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Utils Module

This module provides core utilities used throughout the GNN pipeline, including unified logging, argument parsing, pipeline orchestration, and common helper functions that ensure consistency across all modules.

Module Structure

src/utils/
├── __init__.py                      # Module initialization and exports
├── AGENTS.md                        # AI agent scaffolding documentation
├── README.md                        # This documentation
├── SPEC.md                          # Module specification
│
├── # Logging & Diagnostics
├── logging_utils.py                 # Unified logging utilities (1071 lines)
├── structured_logging.py            # Structured JSON logging (459 lines)
├── visual_logging.py                # Visual log formatting (431 lines)
├── diagnostic_logging.py            # Diagnostic logging utilities
│
├── # Configuration & Arguments
├── argument_utils.py                # Enhanced argument parsing (1225 lines)
├── config_loader.py                 # YAML/JSON config loading
│
├── # Pipeline Infrastructure
├── pipeline.py                      # Pipeline utilities
├── pipeline_template.py             # Standardized pipeline templates (586 lines)
├── pipeline_dependencies.py         # Step dependency management
├── pipeline_monitor.py              # Pipeline health monitoring (696 lines)
├── pipeline_planner.py              # Execution planning
├── pipeline_validator.py            # Validation utilities
│
├── # Dependency Management
├── dependency_audit.py              # Dependency auditing (504 lines)
├── dependency_installer.py          # Dependency installation
├── dependency_manager.py            # Dependency management (475 lines)
├── dependency_validator.py          # Dependency validation (667 lines)
│
├── # Error Handling & Recovery
├── error_handling.py                # Error handling framework
├── error_recovery.py                # Error recovery strategies
├── recovery.py                      # Recovery implementations
│
├── # Resource & Performance
├── resource_manager.py              # Resource monitoring
├── performance_tracker.py           # Performance tracking
├── timeout_manager.py               # Timeout management (445 lines)
├── visualization_optimizer.py       # Viz optimization (445 lines)
│
├── # Testing & Validation
├── test_utils.py                    # Test utilities (1121 lines)
├── script_validator.py              # Script validation (523 lines)
│
├── # Utilities
├── base_processor.py                # Base processor class
├── execution_utils.py               # Execution helpers
├── io_utils.py                      # I/O utilities
├── network_utils.py                 # Network utilities
├── path_utils.py                    # Path utilities
├── system_utils.py                  # System utilities
├── venv_utils.py                    # Virtual environment helpers
│
├── # Specialized
├── mcp.py                           # MCP integration (351 lines)
├── migration_helper.py              # Migration utilities
├── simulation_monitor.py            # Simulation monitoring
└── simulation_utils.py              # Simulation utilities

Core Components

graph TD
    Pipeline[Pipeline Scripts] --> Utils{Utils Module}
    
    Utils --> Log[Unified Logging]
    Utils --> Args[Arg Parser]
    Utils --> Files[File Utils]
    Utils --> Valid[Validation]
    Utils --> Config[Configuration]
    Utils --> Error[Error Recovery]
    Utils --> Perf[Performance Tracking]
    
    Log --> StructLog[Structured Logs]
    Args --> Config[Configuration]
    Files --> IOSafe[Safe IO Ops]
    Valid --> Checks[Path/Config Checks]
    Error --> Recovery[Recovery Strategies]
    Perf --> Metrics[Performance Metrics]
    
    StructLog & Config & IOSafe & Checks & Recovery & Metrics --> Standard[Standardization]
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Utility Module Architecture

graph LR
    subgraph "Logging Utilities"
        LogUtils[logging_utils.py]
        StructLog[structured_logging.py]
        VisualLog[visual_logging.py]
    end
    
    subgraph "Configuration"
        ConfigLoader[config_loader.py]
        ConfigMgr[configuration.py]
    end
    
    subgraph "Pipeline Support"
        ArgUtils[argument_utils.py]
        PipelineUtils[pipeline.py]
        PipelineTemplate[pipeline_template.py]
    end
    
    subgraph "Error Handling"
        ErrorRecovery[error_recovery.py]
        ErrorHandling[error_handling.py]
    end
    
    subgraph "Resource Management"
        ResourceMgr[resource_manager.py]
        PerfTracker[performance_tracker.py]
    end
    
    PipelineUtils --> LogUtils
    PipelineUtils --> ArgUtils
    PipelineUtils --> ConfigLoader
    
    LogUtils --> StructLog
    LogUtils --> VisualLog
    
    ErrorRecovery --> ErrorHandling
    ResourceMgr --> PerfTracker
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Logging Flow

sequenceDiagram
    participant Script as Pipeline Script
    participant Utils as Utils Module
    participant Logger as Logger
    participant Tracker as Performance Tracker
    
    Script->>Utils: setup_step_logging()
    Utils->>Logger: Create logger with correlation ID
    Utils->>Tracker: Initialize performance tracking
    Logger-->>Script: Return logger instance
    
    Script->>Logger: log_step_start()
    Logger->>Tracker: Record start time
    Logger->>Logger: Emit structured log
    
    Script->>Logger: log_step_success()
    Logger->>Tracker: Calculate duration
    Logger->>Logger: Emit success log with metrics
    
    Script->>Logger: log_step_error()
    Logger->>Tracker: Record error
    Logger->>Logger: Emit error log with context
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Unified Logging System

setup_step_logging(module_name: str) -> logging.Logger

Sets up standardized logging for pipeline steps.

Features:

  • Correlation ID generation
  • Structured logging format
  • Performance tracking
  • Error handling
  • Log level management

log_step_start(step_name: str, target_dir: Path, output_dir: Path, verbose: bool) -> None

Logs the start of a pipeline step.

Logging Features:

  • Step identification
  • Input/output directory logging
  • Verbosity level tracking
  • Performance start time
  • Resource usage monitoring

log_step_success(step_name: str, results: Dict[str, Any]) -> None

Logs successful completion of a pipeline step.

Success Features:

  • Results summary
  • Performance metrics
  • Output file counts
  • Processing statistics
  • Success indicators

log_step_error(step_name: str, error: Exception) -> None

Logs errors during pipeline step execution.

Error Features:

  • Error type identification
  • Stack trace logging
  • Error context preservation
  • Recovery suggestions
  • Error categorization

log_step_warning(step_name: str, warning: str, context: Dict[str, Any] = None) -> None

Logs warnings during pipeline step execution.

Warning Features:

  • Warning message logging
  • Context preservation
  • Severity assessment
  • Action recommendations
  • Warning categorization

Argument Parsing

ArgumentParser

Standard argument parser with comprehensive pipeline support.

Features:

  • Graceful degradation
  • Standard argument sets
  • Validation integration
  • Help text generation
  • Error handling

parse_step_arguments() -> argparse.Namespace

Parses arguments for specific pipeline steps with recovery for graceful degradation.

Parsing Features:

  • Standard argument parsing
  • Recovery argument handling
  • Validation integration
  • Error recovery
  • Help text generation

Pipeline Orchestration Utilities

get_output_dir_for_script(script_name: str) -> Path

Gets the output directory for a specific pipeline script.

Features:

  • Standardized output paths
  • Directory creation
  • Path validation
  • Error handling
  • Configuration integration

get_pipeline_config() -> Dict[str, Any]

Gets the pipeline configuration.

Configuration Features:

  • Configuration loading
  • Default value handling
  • Validation integration
  • Error handling
  • Configuration merging

File Handling Utilities

ensure_directory_exists(path: Path) -> bool

Ensures a directory exists, creating it if necessary.

Features:

  • Directory creation
  • Permission handling
  • Error handling
  • Path validation
  • Success verification

safe_file_operation(operation: Callable, *args, **kwargs) -> Any

Safely executes file operations with error handling.

Safety Features:

  • Exception handling
  • Rollback capabilities
  • Error reporting
  • Success verification
  • Resource cleanup

get_file_info(file_path: Path) -> Dict[str, Any]

Gets comprehensive information about a file.

Info Features:

  • File size
  • Modification time
  • File type
  • Permissions
  • Content analysis

Validation Utilities

validate_path(path: Path, must_exist: bool = True) -> bool

Validates a file path.

Validation Features:

  • Path existence checking
  • Permission validation
  • Path format validation
  • Error reporting
  • Success verification

validate_config(config: Dict[str, Any], required_keys: List[str]) -> bool

Validates configuration dictionaries.

Validation Features:

  • Required key checking
  • Type validation
  • Value validation
  • Error reporting
  • Success verification

Common Utilities

format_duration(seconds: float) -> str

Formats duration in human-readable format.

Formatting Features:

  • Time unit conversion
  • Precision handling
  • Readable output
  • Error handling
  • Success verification

format_file_size(bytes: int) -> str

Formats file size in human-readable format.

Formatting Features:

  • Size unit conversion
  • Precision handling
  • Readable output
  • Error handling
  • Success verification

Usage Examples

Basic Logging Setup

from utils import setup_step_logging, log_step_start, log_step_success, log_step_error

# Setup logging for a module
logger = setup_step_logging(__name__)

# Log step start
log_step_start("my_step", target_dir, output_dir, verbose)

try:
    # Perform processing
    results = perform_processing()
    
    # Log success
    log_step_success("my_step", results)
    
except Exception as e:
    # Log error
    log_step_error("my_step", e)
    raise

Argument Parsing

from utils.argument_utils import ArgumentParser

# Parse arguments for a specific step
args = ArgumentParser.parse_step_arguments("step_name")

# Access parsed arguments
target_dir = args.target_dir
output_dir = args.output_dir
verbose = args.verbose

Pipeline Orchestration

from utils import get_output_dir_for_script, get_pipeline_config

# Get output directory for current script
output_dir = get_output_dir_for_script("my_script.py")

# Get pipeline configuration
config = get_pipeline_config()

# Use configuration
verbose = config.get("verbose", False)
log_level = config.get("log_level", "INFO")

File Handling

from utils import ensure_directory_exists, safe_file_operation, get_file_info

# Ensure directory exists
success = ensure_directory_exists(Path("output/my_step/"))

# Safe file operation
def write_file(content, path):
    with open(path, 'w') as f:
        f.write(content)

result = safe_file_operation(write_file, "Hello World", Path("test.txt"))

# Get file information
file_info = get_file_info(Path("test.txt"))
print(f"File size: {file_info['size']}")
print(f"Modified: {file_info['modified']}")

Validation

from utils import validate_path, validate_config

# Validate path
if validate_path(Path("input/"), must_exist=True):
    print("Path is valid")
else:
    print("Path is invalid")

# Validate configuration
config = {
    "verbose": True,
    "output_dir": "output/",
    "log_level": "INFO"
}

required_keys = ["verbose", "output_dir"]
if validate_config(config, required_keys):
    print("Configuration is valid")
else:
    print("Configuration is invalid")

Common Utilities

from utils import format_duration, format_file_size

# Format duration
duration = format_duration(3661.5)  # 1 hour, 1 minute, 1.5 seconds
print(duration)

# Format file size
size = format_file_size(1024 * 1024)  # 1 MB
print(size)

Integration with Pipeline

Standard Module Pattern

Every module should follow this pattern using utils:

# Standard imports
from utils import setup_step_logging, log_step_start, log_step_success, log_step_error
from pipeline import get_output_dir_for_script, get_pipeline_config

# Setup logging
logger = setup_step_logging(__name__)

# Main processing function
def process_my_module(target_dir: Path, output_dir: Path, verbose: bool = False, **kwargs) -> bool:
    """Main function for processing my module tasks."""
    try:
        log_step_start("my_module", target_dir, output_dir, verbose)
        
        # Core processing logic here
        results = perform_my_module_processing(target_dir, output_dir, verbose)
        
        log_step_success("my_module", results)
        return True
        
    except Exception as e:
        log_step_error("my_module", e)
        return False

Argument Parsing Pattern

# Standard argument parsing for pipeline steps
def parse_step_arguments(step_name):
    """Parse arguments for a specific pipeline step."""
    from utils.argument_utils import ArgumentParser
    return ArgumentParser.parse_step_arguments(step_name)

Configuration Options

Logging Configuration

# Logging configuration
logging_config = {
    'log_level': 'INFO',           # Log level
    'log_format': 'structured',     # Log format
    'correlation_ids': True,        # Enable correlation IDs
    'performance_tracking': True,    # Enable performance tracking
    'error_reporting': True         # Enable error reporting
}

Argument Parsing Configuration

# Argument parsing configuration
parser_config = {
    'fallback_enabled': True,       # Enable recovery parsing
    'validation_enabled': True,      # Enable argument validation
    'help_generation': True,         # Enable help text generation
    'error_handling': True           # Enable error handling
}

Pipeline Configuration

# Pipeline configuration
pipeline_config = {
    'output_base_dir': 'output/',    # Base output directory
    'log_base_dir': 'logs/',         # Base log directory
    'temp_dir': 'temp/',             # Temporary directory
    'backup_enabled': True,          # Enable backups
    'cleanup_enabled': True          # Enable cleanup
}

Error Handling

Logging Failures

# Handle logging failures gracefully
try:
    logger = setup_step_logging(__name__)
except Exception as e:
    # Recovery to basic logging
    logging.basicConfig(level=logging.INFO)
    logger = logging.getLogger(__name__)

Argument Parsing Failures

# Handle argument parsing failures gracefully
try:
    args = ArgumentParser.parse_step_arguments("step_name")
except Exception as e:
    # Recovery to basic argument parser
    parser = argparse.ArgumentParser()
    parser.add_argument("--target-dir", type=Path, required=True)
    parser.add_argument("--output-dir", type=Path, required=True)
    args = parser.parse_args()

File Operation Failures

# Handle file operation failures gracefully
try:
    success = safe_file_operation(write_file, content, path)
except Exception as e:
    logger.error(f"File operation failed: {e}")
    # Provide recovery or error reporting

Performance Optimization

Logging Optimization

  • Structured Logging: Use structured logging for better performance
  • Async Logging: Use async logging for non-blocking operations
  • Log Rotation: Implement log rotation to manage file sizes
  • Performance Tracking: Track logging performance impact

Argument Parsing Optimization

  • Caching: Cache parsed arguments for repeated access
  • Lazy Parsing: Parse arguments only when needed
  • Validation Optimization: Optimize argument validation
  • Error Recovery: Implement efficient error recovery

File Operation Optimization

  • Batch Operations: Use batch operations for multiple files
  • Async Operations: Use async operations for I/O intensive tasks
  • Caching: Cache file information for repeated access
  • Error Recovery: Implement efficient error recovery

Testing and Validation

Unit Tests

# Test individual utility functions
def test_setup_step_logging():
    logger = setup_step_logging("test_module")
    assert logger is not None
    assert logger.name == "test_module"

Integration Tests

# Test complete utility pipeline
def test_utility_pipeline():
    # Test logging setup
    logger = setup_step_logging("test_module")
    
    # Test argument parsing
    args = ArgumentParser.parse_step_arguments("step_name")
    
    # Test file operations
    success = ensure_directory_exists(Path("test_dir/"))
    assert success

Validation Tests

# Test validation utilities
def test_validation_utilities():
    # Test path validation
    assert validate_path(Path("."), must_exist=True)
    
    # Test config validation
    config = {"key": "value"}
    assert validate_config(config, ["key"])

Dependencies

Required Dependencies

  • pathlib: Path handling
  • logging: Logging functionality
  • argparse: Argument parsing
  • json: JSON data handling
  • time: Time utilities

Optional Dependencies

  • rich: Rich text formatting
  • click: Advanced command line interface
  • pydantic: Data validation
  • structlog: Structured logging

Performance Metrics

Logging Performance

  • Setup Time: < 10ms for logger setup
  • Log Write Time: < 1ms per log entry
  • Memory Usage: ~5MB base logging overhead
  • File I/O: Optimized for minimal disk impact

Argument Parsing Performance

  • Parse Time: < 5ms for standard arguments
  • Validation Time: < 2ms per argument
  • Memory Usage: ~2MB for argument parser
  • Error Recovery: < 10ms for recovery parsing

File Operation Performance

  • Directory Creation: < 50ms for standard directories
  • File Operations: < 100ms for standard files
  • Validation Time: < 5ms per file validation
  • Error Recovery: < 20ms for error recovery

Troubleshooting

Common Issues

1. Logging Failures

Error: Failed to setup logging - permission denied
Solution: Check log directory permissions and create if necessary

2. Argument Parsing Issues

Error: Argument parsing failed - invalid argument type
Solution: Check argument types and provide proper fallbacks

3. File Operation Issues

Error: File operation failed - disk full
Solution: Check disk space and implement cleanup procedures

4. Validation Issues

Error: Validation failed - invalid path format
Solution: Check path format and implement proper validation

Debug Mode

# Enable debug mode for detailed utility information
import logging
logging.getLogger().setLevel(logging.DEBUG)

Future Enhancements

Planned Features

  • Advanced Logging: AI-powered log analysis and optimization
  • Smart Argument Parsing: Context-aware argument parsing
  • Intelligent File Operations: AI-powered file operation optimization
  • Automated Testing: Automated utility testing and validation

Performance Improvements

  • Advanced Caching: Advanced caching strategies
  • Parallel Processing: Parallel utility operations
  • Incremental Updates: Incremental utility updates
  • Machine Learning: ML-based utility optimization

Summary

The Utils module provides core utilities used throughout the GNN pipeline, including unified logging, argument parsing, pipeline orchestration, and common helper functions. The module ensures consistency across all modules by providing standardized patterns for logging, argument handling, file operations, and validation. These utilities form the foundation for reliable and maintainable pipeline operations.

License and Citation

This module is part of the GeneralizedNotationNotation project. See the main repository for license and citation information.

References

  • Project overview: ../../README.md
  • Comprehensive docs: ../../DOCS.md
  • Architecture guide: ../../ARCHITECTURE.md
  • Pipeline details: ../../doc/pipeline/README.md

Documentation

  • README: Module Overview
  • AGENTS: Agentic Workflows
  • SPEC: Architectural Specification
  • SKILL: Capability API