ASCII Art Blocks

ASCII art blocks are the most powerful visualization feature of pretty-loguru, combining the advantages of ASCII art headers and Rich blocks to create a complete report format that is both eye-catching and informative.

🎯 Basic Concept

ASCII Art Block = ASCII Art Header + Rich Block Content

This combination provides:

A striking ASCII art title
Structured content display
A unified visual style
A complete report format

🚀 Basic Usage

Simple ASCII Block

python

from pretty_loguru import create_logger

# Create logger instance
logger = create_logger(
    name="demo",
    log_path="logs",
    level="INFO"
)

logger.ascii_block(
    "System Status Report",   // Block title
    [                         // Content list
        "CPU Usage: 25%",
        "Memory Usage: 2.1GB", 
        "Disk Space: 120GB Available"
    ],
    ascii_header="STATUS",    // ASCII header text
    ascii_font="standard",    // ASCII font
    border_style="green",     // Border color
    log_level="INFO"          // Log level
)

Full Parameter Example

python

logger.ascii_block(
    title="Deployment Complete Report",
    content=[
        "Application Version: v2.1.0",
        "Deployment Time: 3m 45s",
        "Service Checks: All passed",
        "Load Balancer: Enabled"
    ],
    ascii_header="DEPLOYED",
    ascii_font="block",
    border_style="green",
    log_level="SUCCESS"
)

🎨 Visual Showcase

Success Scenario

python

logger.ascii_block(
    "Startup Complete Report",
    [
        "✅ Configuration file loaded successfully",
        "✅ Database connection normal",
        "✅ Redis cache ready",
        "✅ API service started",
        "✅ Health check passed"
    ],
    ascii_header="READY",
    ascii_font="slant",
    border_style="green",
    log_level="SUCCESS"
)

Warning Scenario

python

logger.ascii_block(
    "System Performance Warning",
    [
        "⚠️  CPU Usage: 87%",
        "⚠️  Memory Usage: 92%",
        "⚠️  Disk I/O: High load",
        "💡 Suggestion: Scale resources or optimize the application"
    ],
    ascii_header="WARNING",
    ascii_font="doom",
    border_style="yellow",
    log_level="WARNING"
)

Error Scenario

python

logger.ascii_block(
    "System Failure Report",
    [
        "❌ Database connection failed",
        "❌ Redis service not responding",
        "❌ API health check failed",
        "🔧 Corrective Action: Restart relevant services"
    ],
    ascii_header="ERROR",
    ascii_font="doom", 
    border_style="red",
    log_level="ERROR"
)

📊 Practical Application Scenarios

Application Startup Sequence

python

def application_startup(app_name, app_version, port, startup_time):
    logger.ascii_block(
        "Startup Checklist",
        [
            "🔧 Loading environment variables",
            "🔧 Parsing configuration file",
            "🔧 Initializing logging system", 
            "🔧 Creating database connection pool"
        ],
        ascii_header="STARTUP",
        ascii_font="slant",
        border_style="blue"
    )
    
    # Execute startup logic...
    
    logger.ascii_block(
        "Startup Complete Summary",
        [
            f"🚀 Application Name: {app_name}",
            f"📦 Version: {app_version}",
            f"🌐 Listening Port: {port}",
            f"⏱️  Startup Time: {startup_time}s"
        ],
        ascii_header="ONLINE",
        ascii_font="block",
        border_style="green",
        log_level="SUCCESS"
    )

Deployment Process Report

python

def deployment_report(deployment_info):
    logger.ascii_block(
        "Deployment Execution Report",
        [
            f"📦 Version: {deployment_info['version']}",
            f"🌍 Environment: {deployment_info['environment']}", 
            f"⏱️  Deployment Time: {deployment_info['duration']}",
            f"🔄 Rolling Update: {deployment_info['rolling_update']}",
            f"✅ Health Check: {deployment_info['health_check']}",
            f"📊 Success Rate: {deployment_info['success_rate']}%"
        ],
        ascii_header="DEPLOYED",
        ascii_font="standard",
        border_style="green" if deployment_info['success_rate'] == 100 else "yellow",
        log_level="SUCCESS" if deployment_info['success_rate'] == 100 else "WARNING"
    )

Data Processing Pipeline

python

def data_pipeline_summary(stats):
    logger.ascii_block(
        "Data Processing Complete Report",
        [
            f"📥 Input Records: {stats['input_records']:,}",
            f"✅ Processed Successfully: {stats['processed']:,}",
            f"❌ Processing Failed: {stats['failed']:,}",
            f"⏱️  Processing Time: {stats['duration']}",
            f"🚀 Processing Speed: {stats['records_per_second']:,} records/sec",
            f"💾 Output Size: {stats['output_size']}"
        ],
        ascii_header="COMPLETE",
        ascii_font="block",
        border_style="green",
        log_level="SUCCESS"
    )

System Monitoring Dashboard

python

def system_health_dashboard():
    import psutil
    import datetime
    
    # Get system information
    cpu_percent = psutil.cpu_percent(interval=1)
    memory = psutil.virtual_memory()
    disk = psutil.disk_usage('/')
    
    # Determine status color
    if cpu_percent > 80 or memory.percent > 80:
        color = "red"
        level = "WARNING"
        header = "ALERT"
    elif cpu_percent > 60 or memory.percent > 60:
        color = "yellow" 
        level = "WARNING"
        header = "CAUTION"
    else:
        color = "green"
        level = "INFO"
        header = "HEALTHY"
    
    logger.ascii_block(
        "System Health Monitoring",
        [
            f"🖥️  CPU Usage: {cpu_percent:.1f}%",
            f"💾 Memory Usage: {memory.percent:.1f}% ({memory.used//1024//1024//1024}GB/{memory.total//1024//1024//1024}GB)",
            f"💿 Disk Usage: {disk.percent:.1f}% ({disk.free//1024//1024//1024}GB Available)",
            f"⏰ Check Time: {datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S')}",
        ],
        ascii_header=header,
        ascii_font="standard",
        border_style=color,
        log_level=level
    )

API Request Summary

python

def api_request_summary(request_stats):
    logger.ascii_block(
        "API Request Statistics Report",
        [
            f"📊 Total Requests: {request_stats['total']:,}",
            f"✅ Successful Requests: {request_stats['success']:,} ({request_stats['success_rate']:.1f}%)",
            f"❌ Failed Requests: {request_stats['failed']:,} ({request_stats['error_rate']:.1f}%)",
            f"⚡ Average Response Time: {request_stats['avg_response_time']:.2f}ms",
            f"🚀 Fastest Response: {request_stats['min_response_time']:.2f}ms",
            f"🐌 Slowest Response: {request_stats['max_response_time']:.2f}ms"
        ],
        ascii_header="API STATS",
        ascii_font="small",
        border_style="blue",
        log_level="INFO"
    )

🔧 Advanced Techniques

Dynamic Content Generation

python

def dynamic_status_report(services):
    content = []
    all_healthy = True
    
    for service, status in services.items():
        if status['healthy']:
            content.append(f"✅ {service}: Running normally")
        else:
            content.append(f"❌ {service}: {status['error']}")
            all_healthy = False
    
    # Add statistics
    healthy_count = sum(1 for s in services.values() if s['healthy'])
    total_count = len(services)
    content.append(f"📊 Healthy Services: {healthy_count}/{total_count}")
    
    logger.ascii_block(
        "Service Health Check",
        content,
        ascii_header="HEALTHY" if all_healthy else "ISSUES",
        ascii_font="slant",
        border_style="green" if all_healthy else "red",
        log_level="SUCCESS" if all_healthy else "ERROR"
    )

Conditional Formatting

python

def build_result_report(build_success, test_results, deployment_ready):
    # Determine overall status based on results
    if build_success and all(test_results.values()) and deployment_ready:
        header = "SUCCESS"
        color = "green"
        level = "SUCCESS"
    elif build_success:
        header = "PARTIAL"
        color = "yellow"
        level = "WARNING"  
    else:
        header = "FAILED"
        color = "red"
        level = "ERROR"
    
    content = [
        f"🔨 Build Status: {'Success' if build_success else 'Failed'}",
        f"🧪 Unit Tests: {'Passed' if test_results.get('unit', False) else 'Failed'}",
        f"🔗 Integration Tests: {'Passed' if test_results.get('integration', False) else 'Failed'}",
        f"🚀 Ready for Deployment: {'Yes' if deployment_ready else 'No'}"
    ]
    
    logger.ascii_block(
        "Build and Test Report",
        content,
        ascii_header=header,
        ascii_font="doom",
        border_style=color,
        log_level=level
    )

⚠️ Usage Recommendations

Content Organization

python

# Recommended - Concise and organized content
logger.ascii_block(
    "Deployment Status",
    [
        "Version: v1.2.0",
        "Environment: Production", 
        "Status: Success"
    ],
    ascii_header="DEPLOY",
    ascii_font="slant"
)

# Not recommended - Overly verbose content
logger.ascii_block(
    "Very detailed deployment status report containing all possible information",
    [
        "This is a very long content line that contains too much information and may affect the visual effect...",
        "Another very long line of content..."
    ],
    ascii_header="VERY LONG HEADER",
    ascii_font="standard"
)

Color Usage Principles

python

# Success - Green
logger.ascii_block(..., border_style="green", log_level="SUCCESS")

# Warning - Yellow
logger.ascii_block(..., border_style="yellow", log_level="WARNING")

# Error - Red  
logger.ascii_block(..., border_style="red", log_level="ERROR")

# Info - Blue
logger.ascii_block(..., border_style="blue", log_level="INFO")

# Special - Purple/Cyan
logger.ascii_block(..., border_style="magenta", log_level="INFO")

🚀 Next Steps

ASCII art blocks are the most powerful feature of pretty-loguru. Now you can:

View complete visualization examples - Practical applications of all visual features
Learn about Rich Blocks - Detailed usage of pure Rich blocks
Explore integration applications - Use these features in web applications
Dive into the API documentation - Complete parameter and option descriptions

Start building professional-grade logging report systems!

ASCII Art Blocks ​

🎯 Basic Concept ​

🚀 Basic Usage ​

Simple ASCII Block ​

Full Parameter Example ​

🎨 Visual Showcase ​

Success Scenario ​

Warning Scenario ​

Error Scenario ​

📊 Practical Application Scenarios ​

Application Startup Sequence ​

Deployment Process Report ​

Data Processing Pipeline ​

System Monitoring Dashboard ​

API Request Summary ​

🔧 Advanced Techniques ​

Dynamic Content Generation ​

Conditional Formatting ​

⚠️ Usage Recommendations ​

Content Organization ​

Color Usage Principles ​

🚀 Next Steps ​

ASCII Art Blocks

🎯 Basic Concept

🚀 Basic Usage

Simple ASCII Block

Full Parameter Example

🎨 Visual Showcase

Success Scenario

Warning Scenario

Error Scenario

📊 Practical Application Scenarios

Application Startup Sequence

Deployment Process Report

Data Processing Pipeline

System Monitoring Dashboard

API Request Summary

🔧 Advanced Techniques

Dynamic Content Generation

Conditional Formatting

⚠️ Usage Recommendations

Content Organization

Color Usage Principles

🚀 Next Steps