大家好，我是威哥。在鸿蒙应用开发里，用户偏好设置的管理是极为重要的一环。HarmonyOS为我们提供了

想做一个简单的数据库表的统计工具。

用 AI，感觉整个人脑子都不好用了。

import pyodbc
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
from datetime import datetime
import warnings
warnings.filterwarnings('ignore')

class MSSQLTableAnalyzer:
    def __init__(self, server, database, username=None, password=None, trusted_connection=True):
        """
        Initialize MSSQL connection
        
        Args:
            server: SQL Server name or IP
            database: Database name
            username: Username (optional if using Windows Authentication)
            password: Password (optional if using Windows Authentication)
            trusted_connection: Use Windows Authentication (default: True)
        """
        self.server = server
        self.database = database
        self.username = username
        self.password = password
        self.trusted_connection = trusted_connection
        self.connection = None
        
    def connect(self):
        """Establish connection to MSSQL database"""
        try:
            if self.trusted_connection:
                # Windows Authentication
                connection_string = f"""
                DRIVER={{ODBC Driver 17 for SQL Server}};
                SERVER={self.server};
                DATABASE={self.database};
                Trusted_Connection=yes;
                """
            else:
                # SQL Server Authentication
                connection_string = f"""
                DRIVER={{ODBC Driver 17 for SQL Server}};
                SERVER={self.server};
                DATABASE={self.database};
                UID={self.username};
                PWD={self.password};
                """
            
            self.connection = pyodbc.connect(connection_string)
            print(f" Successfully connected to {self.database} on {self.server}")
            return True
            
        except Exception as e:
            print(f" Connection failed: {str(e)}")
            return False
    
    def get_table_counts(self):
        """Query all tables and their row counts"""
        if not self.connection:
            print(" No database connection established")
            return None
        
        try:
            # Query to get all user tables and their row counts
            query = """
            SELECT 
                t.TABLE_SCHEMA as [Schema],
                t.TABLE_NAME as [Table_Name],
                p.rows as [Row_Count]
            FROM 
                INFORMATION_SCHEMA.TABLES t
            INNER JOIN 
                sys.tables st ON st.name = t.TABLE_NAME
            INNER JOIN 
                sys.partitions p ON st.object_id = p.object_id
            WHERE 
                t.TABLE_TYPE = 'BASE TABLE'
                AND p.index_id < 2
            ORDER BY 
                p.rows DESC, t.TABLE_SCHEMA, t.TABLE_NAME
            """
            
            df = pd.read_sql_query(query, self.connection)
            print(f" Found {len(df)} tables in database")
            return df
            
        except Exception as e:
            print(f" Query failed: {str(e)}")
            return None
    
    def create_bar_chart(self, df, max_tables=20, chart_type='horizontal'):
        """
        Create bar chart for table row counts
        
        Args:
            df: DataFrame with table information
            max_tables: Maximum number of tables to display
            chart_type: 'horizontal' or 'vertical'
        """
        if df is None or df.empty:
            print(" No data to display")
            return
        
        # Prepare data for visualization
        df_display = df.head(max_tables).copy()
        df_display['Full_Name'] = df_display['Schema'] + '.' + df_display['Table_Name']
        
        # Set up the plot style
        plt.style.use('seaborn-v0_8')
        fig, ax = plt.subplots(figsize=(12, 8))
        
        # Create color palette
        colors = plt.cm.viridis(range(len(df_display)))
        
        if chart_type == 'horizontal':
            bars = ax.barh(df_display['Full_Name'], df_display['Row_Count'], color=colors)
            ax.set_xlabel('Number of Rows', fontsize=12, fontweight='bold')
            ax.set_ylabel('Table Name', fontsize=12, fontweight='bold')
            
            # Add value labels on bars
            for i, bar in enumerate(bars):
                width = bar.get_width()
                ax.text(width + max(df_display['Row_Count']) * 0.01, 
                       bar.get_y() + bar.get_height()/2, 
                       f'{int(width):,}', 
                       ha='left', va='center', fontsize=10)
        else:
            bars = ax.bar(range(len(df_display)), df_display['Row_Count'], color=colors)
            ax.set_xlabel('Table Name', fontsize=12, fontweight='bold')
            ax.set_ylabel('Number of Rows', fontsize=12, fontweight='bold')
            ax.set_xticks(range(len(df_display)))
            ax.set_xticklabels(df_display['Full_Name'], rotation=45, ha='right')
            
            # Add value labels on bars
            for i, bar in enumerate(bars):
                height = bar.get_height()
                ax.text(bar.get_x() + bar.get_width()/2, height + max(df_display['Row_Count']) * 0.01,
                       f'{int(height):,}', 
                       ha='center', va='bottom', fontsize=10)
        
        # Customize the chart
        ax.set_title(f'Database Table Row Counts - {self.database}\n'
                    f'Top {min(max_tables, len(df))} Tables by Row Count', 
                    fontsize=14, fontweight='bold', pad=20)
        
        # Format y-axis to show numbers with commas
        ax.yaxis.set_major_formatter(plt.FuncFormatter(lambda x, p: f'{int(x):,}'))
        if chart_type == 'vertical':
            ax.yaxis.set_major_formatter(plt.FuncFormatter(lambda x, p: f'{int(x):,}'))
        else:
            ax.xaxis.set_major_formatter(plt.FuncFormatter(lambda x, p: f'{int(x):,}'))
        
        # Add grid for better readability
        ax.grid(True, alpha=0.3, axis='x' if chart_type == 'horizontal' else 'y')
        
        # Add timestamp
        timestamp = datetime.now().strftime("%Y-%m-%d %H:%M:%S")
        plt.figtext(0.99, 0.01, f'Generated: {timestamp}', 
                   ha='right', va='bottom', fontsize=8, alpha=0.7)
        
        plt.tight_layout()
        plt.show()
        
        # Print summary statistics
        total_rows = df['Row_Count'].sum()
        avg_rows = df['Row_Count'].mean()
        print(f"\n Summary Statistics:")
        print(f"   Total Tables: {len(df):,}")
        print(f"   Total Rows: {total_rows:,}")
        print(f"   Average Rows per Table: {avg_rows:,.0f}")
        print(f"   Largest Table: {df.iloc[0]['Schema']}.{df.iloc[0]['Table_Name']} ({df.iloc[0]['Row_Count']:,} rows)")
        
    def get_schema_summary(self, df):
        """Create summary by schema"""
        if df is None or df.empty:
            return None
            
        schema_summary = df.groupby('Schema').agg({
            'Table_Name': 'count',
            'Row_Count': ['sum', 'mean', 'max']
        }).round(0)
        
        schema_summary.columns = ['Table_Count', 'Total_Rows', 'Avg_Rows', 'Max_Rows']
        schema_summary = schema_summary.sort_values('Total_Rows', ascending=False)
        
        return schema_summary
    
    def create_schema_chart(self, df):
        """Create bar chart grouped by schema"""
        schema_summary = self.get_schema_summary(df)
        if schema_summary is None:
            return
            
        fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(15, 6))
        
        # Chart 1: Total rows by schema
        colors1 = plt.cm.Set3(range(len(schema_summary)))
        bars1 = ax1.bar(schema_summary.index, schema_summary['Total_Rows'], color=colors1)
        ax1.set_title('Total Rows by Schema', fontsize=12, fontweight='bold')
        ax1.set_xlabel('Schema', fontsize=10)
        ax1.set_ylabel('Total Rows', fontsize=10)
        ax1.tick_params(axis='x', rotation=45)
        
        # Add value labels
        for bar in bars1:
            height = bar.get_height()
            ax1.text(bar.get_x() + bar.get_width()/2, height,
                    f'{int(height):,}', ha='center', va='bottom', fontsize=9)
        
        # Chart 2: Table count by schema
        colors2 = plt.cm.Set2(range(len(schema_summary)))
        bars2 = ax2.bar(schema_summary.index, schema_summary['Table_Count'], color=colors2)
        ax2.set_title('Table Count by Schema', fontsize=12, fontweight='bold')
        ax2.set_xlabel('Schema', fontsize=10)
        ax2.set_ylabel('Number of Tables', fontsize=10)
        ax2.tick_params(axis='x', rotation=45)
        
        # Add value labels
        for bar in bars2:
            height = bar.get_height()
            ax2.text(bar.get_x() + bar.get_width()/2, height,
                    f'{int(height)}', ha='center', va='bottom', fontsize=9)
        
        plt.tight_layout()
        plt.show()
        
        print("\n Schema Summary:")
        print(schema_summary.to_string())
    
    def close_connection(self):
        """Close database connection"""
        if self.connection:
            self.connection.close()
            print(" Database connection closed")

def main():
    """Main execution function"""
    # Database connection parameters
    # Modify these parameters according to your environment
    SERVER = "localhost"  # or your server name/IP
    DATABASE = "YourDatabaseName"  # replace with your database name
    
    # Initialize analyzer
    analyzer = MSSQLTableAnalyzer(
        server=SERVER,
        database=DATABASE,
        trusted_connection=True  # Set to False if using SQL Server Authentication
    )
    
    # Connect to database
    if not analyzer.connect():
        return
    
    try:
        # Get table counts
        print(" Querying table information...")
        df = analyzer.get_table_counts()
        
        if df is not None and not df.empty:
            # Create main bar chart
            print(" Creating bar chart...")
            analyzer.create_bar_chart(df, max_tables=15, chart_type='horizontal')
            
            # Create schema summary chart
            print(" Creating schema summary...")
            analyzer.create_schema_chart(df)
            
            # Export to CSV (optional)
            csv_filename = f"table_counts_{analyzer.database}_{datetime.now().strftime('%Y%m%d_%H%M%S')}.csv"
            df.to_csv(csv_filename, index=False)
            print(f" Data exported to: {csv_filename}")
            
        else:
            print(" No table data retrieved")
            
    except Exception as e:
        print(f" Error during execution: {str(e)}")
    
    finally:
        # Always close the connection
        analyzer.close_connection()

if __name__ == "__main__":
    # Example usage
    print(" MSSQL Table Count Analyzer")
    print("=" * 40)
    
    # You can also use the analyzer directly:
    # analyzer = MSSQLTableAnalyzer("your_server", "your_database")
    # if analyzer.connect():
    #     df = analyzer.get_table_counts()
    #     analyzer.create_bar_chart(df)
    #     analyzer.close_connection()
    
    main()

前言

本文基于Api13

来了一个需求，要实现顶部下拉刷新，并且顶部的标题栏，下拉状态下跟随手势刷新，上拉状态下进行吸顶，也就是tabs需要固定在顶部标题栏的下面，基本的效果可以看下图，下图是一个Demo，实际的需求，顶部标题栏带有渐变显示，不过这些不是重点。

仓颉语言商城应用的页面开发教程接近尾声了，今天要分享的是设置页面：

导航栏还是老样式，介绍过很多次了，今天不再赘述。这个页面的内容主要还是介绍List容器的使用。

可以看出列表内容分为三组，所以我们要用到ListItemGroup，不过第一组是没有标题的，所以可以直接使用ListItem，布局很简单，具体代码如下：

大家好，我是 V 哥。在鸿蒙 NEXT 开发中，@Styles 装饰器是一种非常有用的方法，用于定义可重用的样式。这使得开发者可以将多条样式设置提炼成一个方法，以便在多个组件中复用，从而提高代码的可维护性和可读性。以下是 @Styles 装饰器的详细用法和应用场景案例。

@Styles装饰器的使用说明

AppSolution

Ping| 昨天 10:15

对于 UI 设计师来说，iOS 9 的更新最为激动的是带来了全新设计的中文字体「苹方」以及英文字体「San Francisco」。

中文字体「苹方」

在 iOS 9 前的中文字体「黑体」被人诟病已久：喇叭口设计、仅包含 2 个字重，不仅在 Retina 的屏幕上不能达到很好的显示效果，并且随着 iOS 7 开始的扁平化的设计语言，两个字重的「黑体」已经完全无法胜任设计的需求。

Israelis clash with police during a protest demanding an immediate ceasefire in Gaza and the release of Israeli hostages, in Tel Aviv, Israel, on Jan 4, 2025. [Photo/Xinhua]

This is an editorial from China Daily.

我已经没有精力和兴趣再去描述我的不开心了，我emo了。

“The region represents about 30% of the world's energy supplies， about 20% of global trade passages， about 4% of the world GDP. Imagine all of these three things stop. This means a total collapse of the global economy， and not just Saudi Arabia or the Middle East countries.”

By Pang Xinhua

In the global economic landscape, the China-U.S. economic and trade relationship has always been a focal point of attention. Recently, the news that China and the United States reached an economic and trade framework agreement in London has come as a relief to many.