exercises/exerciseA/heavy_computation.py

@@ -5,10 +5,11 @@ from datetime import datetime
 import time
 
 # Timestamp that will be put in the file name
-timestamp = datetime.now().strftime("%H%M%S%f")
 
 # Get the environment variable for threads
-threads = os.getenv('OMP_NUM_THREADS')
+for threads in range(1,12):
+	for _ in range(3):
+		timestamp = datetime.now().strftime("%H%M%S%f")
 
 		# A relatively large matrix to work on
 		n = 5_000

exercises/exerciseA/plot.py

@@ -1,6 +1,7 @@
 import os
 import numpy as np
 import matplotlib.pyplot as plt
+import pandas as pd
 
 # IO: This loads the timings for you 
 threads, timings = [], []
@@ -11,13 +12,23 @@ for file in os.listdir('timings'):
         timings.append(float(t))
 threads = np.array(threads)
 timings = np.array(timings)
+dat = {'timings': timings, 'threads': threads}
 
-print('This is the data I loaded: threads =', threads, ', timings =',timings)
+data = pd.DataFrame(dat)
 
+averages = data.groupby('threads').aggregate(['mean','std'])
+averages.to_csv('data.csv')
 fig, axs = plt.subplots()
 
 # CREATE YOUR PLOT HERE
 # Remember to label your axis
 # Feel free to make it pretty
+means = averages['timings']['mean']
+stds = averages['timings']['std']
 
+axs.plot(averages.index, means)
+axs.fill_between(averages.index, means-stds, means+stds, alpha=0.3)
+axs.set_xlabel('Num threads')
+axs.set_ylabel('Time (s)')
+plt.show()
 plt.savefig('threads_v_timings.png', dpi=300)