'load and plot *.edf files'

.gitignore

@@ -0,0 +1,3 @@
+edf_file.edf
+__pycache__/*
+*.pyc

app.py

@@ -2,21 +2,18 @@
 import streamlit as st
 import mne
 import matplotlib.pyplot as plt
+import os
+from misc import *
+
 
 # Load the edf file
 edf_file = st.file_uploader("Upload an EEG edf file", type="edf")
+
+
 if edf_file is not None:
-    raw = mne.io.read_raw_edf(edf_file)
-    st.write(f"Loaded {edf_file.name} with {raw.info['nchan']} channels")
 
-    # Select the first channel
-    channel = raw.ch_names[0]
-    st.write(f"Selected channel: {channel}")
+    # Read the file
+    raw = read_file(edf_file)
 
-    # 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)
+    # Preprocess and plot the data
+    preprocessing_and_plotting(raw)

misc.py

@@ -0,0 +1,31 @@
+
+import mne
+import streamlit as st
+import matplotlib.pyplot as plt
+
+
+
+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}")
+    st.pyplot(fig)
+
+def read_file(edf_file):
+    # To read file as bytes:
+    bytes_data = edf_file.getvalue()
+    # Open a file named "output.bin" in the current directory in write binary mode
+    with open('edf_file.edf', "wb") as f:
+        # Write the bytes data to the file
+        f.write(bytes_data)
+    
+    raw = mne.io.read_raw_edf('edf_file.edf')
+    st.write(f"Loaded {edf_file.name} with {raw.info['nchan']} channels")
+    return raw

run.sh

@@ -0,0 +1 @@
+streamlit run app.py