Update app.py

app.py

@@ -33,6 +33,13 @@ def fill_nan_with_zero(value):
     else:
         return value
 
+# Getting current time and yesterday
+now = datetime.now() + timedelta(hours=2)
+yesterday = now - timedelta(days=1)
+
+# Defining scaler
+scaler = StandardScaler()
+
 # Creating tabs for the different features of the application
 tab1,tab2 = st.tabs(['Parking place near Building', 'Parking place near Bikelane'])
 
@@ -113,28 +120,49 @@ with tab1:
         # Immediately rerun the application
         st.experimental_rerun()
 
-    # Get current time in UTC + 2 hours
-    now = datetime.now() + timedelta(hours=2) # Get current time 
-    yesterday = now - timedelta(days=1)
+    # Creating plot for latest magnetic field data for building
+    # Filtering building_new for specific time
+    building_mag_specific_time_range = building_new[(building_new['time'] >= yesterday) & (building_new['time'] <= now)]
+
+    # Defining magnetic field data to normalise
+    building_mag_to_normalize = building_mag_specific_time_range[['x', 'y', 'z']]
+
+    # Applying StandardScaler
+    normalized_building_mag = scaler.fit_transform(building_mag_to_normalize)    
+
+    # Adding normalized data back to the DataFrame
+    building_mag_specific_time_range[['x', 'y', 'z']] = normalized_building_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
+    building_mag_specific_time_range['time'] = building_mag_specific_time_range['time'].astype(str)
+
+    # Plotting using Streamlit's line chart
+    st.line_chart(building_mag_specific_time_range.set_index('time')[['x', 'y', 'z']])
+
+    # Creating plot for latest radar data for building
+    # Filtering building_new for specific time
+    building_rad_specific_time_range = building_new[(building_new['time'] >= yesterday) & (building_new['time'] <= now)]
 
-    df_specific_time_range = building_new[(building_new['time'] >= yesterday) & (building_new['time'] <= now)]
-    data_to_normalize = df_specific_time_range[['x', 'y', 'z']]
+    # Defining magnetic field data to normalise
+    building_rad_to_normalize = building_rad_specific_time_range[['radar_0', 'radar_1', 'radar_2', 'radar_3', 'radar_4', 'radar_5', 'radar_6', 'radar_7']]
 
     # Applying StandardScaler
-    scaler = StandardScaler()
-    normalized_data = scaler.fit_transform(data_to_normalize)    
+    normalized_building_rad = scaler.fit_transform(building_rad_to_normalize)    
 
     # Adding normalized data back to the DataFrame
-    df_specific_time_range[['x', 'y', 'z']] = normalized_data
+    building_rad_specific_time_range[['radar_0', 'radar_1', 'radar_2', 'radar_3', 'radar_4', 'radar_5', 'radar_6', 'radar_7']] = normalized_building_rad
 
     # Streamlit plotting
-    st.title('Normalized values of x, y, z from yesterday to today')
+    st.subheader('Normalized values of radar data from yesterday to today')
 
     # Converting the time column to string for better readability in Streamlit plots
-    df_specific_time_range['time'] = df_specific_time_range['time'].astype(str)
+    building_rad_specific_time_range['time'] = building_rad_specific_time_range['time'].astype(str)
 
     # Plotting using Streamlit's line chart
-    st.line_chart(df_specific_time_range.set_index('time')[['x', 'y', 'z']])
+    st.line_chart(building_rad_specific_time_range.set_index('time')[['radar_0', 'radar_1', 'radar_2', 'radar_3', 'radar_4', 'radar_5', 'radar_6', 'radar_7']])
         
 with tab2: