chart.py

import sqlite3
import pandas as pd
import matplotlib.pyplot as plt
import matplotlib.dates as mdates
from matplotlib.dates import AutoDateLocator
import seaborn as sns
from colorama import Fore, Style

sns.set_theme(style="darkgrid")

def display_banner():
    banner = """
    ██╗░░██╗███████╗███╗░░██╗  ██████╗░██╗░░░░░░█████╗░░█████╗░██╗░░██╗░██████╗
    ╚██╗██╔╝██╔════╝████╗░██║  ██╔══██╗██║░░░░░██╔══██╗██╔══██╗██║░██╔╝██╔════╝
    ░╚███╔╝░█████╗░░██╔██╗██║  ██████╦╝██║░░░░░██║░░██║██║░░╚═╝█████═╝░╚█████╗░
    ░██╔██╗░██╔══╝░░██║╚████║  ██╔══██╗██║░░░░░██║░░██║██║░░██╗██╔═██╗░░╚═══██╗
    ██╔╝╚██╗███████╗██║░╚███║  ██████╦╝███████╗╚█████╔╝╚█████╔╝██║░╚██╗██████╔╝
    ╚═╝░░╚═╝╚══════╝╚═╝░░╚══╝  ╚═════╝░╚══════╝░╚════╝░░╚════╝░╚═╝░░╚═╝╚═════╝░
        XenBlocks Python Block Explorer by TreeCityWes.eth
        Buy Me A Coffee on HashHead.io 
    """
    print(Fore.LIGHTGREEN_EX + banner + Style.RESET_ALL)

def plot_data(df, title):
    if df.empty:
        print(f"No data to plot for {title}")
        return
    
    plt.figure(figsize=(12, 7))
    color = 'tab:blue'
    plt.plot(df['created_at'], df['cumulative_blocks'], color=color, linestyle='-', marker='o')
    plt.fill_between(df['created_at'], df['cumulative_blocks'], color=color, alpha=0.1)
    
    plt.xlabel('Time', fontsize=14)
    plt.ylabel('Blocks', fontsize=14)
    plt.title(title, fontsize=16, fontweight='bold')
    plt.show()

print("Loading data... Please wait.")
conn = sqlite3.connect("blockchainindex.db")
query = "SELECT * FROM blocks"
df = pd.read_sql_query(query, conn)
print("Data loaded successfully!")

df['created_at'] = pd.to_datetime(df['created_at'])
df['account'] = df['account'].str.lower()

# Filter the DataFrame to only include rows where 'created_at' is after September 20th
start_date = '2023-09-15'  # Assuming YYYY-MM-DD format
df = df[df['created_at'] > start_date]

def display_menu():
    display_banner()
    print("\nPlease choose a chart to display:")
    print("Network Statistics:")
    # ... (other menu items remain unchanged)
    return input("Your choice: ")

def display_menu():
    # Assuming display_banner() is a function you have defined elsewhere in your script
    display_banner()  
    print("\nPlease choose a chart to display:")
    print()  # Add a newline for better readability
    print("Network Statistics:")
    print("1. Total Network Blocks Over Time")
    print("2. Total Network Super Blocks Over Time")
    print("3. Total Network XUNI Blocks Over Time")
    print("4. Total Network Distribution (PieChart)")
    print("\nAccount Statistics:")
    print("5. Blocks by Account")
    print("6. XUNI Blocks by Account")
    print("7. Superblocks by Account")
    print("8. Daily Block Distribution for Account")
    print("9. Daily XUNI Block Distribution for Account")
    print("10. Daily Super Block Distribution for Account")
    print("\nDaily Distribution:")
    print("11. Daily Block Distribution")
    print("12. Daily Xuni Block Distribution")
    print("13. Daily Super Block Distribution")
    print("\n14. Exit")
    print()  # Add a newline for better readability
    return input("Your choice: ")

def main():
    while True:
        choice = display_menu()
        
        df_filtered = df  # Modified line: No filtering is done here anymore
        
        if choice == '1':
            # Assuming df is the original DataFrame
            total_blocks_df = df[df['block_type'] != 'xuni'].groupby('created_at').size().reset_index(name='blocks')  # Exclude 'XUNI' blocks
            total_blocks_df['cumulative_blocks'] = total_blocks_df['blocks'].cumsum()
            plot_data(total_blocks_df, 'Total Network Blocks Over Time')

                
        elif choice == '2':
            superblocks_df = df[df['block_type'] == 'super'].groupby('created_at').size().reset_index(name='blocks')
            superblocks_df['cumulative_blocks'] = superblocks_df['blocks'].cumsum()
            plot_data(superblocks_df, 'Total Network Super Blocks Over Time')



        elif choice == '3':
            xuni_blocks_df = df[df['block_type'] == 'xuni'].groupby('created_at').size().reset_index(name='blocks')
            xuni_blocks_df['cumulative_blocks'] = xuni_blocks_df['blocks'].cumsum()
            plot_data(xuni_blocks_df, 'Total Network XUNI Blocks Over Time')


        elif choice == '4':
            block_counts = df['block_type'].value_counts()
            regular_block_count = block_counts.get('regular', 0)
            super_block_count = block_counts.get('super', 0)
            xuni_block_count = block_counts.get('xuni', 0)
            
            total_blocks = regular_block_count + super_block_count + xuni_block_count
            regular_block_percentage = (regular_block_count / total_blocks) * 100
            super_block_percentage = (super_block_count / total_blocks) * 100
            xuni_block_percentage = (xuni_block_count / total_blocks) * 100
            
            plt.pie([regular_block_count, super_block_count, xuni_block_count],
                    labels=['Regular Blocks', 'Super Blocks', 'XUNI Blocks'], 
                    autopct='%1.1f%%',
                    startangle=140, 
                    colors=['#66b3ff', '#99ff99', '#ffcc99'])
            plt.axis('equal')  # Equal aspect ratio ensures that pie is drawn as a circle.
            plt.title('Distribution of Block Types', fontsize=16)
            legend_labels = [
                f'Regular Blocks - {regular_block_percentage:.1f}%', 
                f'Superblocks - {super_block_percentage:.1f}%',
                f'XUNI Blocks - {xuni_block_percentage:.1f}%'
            ]
            legend = plt.legend(loc='upper left', labels=legend_labels)
            legend.get_frame().set_alpha(1.0)
            plt.show()


        elif choice == '5':
            account_name = input("Enter the account name: ").lower()
            account_blocks_df = df_filtered[df_filtered['account'] == account_name].groupby('created_at').size().reset_index(name='blocks')
            account_blocks_df['cumulative_blocks'] = account_blocks_df['blocks'].cumsum()
            plot_data(account_blocks_df, f'Blocks by Account: {account_name}')
        
        elif choice == '6':
            account_name = input("Enter the account name: ").lower()
            xuni_account_blocks_df = df_filtered[(df_filtered['account'] == account_name) & (df_filtered['block_type'] == 'xuni')].groupby('created_at').size().reset_index(name='blocks')
            xuni_account_blocks_df['cumulative_blocks'] = xuni_account_blocks_df['blocks'].cumsum()
            plot_data(xuni_account_blocks_df, f'XUNI Blocks by Account: {account_name}')

        
        elif choice == '7':
            account_name = input("Enter the account name: ").lower()
            superblocks_account_df = df[(df['account'] == account_name) & (df['block_type'] == 'super')].groupby('created_at').size().reset_index(name='blocks')
            superblocks_account_df['cumulative_blocks'] = superblocks_account_df['blocks'].cumsum()
            plot_data(superblocks_account_df, f'Superblocks by Account: {account_name}')


        elif choice == '8':
            account_name = input("Enter the account name: ").lower()
            filtered_df = df_filtered[(df_filtered['account'] == account_name) & (df_filtered['created_at'] >= '2023-09-17')]
            
            if filtered_df.empty:
                print(f"No blocks found for account {account_name}.")
            else:
                filtered_df['created_at'].dt.date.value_counts().sort_index().plot(kind='bar', color='skyblue', edgecolor='black')
                plt.title(f'Daily Block Distribution for {account_name}')
                plt.xlabel('Creation Date')
                plt.ylabel('Frequency')
                plt.gcf().autofmt_xdate()
                plt.show()


        elif choice == '9':
            account_name = input("Enter the account name: ").lower()
            filtered_df = df_filtered[(df_filtered['account'] == account_name) & (df_filtered['block_type'] == 'xuni') & (df_filtered['created_at'] >= '2023-09-17')]
            
            if filtered_df.empty:
                print(f"No XUNI blocks found for account {account_name}.")
            else:
                filtered_df['created_at'].dt.date.value_counts().sort_index().plot(kind='bar', color='#ffcc99', edgecolor='black')
                plt.title(f'Daily XUNI Block Distribution for {account_name}')
                plt.xlabel('Creation Date')
                plt.ylabel('Frequency')
                plt.gcf().autofmt_xdate()
                plt.show()

        elif choice == '10':
            account_name = input("Enter the account name: ").lower()
            filtered_df = df_filtered[(df_filtered['account'] == account_name) & (df_filtered['block_type'] == 'super') & (df_filtered['created_at'] >= '2023-09-17')]
            
            if filtered_df.empty:
                print(f"No superblocks found for account {account_name}.")
            else:
                filtered_df['created_at'].dt.date.value_counts().sort_index().plot(kind='bar', color='#99ff99', edgecolor='black')
                plt.title(f'Daily Super Block Distribution for {account_name}')
                plt.xlabel('Creation Date')
                plt.ylabel('Frequency')
                plt.gcf().autofmt_xdate()
                plt.show()


        elif choice == '11':
            # Set the start date
            start_date = pd.to_datetime('2023-09-17')
            
            # Filter the DataFrame to include only records after the start date
            filtered_df = df[(df['block_type'] != 'xuni') & (df['created_at'] >= start_date)]
            
            # Extract date from datetime and group by the date to get the counts
            filtered_df['created_at'].dt.date.value_counts().sort_index().plot(kind='bar', color='skyblue', edgecolor='black')
            
            plt.title('Daily Blocks Distribution')
            plt.xlabel('Creation Date')
            plt.ylabel('Frequency')
            plt.gcf().autofmt_xdate()  # Auto format the date on the x-axis for better readability
            plt.show()

        elif choice == '12':
            # Set the start date
            start_date = pd.to_datetime('2023-09-17')
            
            # Filter the DataFrame to include only records after the start date and where block_type is 'xuni'
            filtered_df = df[(df['block_type'] == 'xuni') & (df['created_at'] >= start_date)]
            
            # Extract date from datetime and group by the date to get the counts
            filtered_df['created_at'].dt.date.value_counts().sort_index().plot(kind='bar', color='#ffcc99', edgecolor='black')
            
            plt.title('Daily XUNI Block Distribution')
            plt.xlabel('Creation Date')
            plt.ylabel('Frequency')
            plt.gcf().autofmt_xdate()  # Auto format the date on the x-axis for better readability
            plt.show()

        elif choice == '13':
            # Set the start date
            start_date = pd.to_datetime('2023-09-17')
            
            # Filter the DataFrame to include only records after the start date and where block_type is 'super'
            filtered_df = df[(df['block_type'] == 'super') & (df['created_at'] >= start_date)]
            
            # Extract date from datetime and group by the date to get the counts
            filtered_df['created_at'].dt.date.value_counts().sort_index().plot(kind='bar', color='#99ff99', edgecolor='black')
            
            plt.title('Daily Super Block Distribution')
            plt.xlabel('Creation Date')
            plt.ylabel('Frequency')
            plt.gcf().autofmt_xdate()  # Auto format the date on the x-axis for better readability
            plt.show()

        elif choice == '14':
            print("Exiting. Goodbye!")
            break
        
        else:
            print("Invalid choice. Please try again.")

if __name__ == "__main__":
    main()

conn.close()