TimeComparison.py

from pylab import *                    #Necesarry to generate random values
from time import perf_counter     #Necesarry to precisely time the execution
import matplotlib.pyplot as plt     #Necesarry to graph the final result
import matplotlib.patches as mpatches #Also for the graph

#Importing annex files
import QuickSort




#We start with an empty array
Array = [] 

#Defining how many measurments we want
Start = 1 
End = 1000
Increment = 1

# Defines automatically how many iterations will be ran
# from the Start / End / Interations we chose above
Iterations = round( (End-Start) / Increment)
intInterations = int(Iterations)


#Creation of arrays that will save the times it took to execute each sort
TimePythonSort = []
TimeQuickSort = []

#Start of the test:
for Loop in range(Start, End, Increment): 
      
      #We insert a random value into our array to sort
      #This allows for a random array generated. Not the best nor worst
      #cases of each sorting alg, but a realistic case.
      Array.append(randint(0, 10000))
      
      
      
      #Times of the Propriatary Python .sort(): 
      PythonSort = Array.copy()
      
      TimeStart = perf_counter()
      
      PythonSort.sort()
      
      TimeFinish = perf_counter()
      FinalTime = TimeFinish - TimeStart
      TimePythonSort.append(FinalTime)
      
      
      
      
      #Times of the Quick Sort: 
      TimeStart = perf_counter()
      
      SortedArray = QuickSort.QuickSort(Array)
      
      TimeFinish = perf_counter()
      FinalTime = TimeFinish - TimeStart
      TimeQuickSort.append(FinalTime)
      
#For Loop in Range


#Defining the x axis of the graph
x = [] 
for Loop in range(intInterations):
      x.append(Loop + 1)
#for loop in range



#Plotting the graph

fig, ax = plt.subplots()
Red = mpatches.Patch(color='red', label='Python .sort()')
Green = mpatches.Patch(color='green', label='Quick Sort')

ax.legend(handles=[Red, Green])




plt.title("Time comparison of different sorting algs")

plt.scatter(x, TimePythonSort, s=15, c='red')   #Plotting the Python .sort()
plt.scatter(x, TimeQuickSort, s=15, c='green')   #Plotting the QuickSort

plt.xlabel("Number of elements in the array")
plt.ylabel("Time in seconds to sort the array")




plt.show()