From a9a8c3179fd7275adaa23102d9a30936d0f7e79b Mon Sep 17 00:00:00 2001
From: Mohamed feroz khan D <cb.en.p2cys21017@cb.students.amrita.edu>
Date: Thu, 6 Jul 2023 18:21:03 +0530
Subject: [PATCH] Python Program for Visualization
---
.../visual.py | 139 ++++++++++++++++++
1 file changed, 139 insertions(+)
create mode 100644 Assets/Temporal_Graph/Bitcoin/Visualization/Visualization_with_Four_Address_along_with_Timestamp/visual.py
diff --git a/Assets/Temporal_Graph/Bitcoin/Visualization/Visualization_with_Four_Address_along_with_Timestamp/visual.py b/Assets/Temporal_Graph/Bitcoin/Visualization/Visualization_with_Four_Address_along_with_Timestamp/visual.py
new file mode 100644
index 0000000..5cdd3e4
--- /dev/null
+++ b/Assets/Temporal_Graph/Bitcoin/Visualization/Visualization_with_Four_Address_along_with_Timestamp/visual.py
@@ -0,0 +1,139 @@
+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 from the blockchain.info API.
+
+ Args:
+ address (str): The Bitcoin address to fetch data for.
+
+ Returns:
+ dict: The JSON response containing address 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 into a list of transactions.
+
+ Args:
+ data (dict): The address data obtained from the API.
+
+ Returns:
+ list: A list of transformed transactions.
+ """
+ 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"]
+ 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 an Excel file.
+
+ Args:
+ data (list): The data to export.
+ filename (str): The name of the Excel file.
+
+ 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):
+ """
+ Function to create a graph from the data and visualize it.
+
+ Args:
+ df (pandas.DataFrame): The DataFrame containing 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'])
+ 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()
+
+# 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)
+
--
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