Python Program for Visualization

Assets/Temporal_Graph/Bitcoin/Visualization/Visualization_without_UTC/visual.py

+import requests
+import pandas as pd
+import networkx as nx
+import matplotlib.pyplot as plt
+from datetime import datetime
+
+
+def fetch_address_data(address):
+    """
+    Function to fetch address data.
+    
+    Parameters:
+    - address: The Bitcoin address to fetch data for.
+    """
+    url = f"https://blockchain.info/address/{address}?format=json"
+    response = requests.get(url)
+    data = response.json()
+    return data
+
+
+def transform_data(data):
+    """
+    Function to transform address data.
+    
+    Parameters:
+    - data: The address data to transform.
+    """
+    transactions = []
+    for tx in data["txs"]:
+        for out in tx["out"]:
+            address_a = tx["inputs"][0]["prev_out"]["addr"]
+            address_b = out["addr"]
+            timestamp = datetime.fromtimestamp(tx["time"]).strftime("%m-%d-%Y %H:%M")
+            transaction_id = tx["hash"]
+            transaction = {
+                "Address A": address_a,
+                "Address B": address_b,
+                "Timestamp": timestamp,
+                "Transaction ID": transaction_id
+            }
+            transactions.append(transaction)
+    return transactions
+
+
+def export_to_excel(data, filename):
+    """
+    Function to export data to Excel.
+    
+    Parameters:
+    - data: The data to export.
+    - filename: The name of the Excel file to export to.
+    """
+    df = pd.DataFrame(data)
+    df["Timestamp"] = pd.to_datetime(df["Timestamp"])  # Convert Timestamp column to datetime
+    df["Timestamp"] = df["Timestamp"].dt.strftime("%m-%d-%Y %H:%M")  # Format Timestamp column
+    df.to_excel(filename, index=False)
+    print(f"Data exported to {filename}")
+
+
+def create_and_visualize_graph(df, user_addresses):
+    """
+    Function to create graph and visualize it.
+    
+    Parameters:
+    - df: The DataFrame containing the transaction data.
+    - user_addresses: The list of user addresses.
+    """
+    graph = nx.DiGraph()  # Create a networkx graph
+
+    for address in df['Address A'].unique():
+        if address in user_addresses:
+            graph.add_node(address, color='red')  # Highlight user address nodes in red
+        else:
+            graph.add_node(address, color='skyblue')
+
+    for _, row in df.iterrows():
+        source = str(row['Address A'])
+        target = str(row['Address B'])
+        timestamp_str = str(row['Timestamp'])
+        transaction_hash = str(row['Transaction ID'])
+
+        if source in user_addresses or target in user_addresses:
+            graph.add_edge(source, target, color='blue')  # Highlight edges connected to user address in blue
+        else:
+            graph.add_edge(source, target, color='gray')
+
+    plt.figure(figsize=(10, 6))  # Draw the graph using matplotlib
+    pos = nx.spring_layout(graph)
+    node_colors = [graph.nodes[node].get('color', 'skyblue') for node in graph.nodes]
+    edge_colors = [graph.edges[edge]['color'] for edge in graph.edges]
+
+    nx.draw_networkx(graph, pos, with_labels=True, node_size=500, font_size=8, node_color=node_colors, edge_color=edge_colors)
+
+    plt.tight_layout()  # Show the graph
+    plt.show()
+
+
+def main():
+    """
+    Main program.
+    """
+    user_addresses = []
+    for i in range(4):
+        address = input(f"Enter address {i+1} of 4: ")
+        user_addresses.append(address)
+
+    all_transformed_data = []
+
+    for address in user_addresses:
+        address_data = fetch_address_data(address)
+        transformed_data = transform_data(address_data)
+        all_transformed_data.extend(transformed_data)
+
+    export_to_excel(all_transformed_data, "Data.xlsx")
+    df = pd.read_excel('Data.xlsx')
+
+    create_and_visualize_graph(df, user_addresses)
+
+
+if __name__ == "__main__":
+    main()