Add the  quicksort algorithm to this python code. Below is the code to modify # selectionsortanimation.py """Animated selection sort visualization.""" from matplotlib import animation import matplotlib.pyplot as plt import numpy as np import seaborn as sns import sys

Add the  quicksort algorithm to this python code. Below is the code to modify

# selectionsortanimation.py
"""Animated selection sort visualization."""
from matplotlib import animation
import matplotlib.pyplot as plt
import numpy as np
import seaborn as sns
import sys
#from ch11soundutilities import play_sound


def update(frame_data):
"""Display bars representing the current state."""
# unpack info for graph update
data, colors, swaps, comparisons = frame_data
plt.cla() # clear old contents of current Figure

# create barplot and set its xlabel
bar_positions = np.arange(len(data))
axes = sns.barplot(bar_positions, data, palette=colors) # new bars
axes.set(xlabel=f'Comparisons: {comparisons}; Swaps: {swaps}',
xticklabels=data)




def flash_bars(index1, index2, data, colors, swaps, comparisons):
"""Flash the bars about to be swapped and play their notes."""
for x in range(0, 2):
colors[index1], colors[index2] = 'white', 'white'
yield (data, colors, swaps, comparisons)
colors[index1], colors[index2] = 'purple', 'purple'
yield (data, colors, swaps, comparisons)
#play_sound(data[index1], seconds=0.05)
#play_sound(data[index2], seconds=0.05)


def selection_sort(data):
"""Sort data using the selection sort algorithm and
yields values that update uses to visualize the algorithm."""
swaps = 0
comparisons = 0
colors = ['lightgray'] * len(data) # list of bar colors

# display initial bars representing shuffled values
yield (data, colors, swaps, comparisons)

# loop over len(data) - 1 elements
for index1 in range(0, len(data) - 1):
smallest = index1

# loop to find index of smallest element's index
for index2 in range(index1 + 1, len(data)):
comparisons += 1
colors[smallest] = 'purple'
colors[index2] = 'red'
yield (data, colors, swaps, comparisons)
#play_sound(data[index2], seconds=0.05)

# compare elements at positions index and smallest
if data[index2] < data[smallest]:
colors[smallest] = 'lightgray'
smallest = index2
colors[smallest] = 'purple'
yield (data, colors, swaps, comparisons)
else:
colors[index2] = 'lightgray'
yield (data, colors, swaps, comparisons)

# ensure that last bar is not purple
colors[-1] = 'lightgray'

# flash the bars about to be swapped
yield from flash_bars(index1, smallest, data, colors,
swaps, comparisons)

# swap the elements at positions index1 and smallest
swaps += 1
data[smallest], data[index1] = data[index1], data[smallest]

# flash the bars that were just swapped
yield from flash_bars(index1, smallest, data, colors,
swaps, comparisons)

# indicate that bar index1 is now in its final spot
colors[index1] = 'lightgreen'
yield (data, colors, swaps, comparisons)

# indicate that last bar is now in its final spot
colors[-1] = 'lightgreen'
yield (data, colors, swaps, comparisons)
#play_sound(data[-1], seconds=0.05)

# play each bar's note once and color it darker green
for index in range(len(data)):
colors[index] = 'green'
yield (data, colors, swaps, comparisons)
#play_sound(data[index], seconds=0.05)


def main():
number_of_values = int(sys.argv[1]) if len(sys.argv) == 2 else 10

figure = plt.figure('Selection Sort') # Figure to display barplot
numbers = np.arange(1, number_of_values + 1) # create array
np.random.shuffle(numbers) # shuffle the array

# start the animation
anim = animation.FuncAnimation(figure, update, repeat=False,
frames=selection_sort(numbers), interval=50)

plt.show() # display the Figure


# call main if this file is executed as a script
if __name__ == '__main__':
main()

Example of the quick sore algoritim is below

class QuickSort:
# sort the array in it is location in the memory .
@classmethod
# l > low, h > high
def quickSort(cls,array,l,h):
if l < h :
pi =cls.qsort_helper(array, l, h)
# sort the smaller part
cls.quickSort(array, l, pi - 1)
# sort the greater part
cls.quickSort(array, pi + 1, h)

@classmethod
def qsort_helper(cls,array,l,h):
p = array[h]
# print(p)
low = l - 1
# i > poiter for greater element
for j in range(l, h):
# find the smaller element of the pivot, if found swap it with the low element
if array[j] < p :
low += 1
(array[low], array[j]) = (array[j], array[low])

(array[low + 1], array[h]) = (array[h], array[low + 1])
return low + 1

def main ():
array = [5, 8, 5,4, 6, 978, 9, 0, 47, 0, 4, 9]
QuickSort.quickSort(array, 0, len(array) - 1)
print(array)

main()