Python Program for Visualization

Assets/Temporal_Graph/Bitcoin/Visualization/Visualization_with_Four_Address/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):
+    """
+    Fetches address data from blockchain.info API.
+
+    Args:
+        address (str): The Bitcoin address to fetch data for.
+
+    Returns:
+        dict: The fetched address data in JSON format.
+    """
+    url = f"https://blockchain.info/address/{address}?format=json"
+    response = requests.get(url)
+    data = response.json()
+    return data
+
+def transform_data(data):
+    """
+    Transforms address data to extract relevant information.
+
+    Args:
+        data (dict): The address data to be transformed.
+
+    Returns:
+        list: List of transactions with extracted information.
+    """
+    transactions = []
+    for tx in data["txs"]:
+        if "inputs" in tx and "prev_out" in tx["inputs"][0] and "addr" in tx["inputs"][0]["prev_out"]:
+            address_a = tx["inputs"][0]["prev_out"]["addr"]
+        else:
+            address_a = None
+        
+        for out in tx["out"]:
+            if "addr" in out:
+                address_b = out["addr"]
+                transaction_id = tx["hash"]
+                transaction = {
+                    "Address A": address_a,
+                    "Address B": address_b,
+                    "Transaction ID": transaction_id
+                }
+                transactions.append(transaction)
+    return transactions
+
+def export_to_excel(data, filename):
+    """
+    Exports data to an Excel file.
+
+    Args:
+        data (list): The data to be exported.
+        filename (str): The name of the Excel file to be created.
+
+    Returns:
+        None
+    """
+    df = pd.DataFrame(data)
+    df.to_excel(filename, index=False)
+    print(f"Data exported to {filename}")
+
+def create_and_visualize_graph(df, user_addresses):
+    """
+    Creates a graph and visualizes it using matplotlib.
+
+    Args:
+        df (DataFrame): The DataFrame containing the transaction data.
+        user_addresses (list): List of user addresses to highlight in the graph.
+
+    Returns:
+        None
+    """
+    # Create a networkx graph
+    graph = nx.DiGraph()
+
+    # Add nodes to the graph
+    for address in df['Address A'].unique():
+        if address in user_addresses:  # Highlight user address nodes in red
+            graph.add_node(address, color='red')
+        else:
+            graph.add_node(address, color='skyblue')
+
+    # Add edges with attributes to the graph
+    for _, row in df.iterrows():
+        source = str(row['Address A'])
+        target = str(row['Address B'])
+        if source in user_addresses or target in user_addresses:  # Highlight edges connected to user address in blue
+            graph.add_edge(source, target, color='blue')
+            if source in user_addresses:
+                graph.nodes[source]['color'] = 'red'
+            if target in user_addresses:
+                graph.nodes[target]['color'] = 'red'
+        else:
+            graph.add_edge(source, target, color='gray')
+
+    # Draw the graph using matplotlib
+    plt.figure(figsize=(10, 6))
+    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)
+
+
+    # Show the graph
+    plt.tight_layout()
+    plt.show()
+
+# Main program
+num_addresses = int(input("Enter the Number of Addresses to Visualize (1-4): "))
+user_addresses = []
+for i in range(num_addresses):
+    address = input(f"Enter address {i+1} of {num_addresses}: ")
+    user_addresses.append(address)
+
+# Fetch address data for each user 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 all data to a single Excel file
+export_to_excel(all_transformed_data, "Data.xlsx")
+
+# Read the Excel file
+df = pd.read_excel('Data.xlsx')
+
+# Create and visualize the graph, passing the user addresses as an argument
+create_and_visualize_graph(df, user_addresses)