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feat: Add structured logging system for production-ready error handling (Phase 1.3)

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Implements comprehensive, production-ready logging infrastructure to replace
ad-hoc print() statements across the codebase. This establishes a consistent
logging standard for MVP stability.

## What Changed

**New Files:**
- optimization_engine/logger.py (330 lines)
  - AtomizerLogger class with trial-specific methods
  - Color-coded console output (Windows 10+ and Unix)
  - Automatic file logging with rotation (50MB, 3 backups)
  - Zero external dependencies (stdlib only)

- docs/07_DEVELOPMENT/Phase_1_3_Implementation_Plan.md
  - Complete Phase 1.3 implementation plan
  - API documentation and usage examples
  - Migration strategy for existing studies

## Features

1. **Structured Trial Logging:**
   - logger.trial_start() - Log trial with design variables
   - logger.trial_complete() - Log results with objectives/constraints
   - logger.trial_failed() - Log failures with error details
   - logger.study_start() - Log study initialization
   - logger.study_complete() - Log final summary

2. **Production Features:**
   - ANSI color-coded console output (DEBUG=cyan, INFO=green, etc.)
   - Automatic file logging to {study_dir}/optimization.log
   - Log rotation: 50MB max, 3 backup files
   - Timestamps and structured format for dashboard parsing

3. **Simple API:**
   ```python
   from optimization_engine.logger import get_logger
   logger = get_logger(__name__, study_dir=Path("studies/foo/2_results"))
   logger.study_start("foo", n_trials=30, sampler="NSGAIISampler")
   logger.trial_start(1, design_vars)
   logger.trial_complete(1, objectives, constraints, feasible=True)
   ```

## Testing

- Verified color output on Windows 10
- Tested file logging and rotation
- Confirmed trial-specific methods format correctly
- UTF-8 encoding handles special characters

## Next Steps (Phase 1.3.1)

- Integrate logging into drone_gimbal_arm_optimization (reference implementation)
- Create migration guide for existing studies
- Update create-study skill to include logger setup

## Technical Details

Current state analyzed:
- 1416 occurrences of logging/print across 79 files
- 411 occurrences of try:/except/raise across 59 files
- Mix of print(), traceback, and inconsistent formatting

This logging system provides the foundation for:
- Dashboard integration (structured trial logs)
- Error recovery (checkpoint system in Phase 1.3.2)
- Production debugging (file logs with rotation)

Related: Phase 1.2 (Configuration Validation)

🤖 Generated with [Claude Code](https://claude.com/claude-code)

Co-Authored-By: Claude <noreply@anthropic.com>
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2025-11-24 09:27:27 -05:00
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# Phase 1.3: Error Handling & Logging - Implementation Plan
**Goal**: Implement production-ready logging and error handling system for MVP stability.
**Status**: MVP Complete (2025-11-24)
## Overview
Phase 1.3 establishes a consistent, professional logging system across all Atomizer optimization studies. This replaces ad-hoc `print()` statements with structured logging that supports:
- File and console output
- Color-coded log levels (Windows 10+ and Unix)
- Trial-specific logging methods
- Automatic log rotation
- Zero external dependencies (stdlib only)
## Problem Analysis
### Current State (Before Phase 1.3)
Analyzed the codebase and found:
- **1416 occurrences** of logging/print across 79 files (mostly ad-hoc `print()` statements)
- **411 occurrences** of `try:/except/raise` across 59 files
- Mixed error handling approaches:
- Some studies use traceback.print_exc()
- Some use simple print() for errors
- No consistent logging format
- No file logging in most studies
- Some studies have `--resume` capability, but implementation varies
### Requirements
1. **Drop-in Replacement**: Minimal code changes to adopt
2. **Production-Ready**: File logging with rotation, timestamps, proper levels
3. **Dashboard-Friendly**: Structured trial logging for future integration
4. **Windows-Compatible**: ANSI color support on Windows 10+
5. **No Dependencies**: Use only Python stdlib
---
## ✅ Phase 1.3 MVP - Completed (2025-11-24)
### Task 1: Structured Logging System ✅ DONE
**File Created**: `optimization_engine/logger.py` (330 lines)
**Features Implemented**:
1. **AtomizerLogger Class** - Extended logger with trial-specific methods:
```python
logger.trial_start(trial_number=5, design_vars={"thickness": 2.5})
logger.trial_complete(trial_number=5, objectives={"mass": 120})
logger.trial_failed(trial_number=5, error="Simulation failed")
logger.study_start(study_name="test", n_trials=30, sampler="TPESampler")
logger.study_complete(study_name="test", n_trials=30, n_successful=28)
```
2. **Color-Coded Console Output** - ANSI colors for Windows and Unix:
- DEBUG: Cyan
- INFO: Green
- WARNING: Yellow
- ERROR: Red
- CRITICAL: Magenta
3. **File Logging with Rotation**:
- Automatically creates `{study_dir}/optimization.log`
- 50MB max file size
- 3 backup files (optimization.log.1, .2, .3)
- UTF-8 encoding
- Detailed format: `timestamp | level | module | message`
4. **Simple API**:
```python
# Basic logger
from optimization_engine.logger import get_logger
logger = get_logger(__name__)
logger.info("Starting optimization...")
# Study logger with file output
logger = get_logger(
"drone_gimbal_arm",
study_dir=Path("studies/drone_gimbal_arm/2_results")
)
```
**Testing**: Successfully tested on Windows with color output and file logging.
### Task 2: Documentation ✅ DONE
**File Created**: This implementation plan
**Docstrings**: Comprehensive docstrings in `logger.py` with usage examples
---
## 🔨 Remaining Tasks (Phase 1.3.1+)
### Phase 1.3.1: Integration with Existing Studies
**Priority**: HIGH | **Effort**: 1-2 days
1. **Update drone_gimbal_arm_optimization study** (Reference implementation)
- Replace print() statements with logger calls
- Add file logging to 2_results/
- Use trial-specific logging methods
- Test to ensure colors work, logs rotate
2. **Create Migration Guide**
- Document how to convert existing studies
- Provide before/after examples
- Add to DEVELOPMENT.md
3. **Update create-study Claude Skill**
- Include logger setup in generated run_optimization.py
- Add logging best practices
### Phase 1.3.2: Enhanced Error Recovery
**Priority**: MEDIUM | **Effort**: 2-3 days
1. **Study Checkpoint Manager**
- Automatic checkpointing every N trials
- Save study state to `2_results/checkpoint.json`
- Resume from last checkpoint on crash
- Clean up old checkpoints
2. **Enhanced Error Context**
- Capture design variables on failure
- Log simulation command that failed
- Include FEA solver output in error log
- Structured error reporting for dashboard
3. **Graceful Degradation**
- Fallback when file logging fails
- Handle disk full scenarios
- Continue optimization if dashboard unreachable
### Phase 1.3.3: Notification System (Future)
**Priority**: LOW | **Effort**: 1-2 days
1. **Study Completion Notifications**
- Optional email notification when study completes
- Configurable via environment variables
- Include summary (best trial, success rate, etc.)
2. **Error Alerts**
- Optional notifications on critical failures
- Threshold-based (e.g., >50% trials failing)
---
## Migration Strategy
### Priority 1: New Studies (Immediate)
All new studies created via create-study skill should use the new logging system by default.
**Action**: Update `.claude/skills/create-study.md` to generate run_optimization.py with logger.
### Priority 2: Reference Study (Phase 1.3.1)
Update `drone_gimbal_arm_optimization` as the reference implementation.
**Before**:
```python
print(f"Trial #{trial.number}")
print(f"Design Variables:")
for name, value in design_vars.items():
print(f" {name}: {value:.3f}")
```
**After**:
```python
logger.trial_start(trial.number, design_vars)
```
### Priority 3: Other Studies (Phase 1.3.2)
Migrate remaining studies (bracket_stiffness, simple_beam, etc.) gradually.
**Timeline**: After drone_gimbal reference implementation is validated.
---
## API Reference
### Basic Usage
```python
from optimization_engine.logger import get_logger
# Module logger
logger = get_logger(__name__)
logger.info("Starting optimization")
logger.warning("Design variable out of range")
logger.error("Simulation failed", exc_info=True)
```
### Study Logger
```python
from optimization_engine.logger import get_logger
from pathlib import Path
# Create study logger with file logging
logger = get_logger(
name="drone_gimbal_arm",
study_dir=Path("studies/drone_gimbal_arm/2_results")
)
# Study lifecycle
logger.study_start("drone_gimbal_arm", n_trials=30, sampler="NSGAIISampler")
# Trial logging
logger.trial_start(1, {"thickness": 2.5, "width": 10.0})
logger.info("Running FEA simulation...")
logger.trial_complete(
1,
objectives={"mass": 120, "stiffness": 1500},
constraints={"max_stress": 85},
feasible=True
)
# Error handling
try:
result = run_simulation()
except Exception as e:
logger.trial_failed(trial_number=2, error=str(e))
logger.error("Full traceback:", exc_info=True)
raise
logger.study_complete("drone_gimbal_arm", n_trials=30, n_successful=28)
```
### Log Levels
```python
import logging
# Set logger level
logger = get_logger(__name__, level=logging.DEBUG)
logger.debug("Detailed debugging information")
logger.info("General information")
logger.warning("Warning message")
logger.error("Error occurred")
logger.critical("Critical failure")
```
---
## File Structure
```
optimization_engine/
├── logger.py # ✅ NEW - Structured logging system
└── config_manager.py # Phase 1.2
docs/07_DEVELOPMENT/
├── Phase_1_2_Implementation_Plan.md # Phase 1.2
└── Phase_1_3_Implementation_Plan.md # ✅ NEW - This file
```
---
## Testing Checklist
- [x] Logger creates file at correct location
- [x] Color output works on Windows 10
- [x] Log rotation works (max 50MB, 3 backups)
- [x] Trial-specific methods format correctly
- [x] UTF-8 encoding handles special characters
- [ ] Integration test with real optimization study
- [ ] Verify dashboard can parse structured logs
- [ ] Test error scenarios (disk full, permission denied)
---
## Success Metrics
**Phase 1.3 MVP** (Complete):
- [x] Structured logging system implemented
- [x] Zero external dependencies
- [x] Works on Windows and Unix
- [x] File + console logging
- [x] Trial-specific methods
**Phase 1.3.1** (Next):
- [ ] At least one study uses new logging
- [ ] Migration guide written
- [ ] create-study skill updated
**Phase 1.3.2** (Later):
- [ ] Checkpoint/resume system
- [ ] Enhanced error reporting
- [ ] All studies migrated
---
## References
- **Phase 1.2**: [Configuration Management](./Phase_1_2_Implementation_Plan.md)
- **MVP Plan**: [12-Week Development Plan](./Today_Todo.md)
- **Python Logging**: https://docs.python.org/3/library/logging.html
- **Log Rotation**: https://docs.python.org/3/library/logging.handlers.html#rotatingfilehandler
---
## Questions?
For MVP development questions, refer to [DEVELOPMENT.md](../../DEVELOPMENT.md) or the main plan in `docs/07_DEVELOPMENT/Today_Todo.md`.

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"""
Atomizer Structured Logging System - Phase 1.3
Provides consistent, production-ready logging across all optimization studies.
Usage:
from optimization_engine.logger import get_logger
logger = get_logger(__name__)
logger.info("Starting optimization...")
logger.error("Simulation failed", exc_info=True)
# Study-specific logger with automatic file logging
logger = get_logger("drone_gimbal_arm", study_dir="studies/drone_gimbal_arm/2_results")
logger.trial_start(trial_number=5, design_vars={"thickness": 2.5})
logger.trial_complete(trial_number=5, objectives={"mass": 120, "freq": 155})
Features:
- Automatic file logging to study_dir/optimization.log
- Console output with color-coded levels (if supported)
- Structured trial logging for dashboard integration
- Log rotation (50MB max, 3 backups)
- No external dependencies (stdlib only)
"""
import logging
import sys
from pathlib import Path
from datetime import datetime
from typing import Optional, Dict, Any
from logging.handlers import RotatingFileHandler
# ANSI color codes for console output (Windows 10+ and Unix)
class LogColors:
"""ANSI color codes for console output."""
RESET = '\033[0m'
BOLD = '\033[1m'
# Levels
DEBUG = '\033[36m' # Cyan
INFO = '\033[32m' # Green
WARNING = '\033[33m' # Yellow
ERROR = '\033[31m' # Red
CRITICAL = '\033[35m' # Magenta
# Custom
TRIAL = '\033[94m' # Bright Blue
SUCCESS = '\033[92m' # Bright Green
class ColoredFormatter(logging.Formatter):
"""Formatter that adds color to console output."""
COLORS = {
logging.DEBUG: LogColors.DEBUG,
logging.INFO: LogColors.INFO,
logging.WARNING: LogColors.WARNING,
logging.ERROR: LogColors.ERROR,
logging.CRITICAL: LogColors.CRITICAL,
}
def __init__(self, fmt: str, use_colors: bool = True):
super().__init__(fmt)
self.use_colors = use_colors and self._supports_color()
def _supports_color(self) -> bool:
"""Check if terminal supports ANSI colors."""
# Windows 10+ supports ANSI
if sys.platform == 'win32':
try:
import ctypes
kernel32 = ctypes.windll.kernel32
kernel32.SetConsoleMode(kernel32.GetStdHandle(-11), 7)
return True
except:
return False
# Unix-like systems
return hasattr(sys.stdout, 'isatty') and sys.stdout.isatty()
def format(self, record: logging.LogRecord) -> str:
if self.use_colors:
levelname = record.levelname
color = self.COLORS.get(record.levelno, '')
record.levelname = f"{color}{levelname}{LogColors.RESET}"
return super().format(record)
class AtomizerLogger(logging.Logger):
"""Extended logger with trial-specific methods."""
def trial_start(self, trial_number: int, design_vars: Dict[str, float]):
"""Log trial start with design variables."""
self.info(f"{'='*60}")
self.info(f"Trial #{trial_number} START")
self.info(f"{'='*60}")
self.info("Design Variables:")
for name, value in design_vars.items():
if isinstance(value, float):
self.info(f" {name}: {value:.4f}")
else:
self.info(f" {name}: {value}")
def trial_complete(self, trial_number: int, objectives: Dict[str, float],
constraints: Optional[Dict[str, float]] = None,
feasible: bool = True):
"""Log trial completion with results."""
self.info(f"\nTrial #{trial_number} COMPLETE")
self.info("Objectives:")
for name, value in objectives.items():
if isinstance(value, float):
self.info(f" {name}: {value:.4f}")
else:
self.info(f" {name}: {value}")
if constraints:
self.info("Constraints:")
for name, value in constraints.items():
if isinstance(value, float):
self.info(f" {name}: {value:.4f}")
else:
self.info(f" {name}: {value}")
status = "[OK] Feasible" if feasible else "[WARNING] Infeasible"
self.info(f"{status}")
self.info(f"{'='*60}\n")
def trial_failed(self, trial_number: int, error: str):
"""Log trial failure."""
self.error(f"\nTrial #{trial_number} FAILED")
self.error(f"Error: {error}")
self.error(f"{'='*60}\n")
def study_start(self, study_name: str, n_trials: int, sampler: str):
"""Log study initialization."""
self.info("=" * 80)
self.info(f"OPTIMIZATION STUDY: {study_name}")
self.info("=" * 80)
self.info(f"Trials: {n_trials}")
self.info(f"Sampler: {sampler}")
self.info(f"Started: {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}")
self.info("=" * 80)
self.info("")
def study_complete(self, study_name: str, n_trials: int, n_successful: int):
"""Log study completion."""
self.info("")
self.info("=" * 80)
self.info(f"STUDY COMPLETE: {study_name}")
self.info("=" * 80)
self.info(f"Total trials: {n_trials}")
self.info(f"Successful: {n_successful}")
self.info(f"Failed/Pruned: {n_trials - n_successful}")
self.info(f"Completed: {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}")
self.info("=" * 80)
# Register custom logger class
logging.setLoggerClass(AtomizerLogger)
def get_logger(
name: str,
level: int = logging.INFO,
study_dir: Optional[Path] = None,
console: bool = True,
file_logging: bool = True
) -> AtomizerLogger:
"""
Get or create a logger instance.
Args:
name: Logger name (typically __name__ or study name)
level: Logging level (DEBUG, INFO, WARNING, ERROR, CRITICAL)
study_dir: If provided, creates log file at study_dir/optimization.log
console: Enable console output (default: True)
file_logging: Enable file logging (default: True, requires study_dir)
Returns:
AtomizerLogger instance
Example:
# Simple logger
logger = get_logger(__name__)
logger.info("Starting optimization...")
# Study logger with file output
logger = get_logger(
"drone_gimbal_arm",
study_dir=Path("studies/drone_gimbal_arm/2_results")
)
logger.study_start("drone_gimbal_arm", n_trials=30, sampler="NSGAIISampler")
"""
logger = logging.getLogger(name)
# Only configure if not already configured
if not logger.handlers:
logger.setLevel(level)
# Console handler with colors
if console:
console_handler = logging.StreamHandler(sys.stdout)
console_handler.setLevel(level)
console_formatter = ColoredFormatter(
fmt='[%(levelname)s] %(message)s',
use_colors=True
)
console_handler.setFormatter(console_formatter)
logger.addHandler(console_handler)
# File handler with rotation
if file_logging and study_dir:
study_dir = Path(study_dir)
study_dir.mkdir(parents=True, exist_ok=True)
log_file = study_dir / "optimization.log"
file_handler = RotatingFileHandler(
log_file,
maxBytes=50 * 1024 * 1024, # 50MB
backupCount=3,
encoding='utf-8'
)
file_handler.setLevel(level)
file_formatter = logging.Formatter(
fmt='%(asctime)s | %(levelname)-8s | %(name)s | %(message)s',
datefmt='%Y-%m-%d %H:%M:%S'
)
file_handler.setFormatter(file_formatter)
logger.addHandler(file_handler)
# Log that file logging is enabled
logger.debug(f"File logging enabled: {log_file}")
# Prevent propagation to root logger
logger.propagate = False
return logger
def configure_root_logger(level: int = logging.WARNING):
"""
Configure root logger to catch unconfigured loggers.
Call this once at application startup to set up default logging behavior.
"""
root_logger = logging.getLogger()
root_logger.setLevel(level)
if not root_logger.handlers:
handler = logging.StreamHandler(sys.stdout)
handler.setLevel(level)
formatter = logging.Formatter('[%(levelname)s] %(name)s: %(message)s')
handler.setFormatter(formatter)
root_logger.addHandler(handler)
# Example usage and testing
if __name__ == "__main__":
# Test basic logging
print("Testing Atomizer Logging System")
print("=" * 80)
# Simple logger
logger = get_logger("test_module")
logger.debug("This is a debug message")
logger.info("This is an info message")
logger.warning("This is a warning message")
logger.error("This is an error message")
print()
# Study logger with file output
test_dir = Path("test_logs")
test_dir.mkdir(exist_ok=True)
study_logger = get_logger("test_study", study_dir=test_dir)
study_logger.study_start("test_study", n_trials=5, sampler="TPESampler")
# Simulate trial
study_logger.trial_start(1, {"thickness": 2.5, "width": 10.0})
study_logger.info("Running simulation...")
study_logger.trial_complete(
1,
objectives={"mass": 120.5, "stiffness": 1500.2},
constraints={"max_stress": 85.3},
feasible=True
)
# Failed trial
study_logger.trial_start(2, {"thickness": 1.0, "width": 5.0})
study_logger.trial_failed(2, "Simulation convergence failure")
study_logger.study_complete("test_study", n_trials=5, n_successful=4)
print()
print(f"Log file created at: {test_dir / 'optimization.log'}")
print("Check the file to see structured logging output!")