refactor: Major reorganization of optimization_engine module structure
BREAKING CHANGE: Module paths have been reorganized for better maintainability. Backwards compatibility aliases with deprecation warnings are provided. New Structure: - core/ - Optimization runners (runner, intelligent_optimizer, etc.) - processors/ - Data processing - surrogates/ - Neural network surrogates - nx/ - NX/Nastran integration (solver, updater, session_manager) - study/ - Study management (creator, wizard, state, reset) - reporting/ - Reports and analysis (visualizer, report_generator) - config/ - Configuration management (manager, builder) - utils/ - Utilities (logger, auto_doc, etc.) - future/ - Research/experimental code Migration: - ~200 import changes across 125 files - All __init__.py files use lazy loading to avoid circular imports - Backwards compatibility layer supports old import paths with warnings - All existing functionality preserved To migrate existing code: OLD: from optimization_engine.nx_solver import NXSolver NEW: from optimization_engine.nx.solver import NXSolver OLD: from optimization_engine.runner import OptimizationRunner NEW: from optimization_engine.core.runner import OptimizationRunner 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
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298
optimization_engine/utils/logger.py
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298
optimization_engine/utils/logger.py
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"""
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Atomizer Structured Logging System - Phase 1.3
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Provides consistent, production-ready logging across all optimization studies.
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Usage:
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from optimization_engine.utils.logger import get_logger
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logger = get_logger(__name__)
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logger.info("Starting optimization...")
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logger.error("Simulation failed", exc_info=True)
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# Study-specific logger with automatic file logging
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logger = get_logger("drone_gimbal_arm", study_dir="studies/drone_gimbal_arm/2_results")
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logger.trial_start(trial_number=5, design_vars={"thickness": 2.5})
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logger.trial_complete(trial_number=5, objectives={"mass": 120, "freq": 155})
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Features:
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- Automatic file logging to study_dir/optimization.log
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- Console output with color-coded levels (if supported)
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- Structured trial logging for dashboard integration
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- Log rotation (50MB max, 3 backups)
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- No external dependencies (stdlib only)
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"""
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import logging
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import sys
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from pathlib import Path
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from datetime import datetime
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from typing import Optional, Dict, Any
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from logging.handlers import RotatingFileHandler
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# ANSI color codes for console output (Windows 10+ and Unix)
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class LogColors:
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"""ANSI color codes for console output."""
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RESET = '\033[0m'
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BOLD = '\033[1m'
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# Levels
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DEBUG = '\033[36m' # Cyan
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INFO = '\033[32m' # Green
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WARNING = '\033[33m' # Yellow
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ERROR = '\033[31m' # Red
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CRITICAL = '\033[35m' # Magenta
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# Custom
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TRIAL = '\033[94m' # Bright Blue
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SUCCESS = '\033[92m' # Bright Green
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class ColoredFormatter(logging.Formatter):
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"""Formatter that adds color to console output."""
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COLORS = {
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logging.DEBUG: LogColors.DEBUG,
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logging.INFO: LogColors.INFO,
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logging.WARNING: LogColors.WARNING,
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logging.ERROR: LogColors.ERROR,
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logging.CRITICAL: LogColors.CRITICAL,
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}
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def __init__(self, fmt: str, use_colors: bool = True):
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super().__init__(fmt)
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self.use_colors = use_colors and self._supports_color()
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def _supports_color(self) -> bool:
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"""Check if terminal supports ANSI colors."""
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# Windows 10+ supports ANSI
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if sys.platform == 'win32':
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try:
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import ctypes
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kernel32 = ctypes.windll.kernel32
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kernel32.SetConsoleMode(kernel32.GetStdHandle(-11), 7)
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return True
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except:
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return False
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# Unix-like systems
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return hasattr(sys.stdout, 'isatty') and sys.stdout.isatty()
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def format(self, record: logging.LogRecord) -> str:
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if self.use_colors:
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levelname = record.levelname
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color = self.COLORS.get(record.levelno, '')
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record.levelname = f"{color}{levelname}{LogColors.RESET}"
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return super().format(record)
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class AtomizerLogger(logging.Logger):
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"""Extended logger with trial-specific methods."""
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def trial_start(self, trial_number: int, design_vars: Dict[str, float]):
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"""Log trial start with design variables."""
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self.info(f"{'='*60}")
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self.info(f"Trial #{trial_number} START")
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self.info(f"{'='*60}")
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self.info("Design Variables:")
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for name, value in design_vars.items():
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if isinstance(value, float):
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self.info(f" {name}: {value:.4f}")
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else:
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self.info(f" {name}: {value}")
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def trial_complete(self, trial_number: int, objectives: Dict[str, float],
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constraints: Optional[Dict[str, float]] = None,
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feasible: bool = True):
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"""Log trial completion with results."""
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self.info(f"\nTrial #{trial_number} COMPLETE")
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self.info("Objectives:")
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for name, value in objectives.items():
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if isinstance(value, float):
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self.info(f" {name}: {value:.4f}")
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else:
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self.info(f" {name}: {value}")
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if constraints:
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self.info("Constraints:")
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for name, value in constraints.items():
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if isinstance(value, float):
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self.info(f" {name}: {value:.4f}")
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else:
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self.info(f" {name}: {value}")
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status = "[OK] Feasible" if feasible else "[WARNING] Infeasible"
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self.info(f"{status}")
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self.info(f"{'='*60}\n")
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def trial_failed(self, trial_number: int, error: str):
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"""Log trial failure."""
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self.error(f"\nTrial #{trial_number} FAILED")
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self.error(f"Error: {error}")
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self.error(f"{'='*60}\n")
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def study_start(self, study_name: str, n_trials: int, sampler: str):
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"""Log study initialization."""
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self.info("=" * 80)
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self.info(f"OPTIMIZATION STUDY: {study_name}")
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self.info("=" * 80)
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self.info(f"Trials: {n_trials}")
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self.info(f"Sampler: {sampler}")
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self.info(f"Started: {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}")
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self.info("=" * 80)
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self.info("")
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def study_complete(self, study_name: str, n_trials: int, n_successful: int):
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"""Log study completion."""
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self.info("")
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self.info("=" * 80)
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self.info(f"STUDY COMPLETE: {study_name}")
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self.info("=" * 80)
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self.info(f"Total trials: {n_trials}")
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self.info(f"Successful: {n_successful}")
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self.info(f"Failed/Pruned: {n_trials - n_successful}")
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self.info(f"Completed: {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}")
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self.info("=" * 80)
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# Register custom logger class
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logging.setLoggerClass(AtomizerLogger)
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def get_logger(
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name: str,
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level: int = logging.INFO,
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study_dir: Optional[Path] = None,
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console: bool = True,
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file_logging: bool = True
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) -> AtomizerLogger:
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"""
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Get or create a logger instance.
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Args:
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name: Logger name (typically __name__ or study name)
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level: Logging level (DEBUG, INFO, WARNING, ERROR, CRITICAL)
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study_dir: If provided, creates log file at study_dir/optimization.log
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console: Enable console output (default: True)
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file_logging: Enable file logging (default: True, requires study_dir)
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Returns:
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AtomizerLogger instance
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Example:
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# Simple logger
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logger = get_logger(__name__)
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logger.info("Starting optimization...")
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# Study logger with file output
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logger = get_logger(
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"drone_gimbal_arm",
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study_dir=Path("studies/drone_gimbal_arm/2_results")
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)
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logger.study_start("drone_gimbal_arm", n_trials=30, sampler="NSGAIISampler")
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"""
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logger = logging.getLogger(name)
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# Only configure if not already configured
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if not logger.handlers:
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logger.setLevel(level)
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# Console handler with colors
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if console:
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console_handler = logging.StreamHandler(sys.stdout)
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console_handler.setLevel(level)
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console_formatter = ColoredFormatter(
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fmt='[%(levelname)s] %(message)s',
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use_colors=True
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)
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console_handler.setFormatter(console_formatter)
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logger.addHandler(console_handler)
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# File handler with rotation
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if file_logging and study_dir:
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study_dir = Path(study_dir)
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study_dir.mkdir(parents=True, exist_ok=True)
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log_file = study_dir / "optimization.log"
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file_handler = RotatingFileHandler(
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log_file,
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maxBytes=50 * 1024 * 1024, # 50MB
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backupCount=3,
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encoding='utf-8'
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)
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file_handler.setLevel(level)
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file_formatter = logging.Formatter(
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fmt='%(asctime)s | %(levelname)-8s | %(name)s | %(message)s',
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datefmt='%Y-%m-%d %H:%M:%S'
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)
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file_handler.setFormatter(file_formatter)
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logger.addHandler(file_handler)
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# Log that file logging is enabled
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logger.debug(f"File logging enabled: {log_file}")
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# Prevent propagation to root logger
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logger.propagate = False
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return logger
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def configure_root_logger(level: int = logging.WARNING):
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"""
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Configure root logger to catch unconfigured loggers.
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Call this once at application startup to set up default logging behavior.
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"""
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root_logger = logging.getLogger()
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root_logger.setLevel(level)
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if not root_logger.handlers:
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handler = logging.StreamHandler(sys.stdout)
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handler.setLevel(level)
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formatter = logging.Formatter('[%(levelname)s] %(name)s: %(message)s')
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handler.setFormatter(formatter)
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root_logger.addHandler(handler)
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# Example usage and testing
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if __name__ == "__main__":
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# Test basic logging
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print("Testing Atomizer Logging System")
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print("=" * 80)
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# Simple logger
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logger = get_logger("test_module")
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logger.debug("This is a debug message")
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logger.info("This is an info message")
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logger.warning("This is a warning message")
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logger.error("This is an error message")
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print()
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# Study logger with file output
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test_dir = Path("test_logs")
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test_dir.mkdir(exist_ok=True)
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study_logger = get_logger("test_study", study_dir=test_dir)
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study_logger.study_start("test_study", n_trials=5, sampler="TPESampler")
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# Simulate trial
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study_logger.trial_start(1, {"thickness": 2.5, "width": 10.0})
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study_logger.info("Running simulation...")
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study_logger.trial_complete(
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1,
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objectives={"mass": 120.5, "stiffness": 1500.2},
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constraints={"max_stress": 85.3},
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feasible=True
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)
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# Failed trial
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study_logger.trial_start(2, {"thickness": 1.0, "width": 5.0})
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study_logger.trial_failed(2, "Simulation convergence failure")
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study_logger.study_complete("test_study", n_trials=5, n_successful=4)
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print()
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print(f"Log file created at: {test_dir / 'optimization.log'}")
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print("Check the file to see structured logging output!")
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