Create cross_chain_analysis.ipynb

blockchain_integration/pi_network/pinnacle/notebooks/cross_chain_analysis.ipynb

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+# Cross-Chain Analysis Notebook
+
+import pandas as pd
+import matplotlib.pyplot as plt
+import seaborn as sns
+from pinnacle.data import load_cross_chain_data
+
+# Load cross-chain data
+cross_chain_data = load_cross_chain_data()
+
+# Data cleaning and preprocessing
+cross_chain_data['timestamp'] = pd.to_datetime(cross_chain_data['timestamp'], unit='s')
+cross_chain_data.set_index('timestamp', inplace=True)
+
+# Visualize bridge fees over time
+plt.figure(figsize=(12, 6))
+sns.lineplot(x=cross_chain_data.index, y=cross_chain_data['bridge_fee'])
+plt.title('Bridge Fees Over Time')
+plt.xlabel('Timestamp')
+plt.ylabel('Bridge Fee')
+plt.show()
+
+# Analyze bridge fees by chain
+chain_fees = cross_chain_data.groupby('chain')['bridge_fee'].mean()
+print('Average Bridge Fees by Chain:')
+print(chain_fees)
+
+# Analyze bridge fees by token
+token_fees = cross_chain_data.groupby('token')['bridge_fee'].mean()
+print('Average Bridge Fees by Token:')
+print(token_fees)
+
+# Analyze bridge fees by bridge type
+bridge_type_fees = cross_chain_data.groupby('bridge_type')['bridge_fee'].mean()
+print('Average Bridge Fees by Bridge Type:')
+print(bridge_type_fees)
+
+# Correlation analysis
+corr_matrix = cross_chain_data.corr()
+print('Correlation Matrix:')
+print(corr_matrix)
+
+# Heatmap visualization
+plt.figure(figsize=(10, 8))
+sns.heatmap(corr_matrix, annot=True, cmap='coolwarm')
+plt.title('Correlation Heatmap')
+plt.show()