Updated lines 263, 287-308 with: [notation] # Visualization only includes the (4,5) and (5,4) 6 x 6 Grid Map Positions | # Visualization leveraging intrinsic_analysis.csv includes the entire (6x6) Grid-World (Map Positions) # Load the CSV data df = pd.read_csv('intrinsic_analysis.csv') # 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 the Plotly heatmap fig = px.density_heatmap(visitation_counts, x='x', y='y', z='Visitation_Count', title='Goal Position Visitation Counts Heatmap', labels={'x': 'X Coordinate', 'y': 'Y Coordinate', 'Visitation_Count': 'Visitation Count'}) # Display the heatmap using Streamlit st.title('Goal Position Visitation Counts Heatmap Visualization') st.plotly_chart(fig)

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)
 
+# 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()
 visitation_counts.columns = ['State_2D', 'Visitation_Count']
@@ -281,4 +282,27 @@ st.plotly_chart(fig)
 
 df = pd.read_csv('intrinsic_analysis.csv')
 st.write("Intrinsic Analysis DataFrame:")
-st.dataframe(df)
+st.dataframe(df)
+
+# Visualization leveraging intrinsic_analysis.csv includes the entire (6x6) Grid-World (Map Positions) 
+# Load the CSV data
+df = pd.read_csv('intrinsic_analysis.csv')
+
+# 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 the Plotly heatmap
+fig = px.density_heatmap(visitation_counts, x='x', y='y', z='Visitation_Count', 
+                         title='Goal Position Visitation Counts Heatmap',
+                         labels={'x': 'X Coordinate', 'y': 'Y Coordinate', 'Visitation_Count': 'Visitation Count'})
+
+# Display the heatmap using Streamlit
+st.title('Goal Position Visitation Counts Heatmap Visualization')
+st.plotly_chart(fig)