'plot using mne-python'

misc.py

@@ -98,18 +98,31 @@ def build_model(model_name, n_classes, n_chans, input_window_samples, drop_prob=
     
 
 def preprocessing_and_plotting(raw):
-    # Select the first channel
-    channel = raw.ch_names[0]
-    st.write(f"Selected channel: {channel}")
-
-    # Plot the first channel
-    fig, ax = plt.subplots()
-    ax.plot(raw.times, raw[channel][0].T)
-    ax.set_xlabel("Time (s)")
-    ax.set_ylabel("Amplitude (µV)")
-    ax.set_title(f"EEG signal of {channel}")
+    fig = raw.plot(duration=10, scalings='auto',remove_dc=True,show_scrollbars=False) #, n_channels=10
     st.pyplot(fig)
 
+    # # Plot the power spectrum
+    # fig, ax = plt.subplots()
+    # raw.plot_psd(fmin=1, fmax=60, ax=ax)
+    # st.pyplot(fig)
+
+    # # Plot the spectrogram
+    # fig, ax = plt.subplots()
+    # raw.plot_spectrogram(n_fft=512, ax=ax)
+    # st.pyplot(fig)
+
+    # # Select the first channel
+    # channel = raw.ch_names[0]
+    # st.write(f"Selected channel: {channel}")
+
+    # # Plot the first channel
+    # fig, ax = plt.subplots()
+    # ax.plot(raw.times, raw[channel][0].T)
+    # ax.set_xlabel("Time (s)")
+    # ax.set_ylabel("Amplitude (µV)")
+    # ax.set_title(f"EEG signal of {channel}")
+    # st.pyplot(fig)
+
 def read_file(edf_file):
     # To read file as bytes:
     bytes_data = edf_file.getvalue()