Initial commit: Northern Thailand Ping River Monitor v3.1.0

Features:
- Real-time water level monitoring for Ping River Basin (16 stations)
- Coverage from Chiang Dao to Nakhon Sawan in Northern Thailand
- FastAPI web interface with interactive dashboard and station management
- Multi-database support (SQLite, MySQL, PostgreSQL, InfluxDB, VictoriaMetrics)
- Comprehensive monitoring with health checks and metrics collection
- Docker deployment with Grafana integration
- Production-ready architecture with enterprise-grade observability

 CI/CD & Automation:
- Complete Gitea Actions workflows for CI/CD, security, and releases
- Multi-Python version testing (3.9-3.12)
- Multi-architecture Docker builds (amd64, arm64)
- Daily security scanning and dependency monitoring
- Automated documentation generation
- Performance testing and validation

 Production Ready:
- Type safety with Pydantic models and comprehensive type hints
- Data validation layer with range checking and error handling
- Rate limiting and request tracking for API protection
- Enhanced logging with rotation, colors, and performance metrics
- Station management API for dynamic CRUD operations
- Comprehensive documentation and deployment guides

 Technical Stack:
- Python 3.9+ with FastAPI and Pydantic
- Multi-database architecture with adapter pattern
- Docker containerization with multi-stage builds
- Grafana dashboards for visualization
- Gitea Actions for CI/CD automation
- Enterprise monitoring and alerting

 Ready for deployment to B4L infrastructure!

docs/GAP_FILLING_GUIDE.md

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+# Gap Filling and Data Integrity Guide
+
+This guide explains the enhanced gap-filling functionality that addresses data gaps and missing timestamps in the Thailand Water Monitor.
+
+## ✅ **Issues Resolved**
+
+### **1. Data Gaps Problem**
+- **Before**: Tool only fetched current day data, leaving gaps in historical records
+- **After**: Automatically detects and fills missing timestamps for the last 7 days
+
+### **2. Missing Midnight Timestamps**
+- **Before**: Jump from 23:00 to 01:00 (missing 00:00 midnight data)
+- **After**: Specifically checks for and fills midnight hour gaps
+
+### **3. Changed Values**
+- **Before**: No mechanism to update existing data if values changed on the server
+- **After**: Compares existing data with fresh API data and updates changed values
+
+## 🔧 **New Features**
+
+### **Command Line Interface**
+```bash
+# Check for missing data gaps
+python water_scraper_v3.py --check-gaps [days]
+
+# Fill missing data gaps
+python water_scraper_v3.py --fill-gaps [days]
+
+# Update existing data with latest values
+python water_scraper_v3.py --update-data [days]
+
+# Run single test cycle
+python water_scraper_v3.py --test
+
+# Show help
+python water_scraper_v3.py --help
+```
+
+### **Automatic Gap Detection**
+The system now automatically:
+- Generates expected hourly timestamps for the specified time range
+- Compares with existing database records
+- Identifies missing timestamps
+- Groups missing data by date for efficient API calls
+
+### **Intelligent Gap Filling**
+- **Historical Data Fetching**: Retrieves data for specific dates to fill gaps
+- **Selective Insertion**: Only inserts data for actually missing timestamps
+- **API Rate Limiting**: Includes delays between API calls to be respectful
+- **Error Handling**: Continues processing even if some dates fail
+
+### **Data Update Mechanism**
+- **Change Detection**: Compares water levels, discharge rates, and percentages
+- **Precision Checking**: Uses appropriate thresholds (0.001m for water level, 0.1 cms for discharge)
+- **Selective Updates**: Only updates records where values have actually changed
+
+## 📊 **Test Results**
+
+### **Before Enhancement**
+```
+Found 22 missing timestamps in the last 2 days:
+  2025-07-23: Missing hours [9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23]
+  2025-07-24: Missing hours [0, 20, 21, 22, 23]
+  2025-07-25: Missing hours [0, 9]
+```
+
+### **After Gap Filling**
+```
+Gap filling completed. Filled 96 missing data points
+
+Remaining gaps:
+  2025-07-24: Missing hours [10]
+  2025-07-25: Missing hours [0, 10]
+```
+
+**Improvement**: Reduced from 22 missing timestamps to 3 (86% improvement)
+
+## 🚀 **Enhanced Scraping Cycle**
+
+The regular scraping cycle now includes three phases:
+
+### **Phase 1: Current Data Collection**
+```python
+# Fetch and save current data
+water_data = self.fetch_water_data()
+success = self.save_to_database(water_data)
+```
+
+### **Phase 2: Gap Filling (Last 7 Days)**
+```python
+# Check for and fill missing data
+filled_count = self.fill_data_gaps(days_back=7)
+```
+
+### **Phase 3: Data Updates (Last 2 Days)**
+```python
+# Update existing data with latest values
+updated_count = self.update_existing_data(days_back=2)
+```
+
+## 🔧 **Technical Improvements**
+
+### **Database Connection Handling**
+- **SQLite Optimization**: Added timeout and thread safety parameters
+- **Retry Logic**: Exponential backoff for database lock errors
+- **Transaction Management**: Proper use of `engine.begin()` for automatic commits
+
+### **Error Recovery**
+```python
+# Retry logic with exponential backoff
+for attempt in range(max_retries):
+    try:
+        success = self.db_adapter.save_measurements(water_data)
+        if success:
+            return True
+    except Exception as e:
+        if "database is locked" in str(e).lower():
+            time.sleep(2 ** attempt)  # 1s, 2s, 4s delays
+            continue
+```
+
+### **Memory Efficiency**
+- **Selective Data Processing**: Only processes data for missing timestamps
+- **Batch Processing**: Groups operations by date to minimize API calls
+- **Resource Management**: Proper cleanup and connection handling
+
+## 📋 **Usage Examples**
+
+### **Daily Maintenance**
+```bash
+# Check for gaps in the last week
+python water_scraper_v3.py --check-gaps 7
+
+# Fill any found gaps
+python water_scraper_v3.py --fill-gaps 7
+
+# Update recent data for accuracy
+python water_scraper_v3.py --update-data 2
+```
+
+### **Historical Data Recovery**
+```bash
+# Check for gaps in the last month
+python water_scraper_v3.py --check-gaps 30
+
+# Fill gaps for the last month (be patient, this takes time)
+python water_scraper_v3.py --fill-gaps 30
+```
+
+### **Production Monitoring**
+```bash
+# Quick test to ensure system is working
+python water_scraper_v3.py --test
+
+# Check for recent gaps
+python water_scraper_v3.py --check-gaps 1
+```
+
+## 🔍 **Monitoring and Alerts**
+
+### **Gap Detection Output**
+```
+Found 22 missing timestamps:
+  2025-07-23: Missing hours [9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23]
+  2025-07-24: Missing hours [0, 20, 21, 22, 23]
+  2025-07-25: Missing hours [0, 9]
+```
+
+### **Gap Filling Progress**
+```
+Fetching data for 2025-07-24 to fill 5 missing timestamps
+Successfully fetched 368 data points from API for 2025-07-24
+Filled 80 data points for 2025-07-24
+Gap filling completed. Filled 96 missing data points
+```
+
+### **Update Detection**
+```
+Checking for updates on 2025-07-24
+Update needed for P.1 at 2025-07-24 15:00:00
+Updated 5 measurements for 2025-07-24
+Data update completed. Updated 5 measurements
+```
+
+## ⚙️ **Configuration Options**
+
+### **Environment Variables**
+```bash
+# Database configuration
+export DB_TYPE=sqlite
+export WATER_DB_PATH=water_monitoring.db
+
+# Gap filling settings (can be added to config.py)
+export GAP_FILL_DAYS=7        # Days to check for gaps
+export UPDATE_DAYS=2          # Days to check for updates
+export API_DELAY=1            # Seconds between API calls
+export MAX_RETRIES=3          # Database retry attempts
+```
+
+### **Customizable Parameters**
+- **Gap Check Period**: Default 7 days, configurable via command line
+- **Update Period**: Default 2 days, configurable via command line
+- **API Rate Limiting**: 1-second delay between calls (configurable)
+- **Retry Logic**: 3 attempts with exponential backoff (configurable)
+
+## 🛠️ **Troubleshooting**
+
+### **Common Issues**
+
+#### **Database Locked Errors**
+```
+ERROR - Error saving to SQLITE: database is locked
+```
+**Solution**: The retry logic now handles this automatically with exponential backoff.
+
+#### **API Rate Limiting**
+```
+WARNING - Too many requests to API
+```
+**Solution**: Increase delay between API calls or reduce the number of days processed at once.
+
+#### **Missing Data Still Present**
+```
+Found X missing timestamps after gap filling
+```
+**Possible Causes**:
+- Data not available on the Thai government server for those timestamps
+- Network issues during API calls
+- API returned empty data for those specific times
+
+### **Debug Commands**
+```bash
+# Enable debug logging
+export LOG_LEVEL=DEBUG
+python water_scraper_v3.py --check-gaps 1
+
+# Test specific date range
+python water_scraper_v3.py --fill-gaps 1
+
+# Check database directly
+sqlite3 water_monitoring.db "SELECT COUNT(*) FROM water_measurements;"
+sqlite3 water_monitoring.db "SELECT timestamp, COUNT(*) FROM water_measurements GROUP BY timestamp ORDER BY timestamp DESC LIMIT 10;"
+```
+
+## 📈 **Performance Metrics**
+
+### **Gap Filling Efficiency**
+- **API Calls**: Grouped by date to minimize requests
+- **Processing Speed**: ~100-400 data points per API call
+- **Success Rate**: 86% gap reduction in test case
+- **Resource Usage**: Minimal memory footprint with selective processing
+
+### **Database Performance**
+- **SQLite Optimization**: Connection pooling and timeout handling
+- **Transaction Efficiency**: Batch inserts with proper transaction management
+- **Retry Success**: Automatic recovery from temporary lock conditions
+
+## 🎯 **Best Practices**
+
+### **Regular Maintenance**
+1. **Daily**: Run `--check-gaps 1` to monitor recent data quality
+2. **Weekly**: Run `--fill-gaps 7` to catch any missed data
+3. **Monthly**: Run `--update-data 7` to ensure data accuracy
+
+### **Production Deployment**
+1. **Automated Scheduling**: Use cron or systemd timers for regular gap checks
+2. **Monitoring**: Set up alerts for excessive missing data
+3. **Backup**: Regular database backups before major gap-filling operations
+
+### **Data Quality Assurance**
+1. **Validation**: Check for reasonable value ranges after gap filling
+2. **Comparison**: Compare filled data with nearby timestamps for consistency
+3. **Documentation**: Log all gap-filling activities for audit trails
+
+This enhanced gap-filling system ensures comprehensive and accurate water level monitoring with minimal data loss and automatic recovery capabilities.