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