plt.xlim([0, 160]) # for data

import matplotlib.pyplot as plt import numpy as np plt.figure(figsize=(9,7)) plt.xlim([0, 100]) # for web_search #plt.xlim([0, 160]) # for data_mining plt.ylim([0, 100]) x0 = [0, 100] y0 = [0, 100] plt.plot(x0,y0, label='Ideal',linestyle='--',linewidth=2.0,color='g') x1, y1 = np.loadtxt('./goodput_ndp', delimiter=' ', unpack=True) plt.plot(x1,y1, label='NDP',linestyle='-',linewidth=4.0,color='b') x2, y2 = np.loadtxt('./goodput_ndp_aeolus', delimiter=' ', unpack=True) plt.plot(x2,y2, label='NDP+Aeolus',linestyle='-',linewidth=4.0,color='r') x3, y3 = np.loadtxt('./goodput_homa', delimiter=' ', unpack=True) plt.plot(x3,y3, label='HOMA',linestyle='-',linewidth=4.0,color='b') x4, y4 = np.loadtxt('./goodput_homa_aeolus', delimiter=' ', unpack=True) plt.plot(x4,y4, label='HOMA+Aeolus',linestyle='-',linewidth=4.0,color='r') plt.xticks(fontsize=24) plt.yticks(fontsize=24) plt.xlabel('Load (Gbps)',fontsize=24, fontweight='bold') plt.ylabel('Goodput (Gbps)',fontsize=24, fontweight='bold') plt.legend(fontsize=22,loc=2) plt.grid() plt.show()