diff --git a/software/read_txt.py b/software/read_txt.py
index aa654164d5e90ba9bc66ede4eff2698f175c77ea..75f1848c65ac8ad8364eb5cc7ee75066ba75da84 100644
--- a/software/read_txt.py
+++ b/software/read_txt.py
@@ -14,21 +14,30 @@ from numpy.core.arrayprint import LongFloatFormat
import pandas as pd
-#path = 'C:/Users/ANGELICA/Desktop/PySW/samples/'
-#home = str(Path.home())
-#filenames = os.listdir(os.path.join(home, path))
-#print(filenames)
+# FFT
+def fft(signal, Fs):
+ PSD=abs(np.fft.fft(signal))
+ PSD=PSD[1:int(len(PSD)/2)]
+ PSD=PSD/np.argmax(PSD)
+ L = len(PSD)
+ freq = np.arange(0,L,1)*((Fs/2)/L)
+
+ return PSD,freq
+
+# RATE
+def rate_calculation(peak_array):
+ r=np.diff(peak_array)
+ bpm = (1./r)*60
+ rate = np.mean(abs(bpm))
+ return bpm,rate
-#data = pd.read_excel (r'C:/Users/ANGELICA/Desktop/PySW/samples/test8.xlsx')
-#df = pd.DataFrame(data)
-#print (df)
ppg_ir=[]
location=os.path.dirname(os.path.abspath(__file__))
print(location)
-#df = open("muestra3s.txt", 'r')
-df = open("ppg15.txt", 'r')
+#df = open("c:\Users\ANGELICA\Documents\Workspace\proyecto-smart-watch\software\samples\ecg01_200fs.txt", 'r')
+df = open("c:\\Users\ANGELICA\Documents\Workspace\proyecto-smart-watch\software\samples\ppg15_125fs.txt", 'r')
ppg=[]
for i in df.readlines():
@@ -49,42 +58,11 @@ Ts = 1/Fs # s
N = len(ppg_ir)
time = Ts*(np.array(np.arange(0,N,1)))
-plt.figure(figsize=(15,15))
-#plt.subplot(3,1,1)
-#plt.plot(time,ppg_ir)
-#plt.title("Amplitude adjusted")
-#plt.ylabel('Amplitude (V)')
-#plt.xlabel('Time (s)')
-
-
## Signal to zero
ppg_ir = (ppg_ir - np.mean(ppg_ir))/np.std(ppg_ir)
-plt.subplot(2,2,1)
-plt.plot(time,ppg_ir)
-plt.title("Signal to zero")
-plt.ylabel('Amplitude (V)')
-plt.xlabel('Time (s)')
-
-# FFT
-def fft(signal, Fs):
- PSD=abs(np.fft.fft(signal))
- PSD=PSD[1:int(len(PSD)/2)]
- PSD=PSD/np.argmax(PSD)
- L = len(PSD)
- freq = np.arange(0,L,1)*((Fs/2)/L)
-
- return PSD,freq
-
PSD,freq=fft(ppg_ir, Fs)
-
-plt.subplot(2,2,2)
-plt.plot(freq, PSD)
-plt.title("Normalized frequency")
-plt.ylabel('Magnitude')
-plt.xlabel('Frequency (Hz)')
-
#FIR
numtaps=21
f1 = 53
@@ -99,19 +77,18 @@ ppg_ir_filter= signal.filtfilt(b,a,ppg_ir)
PSD_1,freq_1=fft(ppg_ir_filter,Fs)
-plt.subplot(2,2,3)
+plt.figure(figsize=(15,15))
+plt.subplot(2,2,1)
plt.plot(time,ppg_ir_filter)
-plt.title("Filtered signal")
+plt.title("PPG Signal")
plt.ylabel('Amplitude (V)')
plt.xlabel('Time (s)')
-plt.subplot(2,2,4)
+plt.subplot(2,2,2)
plt.plot(freq_1,PSD_1)
-plt.title("Filtered signal")
-plt.ylabel('Amplitude (V)')
-plt.xlabel('Time (s)')
-plt.show()
-
+plt.title("Normalized frequency")
+plt.ylabel('Magnitude')
+plt.xlabel('Frequency (Hz)')
#Heart rate
ppg_ir_p = ppg_ir
@@ -119,26 +96,22 @@ ppg_ir_p = ppg_ir
thres = 3*np.mean(abs(ppg_ir_p))/3
dist = (60/200)*Fs
peaks, _ = find_peaks(ppg_ir_p, distance=dist, height=thres)
-plt.figure(figsize=(15,15))
-plt.subplot(3,1,1)
+plt.subplot(2,2,3)
plt.plot(time,ppg_ir)
plt.plot(time[peaks],ppg_ir[peaks],"x")
- #heart rate calculation
-#derivada discreta
-hr=np.diff(time[peaks]) #time difference
-plt.subplot(3,1,2)
+
+hr,heart_rate=rate_calculation(time[peaks])
+print(heart_rate)
+
+plt.subplot(2,2,4)
plt.stem(hr)
-bpm = (1./hr)*60
-plt.subplot(3,1,3)
-plt.stem(bpm)
+plt.title("Heart Rate")
+plt.ylabel('Beats/min')
+plt.xlabel('Values')
plt.show()
-heart_rate = np.mean(abs(bpm))
-print(heart_rate)
#interpolation because is not good enough distributed
#rearange time
-
-
time_hr=[]
time_hr.append(time[0])
c=0
@@ -162,64 +135,48 @@ f = interpolate.interp1d(x, y)
#xnew = np.arange(0, 9, 0.1)
#ynew = f(xnew) # use interpolation function returned by `interp1d`
#plt.plot(x, y, 'o', xnew, ynew, 'x')
-#plt.show()
#Respiration rate
-rr=ppg_ir[peaks]
+ppg_rr=ppg_ir[peaks]
time_rr=time[peaks]
-plt.figure(figsize=(15,15))
-plt.subplot(2,2,1)
-plt.plot(time_rr,rr)
-
-PSD_r,freq_r=fft(rr, Fs)
-plt.subplot(2,2,2)
-plt.plot(freq_r, PSD_r)
-plt.title("Normalized frequency")
-plt.ylabel('Magnitude')
-plt.xlabel('Frequency (Hz)')
+PSD_r,freq_r=fft(ppg_rr, Fs)
## Signal to zero
-rr = (rr - np.mean(rr))/np.std(rr)
-plt.subplot(2,2,3)
-plt.plot(time_rr,rr)
+ppg_rr = (ppg_rr - np.mean(ppg_rr))/np.std(ppg_rr)
+plt.figure(figsize=(15,15))
+plt.subplot(2,2,1)
+plt.plot(time_rr,ppg_rr)
plt.title("Signal to zero")
plt.ylabel('Amplitude (V)')
plt.xlabel('Time (s)')
-
-PSD_r1,freq_r1=fft(rr, Fs)
-plt.subplot(2,2,4)
+PSD_r1,freq_r1=fft(ppg_rr, Fs)
+plt.subplot(2,2,2)
plt.plot(freq_r1, PSD_r1)
plt.title("Normalized frequency")
plt.ylabel('Magnitude')
plt.xlabel('Frequency (Hz)')
-plt.show()
-
-#Heart rate
+#Respiration rate
#for peak detection
-thres = 3*np.mean(abs(rr))/3
+#thres = 3*np.mean(abs(ppg_rr))/3
#dist = (60/30)*Fs
-peaks, _ = find_peaks(rr, height=0)
-plt.figure(figsize=(15,15))
-plt.subplot(3,1,1)
-plt.plot(time_rr,rr)
-plt.plot(time_rr[peaks],rr[peaks],"x")
-
- #heart rate calculation
-#derivada discreta
-rrr=np.diff(time_rr[peaks]) #time difference
-plt.subplot(3,1,2)
-plt.stem(rrr)
-bpm = (1./rrr)*60
-plt.subplot(3,1,3)
-plt.stem(bpm)
-
-respiration_rate = np.mean(abs(bpm))
-print(respiration_rate)
+peaks, _ = find_peaks(ppg_rr, height=0)
+plt.subplot(2,2,3)
+plt.plot(time_rr,ppg_rr)
+plt.plot(time_rr[peaks],ppg_rr[peaks],"x")
+plt.title("RR Signal")
+plt.ylabel('Amplitude (V)')
+plt.xlabel('Time (s)')
+ #respiration rate calculation
+rr,respiration_rate=rate_calculation(time_rr[peaks])
+print(respiration_rate)
+plt.subplot(2,2,4)
+plt.stem(rr)
+plt.title("Respiration Rate")
+plt.ylabel('Breaths/min')
+plt.xlabel('Values')
plt.show()
-
-