data

    if not data:
        return

    if (algmenu.get() == 'Quick Sort'):
        quick_sort(data, 0, len(data)-1, drawData, speedbar.get())
        drawData(data, ['Green' for x in range(len(data))])


# creating main user interface frame 
# and basic layout by creating a frame
Mainframe = Frame(root, width=600, height=200, bg="Grey")
Mainframe.grid(row=0, column=0, padx=10, pady=5)

canvas = Canvas(root, width=600, height=380, bg="Grey")
canvas.grid(row=1, column=0, padx=10, pady=5)

# creating user interface area in grid manner
# first row components
Label(Mainframe, text="ALGORITHM", 
      bg='Grey').grid(row=0, column=0, 
                      padx=5, pady=5, 
                      sticky=W)

# algorithm menu for showing the 
# name of the sorting algorithm
algmenu = ttk.Combobox(Mainframe, 
                       textvariable=select_alg, 
                       values=["Quick Sort"])
algmenu.grid(row=0, column=1, padx=5, pady=5)
algmenu.current(0)

# creating Start Button to start 
# the sorting visualization process
Button(Mainframe, text="START", 
       bg="Blue", 
       command=start_algorithm).grid(row=1, 
                                     column=3, 
                                     padx=5, 
                                     pady=5)

# creating Speed Bar using scale in Tkinter
speedbar = Scale(Mainframe, from_=0.10, 
                 to=2.0, length=100, digits=2,
                 resolution=0.2, orient=HORIZONTAL, 
                 label="Select Speed")
speedbar.grid(row=0, column=2, 
              padx=5, pady=5)

# second row components
# sizeEntry : scale to select 
# the size/number of data bars
sizeEntry = Scale(Mainframe, from_=3, 
                  to=60, resolution=1,
                  orient=HORIZONTAL, 
                  label="Size")
sizeEntry.grid(row=1, column=0, 
               padx=5, pady=5)

# minEntry : scale to select the 
# minimum value of data bars
minEntry = Scale(Mainframe, from_=0, 
                 to=10, resolution=1,
                 orient=HORIZONTAL, 
                 label="Minimum Value")
minEntry.grid(row=1, column=1, 
              padx=5, pady=5)

# maxEntry : scale to select the 
# maximum value of data bars
maxEntry = Scale(Mainframe, from_=10, 
                 to=100, resolution=1,
                 orient=HORIZONTAL, 
                 label="Maximum Value")
maxEntry.grid(row=1, column=2, 
              padx=5, pady=5)

# creating generate button
Button(Mainframe, text="Generate", 
       bg="Red", 
       command=generate).grid(row=0, 
                              column=3, 
                              padx=5, 
                              pady=5)

# to stop automatic window termination
root.mainloop()

Output:

Visualization of Quick sort using Matplotlib

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Visualizing Quick Sort using Tkinter in Python

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