Python Program for Visualization

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

+import requests
+import pandas as pd
+from datetime import datetime
+import pathpy as pp
+import networkx as nx
+import matplotlib.pyplot as plt
+
+
+def fetch_address_data(address):
+    """
+    Function to fetch address data.
+
+    :param address: The address to fetch data for.
+    :return: The fetched data.
+    """
+    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.
+
+    :param data: The address data to transform.
+    :return: The transformed data.
+    """
+    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):
+    """
+    Function to export data to Excel.
+
+    :param data: The data to export.
+    """
+    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
+    filename = "Data.xlsx"  # Change the filename to "Data.xlsx"
+    df.to_excel(filename, index=False)
+    print(f"Data exported to {filename}")
+
+
+def create_and_visualize_graph(df, user_address):
+    """
+    Function to create graph and visualize it.
+
+    :param df: The DataFrame containing the data.
+    :param user_address: The user's address.
+    """
+    network = pp.TemporalNetwork()  # Create a temporal network from the DataFrame
+    edge_attributes = {}  # Create a dictionary to store edge attributes
+
+    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'])
+
+        timestamp = datetime.strptime(timestamp_str, '%m-%d-%Y %H:%M')
+        timestamp_unix = int(timestamp.timestamp())
+
+        network.add_edge(source, target, ts=timestamp_unix)
+        edge_attributes[(source, target)] = {
+            'timestamp': timestamp_unix,
+            'transaction_hash': transaction_hash
+        }
+
+    graph = nx.DiGraph()  # Create a networkx graph
+
+    for node in network.nodes:
+        if node == user_address:  # Highlight user address node in red
+            graph.add_node(node, color='red')
+        else:
+            graph.add_node(node, color='skyblue')
+
+    for source, target, timestamp in network.tedges:
+        transaction_hash = edge_attributes[(source, target)]['transaction_hash']
+        if source == user_address or target == user_address:  # Highlight edges connected to user address in blue
+            graph.add_edge(source, target, timestamp=timestamp, transaction_hash=transaction_hash, color='blue')
+        else:
+            graph.add_edge(source, target, timestamp=timestamp, transaction_hash=transaction_hash, color='gray')
+
+    plt.figure(figsize=(10, 6))  # Draw the graph using matplotlib
+    pos= nx.spring_layout(graph)
+    node_colors = [graph.nodes[node]['color'] 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)
+    edge_labels = nx.get_edge_attributes(graph, 'timestamp')
+    nx.draw_networkx_edge_labels(graph, pos, edge_labels=edge_labels, font_size=6)
+    plt.tight_layout()  # Show the graph
+    plt.show()
+
+
+def main():
+    """Main program."""
+    while True:
+        user_address = input("Enter the address (or 'q' to quit): ")
+
+        if user_address.lower() == 'q':
+            break
+
+        address_data = fetch_address_data(user_address)
+        transformed_data = transform_data(address_data)
+        export_to_excel(transformed_data)
+        df = pd.read_excel('Data.xlsx')
+        create_and_visualize_graph(df, user_address)
+
+
+if __name__ == '__main__':
+    main()