Update app.py

app.py

@@ -235,4 +235,48 @@ with tab2:
         st.cache_data.clear()
         # Immediately rerun the application
         st.experimental_rerun()
- 
+
+    # Creating plot for latest magnetic field data for bikelane
+    # Filtering bikelane_new for specific time
+    bikelane_mag_specific_time_range = bikelane_new[(bikelane_new['time'] >= yesterday) & (bikelane_new['time'] <= now)]
+
+    # Defining magnetic field data to normalise
+    bikelane_mag_to_normalize = bikelane_mag_specific_time_range[['x', 'y', 'z']]
+
+    # Applying StandardScaler
+    normalized_bikelane_mag = scaler.fit_transform(bikelane_mag_to_normalize)    
+
+    # Adding normalized data back to the DataFrame
+    bikelane_mag_specific_time_range[['x', 'y', 'z']] = normalized_bikelane_mag
+
+    # Streamlit plotting
+    st.subheader('Normalized values of magnetic field data from yesterday to today')
+
+    # Converting the time column to string for better readability in Streamlit plots
+    bikelane_mag_specific_time_range['time'] = bikelane_mag_specific_time_range['time'].astype(str)
+
+    # Plotting using Streamlit's line chart
+    st.line_chart(bikelane_mag_specific_time_range.set_index('time')[['x', 'y', 'z']])
+
+    
+    # Creating plot for latest radar data for bikelane
+    # Filtering bikelane_new for specific time
+    bikelane_rad_specific_time_range = bikelane_new[(bikelane_new['time'] >= yesterday) & (bikelane_new['time'] <= now)]
+
+    # Defining magnetic field data to normalise
+    bikelane_rad_to_normalize = bikelane_rad_specific_time_range[['radar_0', 'radar_1', 'radar_2', 'radar_3', 'radar_4', 'radar_5', 'radar_6', 'radar_7']]
+
+    # Applying StandardScaler
+    normalized_bikelane_rad = scaler.fit_transform(bikelane_rad_to_normalize)    
+
+    # Adding normalized data back to the DataFrame
+    bikelane_rad_specific_time_range[['radar_0', 'radar_1', 'radar_2', 'radar_3', 'radar_4', 'radar_5', 'radar_6', 'radar_7']] = normalized_bikelane_rad
+
+    # Streamlit plotting
+    st.subheader('Normalized values of radar data from yesterday to today')
+
+    # Converting the time column to string for better readability in Streamlit plots
+    bikelane_rad_specific_time_range['time'] = bikelane_rad_specific_time_range['time'].astype(str)
+
+    # Plotting using Streamlit's line chart
+    st.line_chart(bikelane_rad_specific_time_range.set_index('time')[['radar_0', 'radar_1', 'radar_2', 'radar_3', 'radar_4', 'radar_5', 'radar_6', 'radar_7']])