# import matplotlib # matplotlib.use('Agg') import numpy as np import matplotlib.pyplot as plt #### Draw IMU data fig, axs = plt.subplots(2) imu=np.loadtxt('imu_pbp.txt') time=imu[:,0] axs[0].set_title('Gyroscope') axs[1].set_title('Accelerameter') lab_1 = ['gyr-x', 'gyr-y', 'gyr-z'] lab_2 = ['acc-x', 'acc-y', 'acc-z'] for i in range(3): #if i==1: axs[0].plot(time, imu[:,i+1],'.-', label=lab_1[i]) axs[1].plot(time, imu[:,i+4],'.-', label=lab_2[i]) for i in range(2): #axs[i].set_xlim(386,389) axs[i].grid() axs[i].legend() plt.grid() #fig, axs = plt.subplots(5) #axs[0].set_title('miss') #axs[1].set_title('miss') #axs[2].set_title('miss') #axs[3].set_title('miss') #axs[4].set_title('miss') #len_time1 = np.arange(0,1977) #len_time2 = np.arange(1977, 3954) #len_time3 = np.arange(3954,5931) #len_time4 = np.arange(5931,7908) #len_time5 = np.arange(7908,9885) #if i==1: #axs[0].plot(len_time1, time[0:1977],'.-', label='check') #axs[1].plot(len_time2, time[1977:3954],'.-', label='check') #axs[2].plot(len_time3, time[3954:5931],'.-', label='check') #axs[3].plot(len_time4, time[5931:7908],'.-', label='check') #axs[4].plot(len_time5, time[7908:9885],'.-', label='check') #axs[i].set_xlim(386,389) #axs[0].grid() #axs[0].legend() #axs[1].grid() #axs[1].legend() #axs[2].grid() #axs[2].legend() #axs[3].grid() #axs[3].legend() #axs[4].grid() #axs[4].legend() #plt.grid() #fig, axs = plt.subplots(5) #axs[0].set_title('miss') #axs[1].set_title('miss') #axs[2].set_title('miss') #axs[3].set_title('miss') #axs[4].set_title('miss') #len_time1 = np.arange(9885,9885+1977) #len_time2 = np.arange(9885+1977,9885+3954) #len_time3 = np.arange(9885+3954,9885+5931) #len_time4 = np.arange(9885+5931,9885+7908) #len_time5 = np.arange(9885+7908,9885+9885) #if i==1: #axs[0].plot(len_time1, time[9885+0:9885+1977],'.-', label='check') #axs[1].plot(len_time2, time[9885+1977:9885+3954],'.-', label='check') #axs[2].plot(len_time3, time[9885+3954:9885+5931],'.-', label='check') #axs[3].plot(len_time4, time[9885+5931:9885+7908],'.-', label='check') #axs[4].plot(len_time5, time[9885+7908:9885+9885],'.-', label='check') #axs[i].set_xlim(386,389) #axs[0].grid() #axs[0].legend() #axs[1].grid() #axs[1].legend() #axs[2].grid() #axs[2].legend() #axs[3].grid() #axs[3].legend() #axs[4].grid() #axs[4].legend() #plt.grid() # #### Draw time calculation # plt.figure(3) # fig = plt.figure() # font1 = {'family' : 'Times New Roman', # 'weight' : 'normal', # 'size' : 12, # } # c="red" # a_out1=np.loadtxt('Log/mat_out_time_indoor1.txt') # a_out2=np.loadtxt('Log/mat_out_time_indoor2.txt') # a_out3=np.loadtxt('Log/mat_out_time_outdoor.txt') # # n = a_out[:,1].size # # time_mean = a_out[:,1].mean() # # time_se = a_out[:,1].std() / np.sqrt(n) # # time_err = a_out[:,1] - time_mean # # feat_mean = a_out[:,2].mean() # # feat_err = a_out[:,2] - feat_mean # # feat_se = a_out[:,2].std() / np.sqrt(n) # ax1 = fig.add_subplot(111) # ax1.set_ylabel('Effective Feature Numbers',font1) # ax1.boxplot(a_out1[:,2], showfliers=False, positions=[0.9]) # ax1.boxplot(a_out2[:,2], showfliers=False, positions=[1.9]) # ax1.boxplot(a_out3[:,2], showfliers=False, positions=[2.9]) # ax1.set_ylim([0, 3000]) # ax2 = ax1.twinx() # ax2.spines['right'].set_color('red') # ax2.set_ylabel('Compute Time (ms)',font1) # ax2.yaxis.label.set_color('red') # ax2.tick_params(axis='y', colors='red') # ax2.boxplot(a_out1[:,1]*1000, showfliers=False, positions=[1.1],boxprops=dict(color=c),capprops=dict(color=c),whiskerprops=dict(color=c)) # ax2.boxplot(a_out2[:,1]*1000, showfliers=False, positions=[2.1],boxprops=dict(color=c),capprops=dict(color=c),whiskerprops=dict(color=c)) # ax2.boxplot(a_out3[:,1]*1000, showfliers=False, positions=[3.1],boxprops=dict(color=c),capprops=dict(color=c),whiskerprops=dict(color=c)) # ax2.set_xlim([0.5, 3.5]) # ax2.set_ylim([0, 100]) # plt.xticks([1,2,3], ('Outdoor Scene', 'Indoor Scene 1', 'Indoor Scene 2')) # # # print(time_se) # # # print(a_out3[:,2]) # plt.grid() # plt.savefig("time.pdf", dpi=1200) plt.show()