Python program for Visualization

Assets/Temporal_Graph/Bitcoin/Visualization/Visualization_with_Different_Cryptocurrency_with_Timestamp/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, address_type):
+    """
+    Function to fetch address data from different blockchain APIs based on the address type.
+
+    Parameters:
+    - address (str): The address for which data needs to be fetched.
+    - address_type (str): The type of address (bitcoin, ethereum, tron, solana, litecoin, cardano).
+
+    Returns:
+    - data (dict): The fetched address data in JSON format.
+    """
+
+    if address_type == "bitcoin":
+        url = f"https://blockchain.info/address/{address}?format=json"
+    elif address_type == "ethereum":
+        url = f"https://api.blockchain.com/eth/v1/address/{address}/transactions"
+    elif address_type == "tron":
+        url = f"https://blockchain.info/tron/address/{address}?format=json"
+    elif address_type == "solana":
+        url = f"https://blockchain.info/sol/address/{address}?format=json"
+    elif address_type == "litecoin":
+        url = f"https://blockchain.info/ltc/address/{address}?format=json"
+    elif address_type == "cardano":
+        url = f"https://blockchain.info/ada/address/{address}?format=json"
+    else:
+        raise ValueError("Invalid address type")
+
+    response = requests.get(url)
+    data = response.json()
+    return data
+
+
+def transform_data(data, address_type):
+    """
+    Function to transform the fetched address data into a standardized format.
+
+    Parameters:
+    - data (dict): The fetched address data in JSON format.
+    - address_type (str): The type of address (bitcoin, ethereum, tron, solana, litecoin, cardano).
+
+    Returns:
+    - transactions (list): The transformed address data as a list of dictionaries.
+    """
+
+    transactions = []
+
+    if address_type == "bitcoin":
+        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"]
+                    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)
+    else:
+        for tx in data["txs"]:
+            address_a = tx.get("inputs", [{}])[0].get("prev_out", {}).get("addr", None)
+            for out in tx.get("out", []):
+                address_b = out.get("addr", None)
+                timestamp = datetime.fromtimestamp(tx.get("time", 0)).strftime("%m-%d-%Y %H:%M")
+                transaction_id = tx.get("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 an Excel file.
+
+    Parameters:
+    - data (list): The data to be exported as a```python
+    - data (list): The data to be exported as a list of dictionaries.
+    - filename (str): The filename of the Excel file.
+    """
+
+    df = pd.DataFrame(data)
+    df.to_excel(filename, index=False)
+    print(f"Data exported to {filename}")
+
+
+def create_and_visualize_graph(df, user_addresses):
+    """
+    Function to create a graph from the transformed data and visualize it.
+
+    Parameters:
+    - df (DataFrame): The transformed address data as a pandas DataFrame.
+    - user_addresses (list): The list of user addresses.
+
+    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'])
+        timestamp = str(row['Timestamp'])
+        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', timestamp=timestamp)
+            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', timestamp=timestamp)
+
+    # 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]
+    edge_labels = nx.get_edge_attributes(graph, 'timestamp')  # Get edge attributes for labels
+
+    nx.draw_networkx(graph, pos, with_labels=True, node_size=500, font_size=8, node_color=node_colors, edge_color=edge_colors)
+    nx.draw_networkx_edge_labels(graph, pos, edge_labels=edge_labels)  # Draw edge labels
+
+    # Show the graph
+    plt.tight_layout()
+    plt.show()
+
+
+def main():
+    # Main program
+    num_addresses = int(input("Enter the Number of Addresses to Visualize (1-4): "))
+    user_addresses = []
+    address_types = []
+
+    for i in range(num_addresses):
+        address_type = input(f"Enter type of address {i+1} of {num_addresses} (bitcoin, ethereum, tron, solana, litecoin, cardano): ")
+        address = input(f"Enter address {i+1} of {num_addresses}: ")
+        user_addresses.append(address)
+        address_types.append(address_type)
+
+    # Fetch address data for each user address
+    all_transformed_data = []
+    for address, address_type in zip(user_addresses, address_types):
+        address_data = fetch_address_data(address, address_type)
+        transformed_data = transform_data(address_data, address_type)
+        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)
+
+
+if __name__ == "__main__":
+    main()