@@ -0,0 +1,1085 @@
+import numpy as np%0Aimport matplotlib.pyplot as plt%0A%0A# factor for 90%25 coverage with 90%25 confidence using Normal distribution%0A# with 10 samples from table XII in %5B1%5D%0A# %5B1%5D Montgomery, D. C., & Runger, G. C. (2014). Applied statistics and%0A# probability for engineers. Sixth edition. John Wiley & Sons.%0Ak = 2.535%0A%0A%0A%0Arun_times = np.load('intel_i5_6300u/6_break_times.npy')%0An = np.load('intel_i5_6300u/n.npy')%0Arun_times1 = np.load('intel_i5_6300u_gelsy/6_break_times.npy')%0A%0A%0A%0Arun_times_means = run_times.mean(axis=2)%0Arun_times_stds = run_times.std(axis=2, ddof=1)%0A%0A%0A%0Aplt.figure()%0Aplt.grid()%0Aplt.errorbar(n, run_times_means%5B0%5D, yerr=k*run_times_stds%5B0%5D, capsize=2.0, label='Numpy')%0Aplt.errorbar(n, run_times_means1%5B0%5D, yerr=k*run_times_stds1%5B0%5D, capsize=2.0, label='Scipy gelsy')%0A%0A# plt.errorbar(n, run_times_means%5B1%5D, yerr=k*run_times_stds%5B1%5D, capsize=2.0, label='TF GPU')%0Aplt.errorbar(n, run_times_means%5B1%5D, yerr=k*run_times_stds%5B1%5D, capsize=2.0, label='TF CPU')%0A%0Aplt.xlabel('Number of data points')%0Aplt.ylabel('Run time (seconds, Lower is better)')%0Aplt.semilogx()%0Aplt.legend()%0A%0Aplt.show()%0A

add gelsy comparison for intel