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
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-----
IATT = 40
filtered_signal = ourFilters.filterSignal(rawdata,
SAMP_F,
passFr=F_PASS,
stopFr=F_STOP,
LOSS=ILOSS,
ATTENUATION=IATT,
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)
#filter the signal
#the user selects the parameters, with default suggested
filterType = 'butter'
F_PASS = 2
F_STOP = 6
ILOSS = 0.1
IATT = 40
filtered_signal = ourFilters.filterSignal(rawdata, SAMP_F, passFr = F_PASS, stopFr = F_STOP, LOSS = ILOSS, ATTENUATION = IATT, 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)