Build Digital Twins for Automotive Electronics with Synopsys eDT and MLflow

                      
                     
"""
Production implementation for building digital twins for automotive electronics.
Utilizes Synopsys eDT and MLflow for data handling and model management.
"""
from typing import Dict, Any, List, Tuple
import os
import logging
import requests
import json
import time

# Logger setup for tracking events and errors
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)

class Config:
    """
    Configuration class for environment variables.
    """
    database_url: str = os.getenv('DATABASE_URL')
    api_url: str = os.getenv('API_URL')


def validate_input(data: Dict[str, Any]) -> bool:
    """Validate request data.
    
    Args:
        data: Input data to validate
    Returns:
        True if valid
    Raises:
        ValueError: If validation fails
    """
    if 'vehicle_id' not in data:
        raise ValueError('Missing vehicle_id')
    if not isinstance(data['vehicle_id'], str):
        raise ValueError('vehicle_id must be a string')
    return True


def sanitize_fields(data: Dict[str, Any]) -> Dict[str, Any]:
    """Sanitize input fields to prevent injection attacks.
    
    Args:
        data: Input data to sanitize
    Returns:
        Sanitized data
    """
    return {k: str(v).strip() for k, v in data.items()}


def normalize_data(data: Dict[str, Any]) -> Dict[str, Any]:
    """Normalize data fields for model compatibility.
    
    Args:
        data: Raw data to normalize
    Returns:
        Normalized data
    """
    # Example normalization process
    data['timestamp'] = time.time()  # Add current timestamp
    return data


def transform_records(records: List[Dict[str, Any]]) -> List[Dict[str, Any]]:
    """Transform a list of records for MLflow logging.
    
    Args:
        records: List of raw records
    Returns:
        Transformed records
    """
    return [normalize_data(record) for record in records]


def process_batch(batch: List[Dict[str, Any]]) -> None:
    """Process a batch of data and log to MLflow.
    
    Args:
        batch: List of records to process
    """
    try:
        transformed_batch = transform_records(batch)
        # Log to MLflow
        for record in transformed_batch:
            logger.info(f'Logging record to MLflow: {record}')
            # mlflow.log_params(record)  # Uncomment when MLflow is configured
    except Exception as e:
        logger.error(f'Error processing batch: {e}')


def fetch_data(api_url: str) -> List[Dict[str, Any]]:
    """Fetch data from an external API.
    
    Args:
        api_url: URL to fetch data from
    Returns:
        List of records fetched
    Raises:
        Exception: If request fails
    """
    response = requests.get(api_url)
    if response.status_code != 200:
        raise Exception(f'Failed to fetch data: {response.status_code}')
    return response.json()


def save_to_db(data: Dict[str, Any]) -> None:
    """Placeholder function to save data to a database.
    
    Args:
        data: Data to save
    """
    logger.info(f'Saving data to database: {data}')
    # db.save(data)  # Uncomment when database connection is established


def call_api(data: Dict[str, Any]) -> None:
    """Call an external API with the provided data.
    
    Args:
        data: Data to send
    """
    logger.info(f'Calling external API with data: {data}')
    # requests.post(Config.api_url, json=data)  # Uncomment when API is configured


def format_output(data: Any) -> str:
    """Format output data for display.
    
    Args:
        data: Data to format
    Returns:
        Formatted string
    """
    return json.dumps(data, indent=4)


def handle_errors(func):
    """Decorator for handling errors in functions.
    
    Args:
        func: Function to wrap
    """
    def wrapper(*args, **kwargs):
        try:
            return func(*args, **kwargs)
        except Exception as e:
            logger.error(f'Error in {func.__name__}: {e}')
            raise
    return wrapper


class DigitalTwinOrchestrator:
    """Main orchestrator for digital twin operations.
    
    This class ties together various helper functions to create and manage digital twins.
    """
    def __init__(self):
        self.config = Config()

    @handle_errors
    def execute(self) -> None:
        """Execute the digital twin pipeline.
        
        This includes fetching data, processing it, and saving results.
        """
        try:
            raw_data = fetch_data(self.config.api_url)
            process_batch(raw_data)
        except ValueError as ve:
            logger.error(f'Validation error: {ve}')
        except Exception as e:
            logger.error(f'Execution error: {e}')


if __name__ == '__main__':
    # Example usage of the orchestrator
    orchestrator = DigitalTwinOrchestrator()
    orchestrator.execute()