Experimental Results Analysis: Visualizing Data with Matplotlib

Experimental Results Analysis: In this section, you can use the matplotlib library to plot histograms, probability density curves, as well as calculate the mean and standard deviation. Here is an example Python code:

import matplotlib.pyplot as plt
import numpy as np

# Plot histogram
plt.hist(group_runtimes, bins=30, alpha=0.5, color='b')
plt.axvline(x=min(group_runtimes), color='r', linestyle='--', label='Best Case')
plt.axvline(x=max(group_runtimes), color='g', linestyle='--', label='Worst Case')
plt.xlabel('Runtime (seconds)')
plt.ylabel('Frequency')
plt.title('Runtime Histogram')
plt.legend()
plt.show()

# Plot histogram and probability density curve
plt.hist(group_runtimes, bins=30, density=True, alpha=0.5, color='b')
density = np.linspace(min(group_runtimes), max(group_runtimes), 100)
plt.plot(density, np.exp(-0.5 * ((density - np.mean(group_runtimes)) / np.std(group_runtimes))**2) / (np.std(group_runtimes) * np.sqrt(2 * np.pi)), color='r', label='Probability Density Curve')
plt.axvline(x=min(group_runtimes), color='r', linestyle='--', label='Best Case')
plt.axvline(x=max(group_runtimes), color='g', linestyle='--', label='Worst Case')
plt.xlabel('Runtime (seconds)')
plt.ylabel('Probability Density')
plt.title('Runtime Histogram and Probability Density Curve')
plt.legend()
plt.show()

# Calculate mean and standard deviation
average_runtime = np.mean(group_runtimes)
std_deviation = np.std(group_runtimes)

print(f'Mean: {average_runtime} seconds')
print(f'Standard Deviation: {std_deviation}')