Updated lines 348-382 with: # Visualization with the origin initializing at the top-left corner # Goal tiles should appear on the bottom right at two sets of coordinates located at (4,5) and (5,4) # Aggregate the data to count the number of visits to each State_2D visitation_counts = df['State_2D'].value_counts().reset_index() visitation_counts.columns = ['State_2D', 'Visitation_Count'] # Clean the State_2D column to remove any extra characters or spaces visitation_counts['State_2D'] = visitation_counts['State_2D'].str.replace(r'[^\d,]', '', regex=True) # Split the cleaned State_2D into separate columns visitation_counts[['x', 'y']] = visitation_counts['State_2D'].str.split(',', expand=True).astype(int) # Invert the y-coordinates to match the desired orientation grid_size = 6 visitation_counts['y'] = grid_size - 1 - visitation_counts['y'] # Create a 6x6 grid with zero visitation counts for all positions heatmap_data = pd.DataFrame({ 'x': [x for x in range(grid_size) for y in range(grid_size)], 'y': [y for x in range(grid_size) for y in range(grid_size)], 'Visitation_Count': 0 }) # Merge the visitation counts with the grid data heatmap_data = heatmap_data.merge(visitation_counts, on=['x', 'y'], how='left').fillna(0) # Create the Plotly heatmap fig = px.density_heatmap(heatmap_data, x='x', y='y', z='Visitation_Count_y', title='Goal Position Visitation Counts Heatmap', labels={'x': 'X Coordinate', 'y': 'Y Coordinate', 'Visitation_Count_y': 'Visitation Count'}, nbinsx=grid_size, nbinsy=grid_size) # Display the heatmap using Streamlit with a unique key st.title('Goal Position Visitation Counts Heatmap Visualization') st.plotly_chart(fig, key='unique_heatmap_key')

app.py

@@ -260,6 +260,7 @@ fig = px.bar(visitation_counts, x='State_2D', y='Visitation_Count',
 st.title('Goal Position Visitation Counts Visualization')
 st.plotly_chart(fig)
 
+# Duplicate Goal Visitation visualization
 # Visualization only includes the (4,5) and (5,4) 6 x 6 Grid Map Positions
 # Aggregate the data to count the number of visits to each State_2D
 #visitation_counts = df['State_2D'].value_counts().reset_index()
@@ -279,6 +280,7 @@ st.plotly_chart(fig)
 # Display the heatmap using Streamlit
 #st.title('Goal Position Visitation Counts Heatmap Visualization')
 #st.plotly_chart(fig)
+# Duplicate Goal Visitation visualization 
 
 df = pd.read_csv('intrinsic_analysis.csv')
 st.write("Intrinsic Analysis DataFrame:")
@@ -310,6 +312,41 @@ fig = px.density_heatmap(visitation_counts, x='x', y='y', z='Visitation_Count',
 st.title('Goal Position Visitation Counts Heatmap Visualization')
 st.plotly_chart(fig, key='unique_heatmap_key')
 
+# Populates goal tiles at the top right corner at positions (4,5) and (5,4)
+# Aggregate the data to count the number of visits to each State_2D
+#visitation_counts = df['State_2D'].value_counts().reset_index()
+#visitation_counts.columns = ['State_2D', 'Visitation_Count']
+
+# Clean the State_2D column to remove any extra characters or spaces
+#visitation_counts['State_2D'] = visitation_counts['State_2D'].str.replace(r'[^\d,]', '', regex=True)
+
+# Split the cleaned State_2D into separate columns
+#visitation_counts[['x', 'y']] = visitation_counts['State_2D'].str.split(',', expand=True).astype(int)
+
+# Create a 6x6 grid with zero visitation counts for all positions
+#grid_size = 6
+#heatmap_data = pd.DataFrame({
+    #'x': [x for x in range(grid_size) for y in range(grid_size)],
+    #'y': [y for x in range(grid_size) for y in range(grid_size)],
+    #'Visitation_Count': 0
+#})
+
+# Merge the visitation counts with the grid data
+#heatmap_data = heatmap_data.merge(visitation_counts, on=['x', 'y'], how='left').fillna(0)
+
+# Create the Plotly heatmap
+#fig = px.density_heatmap(heatmap_data, x='x', y='y', z='Visitation_Count_y', 
+                         #title='Goal Position Visitation Counts Heatmap',
+                         #labels={'x': 'X Coordinate', 'y': 'Y Coordinate', 'Visitation_Count_y': 'Visitation Count'},
+                         #nbinsx=grid_size, nbinsy=grid_size)
+
+# Display the heatmap using Streamlit
+#st.title('Goal Position Visitation Counts Heatmap Visualization')
+#st.plotly_chart(fig)
+# Populates goal tiles at the top right corner at positions (4,5) and (5,4)
+
+# Visualization with the origin initializing at the top-left corner
+# Goal tiles should appear on the bottom right at two sets of coordinates located at (4,5) and (5,4)
 # Aggregate the data to count the number of visits to each State_2D
 visitation_counts = df['State_2D'].value_counts().reset_index()
 visitation_counts.columns = ['State_2D', 'Visitation_Count']
@@ -320,8 +357,11 @@ visitation_counts['State_2D'] = visitation_counts['State_2D'].str.replace(r'[^\d
 # Split the cleaned State_2D into separate columns
 visitation_counts[['x', 'y']] = visitation_counts['State_2D'].str.split(',', expand=True).astype(int)
 
-# Create a 6x6 grid with zero visitation counts for all positions
+# Invert the y-coordinates to match the desired orientation
 grid_size = 6
+visitation_counts['y'] = grid_size - 1 - visitation_counts['y']
+
+# Create a 6x6 grid with zero visitation counts for all positions
 heatmap_data = pd.DataFrame({
     'x': [x for x in range(grid_size) for y in range(grid_size)],
     'y': [y for x in range(grid_size) for y in range(grid_size)],
@@ -337,6 +377,6 @@ fig = px.density_heatmap(heatmap_data, x='x', y='y', z='Visitation_Count_y',
                          labels={'x': 'X Coordinate', 'y': 'Y Coordinate', 'Visitation_Count_y': 'Visitation Count'},
                          nbinsx=grid_size, nbinsy=grid_size)
 
-# Display the heatmap using Streamlit
+# Display the heatmap using Streamlit with a unique key
 st.title('Goal Position Visitation Counts Heatmap Visualization')
-st.plotly_chart(fig)
+st.plotly_chart(fig, key='unique_heatmap_key')