# import matplotlib.pyplot as plt
import windowing as win
filename = "./data/GSR_F01_F.txt"
T1 = 0.75
T2 = 2
MX = 1
DELTA = 0.02
nFS = 16
gsr_data = tools.load_file(filename, header=8, sep=",") # 8 ","
#TODO GAUSSIANA
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(
if __name__ == '__main__':
#user insertion, the path is substituted with database source
# path = "/home/flavio/Work/PhysioWat/robaNoGit/data/Nicolasdata/"
fileName = "./data/EKG_F01_M.csv"
SAMP_F = 256
#load data from the file
rawdata = loadEKG(fileName)
#DEBUG ONLY, create a new ideal timestamp
temp_ts = np.arange(0, rawdata.shape[0]/SAMP_F, 1.0/SAMP_F)
rawdata[:,0] = temp_ts
#SAMP_F = int(round(1/(rawdata[1,0]-rawdata[0,0]))) se i dati di Nicola non avessero il timestamp buggato
#downsampling
#the user selects the parameters, with default suggested
new_f = SAMP_F
downsampled_data = ourTools.downsampling(rawdata, new_f, switch=False)
#filter
#the user selects the parameters, with default suggested
filterType = None
F_PASS = 0
F_STOP = 0
ILOSS = 0
IATT = 0
filtered_signal = ourFilters.filterSignal(downsampled_data, SAMP_F, passFr = F_PASS, stopFr = F_STOP, LOSS = ILOSS, ATTENUATION = IATT, filterType = filterType)
'''
#filter 2
#the user selects the parameters, with default suggested
start_good_beats = 1290 #this parameter hasn't a default, this number is only for the example used in this algorithm
end_good_beats = 1360 #this parameter hasn't a default, this number is only for the example used in this algorithm
plen_bef = 0.35
] col_acc = ["ACCX", "ACCY", "ACCZ"] col_gyr = ["GYRX", "GYRY", "GYRZ"] col_mag = ["MAGX", "MAGY", "MAGZ"] empaticaAccCoeff = 2 * 9.81 / 128 empaticafsamp = 32 sensAccCoeff = 8 * 9.81 / 32768 sensGyrCoeff = 2000 / 32768 sensMagCoeff = 0.007629 sensfsamp = 100 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-----
#Simulate user app
if __name__ == '__main__':
#user insertion, the path is substituted with database source
path = "/home/flavio/Work/PhysioWat/robaNoGit/data/Nicola's_data/"
fileName = "EKG_F01_F.txt"
SAMP_F = 256
#load data from the file
rawdata = loadEKG(path + fileName)
#downsampling
#the user selects the parameters, with default suggested
downsampling_ratio = 1
new_f = SAMP_F / float(downsampling_ratio)
downsampled_data = ourTools.downsampling(rawdata, SAMP_F, new_f)
#filter
#the user selects the parameters, with default suggested
filterType = None
F_PASS = 0
F_STOP = 0
ILOSS = 0
IATT = 0
filtered_signal = ourFilters.filterSignal(downsampled_data, SAMP_F, passFr = F_PASS, stopFr = F_STOP, LOSS = ILOSS, ATTENUATION = IATT, filterType = filterType)
#extraction of IBI from preprocessed signal
#the user selects the parameters, with default suggested
delta = 0.2
minFr = 40
maxFr = 200
# import matplotlib.pyplot as plt
import windowing as win
filename="./data/GSR_F01_F.txt"
T1=0.75
T2=2
MX=1
DELTA=0.02
nFS=16
gsr_data = tools.load_file(filename, header=8, sep=",") # 8 ","
#TODO GAUSSIANA
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")
col_acc=["ACCX", "ACCY", "ACCZ"] col_gyr=["GYRX", "GYRY", "GYRZ"] col_mag=["MAGX", "MAGY", "MAGZ"] empaticaAccCoeff=2*9.81/128 empaticafsamp=32 sensAccCoeff=8*9.81/32768 sensGyrCoeff=2000/32768 sensMagCoeff=0.007629 sensfsamp=100 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-----