vraag 9 goede plot

main.py

@@ -12,7 +12,6 @@ def load_emg_data(file_path):
     
     emg_data = mat_data['emg_data_walking']
     
-
     channel_names = emg_data['data_headers'][0][0]
     channel_names = [ch[0] for ch in channel_names[0]]
     
@@ -26,7 +25,7 @@ def load_emg_data(file_path):
     return channel_names
 
 
-#file_path = "emg_data_walking.mat"
+file_path = "emg_data_walking.mat"
 #load_emg_data(file_path)
 
 chosen_muscles = {
@@ -56,12 +55,20 @@ class SignalProcessing:
         self.fs = fs
 
     def bandpass_filter(self, data, lowcut, highcut):
-            #komt nog filters
-         pass
+        nyquist = 0.5 * self.fs
+        low = lowcut / nyquist
+        high = highcut / nyquist
+        order = 4
+        b, a = butter(order, [low, high], btype='band')
+        filtered_data = filtfilt(b, a, data)
+        return filtered_data
 
     def power_spectral_analysis(self, data):
-            #methode
-        pass
+        N = len(data)
+        fft_values = np.fft.fft(data)
+        fft_magnitude = np.abs(fft_values[:N//2])
+        freq = np.fft.fftfreq(N, d=1/self.fs)[:N//2]
+        return freq, fft_magnitude
 
 #print(dir(scipy.signal))
 
@@ -70,16 +77,21 @@ def info_emg_data(file_path):
     mat_data = scipy.io.loadmat(file_path)
     emg_data = mat_data['emg_data_walking']
 
+    print("Structuur van emg_data:", emg_data.dtype, emg_data.shape)
+    print("Beschikbare velden in emg_data:", emg_data.dtype.names)
+
     sampling_rate = emg_data['sampling_rate'][0][0][0]
 
     data = emg_data['data'][0][0]
+    data = data.T
+    print("Shape van data:", data.shape)
     num_samples = data.shape[1]
-
     duration = num_samples / sampling_rate
     return sampling_rate, data, num_samples, duration
 
-#file_path = "emg_data_walking.mat"
-#info_emg_data(file_path)
+
+file_path = "emg_data_walking.mat"
+info_emg_data(file_path)
 
 def compute_fft(signal, sampling_rate):
     N = len(signal)
@@ -92,12 +104,15 @@ def plot_emg_data(file_path):
     sampling_rate, data, num_samples, duration = info_emg_data(file_path)
 
     sampling_time = float(1/sampling_rate)
-    time_vector = np.arange(0,duration,sampling_time)
-    channel_rectus_femoris = data[chosen_muscles['rectus_fomoris'], :]
-    channel_tibialis_anterior = data[chosen_muscles['tibialis_anterior'], :]
+    num_samples = int(6 * sampling_rate)
+    num_samples = min(num_samples, data.shape[1])
+    time_vector = np.linspace(0,6,num_samples)
+    channel_rectus_femoris = data[chosen_muscles['rectus_fomoris'], :num_samples]
+    channel_tibialis_anterior = data[chosen_muscles['tibialis_anterior'], :num_samples]
+    
 
-    freq_rf, fft_rf = compute_fft(channel_rectus_femoris, sampling_rate)
-    freq_ta, fft_ta = compute_fft(channel_tibialis_anterior, sampling_rate)
+    freq_rf, fft_rf = compute_fft(channel_rectus_femoris[:num_samples], sampling_rate)
+    freq_ta, fft_ta = compute_fft(channel_tibialis_anterior[:num_samples], sampling_rate)
 
     plt.figure(figsize=(10,6))
 
@@ -106,14 +121,13 @@ def plot_emg_data(file_path):
     plt.title('EMG Data - Rectus femoris')
     plt.xlabel('Time [Seconds]')
     plt.ylabel('Amplitude')
-    plt.legend()
 
     plt.subplot(2, 1, 2)
     plt.plot(time_vector, channel_tibialis_anterior)
     plt.title('EMG Data - Tibialis Anterior')
     plt.xlabel('Time [seconds]')
     plt.ylabel('Amplitude')
-    plt.legend()
+
 
     plt.tight_layout()
     plt.show()
@@ -133,10 +147,12 @@ def plot_emg_data(file_path):
     plt.tight_layout()
     plt.show()
 
-file_path = "emg_data_walking"
-plot_emg_data(file_path)
+    print(num_samples)
 
 
+file_path = "emg_data_walking.mat"
+plot_emg_data(file_path)
+