From f6be079a3c5d6901bcac6119601949e41fb6bfaa Mon Sep 17 00:00:00 2001
From: Mohamed feroz khan D <cb.en.p2cys21017@cb.students.amrita.edu>
Date: Thu, 6 Jul 2023 18:11:30 +0530
Subject: [PATCH] Python program for Visualization
---
.../visual.py | 186 ++++++++++++++++++
1 file changed, 186 insertions(+)
create mode 100644 Assets/Temporal_Graph/Bitcoin/Visualization/Visualization_with_Different_Cryptocurrency_with_Timestamp/visual.py
diff --git a/Assets/Temporal_Graph/Bitcoin/Visualization/Visualization_with_Different_Cryptocurrency_with_Timestamp/visual.py b/Assets/Temporal_Graph/Bitcoin/Visualization/Visualization_with_Different_Cryptocurrency_with_Timestamp/visual.py
new file mode 100644
index 0000000..8d1eb34
--- /dev/null
+++ b/Assets/Temporal_Graph/Bitcoin/Visualization/Visualization_with_Different_Cryptocurrency_with_Timestamp/visual.py
@@ -0,0 +1,186 @@
+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()
--
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