# plt.plot(gsr_data[:,0], gsr_data[:,1]) # plt.xlabel("Time (s)") # plt.ylabel("GSR (uS)") # plt.title("Raw GSR") # t_gsr, gsr = GSR.remove_spikes(gsr_data[:,1], nFS) t_gsr = gsr_data[:, 0] gsr = gsr_data[:, 1] print gsr.shape # print t_gsr.shape, gsr.shape, gsr_data.shape t_driver, driver, phasic_d, tonic_d = GSR.estimate_drivers( t_gsr, gsr, T1, T2, MX, DELTA) outputlabels = ["timestamp", "driver", "phasic", "tonic"] tools.array_labels_to_csv( np.column_stack([t_driver, driver, phasic_d, tonic_d]), np.array(outputlabels), "./output/preproc_" + filename[7:-4] + ".csv") #-----FEATURES----- windows = win.generate_dummy_windows(t_driver, 20, 10) features = GSR.extract_features(phasic_d, t_driver, DELTA, windows) tools.dict_to_csv(features, "./output/feat_" + filename[7:-4] + ".csv") # tools.prepare_json_to_plot_time(t_driver, [driver, phasic_d, tonic_d], ["Driver", "Phasic", "Tonic"]) # plt.figure(2) # plt.plot(t_driver, np.c_[tonic_d, driver, phasic_d]) # plt.legend(["Tonic", "Driver", "Phasic"]) # plt.title("Processed GSR") # plt.xlabel("Time (s)") # plt.ylabel("GSR (uS)") # plt.show()
T2=2 MX=1 DELTA=0.02 FS=4 nFS=4 gsr_data = tools.load_file(filename, header=1, sep=";") # 8 "," #TODO GAUSSIANA # gsr_data=tools.downsampling(gsr_data, FS, nFS) plt.figure(1) plt.plot(gsr_data[:,0], gsr_data[:,1]) plt.xlabel("Time (s)") plt.ylabel("GSR (uS)") plt.title("Raw GSR") # t_gsr, gsr = GSR.remove_spikes(gsr_data[:,1], nFS) t_gsr = gsr_data[:,0] gsr = gsr_data[:,1] # print t_gsr.shape, gsr.shape, gsr_data.shape t_driver, driver, phasic_d, tonic_d= GSR.estimate_drivers(t_gsr, gsr, T1, T2, MX, DELTA, FS=FS) windows=win.generate_dummy_windows(len(phasic_d), 80, 10) features = GSR.extract_features(phasic_d, t_driver, DELTA, windows) # features.to_csv("./output/feat_"+filename[7:-4]+".csv") tools.prepare_json_to_plot_time(t_driver, [driver, phasic_d, tonic_d], ["Driver", "Phasic", "Tonic"]) # plt.figure(2) # plt.plot(t_driver, np.c_[tonic_d, driver, phasic_d]) # plt.legend(["Tonic", "Driver", "Phasic"]) # plt.title("Processed GSR") # plt.xlabel("Time (s)") # plt.ylabel("GSR (uS)") # plt.show()
plt.figure(1) plt.plot(data.timestamp, data[lables_acc]) plt.legend(lables_acc) plt.xlabel("Time (ms)") plt.ylabel("Acceleration (m/s^2)") plt.title("Accelerometer") plt.figure(2) plt.plot(data.timestamp, data[lables_gyr]) plt.legend(lables_gyr) plt.xlabel("Time (ms)") plt.ylabel("Angular Speed (degree/s)") plt.title("Gyroscope") plt.figure(3) plt.plot(data.timestamp, data[lables_mag]) plt.legend(lables_mag) plt.title("Magnetometer") plt.ylabel("uT") plt.xlabel("Time (ms)") plt.show() # data=inertial.convert_units(data, lables[1:], coeff=empaticaAccCoeff) # print data windows=win.generate_dummy_windows(len(data), 100, 50) feats_acc=inertial.extract_features_acc(data, windows, fsamp=sensfsamp, col_acc=lables_acc) feats_gyr=inertial.extract_features_gyr(data, windows, fsamp=sensfsamp, col_gyr=lables_gyr) feats_mag=inertial.extract_features_mag(data, windows, fsamp=sensfsamp, col_mag=lables_mag) feats=pd.concat([feats_acc, feats_gyr, feats_mag], axis=1) print feats.shape feats.to_csv("./output/feat_"+filename[7:-4]+".csv")
gsr_data= tools.downsampling(gsr_data, nFS) # plt.figure(1) # plt.plot(gsr_data[:,0], gsr_data[:,1]) # plt.xlabel("Time (s)") # plt.ylabel("GSR (uS)") # plt.title("Raw GSR") # t_gsr, gsr = GSR.remove_spikes(gsr_data[:,1], nFS) t_gsr = gsr_data[:,0] gsr = gsr_data[:,1] print gsr.shape # print t_gsr.shape, gsr.shape, gsr_data.shape t_driver, driver, phasic_d, tonic_d= GSR.estimate_drivers(t_gsr, gsr, T1, T2, MX, DELTA) outputlabels=["timestamp", "driver", "phasic", "tonic"] tools.array_labels_to_csv(np.column_stack([t_driver, driver, phasic_d, tonic_d]), np.array(outputlabels), "./output/preproc_"+filename[7:-4]+".csv") #-----FEATURES----- windows=win.generate_dummy_windows(t_driver, 20, 10) features = GSR.extract_features(phasic_d, t_driver, DELTA, windows) tools.dict_to_csv(features, "./output/feat_"+filename[7:-4]+".csv") # tools.prepare_json_to_plot_time(t_driver, [driver, phasic_d, tonic_d], ["Driver", "Phasic", "Tonic"]) # plt.figure(2) # plt.plot(t_driver, np.c_[tonic_d, driver, phasic_d]) # plt.legend(["Tonic", "Driver", "Phasic"]) # plt.title("Processed GSR") # plt.xlabel("Time (s)") # plt.ylabel("GSR (uS)") # plt.show()