For this project, you will import the **time** and **random** modules. You will also install the **matplotlib** package and import from it the **pyplot** module. You will also import the wraps() function from the functools module for use in your decorator. Use the **time** module to write a decorator function named sort_timer that times how many seconds it takes the decorated function to run. Since sort functions don't need to return anything, have the decorator's wrapper function return that
For this project, you will import the **time** and **random** modules. You will also install the **matplotlib** package and import from it the **pyplot** module. You will also import the wraps() function from the functools module for use in your decorator.
Use the **time** module to write a decorator function named sort_timer that times how many seconds it takes the decorated function to run. Since sort functions don't need to return anything, have the decorator's wrapper function return that elapsed time.
To get the current time, call time.perf_counter(). Subtracting the begin time from the end time will give you the elapsed time in seconds.
Copy the code for bubble sort and insertion sort from Module 4 and decorate them with sort_timer.
def bubble_sort(a_list):
"""
Sorts a_list in ascending order
"""
for pass_num in range(len(a_list) - 1):
for index in range(len(a_list) - 1 - pass_num):
if a_list[index] > a_list[index + 1]:
temp = a_list[index]
a_list[index] = a_list[index + 1]
a_list[index + 1] = temp
def insertion_sort(a_list):
"""
Sorts a_list in ascending order
"""
for index in range(1, len(a_list)):
value = a_list[index]
pos = index - 1
while pos >= 0 and a_list[pos] > value:
a_list[pos + 1] = a_list[pos]
pos -= 1
a_list[pos + 1] = value
Write a function called **compare_sorts** that takes the two decorated sort functions as parameters. It should randomly generate a list of 1000 numbers and then make a separate copy of that list, which you can do like this:
```
list_2 = list(list_1)
```
Next it should time how long it takes bubble sort to sort one of those copies (using the decorated bubble sort), and then time how long it takes insertion sort to sort the other copy (using the decorated insertion sort). This gets us the first data point for each
To generate random numbers, you will need to import the **random** module. The function call random.randint(a, b) returns a random integer N such that a <= N <= b. You should use a = 1 and b = 10000. It's fine if values appear in the list multiple times.
To generate a graph, you will need to install the **matplotlib** package and import **pyplot** from it. Below is an example of code to produce a graph comparing two series of data points - **you will need to modify it to graph your timing data**.
```
from matplotlib import pyplot
pyplot.plot([1, 2, 3, 4, 10], [1, 4, 9, 16, 100], 'ro--', linewidth=2, label='series 1')
pyplot.plot([1, 2, 3, 4, 10], [1, 3, 7, 20, 150], 'go--', linewidth=2, label='series 2')
pyplot.xlabel("the x label")
pyplot.ylabel("the y label")
pyplot.legend(loc='upper left')
pyplot.show()
```
Each of the calls to the plot function plot a line. The call to the show function displays the graph. In the calls to the plot function, the first list is the list of x-coordinates (which is the same list for both curves you're plotting). The second list is the list of y-coordinates. The 'ro--' tells it to use red circles connected by a dashed line and 'go--' is the same except green instead of red. The linewidth parameter is self-explanatory. The label parameter assigns the label to be used for that line in the legend. The legend() function sets where on the graph the legend should be displayed. The xlabel() and ylabel() functions set the labels for the x and y axes. For your graph, the x axis is the number of elements being sorted, and the y axis is the time in seconds. **Your graph must include a legend and labels for the axes.**
include a line that calls your compare_sorts() function to generate the graph
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