# 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)")
filterType=filterType)
#compact timestamp, signal and labels for the next processes
total_signal = filtered_signal
#extraction peaks from the signal
#the user selects the parameters, with default suggested
delta = 1
peaks = IBI.getPeaksIBI(total_signal, SAMP_F, delta)
#calculation of the IBI
#the user selects the parameters, with default suggested
minFr = 40
maxFr = 200
ibi = IBI.max2interval(peaks, minFr, maxFr)
tools.array_labels_to_csv(ibi, np.array(["timestamp", "IBI", "lables"]),
"./output/preproc_" + fileName[7:-4] + ".csv")
#-----FEATURES EXTRACTION-----
timestamp = ibi[:, 0]
timed_vals = ibi[:, [0, 1]]
lbls = ibi[:, 2]
winds, lbls = windowing.get_windows_contiguos(timestamp, lbls, 100, 50)
feat, lbls = IBI.extract_IBI_features(timed_vals, winds, lbls)
feat_col = np.array(
['RRmean', 'RRSTD', 'pNN50', 'pNN25', 'pNN10', 'RMSSD', 'SDSD'])
tools.array_labels_to_csv(feat, feat_col,
"./output/feat_" + fileName[7:-4] + ".csv")
plen_aft = 1
filtered_signal = ourFilters.matched_filter(downsampled_data, SAMP_F, start_good_beats, end_good_beats, plen_bef, plen_aft)
print 'filtered' '''
#extraction peaks from the signal
#the user selects the parameters, with default suggested
delta = 0.2
peaks = IBI.getPeaksIBI(filtered_signal,SAMP_F, delta)
print 'plotting...'
plt.plot(filtered_signal[:,0],filtered_signal[:,1])
plt.plot(peaks[:,0], peaks[:,1], 'o')
plt.show()
#calculation of the IBI
#the user selects the parameters, with default suggested
minFr = 40
maxFr = 200
ibi = IBI.max2interval(peaks, minFr, maxFr)
ourTools.array_labels_to_csv(ibi, np.array(["timestamp", "IBI", "labels"]), "./output/preproc_"+fileName[7:-4]+".csv")
#-----FEATURES EXTRACTION-----
lbls = np.array([0 for i in ibi[:,0]])
winds, lbls = windowing.get_windows_contiguos(ibi[:,0], lbls, 100, 50)
feat, lbls = IBI.extract_IBI_features(ibi, winds, lbls)
feat_col=np.array(['RRmean', 'RRSTD', 'pNN50', 'pNN25', 'pNN10', 'RMSSD', 'SDSD'])
ourTools.array_labels_to_csv(feat, feat_col, "./output/feat_"+fileName[7:-4]+".csv")
data = tools.load_file(filename, sep=',', header=1)
data = tools.downsampling(data, 50)
t = tools.selectCol(data, columns_in, "TIME")
acc = tools.selectCol(data, columns_in, col_acc)
gyr = tools.selectCol(data, columns_in, col_gyr)
mag = tools.selectCol(data, columns_in, col_mag)
lab = tools.selectCol(data, columns_in, "LAB")
acc = inertial.convert_units(acc, coeff=sensAccCoeff)
gyr = inertial.convert_units(gyr, coeff=sensGyrCoeff)
mag = inertial.convert_units(mag, coeff=sensMagCoeff)
# tools.array_labels_to_csv(np.column_stack([t, acc]), np.array(columns_in), "./output/preproc_"+filename[7:-4]+".csv")
#-----EXTRACT FEATURES-----
windows, winlab = win.get_windows_no_mix(t, lab, 1, 0.5)
feats_acc, fcol_acc = inertial.extract_features_acc(acc, t, col_acc, windows)
feats_gyr, fcol_gyr = inertial.extract_features_gyr(gyr, t, col_gyr, windows)
feats_mag, fcol_mag = inertial.extract_features_mag(mag, t, col_mag, windows)
feats = np.column_stack([feats_acc, feats_gyr, feats_mag, winlab])
columns_out = np.r_[fcol_acc, fcol_gyr, fcol_mag, np.array(["LAB"])]
# print feats.shape
# print columns.shape, columns
tools.array_labels_to_csv(feats, columns_out,
"./output2/feat_" + filename[7:-4] + ".csv")
# # feats.to_csv("./output/feat_6.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()
data = tools.load_file(filename, sep=',', header=1)
data=tools.downsampling(data, 50)
t=tools.selectCol(data, columns_in, "TIME")
acc=tools.selectCol(data, columns_in, col_acc)
gyr=tools.selectCol(data, columns_in, col_gyr)
mag=tools.selectCol(data, columns_in, col_mag)
lab=tools.selectCol(data, columns_in, "LAB")
acc= inertial.convert_units(acc, coeff=sensAccCoeff)
gyr= inertial.convert_units(gyr, coeff=sensGyrCoeff)
mag= inertial.convert_units(mag, coeff=sensMagCoeff)
# tools.array_labels_to_csv(np.column_stack([t, acc]), np.array(columns_in), "./output/preproc_"+filename[7:-4]+".csv")
#-----EXTRACT FEATURES-----
windows, winlab=win.get_windows_no_mix(t,lab , 1, 0.5)
feats_acc, fcol_acc= inertial.extract_features_acc(acc, t, col_acc, windows)
feats_gyr, fcol_gyr= inertial.extract_features_gyr(gyr, t, col_gyr, windows)
feats_mag, fcol_mag= inertial.extract_features_mag(mag, t, col_mag, windows)
feats=np.column_stack([feats_acc, feats_gyr, feats_mag, winlab])
columns_out=np.r_[fcol_acc, fcol_gyr, fcol_mag, np.array(["LAB"])]
# print feats.shape
# print columns.shape, columns
tools.array_labels_to_csv(feats, columns_out, "./output2/feat_"+filename[7:-4]+".csv")
# # feats.to_csv("./output/feat_6.csv")
