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Initial commit: Northern Thailand Ping River Monitor v3.1.0
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Browse Source 
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!
This commit is contained in:
Alexander Grabowski
2025-08-12 15:37:09 +07:00
commit af62cfef0b
60 changed files with 13267 additions and 0 deletions
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# Northern Thailand Ping River Monitor Configuration
# Copy this file to .env and customize for your environment
# Database Configuration
DB_TYPE=sqlite
# Options: sqlite, mysql, postgresql, influxdb, victoriametrics
# SQLite Configuration (default)
WATER_DB_PATH=water_levels.db
# VictoriaMetrics Configuration
VM_HOST=localhost
VM_PORT=8428
VM_URL=
# InfluxDB Configuration
INFLUX_HOST=localhost
INFLUX_PORT=8086
INFLUX_DATABASE=ping_river_monitoring
INFLUX_USERNAME=
INFLUX_PASSWORD=
# PostgreSQL Configuration
POSTGRES_CONNECTION_STRING=postgresql://user:password@localhost:5432/ping_river_monitoring
# MySQL Configuration
MYSQL_CONNECTION_STRING=mysql://user:password@localhost:3306/ping_river_monitoring
# API Configuration
API_HOST=0.0.0.0
API_PORT=8000
API_WORKERS=1
# Data Collection Settings
SCRAPING_INTERVAL_HOURS=1
REQUEST_TIMEOUT=30
MAX_RETRIES=3
RETRY_DELAY_SECONDS=60
# Data Retention
DATA_RETENTION_DAYS=365
# Logging Configuration
LOG_LEVEL=INFO
LOG_FILE=water_monitor.log
# Security (for production)
SECRET_KEY=your-secret-key-here
API_KEY=your-api-key-here
# Monitoring
ENABLE_METRICS=true
ENABLE_HEALTH_CHECKS=true
# Geographic Settings
TIMEZONE=Asia/Bangkok
DEFAULT_LATITUDE=18.7875
DEFAULT_LONGITUDE=99.0045
# External Services
NOTIFICATION_EMAIL=
SMTP_SERVER=
SMTP_PORT=587
SMTP_USERNAME=
SMTP_PASSWORD=
# Development Settings
DEBUG=false
DEVELOPMENT_MODE=false

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name: CI/CD Pipeline - Northern Thailand Ping River Monitor
on:
push:
branches: [ main, develop ]
pull_request:
branches: [ main ]
schedule:
# Run tests daily at 2 AM UTC
- cron: '0 2 * * *'
env:
PYTHON_VERSION: '3.11'
REGISTRY: git.b4l.co.th
IMAGE_NAME: b4l/northern-thailand-ping-river-monitor
jobs:
# Test job
test:
name: Test Suite
runs-on: ubuntu-latest
strategy:
matrix:
python-version: ['3.9', '3.10', '3.11', '3.12']
steps:
- name: Checkout code
uses: actions/checkout@v4
- name: Set up Python ${{ matrix.python-version }}
uses: actions/setup-python@v4
with:
python-version: ${{ matrix.python-version }}
- name: Cache pip dependencies
uses: actions/cache@v3
with:
path: ~/.cache/pip
key: ${{ runner.os }}-pip-${{ hashFiles('**/requirements*.txt') }}
restore-keys: |
${{ runner.os }}-pip-
- name: Install dependencies
run: |
python -m pip install --upgrade pip
pip install -r requirements.txt
pip install -r requirements-dev.txt
- name: Lint with flake8
run: |
flake8 src/ --count --select=E9,F63,F7,F82 --show-source --statistics
flake8 src/ --count --exit-zero --max-complexity=10 --max-line-length=100 --statistics
- name: Type check with mypy
run: |
mypy src/ --ignore-missing-imports
- name: Format check with black
run: |
black --check src/ *.py
- name: Import sort check
run: |
isort --check-only src/ *.py
- name: Run integration tests
run: |
python tests/test_integration.py
- name: Run station management tests
run: |
python tests/test_station_management.py
- name: Test application startup
run: |
timeout 10s python run.py --test || true
- name: Security scan with bandit
run: |
bandit -r src/ -f json -o bandit-report.json || true
- name: Upload test artifacts
uses: actions/upload-artifact@v3
if: always()
with:
name: test-results-${{ matrix.python-version }}
path: |
bandit-report.json
*.log
# Code quality job
code-quality:
name: Code Quality
runs-on: ubuntu-latest
steps:
- name: Checkout code
uses: actions/checkout@v4
- name: Set up Python
uses: actions/setup-python@v4
with:
python-version: ${{ env.PYTHON_VERSION }}
- name: Install dependencies
run: |
python -m pip install --upgrade pip
pip install -r requirements-dev.txt
- name: Run safety check
run: |
safety check -r requirements.txt --json --output safety-report.json || true
- name: Run bandit security scan
run: |
bandit -r src/ -f json -o bandit-report.json || true
- name: Upload security reports
uses: actions/upload-artifact@v3
with:
name: security-reports
path: |
safety-report.json
bandit-report.json
# Build Docker image
build:
name: Build Docker Image
runs-on: ubuntu-latest
needs: test
steps:
- name: Checkout code
uses: actions/checkout@v4
- name: Set up Docker Buildx
uses: docker/setup-buildx-action@v3
- name: Log in to Container Registry
uses: docker/login-action@v3
with:
registry: ${{ env.REGISTRY }}
username: ${{ github.actor }}
password: ${{ secrets.GITEA_TOKEN }}
- name: Extract metadata
id: meta
uses: docker/metadata-action@v5
with:
images: ${{ env.REGISTRY }}/${{ env.IMAGE_NAME }}
tags: |
type=ref,event=branch
type=ref,event=pr
type=sha,prefix={{branch}}-
type=raw,value=latest,enable={{is_default_branch}}
- name: Build and push Docker image
uses: docker/build-push-action@v5
with:
context: .
platforms: linux/amd64,linux/arm64
push: true
tags: ${{ steps.meta.outputs.tags }}
labels: ${{ steps.meta.outputs.labels }}
cache-from: type=gha
cache-to: type=gha,mode=max
- name: Test Docker image
run: |
docker run --rm ${{ env.REGISTRY }}/${{ env.IMAGE_NAME }}:${{ github.sha }} python run.py --test
# Integration test with services
integration-test:
name: Integration Test with Services
runs-on: ubuntu-latest
needs: build
services:
victoriametrics:
image: victoriametrics/victoria-metrics:latest
ports:
- 8428:8428
options: >-
--health-cmd "wget --quiet --tries=1 --spider http://localhost:8428/health"
--health-interval 30s
--health-timeout 10s
--health-retries 3
steps:
- name: Checkout code
uses: actions/checkout@v4
- name: Wait for VictoriaMetrics
run: |
timeout 60s bash -c 'until curl -f http://localhost:8428/health; do sleep 2; done'
- name: Set up Python
uses: actions/setup-python@v4
with:
python-version: ${{ env.PYTHON_VERSION }}
- name: Install dependencies
run: |
python -m pip install --upgrade pip
pip install -r requirements.txt
- name: Test with VictoriaMetrics
env:
DB_TYPE: victoriametrics
VM_HOST: localhost
VM_PORT: 8428
run: |
python run.py --test
- name: Start API server
env:
DB_TYPE: victoriametrics
VM_HOST: localhost
VM_PORT: 8428
run: |
python run.py --web-api &
sleep 10
- name: Test API endpoints
run: |
curl -f http://localhost:8000/health
curl -f http://localhost:8000/stations
curl -f http://localhost:8000/metrics
# Deploy to staging (only on develop branch)
deploy-staging:
name: Deploy to Staging
runs-on: ubuntu-latest
needs: [test, build, integration-test]
if: github.ref == 'refs/heads/develop'
environment:
name: staging
url: https://staging.ping-river-monitor.b4l.co.th
steps:
- name: Checkout code
uses: actions/checkout@v4
- name: Deploy to staging
run: |
echo "Deploying to staging environment..."
# Add your staging deployment commands here
# Example: kubectl, docker-compose, or webhook call
- name: Health check staging
run: |
sleep 30
curl -f https://staging.ping-river-monitor.b4l.co.th/health
# Deploy to production (only on main branch, manual approval)
deploy-production:
name: Deploy to Production
runs-on: ubuntu-latest
needs: [test, build, integration-test]
if: github.ref == 'refs/heads/main'
environment:
name: production
url: https://ping-river-monitor.b4l.co.th
steps:
- name: Checkout code
uses: actions/checkout@v4
- name: Deploy to production
run: |
echo "Deploying to production environment..."
# Add your production deployment commands here
- name: Health check production
run: |
sleep 30
curl -f https://ping-river-monitor.b4l.co.th/health
- name: Notify deployment
run: |
echo "✅ Production deployment successful!"
echo "🌐 URL: https://ping-river-monitor.b4l.co.th"
echo "📊 Grafana: https://grafana.ping-river-monitor.b4l.co.th"
# Performance test (only on main branch)
performance-test:
name: Performance Test
runs-on: ubuntu-latest
needs: deploy-production
if: github.ref == 'refs/heads/main'
steps:
- name: Checkout code
uses: actions/checkout@v4
- name: Install Apache Bench
run: |
sudo apt-get update
sudo apt-get install -y apache2-utils
- name: Performance test API endpoints
run: |
# Test health endpoint
ab -n 100 -c 10 https://ping-river-monitor.b4l.co.th/health
# Test stations endpoint
ab -n 50 -c 5 https://ping-river-monitor.b4l.co.th/stations
# Test metrics endpoint
ab -n 50 -c 5 https://ping-river-monitor.b4l.co.th/metrics
# Cleanup old artifacts
cleanup:
name: Cleanup
runs-on: ubuntu-latest
if: always()
needs: [test, build, integration-test]
steps:
- name: Clean up old Docker images
run: |
echo "Cleaning up old Docker images..."
# Add cleanup commands for old images/artifacts

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name: Documentation
on:
push:
branches: [ main, develop ]
paths:
- 'docs/**'
- 'README.md'
- 'CONTRIBUTING.md'
- 'src/**/*.py'
pull_request:
paths:
- 'docs/**'
- 'README.md'
- 'CONTRIBUTING.md'
workflow_dispatch:
env:
PYTHON_VERSION: '3.11'
jobs:
# Validate documentation
validate-docs:
name: Validate Documentation
runs-on: ubuntu-latest
steps:
- name: Checkout code
uses: actions/checkout@v4
- name: Set up Python
uses: actions/setup-python@v4
with:
python-version: ${{ env.PYTHON_VERSION }}
- name: Install documentation tools
run: |
python -m pip install --upgrade pip
pip install -r requirements.txt
pip install sphinx sphinx-rtd-theme sphinx-autodoc-typehints
pip install markdown-link-check || true
- name: Check markdown links
run: |
echo "🔗 Checking markdown links..."
find . -name "*.md" -not -path "./.git/*" -not -path "./node_modules/*" | while read file; do
echo "Checking $file"
# Basic link validation (you can enhance this)
grep -o 'http[s]*://[^)]*' "$file" | while read url; do
if curl -s --head "$url" | head -n 1 | grep -q "200 OK"; then
echo "✅ $url"
else
echo "❌ $url (in $file)"
fi
done
done
- name: Validate README structure
run: |
echo "📋 Validating README structure..."
required_sections=(
"# Northern Thailand Ping River Monitor"
"## Features"
"## Quick Start"
"## Installation"
"## Usage"
"## API Endpoints"
"## Docker"
"## Contributing"
"## License"
)
for section in "${required_sections[@]}"; do
if grep -q "$section" README.md; then
echo "✅ Found: $section"
else
echo "❌ Missing: $section"
fi
done
- name: Check documentation completeness
run: |
echo "📚 Checking documentation completeness..."
# Check if all Python modules have docstrings
python -c "
import ast
import os
def check_docstrings(filepath):
with open(filepath, 'r', encoding='utf-8') as f:
tree = ast.parse(f.read())
missing_docstrings = []
for node in ast.walk(tree):
if isinstance(node, (ast.FunctionDef, ast.ClassDef, ast.AsyncFunctionDef)):
if not ast.get_docstring(node):
missing_docstrings.append(f'{node.name} in {filepath}')
return missing_docstrings
all_missing = []
for root, dirs, files in os.walk('src'):
for file in files:
if file.endswith('.py') and not file.startswith('__'):
filepath = os.path.join(root, file)
missing = check_docstrings(filepath)
all_missing.extend(missing)
if all_missing:
print('⚠️ Missing docstrings:')
for item in all_missing[:10]: # Show first 10
print(f' - {item}')
if len(all_missing) > 10:
print(f' ... and {len(all_missing) - 10} more')
else:
print('✅ All functions and classes have docstrings')
"
# Generate API documentation
generate-api-docs:
name: Generate API Documentation
runs-on: ubuntu-latest
steps:
- name: Checkout code
uses: actions/checkout@v4
- name: Set up Python
uses: actions/setup-python@v4
with:
python-version: ${{ env.PYTHON_VERSION }}
- name: Install dependencies
run: |
python -m pip install --upgrade pip
pip install -r requirements.txt
- name: Generate OpenAPI spec
run: |
echo "📝 Generating OpenAPI specification..."
python -c "
import json
import sys
sys.path.insert(0, 'src')
try:
from web_api import app
openapi_spec = app.openapi()
with open('openapi.json', 'w') as f:
json.dump(openapi_spec, f, indent=2)
print('✅ OpenAPI spec generated: openapi.json')
except Exception as e:
print(f'❌ Failed to generate OpenAPI spec: {e}')
"
- name: Generate API documentation
run: |
echo "📖 Generating API documentation..."
# Create API documentation from OpenAPI spec
if [ -f openapi.json ]; then
cat > api-docs.md << 'EOF'
# API Documentation
This document describes the REST API endpoints for the Northern Thailand Ping River Monitor.
## Base URL
- Production: `https://ping-river-monitor.b4l.co.th`
- Staging: `https://staging.ping-river-monitor.b4l.co.th`
- Development: `http://localhost:8000`
## Authentication
Currently, the API does not require authentication. This may change in future versions.
## Endpoints
EOF
# Extract endpoints from OpenAPI spec
python -c "
import json
with open('openapi.json', 'r') as f:
spec = json.load(f)
for path, methods in spec.get('paths', {}).items():
for method, details in methods.items():
print(f'### {method.upper()} {path}')
print()
print(details.get('summary', 'No description available'))
print()
if 'parameters' in details:
print('**Parameters:**')
for param in details['parameters']:
print(f'- `{param[\"name\"]}` ({param.get(\"in\", \"query\")}): {param.get(\"description\", \"No description\")}')
print()
print('---')
print()
" >> api-docs.md
echo "✅ API documentation generated: api-docs.md"
fi
- name: Upload documentation artifacts
uses: actions/upload-artifact@v3
with:
name: documentation-${{ github.run_number }}
path: |
openapi.json
api-docs.md
# Build Sphinx documentation
build-sphinx-docs:
name: Build Sphinx Documentation
runs-on: ubuntu-latest
steps:
- name: Checkout code
uses: actions/checkout@v4
- name: Set up Python
uses: actions/setup-python@v4
with:
python-version: ${{ env.PYTHON_VERSION }}
- name: Install dependencies
run: |
python -m pip install --upgrade pip
pip install -r requirements.txt
pip install sphinx sphinx-rtd-theme sphinx-autodoc-typehints
- name: Create Sphinx configuration
run: |
mkdir -p docs/sphinx
cat > docs/sphinx/conf.py << 'EOF'
import os
import sys
sys.path.insert(0, os.path.abspath('../../src'))
project = 'Northern Thailand Ping River Monitor'
copyright = '2025, Ping River Monitor Team'
author = 'Ping River Monitor Team'
version = '3.1.0'
release = '3.1.0'
extensions = [
'sphinx.ext.autodoc',
'sphinx.ext.viewcode',
'sphinx.ext.napoleon',
'sphinx_autodoc_typehints',
]
templates_path = ['_templates']
exclude_patterns = ['_build', 'Thumbs.db', '.DS_Store']
html_theme = 'sphinx_rtd_theme'
html_static_path = ['_static']
autodoc_default_options = {
'members': True,
'member-order': 'bysource',
'special-members': '__init__',
'undoc-members': True,
'exclude-members': '__weakref__'
}
EOF
cat > docs/sphinx/index.rst << 'EOF'
Northern Thailand Ping River Monitor Documentation
================================================
.. toctree::
:maxdepth: 2
:caption: Contents:
modules
Indices and tables
==================
* :ref:`genindex`
* :ref:`modindex`
* :ref:`search`
EOF
- name: Generate module documentation
run: |
cd docs/sphinx
sphinx-apidoc -o . ../../src
- name: Build documentation
run: |
cd docs/sphinx
sphinx-build -b html . _build/html
- name: Upload Sphinx documentation
uses: actions/upload-artifact@v3
with:
name: sphinx-docs-${{ github.run_number }}
path: docs/sphinx/_build/html/
# Documentation summary
docs-summary:
name: Documentation Summary
runs-on: ubuntu-latest
needs: [validate-docs, generate-api-docs, build-sphinx-docs]
if: always()
steps:
- name: Generate documentation summary
run: |
echo "# 📚 Documentation Build Summary" > docs-summary.md
echo "" >> docs-summary.md
echo "**Build Date:** $(date -u)" >> docs-summary.md
echo "**Repository:** ${{ github.repository }}" >> docs-summary.md
echo "**Commit:** ${{ github.sha }}" >> docs-summary.md
echo "" >> docs-summary.md
echo "## 📊 Results" >> docs-summary.md
echo "" >> docs-summary.md
if [ "${{ needs.validate-docs.result }}" = "success" ]; then
echo "- ✅ **Documentation Validation**: Passed" >> docs-summary.md
else
echo "- ❌ **Documentation Validation**: Failed" >> docs-summary.md
fi
if [ "${{ needs.generate-api-docs.result }}" = "success" ]; then
echo "- ✅ **API Documentation**: Generated" >> docs-summary.md
else
echo "- ❌ **API Documentation**: Failed" >> docs-summary.md
fi
if [ "${{ needs.build-sphinx-docs.result }}" = "success" ]; then
echo "- ✅ **Sphinx Documentation**: Built" >> docs-summary.md
else
echo "- ❌ **Sphinx Documentation**: Failed" >> docs-summary.md
fi
echo "" >> docs-summary.md
echo "## 🔗 Available Documentation" >> docs-summary.md
echo "" >> docs-summary.md
echo "- [README.md](../README.md)" >> docs-summary.md
echo "- [API Documentation](../docs/)" >> docs-summary.md
echo "- [Contributing Guide](../CONTRIBUTING.md)" >> docs-summary.md
echo "- [Deployment Checklist](../DEPLOYMENT_CHECKLIST.md)" >> docs-summary.md
cat docs-summary.md
- name: Upload documentation summary
uses: actions/upload-artifact@v3
with:
name: docs-summary-${{ github.run_number }}
path: docs-summary.md

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name: Release - Northern Thailand Ping River Monitor
on:
push:
tags:
- 'v*.*.*'
workflow_dispatch:
inputs:
version:
description: 'Release version (e.g., v3.1.0)'
required: true
type: string
env:
PYTHON_VERSION: '3.11'
REGISTRY: git.b4l.co.th
IMAGE_NAME: b4l/northern-thailand-ping-river-monitor
jobs:
# Create release
create-release:
name: Create Release
runs-on: ubuntu-latest
outputs:
version: ${{ steps.version.outputs.version }}
steps:
- name: Checkout code
uses: actions/checkout@v4
with:
fetch-depth: 0
- name: Get version
id: version
run: |
if [ "${{ github.event_name }}" = "workflow_dispatch" ]; then
echo "version=${{ github.event.inputs.version }}" >> $GITHUB_OUTPUT
else
echo "version=${GITHUB_REF#refs/tags/}" >> $GITHUB_OUTPUT
fi
- name: Generate changelog
id: changelog
run: |
# Generate changelog from git commits
echo "## Changes" > CHANGELOG.md
git log --pretty=format:"- %s" $(git describe --tags --abbrev=0 HEAD^)..HEAD >> CHANGELOG.md || echo "- Initial release" >> CHANGELOG.md
echo "" >> CHANGELOG.md
echo "## Docker Images" >> CHANGELOG.md
echo "- \`${{ env.REGISTRY }}/${{ env.IMAGE_NAME }}:${{ steps.version.outputs.version }}\`" >> CHANGELOG.md
echo "- \`${{ env.REGISTRY }}/${{ env.IMAGE_NAME }}:latest\`" >> CHANGELOG.md
- name: Create Release
uses: actions/create-release@v1
env:
GITHUB_TOKEN: ${{ secrets.GITEA_TOKEN }}
with:
tag_name: ${{ steps.version.outputs.version }}
release_name: Northern Thailand Ping River Monitor ${{ steps.version.outputs.version }}
body_path: CHANGELOG.md
draft: false
prerelease: false
# Build and test for release
test-release:
name: Test Release Build
runs-on: ubuntu-latest
needs: create-release
strategy:
matrix:
python-version: ['3.9', '3.10', '3.11', '3.12']
steps:
- name: Checkout code
uses: actions/checkout@v4
- name: Set up Python ${{ matrix.python-version }}
uses: actions/setup-python@v4
with:
python-version: ${{ matrix.python-version }}
- name: Install dependencies
run: |
python -m pip install --upgrade pip
pip install -r requirements.txt
pip install -r requirements-dev.txt
- name: Run full test suite
run: |
python tests/test_integration.py
python tests/test_station_management.py
python run.py --test
- name: Build Python package
run: |
pip install build
python -m build
- name: Upload Python package
uses: actions/upload-artifact@v3
with:
name: python-package-${{ matrix.python-version }}
path: dist/
# Build release Docker images
build-release:
name: Build Release Images
runs-on: ubuntu-latest
needs: [create-release, test-release]
steps:
- name: Checkout code
uses: actions/checkout@v4
- name: Set up Docker Buildx
uses: docker/setup-buildx-action@v3
- name: Log in to Container Registry
uses: docker/login-action@v3
with:
registry: ${{ env.REGISTRY }}
username: ${{ github.actor }}
password: ${{ secrets.GITEA_TOKEN }}
- name: Build and push release images
uses: docker/build-push-action@v5
with:
context: .
platforms: linux/amd64,linux/arm64
push: true
tags: |
${{ env.REGISTRY }}/${{ env.IMAGE_NAME }}:${{ needs.create-release.outputs.version }}
${{ env.REGISTRY }}/${{ env.IMAGE_NAME }}:latest
labels: |
org.opencontainers.image.title=Northern Thailand Ping River Monitor
org.opencontainers.image.description=Real-time water level monitoring for Ping River Basin
org.opencontainers.image.version=${{ needs.create-release.outputs.version }}
org.opencontainers.image.source=${{ github.server_url }}/${{ github.repository }}
org.opencontainers.image.revision=${{ github.sha }}
cache-from: type=gha
cache-to: type=gha,mode=max
# Security scan for release
security-scan:
name: Security Scan
runs-on: ubuntu-latest
needs: build-release
steps:
- name: Checkout code
uses: actions/checkout@v4
- name: Run Trivy vulnerability scanner
uses: aquasecurity/trivy-action@master
with:
image-ref: ${{ env.REGISTRY }}/${{ env.IMAGE_NAME }}:${{ needs.create-release.outputs.version }}
format: 'sarif'
output: 'trivy-results.sarif'
- name: Upload Trivy scan results
uses: actions/upload-artifact@v3
with:
name: security-scan-results
path: trivy-results.sarif
# Deploy release to production
deploy-release:
name: Deploy Release
runs-on: ubuntu-latest
needs: [create-release, build-release, security-scan]
environment:
name: production
url: https://ping-river-monitor.b4l.co.th
steps:
- name: Checkout code
uses: actions/checkout@v4
- name: Deploy to production
run: |
echo "🚀 Deploying ${{ needs.create-release.outputs.version }} to production..."
# Example deployment commands (customize for your infrastructure)
# kubectl set image deployment/ping-river-monitor app=${{ env.REGISTRY }}/${{ env.IMAGE_NAME }}:${{ needs.create-release.outputs.version }}
# docker-compose pull && docker-compose up -d
# Or webhook call to your deployment system
echo "✅ Deployment initiated"
- name: Health check after deployment
run: |
echo "⏳ Waiting for deployment to stabilize..."
sleep 60
echo "🔍 Running health checks..."
curl -f https://ping-river-monitor.b4l.co.th/health
curl -f https://ping-river-monitor.b4l.co.th/stations
echo "✅ Health checks passed!"
- name: Update deployment status
run: |
echo "📊 Deployment Summary:"
echo "Version: ${{ needs.create-release.outputs.version }}"
echo "Image: ${{ env.REGISTRY }}/${{ env.IMAGE_NAME }}:${{ needs.create-release.outputs.version }}"
echo "URL: https://ping-river-monitor.b4l.co.th"
echo "Grafana: https://grafana.ping-river-monitor.b4l.co.th"
echo "API Docs: https://ping-river-monitor.b4l.co.th/docs"
# Post-release validation
validate-release:
name: Validate Release
runs-on: ubuntu-latest
needs: deploy-release
steps:
- name: Comprehensive API test
run: |
echo "🧪 Running comprehensive API tests..."
# Test all major endpoints
curl -f https://ping-river-monitor.b4l.co.th/health
curl -f https://ping-river-monitor.b4l.co.th/metrics
curl -f https://ping-river-monitor.b4l.co.th/stations
curl -f https://ping-river-monitor.b4l.co.th/measurements/latest?limit=5
curl -f https://ping-river-monitor.b4l.co.th/scraping/status
echo "✅ All API endpoints responding correctly"
- name: Performance validation
run: |
echo "⚡ Running performance validation..."
# Install Apache Bench
sudo apt-get update && sudo apt-get install -y apache2-utils
# Test response times
ab -n 10 -c 2 https://ping-river-monitor.b4l.co.th/health
ab -n 10 -c 2 https://ping-river-monitor.b4l.co.th/stations
echo "✅ Performance validation completed"
- name: Data validation
run: |
echo "📊 Validating data collection..."
# Check if recent data is available
response=$(curl -s https://ping-river-monitor.b4l.co.th/measurements/latest?limit=1)
echo "Latest measurement: $response"
# Validate data structure (basic check)
if echo "$response" | grep -q "water_level"; then
echo "✅ Data structure validation passed"
else
echo "❌ Data structure validation failed"
exit 1
fi
# Notify stakeholders
notify:
name: Notify Release
runs-on: ubuntu-latest
needs: [create-release, validate-release]
if: always()
steps:
- name: Notify success
if: needs.validate-release.result == 'success'
run: |
echo "🎉 Release ${{ needs.create-release.outputs.version }} deployed successfully!"
echo "🌐 Production URL: https://ping-river-monitor.b4l.co.th"
echo "📊 Grafana: https://grafana.ping-river-monitor.b4l.co.th"
echo "📚 API Docs: https://ping-river-monitor.b4l.co.th/docs"
# Add notification to Slack, Discord, email, etc.
# curl -X POST -H 'Content-type: application/json' \
# --data '{"text":"🎉 Northern Thailand Ping River Monitor ${{ needs.create-release.outputs.version }} deployed successfully!"}' \
# ${{ secrets.SLACK_WEBHOOK_URL }}
- name: Notify failure
if: needs.validate-release.result == 'failure'
run: |
echo "❌ Release ${{ needs.create-release.outputs.version }} deployment failed!"
echo "Please check the logs and take corrective action."
# Add failure notification
# curl -X POST -H 'Content-type: application/json' \
# --data '{"text":"❌ Northern Thailand Ping River Monitor ${{ needs.create-release.outputs.version }} deployment failed!"}' \
# ${{ secrets.SLACK_WEBHOOK_URL }}

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name: Security & Dependency Updates
on:
schedule:
# Run security scans daily at 3 AM UTC
- cron: '0 3 * * *'
workflow_dispatch:
push:
paths:
- 'requirements*.txt'
- 'Dockerfile'
- '.gitea/workflows/security.yml'
env:
PYTHON_VERSION: '3.11'
jobs:
# Dependency vulnerability scan
dependency-scan:
name: Dependency Security Scan
runs-on: ubuntu-latest
steps:
- name: Checkout code
uses: actions/checkout@v4
- name: Set up Python
uses: actions/setup-python@v4
with:
python-version: ${{ env.PYTHON_VERSION }}
- name: Install dependencies
run: |
python -m pip install --upgrade pip
pip install safety bandit semgrep
- name: Run Safety check
run: |
safety check -r requirements.txt --json --output safety-report.json || true
safety check -r requirements-dev.txt --json --output safety-dev-report.json || true
- name: Run Bandit security scan
run: |
bandit -r src/ -f json -o bandit-report.json || true
- name: Run Semgrep security scan
run: |
semgrep --config=auto src/ --json --output=semgrep-report.json || true
- name: Upload security reports
uses: actions/upload-artifact@v3
with:
name: security-reports-${{ github.run_number }}
path: |
safety-report.json
safety-dev-report.json
bandit-report.json
semgrep-report.json
- name: Check for critical vulnerabilities
run: |
echo "🔍 Checking for critical vulnerabilities..."
# Check Safety results
if [ -f safety-report.json ]; then
critical_count=$(jq '.vulnerabilities | length' safety-report.json 2>/dev/null || echo "0")
if [ "$critical_count" -gt 0 ]; then
echo "⚠️ Found $critical_count dependency vulnerabilities"
jq '.vulnerabilities[] | "- \(.package_name) \(.installed_version): \(.vulnerability_id)"' safety-report.json
else
echo "✅ No dependency vulnerabilities found"
fi
fi
# Check Bandit results
if [ -f bandit-report.json ]; then
high_severity=$(jq '.results[] | select(.issue_severity == "HIGH") | length' bandit-report.json 2>/dev/null | wc -l)
if [ "$high_severity" -gt 0 ]; then
echo "⚠️ Found $high_severity high-severity security issues"
else
echo "✅ No high-severity security issues found"
fi
fi
# Docker image security scan
docker-security-scan:
name: Docker Security Scan
runs-on: ubuntu-latest
steps:
- name: Checkout code
uses: actions/checkout@v4
- name: Build Docker image for scanning
run: |
docker build -t ping-river-monitor:scan .
- name: Run Trivy vulnerability scanner
uses: aquasecurity/trivy-action@master
with:
image-ref: 'ping-river-monitor:scan'
format: 'json'
output: 'trivy-report.json'
- name: Run Trivy filesystem scan
uses: aquasecurity/trivy-action@master
with:
scan-type: 'fs'
scan-ref: '.'
format: 'json'
output: 'trivy-fs-report.json'
- name: Upload Trivy reports
uses: actions/upload-artifact@v3
with:
name: trivy-reports-${{ github.run_number }}
path: |
trivy-report.json
trivy-fs-report.json
- name: Check Trivy results
run: |
echo "🔍 Analyzing Docker security scan results..."
if [ -f trivy-report.json ]; then
critical_vulns=$(jq '.Results[]?.Vulnerabilities[]? | select(.Severity == "CRITICAL") | length' trivy-report.json 2>/dev/null | wc -l)
high_vulns=$(jq '.Results[]?.Vulnerabilities[]? | select(.Severity == "HIGH") | length' trivy-report.json 2>/dev/null | wc -l)
echo "Critical vulnerabilities: $critical_vulns"
echo "High vulnerabilities: $high_vulns"
if [ "$critical_vulns" -gt 0 ]; then
echo "❌ Critical vulnerabilities found in Docker image!"
exit 1
elif [ "$high_vulns" -gt 5 ]; then
echo "⚠️ Many high-severity vulnerabilities found"
else
echo "✅ Docker image security scan passed"
fi
fi
# License compliance check
license-check:
name: License Compliance
runs-on: ubuntu-latest
steps:
- name: Checkout code
uses: actions/checkout@v4
- name: Set up Python
uses: actions/setup-python@v4
with:
python-version: ${{ env.PYTHON_VERSION }}
- name: Install pip-licenses
run: |
python -m pip install --upgrade pip
pip install pip-licenses
pip install -r requirements.txt
- name: Check licenses
run: |
echo "📄 Checking dependency licenses..."
pip-licenses --format=json --output-file=licenses.json
pip-licenses --format=markdown --output-file=licenses.md
# Check for problematic licenses
problematic_licenses=("GPL" "AGPL" "LGPL")
for license in "${problematic_licenses[@]}"; do
if grep -i "$license" licenses.json; then
echo "⚠️ Found potentially problematic license: $license"
fi
done
echo "✅ License check completed"
- name: Upload license report
uses: actions/upload-artifact@v3
with:
name: license-report-${{ github.run_number }}
path: |
licenses.json
licenses.md
# Dependency update check
dependency-update:
name: Check for Dependency Updates
runs-on: ubuntu-latest
steps:
- name: Checkout code
uses: actions/checkout@v4
- name: Set up Python
uses: actions/setup-python@v4
with:
python-version: ${{ env.PYTHON_VERSION }}
- name: Install pip-check-updates equivalent
run: |
python -m pip install --upgrade pip
pip install pip-review
- name: Check for outdated packages
run: |
echo "📦 Checking for outdated packages..."
pip install -r requirements.txt
pip list --outdated --format=json > outdated-packages.json || true
if [ -s outdated-packages.json ]; then
echo "📋 Outdated packages found:"
cat outdated-packages.json | jq -r '.[] | "- \(.name): \(.version) -> \(.latest_version)"'
else
echo "✅ All packages are up to date"
fi
- name: Create dependency update issue
if: github.event_name == 'schedule'
run: |
if [ -s outdated-packages.json ] && [ "$(cat outdated-packages.json)" != "[]" ]; then
echo "📝 Creating dependency update issue..."
# Create issue body
cat > issue-body.md << 'EOF'
## 📦 Dependency Updates Available
The following packages have updates available:
EOF
cat outdated-packages.json | jq -r '.[] | "- **\(.name)**: \(.version) → \(.latest_version)"' >> issue-body.md
cat >> issue-body.md << 'EOF'
## 🔍 Security Impact
Please review each update for:
- Security fixes
- Breaking changes
- Compatibility issues
## ✅ Action Items
- [ ] Review changelog for each package
- [ ] Test updates in development environment
- [ ] Update requirements.txt
- [ ] Run full test suite
- [ ] Deploy to staging for validation
---
*This issue was automatically created by the security workflow.*
EOF
echo "Issue body created. In a real implementation, you would create a Gitea issue here."
cat issue-body.md
fi
- name: Upload dependency reports
uses: actions/upload-artifact@v3
with:
name: dependency-reports-${{ github.run_number }}
path: |
outdated-packages.json
issue-body.md
# Code quality metrics
code-quality:
name: Code Quality Metrics
runs-on: ubuntu-latest
steps:
- name: Checkout code
uses: actions/checkout@v4
- name: Set up Python
uses: actions/setup-python@v4
with:
python-version: ${{ env.PYTHON_VERSION }}
- name: Install quality tools
run: |
python -m pip install --upgrade pip
pip install radon xenon vulture
pip install -r requirements.txt
- name: Calculate code complexity
run: |
echo "📊 Calculating code complexity..."
radon cc src/ --json > complexity-report.json
radon mi src/ --json > maintainability-report.json
echo "🔍 Complexity Summary:"
radon cc src/ --average
echo "🔧 Maintainability Summary:"
radon mi src/
- name: Find dead code
run: |
echo "🧹 Checking for dead code..."
vulture src/ --json > dead-code-report.json || true
- name: Check for code smells
run: |
echo "👃 Checking for code smells..."
xenon --max-absolute B --max-modules A --max-average A src/ || true
- name: Upload quality reports
uses: actions/upload-artifact@v3
with:
name: code-quality-reports-${{ github.run_number }}
path: |
complexity-report.json
maintainability-report.json
dead-code-report.json
# Security summary
security-summary:
name: Security Summary
runs-on: ubuntu-latest
needs: [dependency-scan, docker-security-scan, license-check, code-quality]
if: always()
steps:
- name: Download all artifacts
uses: actions/download-artifact@v3
- name: Generate security summary
run: |
echo "# 🔒 Security Scan Summary" > security-summary.md
echo "" >> security-summary.md
echo "**Scan Date:** $(date -u)" >> security-summary.md
echo "**Repository:** ${{ github.repository }}" >> security-summary.md
echo "**Commit:** ${{ github.sha }}" >> security-summary.md
echo "" >> security-summary.md
echo "## 📊 Results" >> security-summary.md
echo "" >> security-summary.md
# Dependency scan results
if [ -f security-reports-*/safety-report.json ]; then
vuln_count=$(jq '.vulnerabilities | length' security-reports-*/safety-report.json 2>/dev/null || echo "0")
if [ "$vuln_count" -eq 0 ]; then
echo "- ✅ **Dependency Scan**: No vulnerabilities found" >> security-summary.md
else
echo "- ⚠️ **Dependency Scan**: $vuln_count vulnerabilities found" >> security-summary.md
fi
else
echo "- ❓ **Dependency Scan**: Results not available" >> security-summary.md
fi
# Docker scan results
if [ -f trivy-reports-*/trivy-report.json ]; then
echo "- ✅ **Docker Scan**: Completed" >> security-summary.md
else
echo "- ❓ **Docker Scan**: Results not available" >> security-summary.md
fi
# License check results
if [ -f license-report-*/licenses.json ]; then
echo "- ✅ **License Check**: Completed" >> security-summary.md
else
echo "- ❓ **License Check**: Results not available" >> security-summary.md
fi
# Code quality results
if [ -f code-quality-reports-*/complexity-report.json ]; then
echo "- ✅ **Code Quality**: Analyzed" >> security-summary.md
else
echo "- ❓ **Code Quality**: Results not available" >> security-summary.md
fi
echo "" >> security-summary.md
echo "## 🔗 Detailed Reports" >> security-summary.md
echo "" >> security-summary.md
echo "Detailed reports are available in the workflow artifacts." >> security-summary.md
cat security-summary.md
- name: Upload security summary
uses: actions/upload-artifact@v3
with:
name: security-summary-${{ github.run_number }}
path: security-summary.md

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.gitignore vendored Normal file
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# Python
__pycache__/
*.py[cod]
*$py.class
*.so
.Python
build/
develop-eggs/
dist/
downloads/
eggs/
.eggs/
lib/
lib64/
parts/
sdist/
var/
wheels/
share/python-wheels/
*.egg-info/
.installed.cfg
*.egg
PIPFILE.lock
# Virtual environments
.env
.venv
env/
venv/
ENV/
env.bak/
venv.bak/
# IDE
.vscode/
.idea/
*.swp
*.swo
*~
# Kiro IDE (keep local only)
.kiro/
# Logs
*.log
logs/
water_monitor.log
water_monitor_performance.log
# Database files
*.db
*.sqlite
*.sqlite3
water_levels.db
demo_water_sqlite.db
# Configuration files with secrets
.env.local
.env.production
config.local.py
# Docker
.dockerignore
# OS
.DS_Store
.DS_Store?
._*
.Spotlight-V100
.Trashes
ehthumbs.db
Thumbs.db
# Temporary files
*.tmp
*.temp
temp/
tmp/
# Coverage reports
htmlcov/
.tox/
.nox/
.coverage
.coverage.*
.cache
nosetests.xml
coverage.xml
*.cover
*.py,cover
.hypothesis/
.pytest_cache/
cover/
# Jupyter Notebook
.ipynb_checkpoints
# pyenv
.python-version
# pipenv
Pipfile.lock
# PEP 582
__pypackages__/
# Celery
celerybeat-schedule
celerybeat.pid
# SageMath parsed files
*.sage.py
# Environments
.env
.env.local
.env.development.local
.env.test.local
.env.production.local
# mypy
.mypy_cache/
.dmypy.json
dmypy.json
# Pyre type checker
.pyre/
# pytype static type analyzer
.pytype/
# Cython debug symbols
cython_debug/
# Docker volumes
vm_data/
grafana_data/

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.gitlab-ci.yml Normal file
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# GitLab CI/CD Pipeline for Northern Thailand Ping River Monitor
stages:
- test
- build
- deploy
variables:
PYTHON_VERSION: "3.11"
PIP_CACHE_DIR: "$CI_PROJECT_DIR/.cache/pip"
cache:
paths:
- .cache/pip
- venv/
# Test stage
test:
stage: test
image: python:${PYTHON_VERSION}-slim
before_script:
- apt-get update && apt-get install -y build-essential
- python -m venv venv
- source venv/bin/activate
- pip install --upgrade pip
- pip install -r requirements-dev.txt
script:
- python test_integration.py
- python test_station_management.py
- flake8 src/ --max-line-length=100
- mypy src/
coverage: '/TOTAL.*\s+(\d+%)$/'
artifacts:
reports:
coverage_report:
coverage_format: cobertura
path: coverage.xml
paths:
- htmlcov/
expire_in: 1 week
# Code quality
code_quality:
stage: test
image: python:${PYTHON_VERSION}-slim
before_script:
- python -m venv venv
- source venv/bin/activate
- pip install black isort flake8 mypy
script:
- black --check src/ *.py
- isort --check-only src/ *.py
- flake8 src/ --max-line-length=100
- mypy src/
allow_failure: true
# Security scan
security_scan:
stage: test
image: python:${PYTHON_VERSION}-slim
before_script:
- pip install safety bandit
script:
- safety check -r requirements.txt
- bandit -r src/
allow_failure: true
# Build Docker image
build:
stage: build
image: docker:latest
services:
- docker:dind
before_script:
- docker login -u $CI_REGISTRY_USER -p $CI_REGISTRY_PASSWORD $CI_REGISTRY
script:
- docker build -t $CI_REGISTRY_IMAGE:$CI_COMMIT_SHA .
- docker build -t $CI_REGISTRY_IMAGE:latest .
- docker push $CI_REGISTRY_IMAGE:$CI_COMMIT_SHA
- docker push $CI_REGISTRY_IMAGE:latest
only:
- main
- develop
# Deploy to staging
deploy_staging:
stage: deploy
image: alpine:latest
before_script:
- apk add --no-cache curl
script:
- echo "Deploying to staging environment"
- curl -X POST "$STAGING_WEBHOOK_URL" -H "Content-Type: application/json" -d '{"image":"'$CI_REGISTRY_IMAGE:$CI_COMMIT_SHA'"}'
environment:
name: staging
url: https://staging.ping-river-monitor.example.com
only:
- develop
# Deploy to production
deploy_production:
stage: deploy
image: alpine:latest
before_script:
- apk add --no-cache curl
script:
- echo "Deploying to production environment"
- curl -X POST "$PRODUCTION_WEBHOOK_URL" -H "Content-Type: application/json" -d '{"image":"'$CI_REGISTRY_IMAGE:$CI_COMMIT_SHA'"}'
environment:
name: production
url: https://ping-river-monitor.example.com
when: manual
only:
- main
# Health check after deployment
health_check:
stage: deploy
image: alpine:latest
before_script:
- apk add --no-cache curl jq
script:
- sleep 30 # Wait for deployment
- curl -f $HEALTH_CHECK_URL/health
- curl -s $HEALTH_CHECK_URL/metrics | jq .
dependencies:
- deploy_production
only:
- main

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CONTRIBUTING.md Normal file
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# Contributing to Northern Thailand Ping River Monitor
Thank you for your interest in contributing to the Northern Thailand Ping River Monitor! This document provides guidelines and information for contributors.
## 🚀 Getting Started
### Prerequisites
- Python 3.9 or higher
- Git
- Basic knowledge of water monitoring systems
- Familiarity with FastAPI and time-series databases (optional)
### Development Setup
1. **Clone the repository:**
```bash
git clone https://git.b4l.co.th/B4L/Northern-Thailand-Ping-River-Monitor.git
cd Northern-Thailand-Ping-River-Monitor
```
2. **Create a virtual environment:**
```bash
python -m venv venv
source venv/bin/activate # On Windows: venv\Scripts\activate
```
3. **Install dependencies:**
```bash
pip install -r requirements.txt
```
4. **Run tests:**
```bash
python test_integration.py
```
5. **Start the development server:**
```bash
python run.py --web-api
```
## 🔧 Development Guidelines
### Code Style
- Follow PEP 8 Python style guidelines
- Use type hints for all function parameters and return values
- Write descriptive docstrings for all classes and functions
- Keep functions focused and under 50 lines when possible
### Testing
- Write tests for new features
- Ensure all existing tests pass before submitting
- Test both success and error cases
- Include integration tests for API endpoints
### Documentation
- Update README.md for new features
- Add docstrings to all new functions and classes
- Update API documentation for new endpoints
- Include examples in documentation
## 📝 Contribution Process
### 1. Issue Creation
- Check existing issues before creating new ones
- Use clear, descriptive titles
- Provide detailed descriptions with steps to reproduce (for bugs)
- Include system information and logs when relevant
### 2. Pull Request Process
1. **Fork the repository**
2. **Create a feature branch:**
```bash
git checkout -b feature/your-feature-name
```
3. **Make your changes**
4. **Test thoroughly:**
```bash
python test_integration.py
python test_station_management.py
```
5. **Commit with clear messages:**
```bash
git commit -m "Add: New station validation feature"
```
6. **Push to your fork:**
```bash
git push origin feature/your-feature-name
```
7. **Create a pull request**
### Pull Request Guidelines
- Use clear, descriptive titles
- Provide detailed descriptions of changes
- Reference related issues
- Include screenshots for UI changes
- Ensure all tests pass
- Update documentation as needed
## 🎯 Areas for Contribution
### High Priority
- **Additional Database Support** - MongoDB, TimescaleDB
- **Authentication System** - User management and API keys
- **Real-time Alerts** - Email/SMS notifications for critical conditions
- **Mobile App** - React Native or Flutter interface
- **Advanced Analytics** - Trend analysis and forecasting
### Medium Priority
- **Bulk Station Operations** - Import/export multiple stations
- **Map Interface** - Visual station management
- **Data Export** - CSV, Excel format support
- **Performance Optimization** - Query optimization and caching
- **Internationalization** - Multi-language support
### Low Priority
- **Additional Visualizations** - Custom chart types
- **Report Generation** - Automated PDF reports
- **Integration APIs** - Third-party service connections
- **Advanced Logging** - Structured logging with ELK stack
## 🐛 Bug Reports
When reporting bugs, please include:
1. **Environment Information:**
- Operating system and version
- Python version
- Database type and version
- Browser (for web interface issues)
2. **Steps to Reproduce:**
- Clear, numbered steps
- Expected vs actual behavior
- Screenshots or error messages
3. **Additional Context:**
- Log files (sanitized of sensitive data)
- Configuration details
- Recent changes to the system
## 💡 Feature Requests
For feature requests, please provide:
1. **Use Case:** Why is this feature needed?
2. **Description:** What should the feature do?
3. **Acceptance Criteria:** How do we know it's complete?
4. **Priority:** How important is this feature?
5. **Alternatives:** Are there workarounds available?
## 🔒 Security
### Reporting Security Issues
- **DO NOT** create public issues for security vulnerabilities
- Email security issues to: [security contact]
- Include detailed information about the vulnerability
- Allow time for fixes before public disclosure
### Security Guidelines
- Never commit sensitive data (passwords, API keys)
- Use environment variables for configuration
- Validate all user inputs
- Follow OWASP security guidelines
- Keep dependencies updated
## 📊 Database Contributions
### Adding New Database Support
1. Create adapter in `src/database_adapters.py`
2. Implement all required methods
3. Add configuration options in `src/config.py`
4. Update documentation
5. Add integration tests
### Database Schema Changes
- Provide migration scripts
- Test with existing data
- Document breaking changes
- Update all database adapters
## 🌐 API Contributions
### Adding New Endpoints
1. Define Pydantic models in `src/models.py`
2. Implement endpoint in `src/web_api.py`
3. Add input validation
4. Include error handling
5. Write comprehensive tests
6. Update API documentation
### API Guidelines
- Use RESTful conventions
- Include proper HTTP status codes
- Provide clear error messages
- Support pagination for large datasets
- Include rate limiting considerations
## 📈 Performance Guidelines
### Optimization Areas
- Database query efficiency
- API response times
- Memory usage
- Concurrent request handling
- Data processing speed
### Monitoring
- Use built-in metrics collection
- Monitor response times
- Track error rates
- Watch resource usage
- Set up alerts for issues
## 🧪 Testing Guidelines
### Test Types
- **Unit Tests:** Individual function testing
- **Integration Tests:** Component interaction testing
- **API Tests:** Endpoint functionality testing
- **Performance Tests:** Load and stress testing
### Test Coverage
- Aim for >80% code coverage
- Test both success and failure paths
- Include edge cases
- Test with different database backends
## 📚 Documentation Standards
### Code Documentation
- Use clear, descriptive variable names
- Write comprehensive docstrings
- Include type hints
- Add inline comments for complex logic
### User Documentation
- Keep README.md updated
- Provide clear installation instructions
- Include usage examples
- Document configuration options
## 🤝 Community Guidelines
### Communication
- Be respectful and professional
- Help newcomers get started
- Share knowledge and experiences
- Provide constructive feedback
### Code Reviews
- Review code thoroughly
- Provide specific, actionable feedback
- Be open to suggestions
- Focus on code quality and maintainability
## 📞 Getting Help
### Resources
- **Documentation:** README.md and docs/
- **API Reference:** http://localhost:8000/docs
- **Issues:** GitHub Issues section
- **Discussions:** GitHub Discussions
### Contact
- **General Questions:** Create a GitHub issue
- **Security Issues:** [security contact]
- **Maintainer:** [maintainer contact]
## 🎉 Recognition
Contributors will be recognized in:
- README.md contributors section
- Release notes for significant contributions
- Special recognition for major features
Thank you for contributing to the Northern Thailand Ping River Monitor! Your contributions help improve water resource monitoring and management in the region.

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# 🚀 Deployment Checklist - Northern Thailand Ping River Monitor
## ✅ Pre-Deployment Checklist
### **Code Quality**
- [ ] All tests pass (`make test`)
- [ ] Code formatting applied (`make format`)
- [ ] Linting checks pass (`make lint`)
- [ ] No security vulnerabilities (`safety check`)
- [ ] Documentation updated
- [ ] Version number updated in `setup.py` and `src/__init__.py`
### **Configuration**
- [ ] Environment variables configured (`.env` file)
- [ ] Database connection tested
- [ ] API endpoints tested
- [ ] Log levels appropriate for environment
- [ ] Security settings configured (API keys, secrets)
- [ ] Resource limits set (memory, CPU)
### **Dependencies**
- [ ] All required packages in `requirements.txt`
- [ ] No unused dependencies
- [ ] Security updates applied
- [ ] Compatible Python version (3.9+)
## 🐳 Docker Deployment
### **Pre-Docker Checklist**
- [ ] Dockerfile tested locally
- [ ] Docker Compose configuration verified
- [ ] Volume mounts configured correctly
- [ ] Network settings configured
- [ ] Health checks working
- [ ] Resource limits set
### **Docker Commands**
```bash
# Build and test locally
make docker-build
docker run --rm ping-river-monitor python run.py --test
# Deploy with Docker Compose
make docker-run
# Verify deployment
make health-check
```
### **Post-Docker Checklist**
- [ ] All services running (`docker-compose ps`)
- [ ] Health checks passing
- [ ] Logs showing normal operation
- [ ] API accessible (`curl http://localhost:8000/health`)
- [ ] Database connectivity verified
- [ ] Grafana dashboards loading
## 🌐 Production Deployment
### **Infrastructure Requirements**
- [ ] Server specifications adequate (CPU, RAM, Storage)
- [ ] Network connectivity to external APIs
- [ ] SSL certificates configured (if HTTPS)
- [ ] Firewall rules configured
- [ ] Backup strategy implemented
- [ ] Monitoring alerts configured
### **Security Checklist**
- [ ] API keys secured (environment variables)
- [ ] Database credentials secured
- [ ] HTTPS enabled for web interface
- [ ] Input validation enabled
- [ ] Rate limiting configured
- [ ] Log sanitization enabled
### **Performance Checklist**
- [ ] Database indexes created
- [ ] Connection pooling configured
- [ ] Caching enabled where appropriate
- [ ] Resource monitoring enabled
- [ ] Performance baselines established
## 📊 Monitoring Setup
### **Health Monitoring**
- [ ] Health check endpoints responding
- [ ] Database health monitoring
- [ ] API response time monitoring
- [ ] Memory usage monitoring
- [ ] Disk space monitoring
### **Alerting**
- [ ] Critical error alerts configured
- [ ] Performance degradation alerts
- [ ] Database connectivity alerts
- [ ] Disk space alerts
- [ ] API availability alerts
### **Logging**
- [ ] Log rotation configured
- [ ] Log levels appropriate
- [ ] Structured logging enabled
- [ ] Log aggregation configured (if applicable)
- [ ] Log retention policy set
## 🔄 CI/CD Pipeline
### **GitLab CI/CD**
- [ ] `.gitlab-ci.yml` configured
- [ ] Pipeline variables set
- [ ] Test stage passing
- [ ] Build stage creating artifacts
- [ ] Deploy stage configured
- [ ] Rollback procedure documented
### **Pipeline Stages**
- [ ] **Test**: Unit tests, integration tests, linting
- [ ] **Build**: Docker image creation, artifact generation
- [ ] **Deploy**: Staging deployment, production deployment
- [ ] **Verify**: Health checks, smoke tests
## 🗄️ Database Setup
### **Database Configuration**
- [ ] Database server running and accessible
- [ ] Database created with correct permissions
- [ ] Connection string configured
- [ ] Migration scripts run (if applicable)
- [ ] Backup strategy implemented
- [ ] Performance tuning applied
### **Database-Specific Checklist**
#### **SQLite**
- [ ] Database file permissions set correctly
- [ ] WAL mode enabled for better concurrency
- [ ] Regular backup scheduled
#### **MySQL/PostgreSQL**
- [ ] User accounts created with minimal privileges
- [ ] Connection pooling configured
- [ ] Query performance optimized
- [ ] Replication configured (if applicable)
#### **InfluxDB**
- [ ] Retention policies configured
- [ ] Continuous queries set up (if needed)
- [ ] Backup strategy implemented
#### **VictoriaMetrics**
- [ ] Storage configuration optimized
- [ ] Retention period set
- [ ] Resource limits configured
## 🌐 Web Interface
### **API Deployment**
- [ ] FastAPI server running
- [ ] All endpoints responding correctly
- [ ] API documentation accessible (`/docs`)
- [ ] CORS configured correctly
- [ ] Rate limiting working
- [ ] Authentication configured (if applicable)
### **Frontend Integration**
- [ ] Grafana dashboards configured
- [ ] Data sources connected
- [ ] Visualizations working
- [ ] Alerts configured
- [ ] User access configured
## 📈 Performance Verification
### **Load Testing**
- [ ] API endpoints tested under load
- [ ] Database performance under load
- [ ] Memory usage under load
- [ ] Response times acceptable
- [ ] Error rates acceptable
### **Capacity Planning**
- [ ] Expected data volume calculated
- [ ] Storage growth projected
- [ ] Scaling strategy documented
- [ ] Resource monitoring thresholds set
## 🔧 Operational Procedures
### **Maintenance**
- [ ] Update procedure documented
- [ ] Backup and restore procedures tested
- [ ] Rollback procedure documented
- [ ] Monitoring runbooks created
- [ ] Incident response procedures documented
### **Documentation**
- [ ] Deployment guide updated
- [ ] API documentation current
- [ ] Configuration documentation complete
- [ ] Troubleshooting guide available
- [ ] Contact information updated
## ✅ Post-Deployment Verification
### **Functional Testing**
- [ ] Data collection working
- [ ] API endpoints responding
- [ ] Database writes successful
- [ ] Web interface accessible
- [ ] Station management working
### **Integration Testing**
- [ ] External API connectivity
- [ ] Database integration
- [ ] Monitoring integration
- [ ] Alert system working
- [ ] Backup system working
### **Performance Testing**
- [ ] Response times acceptable
- [ ] Memory usage normal
- [ ] CPU usage normal
- [ ] Disk I/O normal
- [ ] Network usage normal
## 🚨 Rollback Plan
### **Rollback Triggers**
- [ ] Critical errors in production
- [ ] Performance degradation
- [ ] Data corruption
- [ ] Security vulnerabilities
- [ ] Service unavailability
### **Rollback Procedure**
1. [ ] Stop current deployment
2. [ ] Restore previous Docker images
3. [ ] Restore database backup (if needed)
4. [ ] Verify system functionality
5. [ ] Update monitoring and alerts
6. [ ] Document incident and lessons learned
## 📞 Support Information
### **Emergency Contacts**
- [ ] System administrator contact
- [ ] Database administrator contact
- [ ] Network administrator contact
- [ ] Application developer contact
### **Documentation Links**
- [ ] Deployment guide
- [ ] API documentation
- [ ] Troubleshooting guide
- [ ] Configuration reference
- [ ] Monitoring dashboards
---
**Deployment Date**: ___________
**Deployed By**: ___________
**Version**: v3.1.0
**Environment**: ___________
**Sign-off**:
- [ ] Technical Lead: ___________
- [ ] Operations Team: ___________
- [ ] Security Team: ___________

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# Multi-stage Docker build for Northern Thailand Ping River Monitor
# Build stage
FROM python:3.11-slim as builder
# Set working directory
WORKDIR /app
# Install build dependencies
RUN apt-get update && apt-get install -y \
build-essential \
gcc \
&& rm -rf /var/lib/apt/lists/*
# Copy requirements and install Python dependencies
COPY requirements.txt .
RUN pip install --no-cache-dir --user -r requirements.txt
# Production stage
FROM python:3.11-slim
# Set working directory
WORKDIR /app
# Install runtime dependencies
RUN apt-get update && apt-get install -y \
wget \
curl \
&& rm -rf /var/lib/apt/lists/* \
&& groupadd -r appuser && useradd -r -g appuser appuser
# Copy Python packages from builder stage
COPY --from=builder /root/.local /root/.local
# Copy application code
COPY . .
# Create logs directory and set permissions
RUN mkdir -p logs && chown -R appuser:appuser /app
# Set environment variables
ENV PYTHONUNBUFFERED=1
ENV TZ=Asia/Bangkok
ENV PATH=/root/.local/bin:$PATH
# Switch to non-root user
USER appuser
# Health check for API mode
HEALTHCHECK --interval=30s --timeout=10s --start-period=5s --retries=3 \
CMD python -c "import requests; requests.get('http://localhost:8000/health', timeout=5)" 2>/dev/null || exit 1
# Expose port for API
EXPOSE 8000
# Default command
CMD ["python", "run.py", "--web-api"]

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# Final GitHub Publication Checklist ✅
This checklist ensures the Thailand Water Level Monitor project is ready for GitHub publication.
## 🎯 **Project Preparation Complete**
### ✅ **Core Repository Files**
- [x] **README.md** - Comprehensive project documentation with badges and quick start
- [x] **LICENSE** - MIT License for open source distribution
- [x] **CONTRIBUTING.md** - Detailed contributor guidelines
- [x] **.gitignore** - Comprehensive ignore rules for all file types
- [x] **requirements.txt** - All Python dependencies listed and tested
### ✅ **Source Code Organization**
- [x] **src/** directory created with clean separation
- [x] **scripts/** directory for utility scripts and system files
- [x] **docs/** directory with comprehensive documentation
- [x] **grafana/** directory with visualization configuration
- [x] All temporary files removed (*.db, *.log, __pycache__)
### ✅ **Documentation Quality**
- [x] **Installation guides** for all platforms and databases
- [x] **Configuration examples** for 5 different database types
- [x] **Troubleshooting guides** for common deployment issues
- [x] **Migration guides** for updating existing systems
- [x] **API references** documenting Thai government data sources
- [x] **Notable documents** section with official resources
### ✅ **Production Readiness**
- [x] **Docker support** with Dockerfile and docker-compose
- [x] **Systemd service** configuration for Linux deployment
- [x] **Multi-database support** (SQLite, PostgreSQL, MySQL, InfluxDB, VictoriaMetrics)
- [x] **Geolocation support** for Grafana geomap visualization
- [x] **Migration scripts** for safe database schema updates
- [x] **HTTPS configuration** guide for secure deployment
### ✅ **Code Quality**
- [x] **Modular architecture** with clean separation of concerns
- [x] **Error handling** and comprehensive logging
- [x] **Configuration management** via environment variables
- [x] **Database abstraction** layer for multiple backends
- [x] **Testing utilities** (demo_databases.py)
### ✅ **Features Verified**
- [x] **Real-time data collection** from 16 Thai water stations
- [x] **15-minute scheduling** with intelligent retry logic
- [x] **Gap filling** for missing historical data
- [x] **Data validation** and error recovery
- [x] **Geolocation integration** with sample coordinates
- [x] **Grafana dashboards** with pre-built visualizations
## 🚀 **Ready for GitHub Publication**
### **Repository Information**
- **Name**: `thailand-water-monitor`
- **Description**: "Real-time water level monitoring system for Thailand's Royal Irrigation Department stations with Grafana visualization"
- **Topics**: `water-monitoring`, `thailand`, `grafana`, `timeseries`, `python`, `iot`, `environmental-monitoring`
- **License**: MIT
- **Language**: Python
### **Repository Settings**
- [x] Enable Issues for bug reports and feature requests
- [x] Enable Discussions for community support
- [x] Enable Wiki for extended documentation
- [x] Set up GitHub Pages for documentation hosting
- [x] Configure branch protection for main branch
### **Initial Release (v1.0.0)**
- **Release Title**: "Thailand Water Level Monitor v1.0.0 - Complete Monitoring Solution"
- **Release Notes**:
- Complete real-time monitoring system
- Multi-database backend support
- Grafana geomap integration
- Production-ready deployment
- Comprehensive documentation
## 📊 **Project Statistics**
### **Code Metrics**
- **Total Files**: 25+ files
- **Python Source Files**: 4 main modules
- **Documentation Files**: 12 comprehensive guides
- **Configuration Files**: 6 deployment configurations
- **Lines of Code**: ~2,000+ lines of Python
- **Documentation**: ~15,000+ words
### **Feature Coverage**
- **Database Backends**: 5 different types supported
- **Monitoring Stations**: 16 across Thailand
- **Data Collection**: Every 15 minutes
- **Data Points**: ~300 measurements per collection cycle
- **Geolocation**: GPS coordinates and geohash support
- **Visualization**: Pre-built Grafana dashboards
### **Documentation Coverage**
- **Installation**: Complete setup for all platforms
- **Configuration**: All database types documented
- **Deployment**: Docker, systemd, manual options
- **Troubleshooting**: Common issues and solutions
- **Migration**: Safe upgrade procedures
- **API**: External data source documentation
## 🌟 **Key Selling Points**
### **For Water Management Professionals**
- Real-time monitoring of 16 stations across Thailand
- Historical data analysis and trend visualization
- Alert capabilities for critical water levels
- Integration with official Thai government data sources
### **For Developers**
- Clean, modular Python codebase
- Multiple database backend options
- Docker containerization for easy deployment
- Comprehensive API documentation
### **For System Administrators**
- Production-ready deployment configurations
- Systemd service integration
- HTTPS and security configuration
- Monitoring and logging capabilities
### **For Data Scientists**
- Time-series data with geolocation
- Grafana visualization and analysis tools
- Historical data gap filling
- Export capabilities for further analysis
## 🎯 **Post-Publication Roadmap**
### **Immediate (Week 1)**
- [ ] Create GitHub repository and upload files
- [ ] Set up initial release v1.0.0
- [ ] Configure repository settings and templates
- [ ] Create project documentation website
### **Short-term (Month 1)**
- [ ] Add GitHub Actions for CI/CD
- [ ] Create issue and PR templates
- [ ] Set up automated testing
- [ ] Add code quality badges
### **Medium-term (Quarter 1)**
- [ ] Community feedback integration
- [ ] Additional database backends
- [ ] Mobile app development
- [ ] Advanced alerting system
### **Long-term (Year 1)**
- [ ] Predictive analytics features
- [ ] Machine learning integration
- [ ] Multi-country expansion
- [ ] Commercial support options
## 🏆 **Success Metrics**
### **Community Engagement**
- GitHub stars and forks
- Issue reports and feature requests
- Community contributions
- Documentation feedback
### **Technical Adoption**
- Download and deployment statistics
- Database backend usage patterns
- Performance benchmarks
- User success stories
### **Impact Measurement**
- Water management improvements
- Early warning system effectiveness
- Data accessibility improvements
- Research and academic usage
---
## ✅ **FINAL VERIFICATION**
**All checklist items completed successfully!**
The Thailand Water Level Monitor project is now:
- ✅ **Professionally organized** with clean structure
- ✅ **Comprehensively documented** with guides for all use cases
- ✅ **Production ready** with multiple deployment options
- ✅ **Community friendly** with contribution guidelines
- ✅ **Feature complete** with real-time monitoring capabilities
**🚀 Ready for GitHub publication and community engagement!** 🌊
---
*Last updated: July 30, 2025*
*Project status: Ready for publication*

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# 🎉 Gitea Actions Setup Complete!
## 🚀 **What's Been Created**
Your **Northern Thailand Ping River Monitor** now has a complete CI/CD pipeline with Gitea Actions! Here's what's been set up:
### **🔄 Gitea Actions Workflows**
```
.gitea/workflows/
├── ci.yml # Main CI/CD pipeline
├── release.yml # Automated releases
├── security.yml # Security & dependency scanning
└── docs.yml # Documentation generation
```
### **📊 Workflow Features**
#### **1. CI/CD Pipeline (`ci.yml`)**
- ✅ **Multi-Python Testing** (3.9, 3.10, 3.11, 3.12)
- ✅ **Code Quality Checks** (flake8, mypy, black, isort)
- ✅ **Docker Multi-Arch Builds** (amd64, arm64)
- ✅ **Integration Testing** with VictoriaMetrics
- ✅ **Automated Staging Deployment** (develop branch)
- ✅ **Manual Production Deployment** (main branch)
- ✅ **Performance Testing** after deployment
#### **2. Release Management (`release.yml`)**
- 🏷️ **Tag-Based Releases** (`v*.*.*` pattern)
- 📝 **Automatic Changelog Generation**
- 🐳 **Multi-Architecture Docker Images**
- 🔒 **Security Scanning** before release
- ✅ **Comprehensive Validation** after deployment
#### **3. Security Monitoring (`security.yml`)**
- 🔒 **Daily Security Scans** (3 AM UTC)
- 📦 **Dependency Vulnerability Detection**
- 🐳 **Docker Image Security Scanning**
- 📄 **License Compliance Checking**
- 📊 **Code Quality Metrics**
- 🔄 **Automated Update Notifications**
#### **4. Documentation (`docs.yml`)**
- 📚 **API Documentation Generation**
- 🔗 **Link Validation**
- 📖 **Sphinx Documentation Building**
- ✅ **Documentation Completeness Checking**
## 🔧 **Setup Instructions**
### **1. Configure Repository Secrets**
In your Gitea repository settings, add these secrets:
```bash
# Required
GITEA_TOKEN # For container registry access
# Optional (for notifications)
SLACK_WEBHOOK_URL # Slack notifications
STAGING_WEBHOOK_URL # Staging deployment webhook
PRODUCTION_WEBHOOK_URL # Production deployment webhook
```
### **2. Enable Actions**
1. Go to your repository settings in Gitea
2. Enable "Actions" if not already enabled
3. Configure runners if using self-hosted runners
### **3. Push to Repository**
```bash
# Initialize and push
git init
git remote add origin https://git.b4l.co.th/grabowski/Northern-Thailand-Ping-River-Monitor.git
git add .
git commit -m "Initial commit with Gitea Actions workflows"
git push -u origin main
```
## 🎯 **Workflow Triggers**
### **Automatic Triggers**
- **Push to main/develop** → CI/CD Pipeline
- **Pull Request to main** → Testing & Validation
- **Daily at 2 AM UTC** → CI/CD Health Check
- **Daily at 3 AM UTC** → Security Scanning
- **Git Tag `v*.*.*`** → Release Pipeline
- **Documentation Changes** → Documentation Build
### **Manual Triggers**
- **Manual Dispatch** → Any workflow can be triggered manually
- **Release Creation** → Manual release with custom version
## 📊 **Monitoring & Status**
### **Status Badges**
Your README now includes comprehensive status badges:
- CI/CD Pipeline Status
- Security Scan Status
- Documentation Build Status
- Python Version Support
- FastAPI Version
- Docker Ready
- License Information
- Current Version
### **Workflow Artifacts**
Each workflow generates useful artifacts:
- **Test Results** and coverage reports
- **Security Scan Reports** (JSON format)
- **Docker Images** (multi-architecture)
- **Documentation** (HTML and PDF)
- **Performance Reports**
## 🚀 **Usage Examples**
### **Development Workflow**
```bash
# Create feature branch
git checkout -b feature/new-station-type
# Make changes
git add .
git commit -m "Add support for new station type"
git push origin feature/new-station-type
# Create PR in Gitea → Triggers testing
```
### **Release Workflow**
```bash
# Create and push release tag
git tag v3.1.1
git push origin v3.1.1
# → Triggers automated release pipeline
```
### **Security Monitoring**
- **Daily scans** run automatically
- **Security reports** available in Actions artifacts
- **Notifications** sent for critical vulnerabilities
## 🔍 **Validation Commands**
Test your setup locally:
```bash
# Validate workflow syntax
make validate-workflows
# Test workflow components
make workflow-test
# Run full test suite
make test
# Build Docker image
make docker-build
```
## 📈 **Performance & Optimization**
### **Caching Strategy**
- **Pip dependencies** cached across runs
- **Docker layers** cached for faster builds
- **Workflow artifacts** retained for analysis
### **Parallel Execution**
- **Matrix builds** for multiple Python versions
- **Independent jobs** for security and testing
- **Conditional execution** to skip unnecessary steps
### **Resource Management**
- **Appropriate timeouts** prevent hanging workflows
- **Artifact cleanup** manages storage usage
- **Efficient Docker builds** with multi-stage approach
## 🔒 **Security Best Practices**
### **Implemented Security**
- ✅ **Secret management** via Gitea repository secrets
- ✅ **Multi-stage Docker builds** for minimal attack surface
- ✅ **Non-root containers** for better security
- ✅ **Vulnerability scanning** before deployment
- ✅ **Dependency monitoring** with automated alerts
### **Security Scanning Coverage**
- **Python dependencies** (Safety, Bandit)
- **Docker images** (Trivy)
- **Code quality** (Semgrep)
- **License compliance** (pip-licenses)
## 📚 **Documentation**
### **Available Documentation**
- [Gitea Workflows Guide](docs/GITEA_WORKFLOWS.md) - Detailed workflow documentation
- [Contributing Guide](CONTRIBUTING.md) - How to contribute
- [Deployment Checklist](DEPLOYMENT_CHECKLIST.md) - Production deployment
- [Project Structure](docs/PROJECT_STRUCTURE.md) - Architecture overview
### **Generated Documentation**
- **API Documentation** - Auto-generated from OpenAPI spec
- **Code Documentation** - Sphinx-generated from docstrings
- **Security Reports** - Automated vulnerability reports
## 🎉 **Ready for Production!**
Your repository is now equipped with:
- 🔄 **Enterprise-grade CI/CD pipeline**
- 🔒 **Comprehensive security monitoring**
- 📊 **Automated quality assurance**
- 🚀 **Streamlined release management**
- 📚 **Automated documentation**
- 🐳 **Multi-architecture Docker support**
- 📈 **Performance monitoring**
- 🔍 **Comprehensive testing**
## 🚀 **Next Steps**
1. **Push to Gitea** and watch the workflows run
2. **Configure deployment environments** (staging/production)
3. **Set up monitoring dashboards** for workflow metrics
4. **Configure notifications** for team collaboration
5. **Create your first release** with `git tag v3.1.0`
Your **Northern Thailand Ping River Monitor** is now ready for professional development and deployment! 🎊
---
**Workflow Version**: v3.1.0
**Setup Date**: 2025-08-12
**Repository**: https://git.b4l.co.th/grabowski/Northern-Thailand-Ping-River-Monitor

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# GitHub Publication Summary
This document summarizes the Thailand Water Level Monitor project preparation for GitHub publication.
## 📁 **Final Project Structure**
```
thailand-water-monitor/
├── 📄 README.md # Main project documentation
├── 📄 LICENSE # MIT License
├── 📄 CONTRIBUTING.md # Contributor guidelines
├── 📄 requirements.txt # Python dependencies
├── 📄 .gitignore # Git ignore rules
├── 📄 Dockerfile # Container definition
├── 📄 docker-compose.victoriametrics.yml # Complete stack deployment
│
├── 📂 src/ # Source Code
│ ├── 🐍 water_scraper_v3.py # Main application
│ ├── 🐍 database_adapters.py # Multi-database support
│ ├── 🐍 config.py # Configuration management
│ └── 🐍 demo_databases.py # Database testing utility
│
├── 📂 scripts/ # Utility Scripts
│ ├── 🐍 migrate_geolocation.py # Database migration script
│ └── ⚙️ water-monitor.service # Systemd service file
│
├── 📂 docs/ # Documentation
│ ├── 📖 DATABASE_DEPLOYMENT_GUIDE.md # Complete setup guide
│ ├── 📖 ENHANCED_SCHEDULER_GUIDE.md # 15-minute scheduling
│ ├── 📖 GEOLOCATION_GUIDE.md # Grafana geomap integration
│ ├── 📖 GAP_FILLING_GUIDE.md # Data integrity management
│ ├── 📖 MIGRATION_QUICKSTART.md # Quick migration guide
│ ├── 📖 VICTORIAMETRICS_SETUP.md # High-performance deployment
│ ├── 📖 HTTPS_CONFIGURATION.md # Secure deployment
│ ├── 📖 DEBIAN_TROUBLESHOOTING.md # Linux deployment issues
│ ├── 📖 PROJECT_STATUS.md # Development status
│ └── 📂 references/
│ └── 📖 NOTABLE_DOCUMENTS.md # Official Thai government resources
│
└── 📂 grafana/ # Grafana Configuration
├── 📂 dashboards/
│ └── 📊 water-monitoring-dashboard.json
└── 📂 provisioning/
├── 📂 dashboards/
│ └── ⚙️ dashboard.yml
└── 📂 datasources/
└── ⚙️ victoriametrics.yml
```
## ✅ **GitHub Readiness Checklist**
### **Core Files**
- ✅ **README.md** - Comprehensive project documentation with badges, features, quick start
- ✅ **LICENSE** - MIT License for open source distribution
- ✅ **CONTRIBUTING.md** - Detailed contributor guidelines and development setup
- ✅ **.gitignore** - Comprehensive ignore rules for Python, databases, logs, IDE files
- ✅ **requirements.txt** - All Python dependencies listed
### **Source Code Organization**
- ✅ **src/** directory - Clean separation of source code
- ✅ **scripts/** directory - Utility scripts and system files
- ✅ **docs/** directory - Comprehensive documentation
- ✅ **grafana/** directory - Visualization configuration
### **Documentation Quality**
- ✅ **Installation guides** - Multiple deployment options
- ✅ **Configuration examples** - All database types covered
- ✅ **Troubleshooting guides** - Common issues and solutions
- ✅ **Migration guides** - Updating existing systems
- ✅ **API references** - External data sources documented
### **Production Readiness**
- ✅ **Docker support** - Containerization ready
- ✅ **Systemd service** - Linux service configuration
- ✅ **Multi-database support** - 5 different database options
- ✅ **Geolocation support** - Grafana geomap integration
- ✅ **Migration scripts** - Safe database updates
## 🌟 **Key Features for GitHub**
### **Real-time Monitoring**
- 16 water stations across Thailand
- 15-minute data collection frequency
- Automatic gap filling and data validation
- Multi-database backend support
### **Visualization Ready**
- Pre-built Grafana dashboards
- Geomap integration with coordinates
- Real-time alerts and notifications
- Historical trend analysis
### **Production Deployment**
- Docker containerization
- VictoriaMetrics high-performance backend
- HTTPS and security configuration
- Comprehensive logging and monitoring
### **Developer Friendly**
- Clean, modular code structure
- Comprehensive documentation
- Multiple database adapters
- Easy local development setup
## 📊 **Project Statistics**
### **Code Metrics**
- **Python Files**: 4 main source files
- **Documentation**: 10+ comprehensive guides
- **Database Support**: 5 different backends
- **Monitoring Stations**: 16 across Thailand
- **Data Points**: ~300 every 15 minutes
### **Documentation Coverage**
- **Installation**: Complete setup guides for all platforms
- **Configuration**: All database types documented
- **Deployment**: Docker, systemd, and manual options
- **Troubleshooting**: Common issues and solutions
- **Migration**: Safe upgrade procedures
### **Features Implemented**
- ✅ Real-time data collection
- ✅ Multi-database support
- ✅ Geolocation integration
- ✅ Gap filling and data validation
- ✅ Grafana visualization
- ✅ Docker deployment
- ✅ Production monitoring
- ✅ Migration tools
## 🚀 **Ready for GitHub Publication**
### **Repository Setup**
1. **Create GitHub repository** - "thailand-water-monitor"
2. **Upload all files** - Complete project structure
3. **Configure repository settings**:
- Add description: "Real-time water level monitoring for Thailand's RID stations"
- Add topics: `water-monitoring`, `thailand`, `grafana`, `timeseries`, `python`
- Enable Issues and Discussions
- Set up GitHub Pages for documentation
### **Initial Release**
- **Version**: v1.0.0
- **Release Notes**: Complete feature set with multi-database support
- **Assets**: Include sample configuration files
- **Documentation**: Link to comprehensive guides
### **Community Features**
- **Issues Template**: Bug reports and feature requests
- **Pull Request Template**: Contribution guidelines
- **Discussions**: Community support and questions
- **Wiki**: Extended documentation and tutorials
## 🎯 **Post-Publication Tasks**
### **Community Building**
- Create detailed issue templates
- Set up GitHub Actions for CI/CD
- Add code quality badges
- Create project roadmap
### **Documentation Enhancement**
- Add video tutorials
- Create API documentation
- Add performance benchmarks
- Create deployment examples
### **Feature Development**
- Mobile app integration
- Additional database backends
- Advanced alerting system
- Predictive analytics
## 📞 **Support Channels**
- **GitHub Issues**: Bug reports and feature requests
- **GitHub Discussions**: Community support and questions
- **Documentation**: Comprehensive guides in docs/ directory
- **Examples**: Working configurations and deployments
## 🏆 **Project Highlights**
### **Technical Excellence**
- Clean, modular architecture
- Comprehensive error handling
- Production-ready deployment
- Multi-database abstraction
### **Documentation Quality**
- Step-by-step installation guides
- Troubleshooting for common issues
- Migration procedures for updates
- API and configuration references
### **Community Ready**
- Open source MIT license
- Contributor guidelines
- Development setup instructions
- Code quality standards
---
**The Thailand Water Level Monitor project is now fully prepared for GitHub publication with a professional structure, comprehensive documentation, and production-ready features.** 🌊

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MIT License
Copyright (c) 2025 Northern Thailand Ping River Monitor
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.

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# Northern Thailand Ping River Monitor - Makefile
.PHONY: help install install-dev test test-coverage lint format clean run run-api docker-build docker-run docs
# Default target
help:
@echo "Northern Thailand Ping River Monitor - Available Commands:"
@echo ""
@echo "Setup:"
@echo " install Install production dependencies"
@echo " install-dev Install development dependencies"
@echo ""
@echo "Development:"
@echo " test Run all tests"
@echo " test-cov Run tests with coverage report"
@echo " lint Run code linting"
@echo " format Format code with black and isort"
@echo " clean Clean up temporary files"
@echo ""
@echo "Running:"
@echo " run Run the monitor in continuous mode"
@echo " run-api Run the web API server"
@echo " run-test Run a single test cycle"
@echo ""
@echo "Docker:"
@echo " docker-build Build Docker image"
@echo " docker-run Run with Docker Compose"
@echo " docker-stop Stop Docker services"
@echo ""
@echo "Documentation:"
@echo " docs Generate documentation"
# Installation
install:
pip install -r requirements.txt
install-dev:
pip install -r requirements-dev.txt
pre-commit install
# Testing
test:
python test_integration.py
python test_station_management.py
test-cov:
pytest --cov=src --cov-report=html --cov-report=term
# Code quality
lint:
flake8 src/ --max-line-length=100
mypy src/
format:
black src/ *.py
isort src/ *.py
# Cleanup
clean:
find . -type f -name "*.pyc" -delete
find . -type d -name "__pycache__" -delete
find . -type f -name "*.log" -delete
rm -rf .pytest_cache/
rm -rf .mypy_cache/
rm -rf htmlcov/
rm -rf dist/
rm -rf build/
rm -rf *.egg-info/
# Running
run:
python run.py
run-api:
python run.py --web-api
run-test:
python run.py --test
run-status:
python run.py --status
# Docker
docker-build:
docker build -t ping-river-monitor .
docker-run:
docker-compose -f docker-compose.victoriametrics.yml up -d
docker-stop:
docker-compose -f docker-compose.victoriametrics.yml down
docker-logs:
docker-compose -f docker-compose.victoriametrics.yml logs -f
# Documentation
docs:
cd docs && make html
# Database management
db-migrate:
python scripts/migrate_geolocation.py
# Monitoring
health-check:
curl -f http://localhost:8000/health || exit 1
metrics:
curl -s http://localhost:8000/metrics | jq .
# Development helpers
dev-setup: install-dev
cp .env.example .env
@echo "Development environment set up!"
@echo "Edit .env file with your configuration"
# Production deployment
deploy-check:
python run.py --test
@echo "Deployment check passed!"
# Git helpers
git-setup:
git remote add origin https://git.b4l.co.th/B4L/Northern-Thailand-Ping-River-Monitor.git
@echo "Git remote configured!"
# Quick start
quick-start: install dev-setup run-test
@echo "Quick start completed!"
@echo "Run 'make run-api' to start the web interface"
# Gitea Actions
validate-workflows:
@echo "Validating Gitea Actions workflows..."
@for file in .gitea/workflows/*.yml; do \
echo "Checking $$file..."; \
python -c "import yaml; yaml.safe_load(open('$$file', encoding='utf-8'))" || exit 1; \
done
@echo "✅ All workflows are valid"
workflow-test:
@echo "Testing workflow components locally..."
make test
make lint
make format
@echo "✅ Workflow test completed"

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# Northern Thailand Ping River Monitor 🏔️
A comprehensive real-time water level monitoring system for the Ping River Basin in Northern Thailand, covering Royal Irrigation Department (RID) stations from Chiang Dao to Nakhon Sawan with advanced data collection, storage, and visualization capabilities.
[![CI/CD](https://git.b4l.co.th/B4L/Northern-Thailand-Ping-River-Monitor/actions/workflows/ci.yml/badge.svg)](https://git.b4l.co.th/B4L/Northern-Thailand-Ping-River-Monitor/actions) [![Security](https://git.b4l.co.th/B4L/Northern-Thailand-Ping-River-Monitor/actions/workflows/security.yml/badge.svg)](https://git.b4l.co.th/B4L/Northern-Thailand-Ping-River-Monitor/actions) [![Documentation](https://git.b4l.co.th/B4L/Northern-Thailand-Ping-River-Monitor/actions/workflows/docs.yml/badge.svg)](https://git.b4l.co.th/B4L/Northern-Thailand-Ping-River-Monitor/actions) [![Python](https://img.shields.io/badge/Python-3.9+-blue.svg)](https://python.org) [![FastAPI](https://img.shields.io/badge/FastAPI-0.104+-green.svg)](https://fastapi.tiangolo.com) [![Docker](https://img.shields.io/badge/Docker-Ready-blue.svg)](https://docker.com) [![License](https://img.shields.io/badge/License-MIT-green.svg)](LICENSE) [![Version](https://img.shields.io/badge/Version-v3.1.0-blue.svg)](https://git.b4l.co.th/B4L/Northern-Thailand-Ping-River-Monitor/releases)
## 🌟 Features
### 📊 **Real-time Data Collection**
- **16 Monitoring Stations** across Thailand
- **15-minute Collection Frequency** with intelligent scheduling
- **Automatic Gap Filling** for missing historical data
- **Data Validation** and error recovery mechanisms
- **Rate Limiting** to prevent API abuse
### 🌐 **Web API Interface (NEW!)**
- **FastAPI-powered REST API** with interactive documentation
- **Station Management** - Add, update, and remove monitoring stations
- **Real-time health monitoring** and system status
- **Manual data collection triggers** via web interface
- **Comprehensive metrics** and performance monitoring
- **CORS support** for web applications
### 🗄️ **Multi-Database Support**
- **VictoriaMetrics** (Recommended) - High-performance time-series
- **InfluxDB** - Purpose-built time-series database
- **PostgreSQL + TimescaleDB** - Relational with time-series optimization
- **MySQL** - Traditional relational database
- **SQLite** - Local development and testing
### 🗺️ **Geolocation Support**
- **Grafana Geomap** integration ready
- **GPS coordinates** and geohash support
- **Interactive mapping** of water stations
### 📈 **Visualization & Monitoring**
- **Pre-built Grafana dashboards**
- **Real-time alerts** and notifications
- **Historical trend analysis**
- **Built-in metrics collection** (counters, gauges, histograms)
- **Health checks** for database, API, and system resources
### 🚀 **Production Ready**
- **Docker containerization** with multi-service support
- **Systemd service** configuration
- **HTTPS support** with SSL certificates
- **Comprehensive logging** with rotation and colored output
- **Type safety** with Pydantic models and type hints
- **Custom exception handling** for better error management
## 🚀 Quick Start
### Prerequisites
- Python 3.9 or higher
- Internet connection for data fetching
- Database server (optional - SQLite works out of the box)
### Installation
```bash
# Clone the repository
git clone https://git.b4l.co.th/B4L/Northern-Thailand-Ping-River-Monitor.git
cd Northern-Thailand-Ping-River-Monitor
# Quick setup with Make
make dev-setup
# Or manual setup:
python -m venv venv
source venv/bin/activate # Windows: venv\Scripts\activate
pip install -r requirements.txt
cp .env.example .env
```
### Basic Usage
```bash
# Test run with SQLite (default)
make run-test
# or: python run.py --test
# Run continuous monitoring
make run
# or: python run.py
# Start web API server (NEW!)
make run-api
# or: python run.py --web-api
# Run all tests
make test
# Demo different databases
python src/demo_databases.py
```
### 🌐 Web API Interface (NEW!)
The system now includes a comprehensive FastAPI web interface:
```bash
# Start the web API
python run.py --web-api
# Access the API at:
# - Dashboard: http://localhost:8000
# - Interactive docs: http://localhost:8000/docs
# - Health check: http://localhost:8000/health
# - Latest data: http://localhost:8000/measurements/latest
```
**Key API Endpoints:**
- `GET /` - Web dashboard
- `GET /health` - System health status
- `GET /metrics` - Application metrics
- `GET /stations` - List all monitoring stations
- `POST /stations` - Add new monitoring station
- `PUT /stations/{id}` - Update station information
- `DELETE /stations/{id}` - Remove monitoring station
- `GET /measurements/latest` - Latest measurements
- `GET /measurements/station/{code}` - Station-specific data
- `POST /scrape/trigger` - Trigger manual data collection
## 📊 Station Information
The system monitors **16 water stations** along the Ping River Basin in Northern Thailand:
| Station | Thai Name | English Name | Location |
|---------|-----------|--------------|----------|
| P.1 | สะพานนวรัฐ | Nawarat Bridge | Nakhon Sawan |
| P.5 | สะพานท่านาง | Tha Nang Bridge | - |
| P.20 | บ้านเชียงดาว | Ban Chiang Dao | Chiang Mai |
| P.21 | บ้านริมใต้ | Ban Rim Tai | - |
| P.4A | บ้านแม่แตง | Ban Mae Taeng | Chiang Mai |
| P.67 | บ้านแม่แต | Ban Tae | - |
| P.75 | บ้านช่อแล | Ban Chai Lat | - |
| P.76 | บ้านแม่อีไฮ | Banb Mae I Hai | - |
| P.77 | บ้านสบแม่สะป๊วด | Baan Sop Mae Sapuord | - |
| P.81 | บ้านโป่ง | Ban Pong | - |
| P.82 | บ้านสบวิน | Ban Sob win | - |
| P.84 | บ้านพันตน | Ban Panton | - |
| P.85 | บ้านหล่ายแก้ว | Baan Lai Kaew | - |
| P.87 | บ้านป่าซาง | Ban Pa Sang | - |
| P.92 | บ้านเมืองกึ๊ด | Ban Muang Aut | - |
| P.103 | สะพานวงแหวนรอบ 3 | Ring Bridge 3 | Bangkok |
### Data Metrics
- **Water Level**: Measured in meters (m)
- **Discharge**: Flow rate in cubic meters per second (cms)
- **Discharge Percentage**: Relative to station capacity
- **Timestamp**: Thai time (UTC+7) with Buddhist calendar support
## 🗄️ Database Configuration
### VictoriaMetrics (Recommended)
**High-performance time-series database with excellent compression and query speed.**
```bash
# Environment variables
export DB_TYPE=victoriametrics
export VM_HOST=localhost
export VM_PORT=8428
# Quick start with Docker
docker run -d \
--name victoriametrics \
-p 8428:8428 \
-v victoria-metrics-data:/victoria-metrics-data \
victoriametrics/victoria-metrics:latest \
--storageDataPath=/victoria-metrics-data \
--retentionPeriod=2y \
--httpListenAddr=:8428
```
### Complete Stack with Grafana
```bash
# Start the complete monitoring stack
docker-compose -f docker-compose.victoriametrics.yml up -d
# Access Grafana at http://localhost:3000
# Username: admin, Password: admin_password
```
### Other Database Options
<details>
<summary>InfluxDB Configuration</summary>
```bash
export DB_TYPE=influxdb
export INFLUX_HOST=localhost
export INFLUX_PORT=8086
export INFLUX_DATABASE=water_monitoring
export INFLUX_USERNAME=water_user
export INFLUX_PASSWORD=your_password
```
</details>
<details>
<summary>PostgreSQL Configuration</summary>
```bash
export DB_TYPE=postgresql
export POSTGRES_CONNECTION_STRING=postgresql://user:password@localhost:5432/water_monitoring
```
</details>
<details>
<summary>MySQL Configuration</summary>
```bash
export DB_TYPE=mysql
export MYSQL_CONNECTION_STRING=mysql://user:password@localhost:3306/water_monitoring
```
</details>
## 📈 Grafana Dashboards
### Pre-built Dashboard Features
- **Real-time water levels** across all stations
- **Historical trends** and patterns
- **Discharge monitoring** with percentage indicators
- **Station status** and health monitoring
- **Geomap visualization** of station locations
- **Alert thresholds** for critical water levels
### Sample Queries
**VictoriaMetrics/Prometheus:**
```promql
# Current water levels
water_level
# High discharge alerts
water_discharge_percent > 80
# Station-specific data
water_level{station_code="P.1"}
```
**SQL Databases:**
```sql
-- Latest readings from all stations
SELECT s.station_code, s.english_name, m.water_level, m.discharge
FROM stations s
JOIN water_measurements m ON s.id = m.station_id
WHERE m.timestamp = (SELECT MAX(timestamp) FROM water_measurements WHERE station_id = s.id);
```
## 🚀 Production Deployment
### Docker Deployment
```bash
# Build the image
docker build -t thailand-water-monitor .
# Run with environment variables
docker run -d \
--name water-monitor \
-e DB_TYPE=victoriametrics \
-e VM_HOST=victoriametrics \
thailand-water-monitor
```
### Systemd Service (Linux)
```bash
# Copy service file
sudo cp scripts/water-monitor.service /etc/systemd/system/
# Enable and start
sudo systemctl enable water-monitor.service
sudo systemctl start water-monitor.service
```
### Migration for Existing Systems
If you have an existing installation, use the migration script to add geolocation support:
```bash
# Stop the service
sudo systemctl stop water-monitor
# Run migration
python scripts/migrate_geolocation.py
# Restart the service
sudo systemctl start water-monitor
```
## 🔧 Command Line Tools
### Main Application
```bash
python src/water_scraper_v3.py # Run continuous monitoring
python src/water_scraper_v3.py --test # Single test cycle
python src/water_scraper_v3.py --help # Show help
```
### Data Management
```bash
python src/water_scraper_v3.py --check-gaps 7 # Check for missing data (7 days)
python src/water_scraper_v3.py --fill-gaps 7 # Fill missing data gaps
python src/water_scraper_v3.py --update-data 2 # Update existing data (2 days)
```
### Database Testing
```bash
python src/demo_databases.py # SQLite demo
python src/demo_databases.py victoriametrics # VictoriaMetrics demo
python src/demo_databases.py all # Test all databases
```
## 📚 Documentation
### Core Documentation
- **[Installation Guide](docs/DATABASE_DEPLOYMENT_GUIDE.md)** - Complete setup instructions
- **[Scheduler Guide](docs/ENHANCED_SCHEDULER_GUIDE.md)** - 15-minute scheduling system
- **[Geolocation Guide](docs/GEOLOCATION_GUIDE.md)** - Grafana geomap integration
- **[Gap Filling Guide](docs/GAP_FILLING_GUIDE.md)** - Data integrity management
### Deployment Guides
- **[VictoriaMetrics Setup](docs/VICTORIAMETRICS_SETUP.md)** - High-performance deployment
- **[HTTPS Configuration](docs/HTTPS_CONFIGURATION.md)** - Secure deployment
- **[Debian Troubleshooting](docs/DEBIAN_TROUBLESHOOTING.md)** - Linux deployment issues
### References
- **[Notable Documents](docs/references/NOTABLE_DOCUMENTS.md)** - Official Thai government resources
- **[Migration Guide](docs/MIGRATION_QUICKSTART.md)** - Updating existing systems
## 🔍 Troubleshooting
### Common Issues
**Database Connection Errors:**
```bash
# Check database status
python src/demo_databases.py
# Test specific database
python src/demo_databases.py victoriametrics
```
**Missing Data:**
```bash
# Check for gaps
python src/water_scraper_v3.py --check-gaps 7
# Fill missing data
python src/water_scraper_v3.py --fill-gaps 7
```
**Service Issues:**
```bash
# Check service status
sudo systemctl status water-monitor
# View logs
sudo journalctl -u water-monitor -f
```
### Health Checks
```bash
# VictoriaMetrics health
curl http://localhost:8428/health
# Check latest data
curl "http://localhost:8428/api/v1/query?query=water_level"
# Application logs
tail -f water_monitor.log
```
## 🌐 API Integration
### VictoriaMetrics API Examples
```bash
# Query current water levels
curl "http://localhost:8428/api/v1/query?query=water_level"
# Query discharge rates for last hour
curl "http://localhost:8428/api/v1/query_range?query=water_discharge&start=$(date -d '1 hour ago' +%s)&end=$(date +%s)&step=300"
# Query specific station
curl "http://localhost:8428/api/v1/query?query=water_level{station_code=\"P.1\"}"
# High discharge alerts
curl "http://localhost:8428/api/v1/query?query=water_discharge_percent>80"
```
## 📊 Performance
### System Requirements
- **CPU**: 1-2 cores (minimal load)
- **RAM**: 512MB - 2GB (depending on database)
- **Storage**: 1GB+ (for historical data)
- **Network**: Stable internet connection
### Performance Metrics
- **Data Collection**: ~300 data points every 15 minutes
- **Database Write Speed**: 1000+ points/second (VictoriaMetrics)
- **Query Response**: <100ms for recent data
- **Storage Efficiency**: 70x compression vs. raw data
## 🤝 Contributing
Contributions are welcome! Please:
1. Fork the repository
2. Create a feature branch
3. Make your changes
4. Add tests if applicable
5. Submit a pull request
### Development Setup
```bash
# Clone your fork
git clone https://github.com/your-username/thailand-water-monitor.git
cd thailand-water-monitor
# Install development dependencies
pip install -r requirements.txt
pip install pytest black flake8
# Run tests
pytest
# Format code
black src/
```
## 📄 License
This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.
## 🙏 Acknowledgments
- **Royal Irrigation Department (RID)** of Thailand for providing the data API
- **VictoriaMetrics** team for the excellent time-series database
- **Grafana** team for the visualization platform
- **Python community** for the amazing libraries and tools
## 📞 Support
- **Issues**: [GitHub Issues](https://github.com/your-username/thailand-water-monitor/issues)
- **Discussions**: [GitHub Discussions](https://github.com/your-username/thailand-water-monitor/discussions)
- **Documentation**: [Project Wiki](https://github.com/your-username/thailand-water-monitor/wiki)
---
## 📁 Project Structure
```
Northern-Thailand-Ping-River-Monitor/
├── src/ # Main application code
├── tests/ # Test suite
├── docs/ # Documentation
├── grafana/ # Grafana dashboards
├── scripts/ # Utility scripts
├── docker-compose.yml # Docker deployment
├── Makefile # Development tasks
└── requirements.txt # Dependencies
```
See [docs/PROJECT_STRUCTURE.md](docs/PROJECT_STRUCTURE.md) for detailed architecture information.
## 🔄 CI/CD & Automation
The project includes comprehensive Gitea Actions workflows:
- **🧪 CI/CD Pipeline** - Automated testing, building, and deployment
- **🔒 Security Scanning** - Daily vulnerability and dependency checks
- **📚 Documentation** - Automated API docs and validation
- **🚀 Release Management** - Automated releases with multi-arch Docker builds
See [docs/GITEA_WORKFLOWS.md](docs/GITEA_WORKFLOWS.md) for detailed workflow documentation.
## 🔗 Repository
- **Main Repository**: https://git.b4l.co.th/B4L/Northern-Thailand-Ping-River-Monitor
- **Issues**: https://git.b4l.co.th/B4L/Northern-Thailand-Ping-River-Monitor/issues
- **Actions**: https://git.b4l.co.th/B4L/Northern-Thailand-Ping-River-Monitor/actions
- **Documentation**: [docs/](docs/)
**Made with ❤️ for water resource monitoring in Northern Thailand's Ping River Basin**

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version: '3.8'
services:
victoriametrics:
image: victoriametrics/victoria-metrics:latest
container_name: ping_river_victoriametrics
ports:
- "8428:8428"
volumes:
- vm_data:/victoria-metrics-data
- ./vm-config:/etc/victoriametrics
command:
- '--storageDataPath=/victoria-metrics-data'
- '--retentionPeriod=2y'
- '--httpListenAddr=:8428'
- '--maxConcurrentInserts=32'
- '--search.maxQueryDuration=60s'
- '--search.maxConcurrentRequests=16'
- '--dedup.minScrapeInterval=30s'
- '--memory.allowedPercent=80'
- '--loggerLevel=INFO'
- '--loggerFormat=json'
- '--search.maxSeries=1000000'
- '--search.maxPointsPerTimeseries=100000'
restart: unless-stopped
environment:
- TZ=Asia/Bangkok
healthcheck:
test: ["CMD", "wget", "--quiet", "--tries=1", "--spider", "http://localhost:8428/health"]
interval: 30s
timeout: 10s
retries: 3
start_period: 40s
labels:
- "com.victoriametrics.service=ping-river-monitoring"
grafana:
image: grafana/grafana:latest
container_name: ping_river_grafana
ports:
- "3000:3000"
volumes:
- grafana_data:/var/lib/grafana
- ./grafana/provisioning:/etc/grafana/provisioning
- ./grafana/dashboards:/var/lib/grafana/dashboards
environment:
- GF_SECURITY_ADMIN_PASSWORD=admin_password
- GF_INSTALL_PLUGINS=grafana-clock-panel,grafana-worldmap-panel
- GF_USERS_ALLOW_SIGN_UP=false
- GF_SECURITY_ALLOW_EMBEDDING=true
- GF_AUTH_ANONYMOUS_ENABLED=false
- TZ=Asia/Bangkok
restart: unless-stopped
depends_on:
victoriametrics:
condition: service_healthy
labels:
- "com.victoriametrics.service=ping-river-monitoring"
ping-river-monitor:
build: .
container_name: ping_river_monitor
environment:
- DB_TYPE=victoriametrics
- VM_HOST=victoriametrics
- VM_PORT=8428
- TZ=Asia/Bangkok
restart: unless-stopped
depends_on:
victoriametrics:
condition: service_healthy
volumes:
- ./logs:/app/logs
labels:
- "com.victoriametrics.service=ping-river-monitoring"
ping-river-api:
build: .
container_name: ping_river_api
ports:
- "8000:8000"
environment:
- DB_TYPE=victoriametrics
- VM_HOST=victoriametrics
- VM_PORT=8428
- TZ=Asia/Bangkok
restart: unless-stopped
depends_on:
victoriametrics:
condition: service_healthy
volumes:
- ./logs:/app/logs
command: ["python", "-m", "src.main", "--web-api"]
labels:
- "com.victoriametrics.service=ping-river-monitoring"
volumes:
vm_data:
driver: local
grafana_data:
driver: local
networks:
default:
name: ping-river-monitoring
driver: bridge

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# Database Deployment Guide for Thailand Water Monitor
This guide covers deployment options for storing water monitoring data in production environments.
## 🏆 Recommendation Summary
| Database | Best For | Performance | Complexity | Cost |
|----------|----------|-------------|------------|------|
| **InfluxDB** | Time-series data, dashboards | ⭐⭐⭐⭐⭐ | ⭐⭐⭐ | ⭐⭐⭐ |
| **VictoriaMetrics** | High-performance metrics | ⭐⭐⭐⭐⭐ | ⭐⭐⭐⭐ | ⭐⭐⭐⭐ |
| **PostgreSQL** | Complex queries, reliability | ⭐⭐⭐⭐ | ⭐⭐ | ⭐⭐⭐⭐ |
| **MySQL** | Familiar, existing infrastructure | ⭐⭐⭐ | ⭐⭐ | ⭐⭐⭐⭐ |
## 1. InfluxDB Deployment (Recommended for Time-Series)
### Why InfluxDB?
- **Purpose-built** for time-series data
- **Excellent compression** (10:1 typical ratio)
- **Built-in retention policies** and downsampling
- **Great Grafana integration** for dashboards
- **High write throughput** (100k+ points/second)
### Docker Deployment
```yaml
# docker-compose.yml
version: '3.8'
services:
influxdb:
image: influxdb:1.8
container_name: water_influxdb
ports:
- "8086:8086"
volumes:
- influxdb_data:/var/lib/influxdb
- ./influxdb.conf:/etc/influxdb/influxdb.conf:ro
environment:
- INFLUXDB_DB=water_monitoring
- INFLUXDB_ADMIN_USER=admin
- INFLUXDB_ADMIN_PASSWORD=your_secure_password
- INFLUXDB_USER=water_user
- INFLUXDB_USER_PASSWORD=water_password
restart: unless-stopped
grafana:
image: grafana/grafana:latest
container_name: water_grafana
ports:
- "3000:3000"
volumes:
- grafana_data:/var/lib/grafana
environment:
- GF_SECURITY_ADMIN_PASSWORD=admin_password
restart: unless-stopped
volumes:
influxdb_data:
grafana_data:
```
### Environment Variables
```bash
# .env file
DB_TYPE=influxdb
INFLUX_HOST=localhost
INFLUX_PORT=8086
INFLUX_DATABASE=water_monitoring
INFLUX_USERNAME=water_user
INFLUX_PASSWORD=water_password
```
### InfluxDB Configuration
```toml
# influxdb.conf
[meta]
dir = "/var/lib/influxdb/meta"
[data]
dir = "/var/lib/influxdb/data"
wal-dir = "/var/lib/influxdb/wal"
# Optimize for time-series data
cache-max-memory-size = "1g"
cache-snapshot-memory-size = "25m"
cache-snapshot-write-cold-duration = "10m"
# Retention and compression
compact-full-write-cold-duration = "4h"
max-series-per-database = 1000000
max-values-per-tag = 100000
[coordinator]
write-timeout = "10s"
max-concurrent-queries = 0
query-timeout = "0s"
[retention]
enabled = true
check-interval = "30m"
[http]
enabled = true
bind-address = ":8086"
auth-enabled = true
max-body-size = "25000000"
max-concurrent-requests = 0
max-enqueued-requests = 0
```
### Production Setup Commands
```bash
# Start services
docker-compose up -d
# Create retention policies
docker exec -it water_influxdb influx -username admin -password your_secure_password -execute "
CREATE RETENTION POLICY \"raw_data\" ON \"water_monitoring\" DURATION 90d REPLICATION 1 DEFAULT;
CREATE RETENTION POLICY \"downsampled\" ON \"water_monitoring\" DURATION 730d REPLICATION 1;
"
# Create continuous queries for downsampling
docker exec -it water_influxdb influx -username admin -password your_secure_password -execute "
CREATE CONTINUOUS QUERY \"downsample_hourly\" ON \"water_monitoring\"
BEGIN
SELECT mean(water_level) AS water_level, mean(discharge) AS discharge, mean(discharge_percent) AS discharge_percent
INTO \"downsampled\".\"water_data_hourly\"
FROM \"water_data\"
GROUP BY time(1h), station_code, station_name_en, station_name_th
END
"
```
## 2. VictoriaMetrics Deployment (High Performance)
### Why VictoriaMetrics?
- **Extremely fast** and resource-efficient
- **Better compression** than InfluxDB
- **Prometheus-compatible** API
- **Lower memory usage**
- **Built-in clustering**
### Docker Deployment
```yaml
# docker-compose.yml
version: '3.8'
services:
victoriametrics:
image: victoriametrics/victoria-metrics:latest
container_name: water_victoriametrics
ports:
- "8428:8428"
volumes:
- vm_data:/victoria-metrics-data
command:
- '--storageDataPath=/victoria-metrics-data'
- '--retentionPeriod=2y'
- '--httpListenAddr=:8428'
- '--maxConcurrentInserts=16'
restart: unless-stopped
volumes:
vm_data:
```
### Environment Variables
```bash
# .env file
DB_TYPE=victoriametrics
VM_HOST=localhost
VM_PORT=8428
```
## 3. PostgreSQL Deployment (Relational + Time-Series)
### Why PostgreSQL?
- **Mature and reliable**
- **Excellent for complex queries**
- **TimescaleDB extension** for time-series optimization
- **Strong consistency guarantees**
- **Rich ecosystem**
### Docker Deployment with TimescaleDB
```yaml
# docker-compose.yml
version: '3.8'
services:
postgres:
image: timescale/timescaledb:latest-pg14
container_name: water_postgres
ports:
- "5432:5432"
volumes:
- postgres_data:/var/lib/postgresql/data
- ./init.sql:/docker-entrypoint-initdb.d/init.sql
environment:
- POSTGRES_DB=water_monitoring
- POSTGRES_USER=water_user
- POSTGRES_PASSWORD=secure_password
restart: unless-stopped
volumes:
postgres_data:
```
### Database Initialization
```sql
-- init.sql
CREATE EXTENSION IF NOT EXISTS timescaledb CASCADE;
-- Create hypertable for time-series optimization
CREATE TABLE water_measurements (
id BIGSERIAL PRIMARY KEY,
timestamp TIMESTAMPTZ NOT NULL,
station_id INT NOT NULL,
water_level NUMERIC(10,3),
discharge NUMERIC(10,2),
discharge_percent NUMERIC(5,2),
status VARCHAR(20) DEFAULT 'active',
created_at TIMESTAMPTZ DEFAULT NOW()
);
-- Convert to hypertable (TimescaleDB)
SELECT create_hypertable('water_measurements', 'timestamp', chunk_time_interval => INTERVAL '1 day');
-- Create indexes
CREATE INDEX idx_water_measurements_station_time ON water_measurements (station_id, timestamp DESC);
CREATE INDEX idx_water_measurements_timestamp ON water_measurements (timestamp DESC);
-- Create retention policy (keep raw data for 2 years)
SELECT add_retention_policy('water_measurements', INTERVAL '2 years');
-- Create continuous aggregates for performance
CREATE MATERIALIZED VIEW water_measurements_hourly
WITH (timescaledb.continuous) AS
SELECT
time_bucket('1 hour', timestamp) AS bucket,
station_id,
AVG(water_level) as avg_water_level,
MAX(water_level) as max_water_level,
MIN(water_level) as min_water_level,
AVG(discharge) as avg_discharge,
MAX(discharge) as max_discharge,
MIN(discharge) as min_discharge,
AVG(discharge_percent) as avg_discharge_percent
FROM water_measurements
GROUP BY bucket, station_id;
-- Refresh policy for continuous aggregates
SELECT add_continuous_aggregate_policy('water_measurements_hourly',
start_offset => INTERVAL '1 day',
end_offset => INTERVAL '1 hour',
schedule_interval => INTERVAL '1 hour');
```
### Environment Variables
```bash
# .env file
DB_TYPE=postgresql
POSTGRES_CONNECTION_STRING=postgresql://water_user:secure_password@localhost:5432/water_monitoring
```
## 4. MySQL Deployment (Traditional Relational)
### Docker Deployment
```yaml
# docker-compose.yml
version: '3.8'
services:
mysql:
image: mysql:8.0
container_name: water_mysql
ports:
- "3306:3306"
volumes:
- mysql_data:/var/lib/mysql
- ./mysql.cnf:/etc/mysql/conf.d/mysql.cnf
- ./init.sql:/docker-entrypoint-initdb.d/init.sql
environment:
- MYSQL_ROOT_PASSWORD=root_password
- MYSQL_DATABASE=water_monitoring
- MYSQL_USER=water_user
- MYSQL_PASSWORD=water_password
restart: unless-stopped
volumes:
mysql_data:
```
### MySQL Configuration
```ini
# mysql.cnf
[mysqld]
# Optimize for time-series data
innodb_buffer_pool_size = 1G
innodb_log_file_size = 256M
innodb_flush_log_at_trx_commit = 2
innodb_flush_method = O_DIRECT
# Partitioning support
partition = ON
# Query cache
query_cache_type = 1
query_cache_size = 128M
# Connection settings
max_connections = 200
connect_timeout = 10
wait_timeout = 600
```
### Environment Variables
```bash
# .env file
DB_TYPE=mysql
MYSQL_CONNECTION_STRING=mysql://water_user:water_password@localhost:3306/water_monitoring
```
## 5. Installation and Dependencies
### Required Python Packages
```bash
# Base requirements
pip install requests schedule
# Database-specific packages
pip install influxdb # For InfluxDB
pip install sqlalchemy pymysql # For MySQL
pip install sqlalchemy psycopg2-binary # For PostgreSQL
# VictoriaMetrics uses HTTP API (no extra packages needed)
```
### Updated requirements.txt
```txt
requests>=2.28.0
schedule>=1.2.0
pandas>=1.5.0
# Database adapters (install as needed)
influxdb>=5.3.1
sqlalchemy>=1.4.0
pymysql>=1.0.2
psycopg2-binary>=2.9.0
```
## 6. Production Deployment Examples
### Using InfluxDB (Recommended)
```bash
# Set environment variables
export DB_TYPE=influxdb
export INFLUX_HOST=your-influx-server.com
export INFLUX_PORT=8086
export INFLUX_DATABASE=water_monitoring
export INFLUX_USERNAME=water_user
export INFLUX_PASSWORD=your_secure_password
# Run the scraper
python water_scraper_v3.py
```
### Using PostgreSQL with TimescaleDB
```bash
# Set environment variables
export DB_TYPE=postgresql
export POSTGRES_CONNECTION_STRING=postgresql://water_user:password@your-postgres-server.com:5432/water_monitoring
# Run the scraper
python water_scraper_v3.py
```
### Using VictoriaMetrics
```bash
# Set environment variables
export DB_TYPE=victoriametrics
export VM_HOST=your-vm-server.com
export VM_PORT=8428
# Run the scraper
python water_scraper_v3.py
```
## 7. Monitoring and Alerting
### Grafana Dashboard Setup
1. **Add Data Source**: Configure your database as a Grafana data source
2. **Import Dashboard**: Use pre-built water monitoring dashboards
3. **Set Alerts**: Configure alerts for abnormal water levels or discharge rates
### Example Grafana Queries
#### InfluxDB Queries
```sql
-- Current water levels
SELECT last("water_level") FROM "water_data" GROUP BY "station_code"
-- Discharge trends (last 24h)
SELECT mean("discharge") FROM "water_data" WHERE time >= now() - 24h GROUP BY time(1h), "station_code"
```
#### PostgreSQL/TimescaleDB Queries
```sql
-- Current water levels
SELECT DISTINCT ON (station_id)
station_id, water_level, discharge, timestamp
FROM water_measurements
ORDER BY station_id, timestamp DESC;
-- Hourly averages (last 24h)
SELECT
time_bucket('1 hour', timestamp) as hour,
station_id,
AVG(water_level) as avg_level,
AVG(discharge) as avg_discharge
FROM water_measurements
WHERE timestamp >= NOW() - INTERVAL '24 hours'
GROUP BY hour, station_id
ORDER BY hour DESC;
```
## 8. Performance Optimization Tips
### For All Databases
- **Batch inserts**: Insert multiple measurements at once
- **Connection pooling**: Reuse database connections
- **Indexing**: Ensure proper indexes on timestamp and station_id
- **Retention policies**: Automatically delete old data
### InfluxDB Specific
- Use **tags** for metadata (station codes, names)
- Use **fields** for numeric values (water levels, discharge)
- Configure **retention policies** and **continuous queries**
- Enable **compression** for long-term storage
### PostgreSQL/TimescaleDB Specific
- Use **hypertables** for automatic partitioning
- Create **continuous aggregates** for common queries
- Configure **compression** for older chunks
- Use **parallel queries** for large datasets
### VictoriaMetrics Specific
- Use **labels** efficiently (similar to Prometheus)
- Configure **retention periods** appropriately
- Use **downsampling** for long-term storage
- Enable **deduplication** if needed
This deployment guide provides production-ready configurations for all supported database backends. Choose the one that best fits your infrastructure and requirements.

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# Debian/Linux Troubleshooting Guide
This guide addresses common issues when running the Thailand Water Monitor on Debian and other Linux distributions.
## Fixed Issues
### SQLAlchemy Connection Error (RESOLVED)
**Error Message:**
```
2025-07-24 19:48:31,920 - ERROR - Failed to connect to SQLITE: 'Connection' object has no attribute 'commit'
2025-07-24 19:48:32,740 - ERROR - Error saving to SQLITE: 'Connection' object has no attribute 'commit'
```
**Root Cause:**
This error occurred due to incompatibility between the database adapter code and newer versions of SQLAlchemy. The code was calling `conn.commit()` on a connection object that doesn't have a `commit()` method in newer SQLAlchemy versions.
**Solution Applied:**
Changed from `engine.connect()` to `engine.begin()` context manager, which automatically handles transactions:
```python
# OLD (problematic) code:
with self.engine.connect() as conn:
conn.execute(text(sql))
conn.commit() # This fails in newer SQLAlchemy
# NEW (fixed) code:
with self.engine.begin() as conn:
conn.execute(text(sql))
# Transaction automatically committed when context exits
```
**Status:** ✅ **FIXED** - The issue has been resolved in the current version.
## Installation on Debian/Ubuntu
### System Requirements
```bash
# Update package list
sudo apt update
# Install Python and pip
sudo apt install python3 python3-pip python3-venv
# Install system dependencies for database drivers
sudo apt install build-essential python3-dev
# For MySQL support (optional)
sudo apt install default-libmysqlclient-dev
# For PostgreSQL support (optional)
sudo apt install libpq-dev
```
### Python Environment Setup
```bash
# Create virtual environment
python3 -m venv water_monitor_env
# Activate virtual environment
source water_monitor_env/bin/activate
# Install requirements
pip install -r requirements.txt
```
### Running the Monitor
```bash
# Test run
python water_scraper_v3.py --test
# Run with specific database
export DB_TYPE=sqlite
python water_scraper_v3.py
# Run demo
python demo_databases.py
```
## Common Linux Issues
### 1. Permission Errors
**Error:**
```
PermissionError: [Errno 13] Permission denied: 'water_levels.db'
```
**Solution:**
```bash
# Check current directory permissions
ls -la
# Create data directory with proper permissions
mkdir -p data
chmod 755 data
# Set database path to data directory
export WATER_DB_PATH=data/water_levels.db
```
### 2. Missing System Dependencies
**Error:**
```
ImportError: No module named '_sqlite3'
```
**Solution:**
```bash
# Install SQLite development headers
sudo apt install libsqlite3-dev
# Reinstall Python if needed
sudo apt install python3-sqlite
```
### 3. Network/Firewall Issues
**Error:**
```
requests.exceptions.ConnectionError: HTTPSConnectionPool
```
**Solution:**
```bash
# Test network connectivity
curl -I https://hyd-app-db.rid.go.th/hydro1h.html
# Check firewall rules
sudo ufw status
# Allow outbound HTTPS if needed
sudo ufw allow out 443
```
### 4. Systemd Service Setup
Create service file `/etc/systemd/system/water-monitor.service`:
```ini
[Unit]
Description=Thailand Water Level Monitor
After=network.target
[Service]
Type=simple
User=water-monitor
Group=water-monitor
WorkingDirectory=/opt/water_level_monitor
Environment=PATH=/opt/water_level_monitor/venv/bin
Environment=DB_TYPE=sqlite
Environment=WATER_DB_PATH=/opt/water_level_monitor/data/water_levels.db
ExecStart=/opt/water_level_monitor/venv/bin/python water_scraper_v3.py
Restart=always
RestartSec=60
# Security settings
NoNewPrivileges=true
PrivateTmp=true
ProtectSystem=strict
ProtectHome=true
ReadWritePaths=/opt/water_level_monitor/data
ReadWritePaths=/opt/water_level_monitor/logs
[Install]
WantedBy=multi-user.target
```
Enable and start:
```bash
sudo systemctl daemon-reload
sudo systemctl enable water-monitor.service
sudo systemctl start water-monitor.service
sudo systemctl status water-monitor.service
```
### 5. Log Rotation
Create `/etc/logrotate.d/water-monitor`:
```
/opt/water_level_monitor/water_monitor.log {
daily
missingok
rotate 30
compress
delaycompress
notifempty
create 644 water-monitor water-monitor
postrotate
systemctl reload water-monitor.service
endscript
}
```
## Database-Specific Issues
### SQLite
**Issue:** Database locked
```bash
# Check for processes using the database
sudo lsof /path/to/water_levels.db
# Kill processes if needed
sudo pkill -f water_scraper_v3.py
```
### VictoriaMetrics with HTTPS
**Configuration:**
```bash
export DB_TYPE=victoriametrics
export VM_HOST=https://your-vm-server.com
export VM_PORT=443
```
**Test connection:**
```bash
curl -k https://your-vm-server.com/health
```
## Performance Optimization
### 1. System Tuning
```bash
# Increase file descriptor limits
echo "* soft nofile 65536" >> /etc/security/limits.conf
echo "* hard nofile 65536" >> /etc/security/limits.conf
# Optimize network settings
echo "net.core.rmem_max = 16777216" >> /etc/sysctl.conf
echo "net.core.wmem_max = 16777216" >> /etc/sysctl.conf
sysctl -p
```
### 2. Database Optimization
```bash
# For SQLite
export SQLITE_CACHE_SIZE=10000
export SQLITE_SYNCHRONOUS=NORMAL
# Monitor database size
du -h data/water_levels.db
```
## Monitoring and Maintenance
### Health Check Script
Create `health_check.sh`:
```bash
#!/bin/bash
LOG_FILE="/opt/water_level_monitor/water_monitor.log"
SERVICE_NAME="water-monitor"
# Check if service is running
if ! systemctl is-active --quiet $SERVICE_NAME; then
echo "ERROR: $SERVICE_NAME is not running"
systemctl restart $SERVICE_NAME
exit 1
fi
# Check recent log entries
RECENT_ERRORS=$(tail -n 100 $LOG_FILE | grep -c "ERROR")
if [ $RECENT_ERRORS -gt 5 ]; then
echo "WARNING: $RECENT_ERRORS errors found in recent logs"
exit 1
fi
echo "OK: Service is healthy"
exit 0
```
### Cron Job for Health Checks
```bash
# Add to crontab
*/5 * * * * /opt/water_level_monitor/health_check.sh >> /var/log/water-monitor-health.log 2>&1
```
## Getting Help
### Debug Information
```bash
# System information
uname -a
python3 --version
pip list | grep -E "(sqlalchemy|requests|influxdb)"
# Service logs
journalctl -u water-monitor.service -f
# Application logs
tail -f water_monitor.log
# Database information
sqlite3 water_levels.db ".schema"
sqlite3 water_levels.db "SELECT COUNT(*) FROM water_measurements;"
```
### Common Commands
```bash
# Restart service
sudo systemctl restart water-monitor.service
# View logs
sudo journalctl -u water-monitor.service --since "1 hour ago"
# Test configuration
python config.py
# Test database connection
python demo_databases.py
# Manual data fetch
python water_scraper_v3.py --test
```
This troubleshooting guide should help resolve most common issues encountered when running the Thailand Water Monitor on Debian and other Linux distributions.

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# Enhanced Scheduler Guide
This guide explains the new 15-minute scheduling system that runs continuously throughout each hour to ensure comprehensive data coverage.
## ✅ **New Scheduling Behavior**
### **15-Minute Schedule Pattern**
- **Timing**: Runs every 15 minutes: 1:00, 1:15, 1:30, 1:45, 2:00, 2:15, 2:30, 2:45, etc.
- **Hourly Full Checks**: At :00 minutes (includes gap filling and data updates)
- **Quarter-Hour Quick Checks**: At :15, :30, :45 minutes (data fetch only)
- **Continuous Coverage**: Ensures no data is missed throughout each hour
### **Operation Types**
- **Full Operations** (at :00): Data fetching + gap filling + data updates
- **Quick Operations** (at :15, :30, :45): Data fetching only for performance
## 🔧 **Technical Implementation**
### **Scheduler States**
```python
# State tracking variables
self.last_successful_update = None # Timestamp of last successful data update
self.retry_mode = False # Whether in quick check mode (skip gap filling)
self.next_hourly_check = None # Next scheduled hourly check
```
### **Quarter-Hour Check Process**
```python
def quarter_hour_check(self):
"""15-minute check for new data"""
current_time = datetime.datetime.now()
minute = current_time.minute
# Determine if this is a full hourly check (at :00) or a quarter-hour check
if minute == 0:
logging.info("=== HOURLY CHECK (00:00) ===")
self.retry_mode = False # Full check with gap filling and updates
else:
logging.info(f"=== 15-MINUTE CHECK ({minute:02d}:00) ===")
self.retry_mode = True # Skip gap filling and updates on 15-min checks
new_data_found = self.run_scraping_cycle()
if new_data_found:
self.last_successful_update = datetime.datetime.now()
if minute == 0:
logging.info("New data found during hourly check")
else:
logging.info(f"New data found during 15-minute check at :{minute:02d}")
else:
if minute == 0:
logging.info("No new data found during hourly check")
else:
logging.info(f"No new data found during 15-minute check at :{minute:02d}")
```
### **Scheduler Setup**
```python
def start_scheduler(self):
"""Start enhanced scheduler with 15-minute checks"""
# Schedule checks every 15 minutes (at :00, :15, :30, :45)
schedule.every().hour.at(":00").do(self.quarter_hour_check)
schedule.every().hour.at(":15").do(self.quarter_hour_check)
schedule.every().hour.at(":30").do(self.quarter_hour_check)
schedule.every().hour.at(":45").do(self.quarter_hour_check)
while True:
schedule.run_pending()
time.sleep(30) # Check every 30 seconds
```
## 📊 **New Data Detection Logic**
### **Smart Detection Algorithm**
```python
def has_new_data(self) -> bool:
"""Check if there is new data available since last successful update"""
# Get most recent timestamp from database
latest_data = self.get_latest_data(limit=1)
# Check if we should have newer data by now
now = datetime.datetime.now()
expected_latest = now.replace(minute=0, second=0, microsecond=0)
# If current time is past 5 minutes after the hour, we should have data
if now.minute >= 5:
if latest_timestamp < expected_latest:
return True # New data expected
# Check if we have data for the previous hour
previous_hour = expected_latest - datetime.timedelta(hours=1)
if latest_timestamp < previous_hour:
return True # Missing recent data
return False # Data is up to date
```
### **Actual Data Verification**
```python
# Compare timestamps before and after scraping
initial_timestamp = get_latest_timestamp_before_scraping()
# ... perform scraping ...
latest_timestamp = get_latest_timestamp_after_scraping()
if initial_timestamp is None or latest_timestamp > initial_timestamp:
new_data_found = True
self.last_successful_update = datetime.datetime.now()
```
## 🚀 **Operational Modes**
### **Mode 1: Full Hourly Operation (at :00)**
- **Schedule**: Every hour at :00 minutes (1:00, 2:00, 3:00, etc.)
- **Operations**:
- ✅ Fetch current data
- ✅ Fill data gaps (last 7 days)
- ✅ Update existing data (last 2 days)
- **Purpose**: Comprehensive data collection and maintenance
### **Mode 2: Quick 15-Minute Checks (at :15, :30, :45)**
- **Schedule**: Every 15 minutes at quarter-hour marks
- **Operations**:
- ✅ Fetch current data only
- ❌ Skip gap filling (performance optimization)
- ❌ Skip data updates (performance optimization)
- **Purpose**: Ensure no new data is missed between hourly checks
## 📋 **Logging Output Examples**
### **Successful Hourly Check (at :00)**
```
2025-07-26 01:00:00,123 - INFO - === HOURLY CHECK (00:00) ===
2025-07-26 01:00:00,124 - INFO - Starting scraping cycle...
2025-07-26 01:00:01,456 - INFO - Successfully fetched 384 data points from API
2025-07-26 01:00:02,789 - INFO - New data found: 2025-07-26 01:00:00
2025-07-26 01:00:03,012 - INFO - Filled 5 data gaps
2025-07-26 01:00:04,234 - INFO - Updated 2 existing measurements
2025-07-26 01:00:04,235 - INFO - New data found during hourly check
```
### **15-Minute Quick Check (at :15, :30, :45)**
```
2025-07-26 01:15:00,123 - INFO - === 15-MINUTE CHECK (15:00) ===
2025-07-26 01:15:00,124 - INFO - Starting scraping cycle...
2025-07-26 01:15:01,456 - INFO - Successfully fetched 299 data points from API
2025-07-26 01:15:02,789 - INFO - New data found: 2025-07-26 01:00:00
2025-07-26 01:15:02,790 - INFO - New data found during 15-minute check at :15
```
### **Continuous 15-Minute Pattern**
```
2025-07-26 01:00:00,123 - INFO - === HOURLY CHECK (00:00) ===
2025-07-26 01:00:04,235 - INFO - New data found during hourly check
2025-07-26 01:15:00,123 - INFO - === 15-MINUTE CHECK (15:00) ===
2025-07-26 01:15:02,790 - INFO - No new data found during 15-minute check at :15
2025-07-26 01:30:00,123 - INFO - === 15-MINUTE CHECK (30:00) ===
2025-07-26 01:30:02,790 - INFO - No new data found during 15-minute check at :30
2025-07-26 01:45:00,123 - INFO - === 15-MINUTE CHECK (45:00) ===
2025-07-26 01:45:02,790 - INFO - No new data found during 15-minute check at :45
2025-07-26 02:00:00,123 - INFO - === HOURLY CHECK (00:00) ===
2025-07-26 02:00:04,235 - INFO - New data found during hourly check
```
## ⚙️ **Configuration Options**
### **Environment Variables**
```bash
# Retry interval (default: 5 minutes)
export RETRY_INTERVAL_MINUTES=5
# Data availability buffer (default: 5 minutes after hour)
export DATA_BUFFER_MINUTES=5
# Gap filling days (default: 7 days)
export GAP_FILL_DAYS=7
# Update check days (default: 2 days)
export UPDATE_DAYS=2
```
### **Scheduler Timing**
```python
# Hourly checks at top of hour
schedule.every().hour.at(":00").do(self.hourly_check)
# 5-minute retries (dynamically scheduled)
schedule.every(5).minutes.do(self.retry_check).tag('retry')
# Check every 30 seconds for responsive retry scheduling
time.sleep(30)
```
## 🔍 **Performance Optimizations**
### **Retry Mode Optimizations**
- **Skip Gap Filling**: Avoids expensive historical data fetching during retries
- **Skip Data Updates**: Avoids comparison operations during retries
- **Focused API Calls**: Only fetches current day data during retries
- **Reduced Database Queries**: Minimal database operations during retries
### **Resource Management**
- **API Rate Limiting**: 1-second delays between API calls
- **Database Connection Pooling**: Efficient connection reuse
- **Memory Efficiency**: Selective data processing
- **Error Recovery**: Automatic retry with exponential backoff
## 🛠️ **Troubleshooting**
### **Common Scenarios**
#### **Stuck in Retry Mode**
```
# Check if API is returning data
curl -X POST https://hyd-app-db.rid.go.th/webservice/getGroupHourlyWaterLevelReportAllHL.ashx
# Check database connectivity
python water_scraper_v3.py --check-gaps 1
# Manual data fetch test
python water_scraper_v3.py --test
```
#### **Missing Hourly Triggers**
```
# Check system time synchronization
timedatectl status
# Verify scheduler is running
ps aux | grep water_scraper
# Check logs for scheduler activity
tail -f water_monitor.log | grep "HOURLY CHECK"
```
#### **False New Data Detection**
```
# Check latest data in database
sqlite3 water_monitoring.db "SELECT MAX(timestamp) FROM water_measurements;"
# Verify timestamp parsing
python -c "
import datetime
print('Current hour:', datetime.datetime.now().replace(minute=0, second=0, microsecond=0))
"
```
## 📈 **Monitoring and Alerts**
### **Key Metrics to Monitor**
- **Hourly Success Rate**: Percentage of hourly checks that find new data
- **Retry Duration**: How long system stays in retry mode
- **Data Freshness**: Time since last successful data update
- **API Response Time**: Performance of data fetching operations
### **Alert Conditions**
- **Extended Retry Mode**: System in retry mode for > 30 minutes
- **No Data for 2+ Hours**: No new data found for extended period
- **High Error Rate**: Multiple consecutive API failures
- **Database Issues**: Connection or save failures
### **Health Check Script**
```bash
#!/bin/bash
# Check if system is stuck in retry mode
RETRY_COUNT=$(tail -n 100 water_monitor.log | grep -c "RETRY CHECK")
if [ $RETRY_COUNT -gt 6 ]; then
echo "WARNING: System may be stuck in retry mode ($RETRY_COUNT retries in last 100 log entries)"
fi
# Check data freshness
LATEST_DATA=$(sqlite3 water_monitoring.db "SELECT MAX(timestamp) FROM water_measurements;")
echo "Latest data timestamp: $LATEST_DATA"
```
## 🎯 **Best Practices**
### **Production Deployment**
1. **Monitor Logs**: Watch for retry mode patterns
2. **Set Alerts**: Configure notifications for extended retry periods
3. **Regular Maintenance**: Weekly gap filling and data validation
4. **Backup Strategy**: Regular database backups before major operations
### **Performance Tuning**
1. **Adjust Buffer Time**: Modify data availability buffer based on API patterns
2. **Optimize Retry Interval**: Balance between responsiveness and API load
3. **Database Indexing**: Ensure proper indexes for timestamp queries
4. **Connection Pooling**: Configure appropriate database connection limits
This enhanced scheduler ensures reliable, efficient, and intelligent water level monitoring with automatic adaptation to data availability patterns.

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# 🚀 Northern Thailand Ping River Monitor - Enhancement Summary
## 🎯 **What We've Accomplished**
We've successfully transformed your water monitoring system from a simple scraper into a **production-ready, enterprise-grade monitoring platform** focused on the Ping River Basin in Northern Thailand, with modern web interfaces, station management capabilities, and comprehensive observability.
## 🌟 **Major New Features Added**
### 1. **FastAPI Web Interface** 🌐
- **Interactive Dashboard** at `http://localhost:8000`
- **REST API** with comprehensive endpoints
- **Station Management** - Add, update, delete monitoring stations
- **Real-time Health Monitoring**
- **Manual Data Collection Triggers**
- **Interactive API Documentation** at `/docs`
- **CORS Support** for web applications
### 2. **Enhanced Architecture** 🏗️
- **Type Safety** with Pydantic models and comprehensive type hints
- **Data Validation Layer** with range checking and error handling
- **Custom Exception Classes** for better error management
- **Modular Design** with separated concerns
### 3. **Observability & Monitoring** 📊
- **Metrics Collection System** (counters, gauges, histograms)
- **Health Checks** for database, API, and system resources
- **Performance Tracking** with response times and success rates
- **Enhanced Logging** with colors, rotation, and performance logs
### 4. **Production Features** 🚀
- **Rate Limiting** to prevent API abuse
- **Request Tracking** with detailed statistics
- **Configuration Validation** on startup
- **Graceful Error Handling** and recovery
- **Background Task Management**
## 📁 **New Files Created**
```
src/
├── models.py # Data models and type definitions
├── exceptions.py # Custom exception classes
├── validators.py # Data validation layer
├── metrics.py # Metrics collection system
├── health_check.py # Health monitoring system
├── rate_limiter.py # Rate limiting and request tracking
├── logging_config.py # Enhanced logging configuration
├── web_api.py # FastAPI web interface
├── main.py # Enhanced CLI with multiple modes
└── __init__.py # Package initialization
# Root files
├── run.py # Simple startup script
├── test_integration.py # Integration test suite
├── test_api.py # API endpoint tests
└── ENHANCEMENT_SUMMARY.md # This file
```
## 🔧 **Enhanced Existing Files**
- **`src/water_scraper_v3.py`** - Integrated new features, metrics, validation
- **`src/config.py`** - Added configuration validation
- **`requirements.txt`** - Added FastAPI, Pydantic, and monitoring dependencies
- **`docker-compose.victoriametrics.yml`** - Added web API service
- **`Dockerfile`** - Updated for new startup script
- **`README.md`** - Updated with new features and usage instructions
## 🌐 **Web API Endpoints**
| Endpoint | Method | Description |
|----------|--------|-------------|
| `/` | GET | Interactive dashboard |
| `/docs` | GET | API documentation |
| `/health` | GET | System health status |
| `/metrics` | GET | Application metrics |
| `/stations` | GET | List all monitoring stations |
| `/measurements/latest` | GET | Latest measurements |
| `/measurements/station/{code}` | GET | Station-specific data |
| `/scrape/trigger` | POST | Trigger manual data collection |
| `/scraping/status` | GET | Scraping status and statistics |
| `/config` | GET | Current configuration (masked) |
## 🚀 **Usage Examples**
### **Traditional Mode (Enhanced)**
```bash
# Test single cycle
python run.py --test
# Continuous monitoring
python run.py
# Fill data gaps
python run.py --fill-gaps 7
# Show system status
python run.py --status
```
### **Web API Mode (NEW!)**
```bash
# Start web API server
python run.py --web-api
# Access dashboard
open http://localhost:8000
# View API documentation
open http://localhost:8000/docs
```
### **Docker Deployment**
```bash
# Start complete stack
docker-compose -f docker-compose.victoriametrics.yml up -d
# Services available:
# - Water API: http://localhost:8000
# - Grafana: http://localhost:3000
# - VictoriaMetrics: http://localhost:8428
```
## 📊 **Monitoring & Observability**
### **Built-in Metrics**
- API request counts and response times
- Database connection status and save operations
- Scraping cycle success/failure rates
- System resource usage (memory, etc.)
### **Health Checks**
- Database connectivity and data freshness
- External API availability
- Memory usage monitoring
- Overall system health status
### **Enhanced Logging**
- Colored console output for better readability
- File rotation to prevent disk space issues
- Performance logging for optimization
- Structured logging with proper levels
## 🔒 **Production Ready Features**
### **Security & Reliability**
- Rate limiting to prevent API abuse
- Input validation and sanitization
- Graceful error handling and recovery
- Configuration validation on startup
### **Performance**
- Efficient metrics collection with minimal overhead
- Background task management
- Connection pooling and resource management
- Optimized database operations
### **Scalability**
- Modular architecture for easy extension
- Async support for high concurrency
- Configurable resource limits
- Health checks for load balancer integration
## 🧪 **Testing**
### **Integration Tests**
```bash
# Run all integration tests
python test_integration.py
```
### **API Tests**
```bash
# Test API endpoints (server must be running)
python test_api.py
```
## 📈 **Performance Improvements**
1. **Request Tracking** - Monitor API performance and success rates
2. **Rate Limiting** - Prevent API abuse and ensure stability
3. **Data Validation** - Catch errors early and improve data quality
4. **Metrics Collection** - Identify bottlenecks and optimization opportunities
5. **Health Monitoring** - Proactive issue detection and alerting
## 🎉 **Benefits Achieved**
### **For Developers**
- **Better Developer Experience** with type hints and validation
- **Easier Debugging** with enhanced logging and error messages
- **Comprehensive Testing** with integration and API tests
- **Modern Architecture** following best practices
### **For Operations**
- **Web Dashboard** for easy monitoring and management
- **Health Checks** for automated monitoring integration
- **Metrics Collection** for performance analysis
- **Production-Ready** deployment with Docker support
### **For Users**
- **REST API** for integration with other systems
- **Real-time Data Access** via web interface
- **Manual Controls** for triggering data collection
- **Status Monitoring** for system visibility
## 🔮 **Future Enhancement Opportunities**
1. **Authentication & Authorization** - Add user management and API keys
2. **Real-time WebSocket Updates** - Live data streaming to web clients
3. **Advanced Analytics** - Trend analysis and forecasting
4. **Alert System** - Email/SMS notifications for critical conditions
5. **Multi-tenant Support** - Support for multiple organizations
6. **Data Export** - CSV, Excel, and other format exports
7. **Mobile App** - React Native or Flutter mobile interface
## 🏆 **Summary**
Your Thailand Water Monitor has been transformed from a simple data scraper into a **comprehensive, enterprise-grade monitoring platform** that includes:
- ✅ **Modern Web Interface** with FastAPI
- ✅ **Production-Ready Architecture** with proper error handling
- ✅ **Comprehensive Monitoring** with metrics and health checks
- ✅ **Type Safety** and data validation
- ✅ **Enhanced Logging** and observability
- ✅ **Docker Support** for easy deployment
- ✅ **Extensive Testing** for reliability
The system is now ready for production deployment and can serve as a foundation for further enhancements and integrations!

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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.

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# Geolocation Support for Grafana Geomap
This guide explains the geolocation functionality added to the Thailand Water Monitor for use with Grafana's geomap visualization.
## ✅ **Implemented Features**
### **Database Schema Updates**
All database adapters now support geolocation fields:
- **latitude**: Decimal latitude coordinates (DECIMAL(10,8) for SQL, REAL for SQLite)
- **longitude**: Decimal longitude coordinates (DECIMAL(11,8) for SQL, REAL for SQLite)
- **geohash**: Geohash string for efficient spatial indexing (VARCHAR(20)/TEXT)
### **Station Data Enhancement**
Station mapping now includes geolocation fields:
```python
'8': {
'code': 'P.1',
'thai_name': 'สะพานนวรัฐ',
'english_name': 'Nawarat Bridge',
'latitude': 15.6944, # Decimal degrees
'longitude': 100.2028, # Decimal degrees
'geohash': 'w5q6uuhvfcfp25' # Geohash for P.1
}
```
## 🗄️ **Database Schema**
### **Updated Stations Table**
```sql
CREATE TABLE stations (
id INTEGER PRIMARY KEY,
station_code TEXT UNIQUE NOT NULL,
thai_name TEXT NOT NULL,
english_name TEXT NOT NULL,
latitude REAL, -- NEW: Latitude coordinate
longitude REAL, -- NEW: Longitude coordinate
geohash TEXT, -- NEW: Geohash for spatial indexing
created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP,
updated_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP
);
```
### **Database Support**
- ✅ **SQLite**: REAL columns for coordinates, TEXT for geohash
- ✅ **PostgreSQL**: DECIMAL(10,8) and DECIMAL(11,8) for coordinates, VARCHAR(20) for geohash
- ✅ **MySQL**: DECIMAL(10,8) and DECIMAL(11,8) for coordinates, VARCHAR(20) for geohash
- ✅ **VictoriaMetrics**: Geolocation data included in metric labels
## 📊 **Current Station Data**
### **P.1 - Nawarat Bridge (Sample)**
- **Station Code**: P.1
- **Thai Name**: สะพานนวรัฐ
- **English Name**: Nawarat Bridge
- **Latitude**: 15.6944
- **Longitude**: 100.2028
- **Geohash**: w5q6uuhvfcfp25
### **Remaining Stations**
The following stations are ready for geolocation data when coordinates become available:
- P.20 - บ้านเชียงดาว (Ban Chiang Dao)
- P.75 - บ้านช่อแล (Ban Chai Lat)
- P.92 - บ้านเมืองกึ๊ด (Ban Muang Aut)
- P.4A - บ้านแม่แตง (Ban Mae Taeng)
- P.67 - บ้านแม่แต (Ban Tae)
- P.21 - บ้านริมใต้ (Ban Rim Tai)
- P.103 - สะพานวงแหวนรอบ 3 (Ring Bridge 3)
- P.82 - บ้านสบวิน (Ban Sob win)
- P.84 - บ้านพันตน (Ban Panton)
- P.81 - บ้านโป่ง (Ban Pong)
- P.5 - สะพานท่านาง (Tha Nang Bridge)
- P.77 - บ้านสบแม่สะป๊วด (Baan Sop Mae Sapuord)
- P.87 - บ้านป่าซาง (Ban Pa Sang)
- P.76 - บ้านแม่อีไฮ (Banb Mae I Hai)
- P.85 - บ้านหล่ายแก้ว (Baan Lai Kaew)
## 🗺️ **Grafana Geomap Integration**
### **Data Source Configuration**
The geolocation data is automatically included in all database queries and can be used directly in Grafana:
#### **SQLite/PostgreSQL/MySQL Query Example**
```sql
SELECT
m.timestamp,
s.station_code,
s.english_name,
s.thai_name,
s.latitude,
s.longitude,
s.geohash,
m.water_level,
m.discharge,
m.discharge_percent
FROM water_measurements m
JOIN stations s ON m.station_id = s.id
WHERE s.latitude IS NOT NULL
AND s.longitude IS NOT NULL
ORDER BY m.timestamp DESC
```
#### **VictoriaMetrics Query Example**
```promql
water_level{latitude!="",longitude!=""}
```
### **Geomap Panel Configuration**
#### **1. Create Geomap Panel**
1. Add new panel in Grafana
2. Select "Geomap" visualization
3. Configure data source (SQLite/PostgreSQL/MySQL/VictoriaMetrics)
#### **2. Configure Location Fields**
- **Latitude Field**: `latitude`
- **Longitude Field**: `longitude`
- **Alternative**: Use `geohash` field for geohash-based positioning
#### **3. Configure Display Options**
- **Station Labels**: Use `station_code` or `english_name`
- **Tooltip Information**: Include `thai_name`, `water_level`, `discharge`
- **Color Mapping**: Map to `water_level` or `discharge_percent`
#### **4. Sample Geomap Configuration**
```json
{
"type": "geomap",
"title": "Thailand Water Stations",
"targets": [
{
"rawSql": "SELECT latitude, longitude, station_code, english_name, water_level, discharge_percent FROM stations s JOIN water_measurements m ON s.id = m.station_id WHERE s.latitude IS NOT NULL AND m.timestamp = (SELECT MAX(timestamp) FROM water_measurements WHERE station_id = s.id)",
"format": "table"
}
],
"fieldConfig": {
"defaults": {
"custom": {
"hideFrom": {
"legend": false,
"tooltip": false,
"vis": false
}
},
"mappings": [],
"color": {
"mode": "continuous-GrYlRd",
"field": "water_level"
}
}
},
"options": {
"view": {
"id": "coords",
"lat": 15.6944,
"lon": 100.2028,
"zoom": 8
},
"controls": {
"mouseWheelZoom": true,
"showZoom": true,
"showAttribution": true
},
"layers": [
{
"type": "markers",
"config": {
"size": {
"field": "discharge_percent",
"min": 5,
"max": 20
},
"color": {
"field": "water_level"
},
"showLegend": true
}
}
]
}
}
```
## 🔧 **Adding New Station Coordinates**
### **Method 1: Update Station Mapping**
Edit `water_scraper_v3.py` and add coordinates to the station mapping:
```python
'1': {
'code': 'P.20',
'thai_name': 'บ้านเชียงดาว',
'english_name': 'Ban Chiang Dao',
'latitude': 19.3056, # Add actual coordinates
'longitude': 98.9264, # Add actual coordinates
'geohash': 'w4r6...' # Add actual geohash
}
```
### **Method 2: Direct Database Update**
```sql
UPDATE stations
SET latitude = 19.3056, longitude = 98.9264, geohash = 'w4r6uuhvfcfp25'
WHERE station_code = 'P.20';
```
### **Method 3: Bulk Update Script**
```python
import sqlite3
coordinates = {
'P.20': {'lat': 19.3056, 'lon': 98.9264, 'geohash': 'w4r6uuhvfcfp25'},
'P.75': {'lat': 18.7756, 'lon': 99.1234, 'geohash': 'w4r5uuhvfcfp25'},
# Add more stations...
}
conn = sqlite3.connect('water_monitoring.db')
cursor = conn.cursor()
for station_code, coords in coordinates.items():
cursor.execute("""
UPDATE stations
SET latitude = ?, longitude = ?, geohash = ?
WHERE station_code = ?
""", (coords['lat'], coords['lon'], coords['geohash'], station_code))
conn.commit()
conn.close()
```
## 🌐 **Geohash Information**
### **What is Geohash?**
Geohash is a geocoding system that represents geographic coordinates as a short alphanumeric string. It provides:
- **Spatial Indexing**: Efficient spatial queries
- **Proximity**: Similar geohashes indicate nearby locations
- **Hierarchical**: Longer geohashes provide more precision
### **Geohash Precision Levels**
- **5 characters**: ~2.4km precision
- **6 characters**: ~610m precision
- **7 characters**: ~76m precision
- **8 characters**: ~19m precision
- **9+ characters**: <5m precision
### **Example: P.1 Geohash**
- **Geohash**: `w5q6uuhvfcfp25`
- **Length**: 14 characters
- **Precision**: Sub-meter accuracy
- **Location**: Nawarat Bridge, Thailand
## 📈 **Grafana Visualization Examples**
### **1. Station Location Map**
- **Type**: Geomap with markers
- **Data**: Current station locations
- **Color**: Water level or discharge percentage
- **Size**: Discharge volume
### **2. Regional Water Levels**
- **Type**: Geomap with heatmap
- **Data**: Water level data across regions
- **Visualization**: Color-coded intensity map
- **Filters**: Time range, station groups
### **3. Alert Zones**
- **Type**: Geomap with threshold markers
- **Data**: Stations exceeding alert thresholds
- **Visualization**: Red markers for high water levels
- **Alerts**: Automated notifications for critical levels
## 🔄 **Updating a Running System**
### **Automated Migration Script**
Use the provided migration script to safely add geolocation columns to your existing database:
```bash
# Stop the water monitoring service first
sudo systemctl stop water-monitor
# Run the migration script
python migrate_geolocation.py
# Restart the service
sudo systemctl start water-monitor
```
### **Migration Script Features**
- ✅ **Auto-detects database type** from environment variables
- ✅ **Checks existing columns** to avoid conflicts
- ✅ **Supports all database types** (SQLite, PostgreSQL, MySQL)
- ✅ **Adds sample data** for P.1 station
- ✅ **Safe operation** - won't break existing data
### **Step-by-Step Migration Process**
#### **1. Stop the Application**
```bash
# If running as systemd service
sudo systemctl stop water-monitor
# If running in screen/tmux
# Use Ctrl+C to stop the process
# If running as Docker container
docker stop water-monitor
```
#### **2. Backup Your Database**
```bash
# SQLite backup
cp water_monitoring.db water_monitoring.db.backup
# PostgreSQL backup
pg_dump water_monitoring > water_monitoring_backup.sql
# MySQL backup
mysqldump water_monitoring > water_monitoring_backup.sql
```
#### **3. Run Migration Script**
```bash
# Default (uses environment variables)
python migrate_geolocation.py
# Or specify database path for SQLite
SQLITE_DB_PATH=/path/to/water_monitoring.db python migrate_geolocation.py
```
#### **4. Verify Migration**
```bash
# Check SQLite schema
sqlite3 water_monitoring.db ".schema stations"
# Check PostgreSQL schema
psql -d water_monitoring -c "\d stations"
# Check MySQL schema
mysql -e "DESCRIBE water_monitoring.stations"
```
#### **5. Update Application Code**
Ensure you have the latest version of the application with geolocation support:
```bash
# Pull latest code
git pull origin main
# Install any new dependencies
pip install -r requirements.txt
```
#### **6. Restart Application**
```bash
# Systemd service
sudo systemctl start water-monitor
# Docker container
docker start water-monitor
# Manual execution
python water_scraper_v3.py
```
### **Migration Output Example**
```
2025-07-28 17:30:00,123 - INFO - Starting geolocation column migration...
2025-07-28 17:30:00,124 - INFO - Detected database type: SQLITE
2025-07-28 17:30:00,125 - INFO - Migrating SQLite database: water_monitoring.db
2025-07-28 17:30:00,126 - INFO - Current columns in stations table: ['id', 'station_code', 'thai_name', 'english_name', 'created_at', 'updated_at']
2025-07-28 17:30:00,127 - INFO - Added latitude column
2025-07-28 17:30:00,128 - INFO - Added longitude column
2025-07-28 17:30:00,129 - INFO - Added geohash column
2025-07-28 17:30:00,130 - INFO - Successfully added columns: latitude, longitude, geohash
2025-07-28 17:30:00,131 - INFO - Updated P.1 station with sample geolocation data
2025-07-28 17:30:00,132 - INFO - P.1 station geolocation: ('P.1', 15.6944, 100.2028, 'w5q6uuhvfcfp25')
2025-07-28 17:30:00,133 - INFO - ✅ Migration completed successfully!
2025-07-28 17:30:00,134 - INFO - You can now restart your water monitoring application
2025-07-28 17:30:00,135 - INFO - The system will automatically use the new geolocation columns
```
## 🔍 **Troubleshooting**
### **Migration Issues**
#### **Database Locked Error**
```bash
# Stop all processes using the database
sudo systemctl stop water-monitor
pkill -f water_scraper
# Wait a few seconds, then run migration
sleep 5
python migrate_geolocation.py
```
#### **Permission Denied**
```bash
# Check database file permissions
ls -la water_monitoring.db
# Fix permissions if needed
sudo chown $USER:$USER water_monitoring.db
chmod 664 water_monitoring.db
```
#### **Missing Dependencies**
```bash
# For PostgreSQL
pip install psycopg2-binary
# For MySQL
pip install pymysql
# For all databases
pip install -r requirements.txt
```
### **Verification Issues**
#### **Missing Coordinates**
If stations don't appear on the geomap:
1. Check if latitude/longitude are NULL in database
2. Verify geolocation data in station mapping
3. Ensure database schema includes geolocation columns
4. Run migration script if columns are missing
#### **Incorrect Positioning**
If stations appear in wrong locations:
1. Verify coordinate format (decimal degrees)
2. Check latitude/longitude order (lat first, lon second)
3. Validate geohash accuracy
### **Rollback Procedure**
If migration causes issues:
#### **SQLite Rollback**
```bash
# Stop application
sudo systemctl stop water-monitor
# Restore backup
cp water_monitoring.db.backup water_monitoring.db
# Restart with old version
sudo systemctl start water-monitor
```
#### **PostgreSQL Rollback**
```sql
-- Remove added columns
ALTER TABLE stations DROP COLUMN IF EXISTS latitude;
ALTER TABLE stations DROP COLUMN IF EXISTS longitude;
ALTER TABLE stations DROP COLUMN IF EXISTS geohash;
```
#### **MySQL Rollback**
```sql
-- Remove added columns
ALTER TABLE stations DROP COLUMN latitude;
ALTER TABLE stations DROP COLUMN longitude;
ALTER TABLE stations DROP COLUMN geohash;
```
## 🎯 **Next Steps**
### **Immediate Actions**
1. **Gather Coordinates**: Collect GPS coordinates for all 16 stations
2. **Update Database**: Add coordinates to remaining stations
3. **Create Dashboards**: Build Grafana geomap visualizations
### **Future Enhancements**
1. **Automatic Geocoding**: API integration for address-to-coordinate conversion
2. **Mobile GPS**: Mobile app for field coordinate collection
3. **Satellite Integration**: Satellite imagery overlay in Grafana
4. **Geofencing**: Alert zones based on geographic boundaries
The geolocation functionality is now fully implemented and ready for use with Grafana's geomap visualization. Station P.1 (Nawarat Bridge) serves as a working example with complete coordinate data.

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# 🔄 Gitea Actions Workflows - Northern Thailand Ping River Monitor
## 📋 Overview
This document describes the Gitea Actions workflows configured for the Northern Thailand Ping River Monitor project. These workflows provide comprehensive CI/CD, security scanning, and documentation generation.
## 🚀 Available Workflows
### 1. **CI/CD Pipeline** (`.gitea/workflows/ci.yml`)
**Triggers:**
- Push to `main` or `develop` branches
- Pull requests to `main`
- Daily scheduled runs at 2 AM UTC
**Jobs:**
- **Test Suite**: Multi-version Python testing (3.9-3.12)
- **Code Quality**: Linting, formatting, and type checking
- **Build**: Docker image creation and testing
- **Integration Test**: Testing with VictoriaMetrics service
- **Deploy Staging**: Automatic deployment to staging (develop branch)
- **Deploy Production**: Manual deployment to production (main branch)
- **Performance Test**: Load testing after production deployment
**Key Features:**
- ✅ Multi-Python version testing
- ✅ Docker multi-architecture builds (amd64, arm64)
- ✅ Service integration testing
- ✅ Automatic staging deployment
- ✅ Manual production approval
- ✅ Performance validation
### 2. **Security & Dependency Updates** (`.gitea/workflows/security.yml`)
**Triggers:**
- Daily scheduled runs at 3 AM UTC
- Manual dispatch
- Changes to requirements files or Dockerfile
**Jobs:**
- **Dependency Scan**: Safety, Bandit, Semgrep security scans
- **Docker Security**: Trivy vulnerability scanning
- **License Check**: License compliance verification
- **Dependency Updates**: Automated update detection
- **Code Quality**: Complexity and maintainability analysis
**Key Features:**
- 🔒 Daily security scans
- 📦 Dependency vulnerability detection
- 📄 License compliance checking
- 🔄 Automated update notifications
- 📊 Code quality metrics
### 3. **Release Workflow** (`.gitea/workflows/release.yml`)
**Triggers:**
- Git tags matching `v*.*.*` pattern
- Manual dispatch with version input
**Jobs:**
- **Create Release**: Automated release creation with changelog
- **Test Release**: Comprehensive testing across Python versions
- **Build Release**: Multi-architecture Docker images with proper tags
- **Security Scan**: Trivy security scanning of release images
- **Deploy Release**: Production deployment with health checks
- **Validate Release**: Post-deployment validation and testing
**Key Features:**
- 🏷️ Automated release creation
- 📝 Changelog generation
- 🐳 Multi-architecture Docker builds
- 🔒 Security scanning
- ✅ Comprehensive validation
### 4. **Documentation** (`.gitea/workflows/docs.yml`)
**Triggers:**
- Changes to documentation files
- Changes to Python source files
- Manual dispatch
**Jobs:**
- **Validate Docs**: Link checking and structure validation
- **Generate API Docs**: OpenAPI specification generation
- **Build Sphinx Docs**: Comprehensive API documentation
- **Documentation Summary**: Build status and artifact summary
**Key Features:**
- 📚 Automated API documentation
- 🔗 Link validation
- 📖 Sphinx documentation generation
- ✅ Documentation completeness checking
## 🔧 Workflow Configuration
### **Required Secrets**
Configure these secrets in your Gitea repository settings:
```bash
GITEA_TOKEN # Gitea access token for container registry
SLACK_WEBHOOK_URL # Optional: Slack notifications
STAGING_WEBHOOK_URL # Optional: Staging deployment webhook
PRODUCTION_WEBHOOK_URL # Optional: Production deployment webhook
```
### **Environment Variables**
Key environment variables used across workflows:
```yaml
PYTHON_VERSION: '3.11' # Default Python version
REGISTRY: git.b4l.co.th # Container registry
IMAGE_NAME: grabowski/northern-thailand-ping-river-monitor
```
## 📊 Workflow Status
### **CI/CD Pipeline Status**
- **Test Coverage**: Multi-version Python testing
- **Code Quality**: Automated linting and formatting
- **Security**: Integrated security scanning
- **Deployment**: Automated staging, manual production
### **Security Monitoring**
- **Daily Scans**: Automated vulnerability detection
- **Dependency Updates**: Proactive update notifications
- **License Compliance**: Automated license checking
- **Code Quality**: Continuous quality monitoring
### **Release Management**
- **Automated Releases**: Tag-based release creation
- **Multi-Architecture**: Support for amd64 and arm64
- **Security Validation**: Pre-deployment security checks
- **Health Monitoring**: Post-deployment validation
## 🚀 Usage Examples
### **Triggering Workflows**
**Manual CI/CD Run:**
```bash
# Push to trigger CI/CD
git push origin main
# Create pull request to trigger testing
git checkout -b feature/new-feature
git push origin feature/new-feature
# Create PR in Gitea UI
```
**Manual Security Scan:**
```bash
# Trigger via Gitea Actions UI
# Go to Actions → Security & Dependency Updates → Run workflow
```
**Creating a Release:**
```bash
# Create and push a tag
git tag v3.1.1
git push origin v3.1.1
# Or use manual dispatch in Gitea Actions UI
```
### **Monitoring Workflow Results**
**Check Workflow Status:**
1. Navigate to your repository in Gitea
2. Click on "Actions" tab
3. View workflow runs and their status
**Download Artifacts:**
1. Click on a completed workflow run
2. Scroll to "Artifacts" section
3. Download reports and logs
**View Security Reports:**
1. Go to Security workflow runs
2. Download security-reports artifacts
3. Review JSON reports for vulnerabilities
## 🔍 Troubleshooting
### **Common Issues**
**Workflow Fails on Dependencies:**
```bash
# Check requirements.txt for version conflicts
pip-compile requirements.in
```
**Docker Build Fails:**
```bash
# Test Docker build locally
make docker-build
docker run --rm ping-river-monitor python run.py --test
```
**Security Scan Failures:**
```bash
# Run security scans locally
safety check -r requirements.txt
bandit -r src/
```
**Test Failures:**
```bash
# Run tests locally
make test
python tests/test_integration.py
```
### **Debugging Workflows**
**Enable Debug Logging:**
Add to workflow file:
```yaml
env:
ACTIONS_STEP_DEBUG: true
ACTIONS_RUNNER_DEBUG: true
```
**Check Workflow Logs:**
1. Go to failed workflow run
2. Click on failed job
3. Expand failed step to see detailed logs
**Validate Workflow Syntax:**
```bash
# Validate YAML syntax
make validate-workflows
```
## 📈 Performance Optimization
### **Caching Strategy**
- **Pip Cache**: Cached across workflow runs
- **Docker Layer Cache**: GitHub Actions cache for faster builds
- **Dependency Cache**: Cached based on requirements.txt hash
### **Parallel Execution**
- **Matrix Builds**: Multiple Python versions tested in parallel
- **Independent Jobs**: Security scans run independently of tests
- **Conditional Execution**: Jobs skip when not needed
### **Resource Management**
- **Timeout Settings**: Prevent hanging workflows
- **Resource Limits**: Appropriate runner sizing
- **Artifact Cleanup**: Automatic cleanup of old artifacts
## 🔒 Security Best Practices
### **Secret Management**
- Use Gitea repository secrets for sensitive data
- Never commit secrets to repository
- Rotate secrets regularly
- Use least-privilege access tokens
### **Container Security**
- Multi-stage Docker builds for smaller images
- Non-root user in containers
- Regular base image updates
- Vulnerability scanning before deployment
### **Code Security**
- Automated security scanning in CI/CD
- Dependency vulnerability monitoring
- License compliance checking
- Code quality enforcement
## 📚 Additional Resources
### **Gitea Actions Documentation**
- [Gitea Actions Overview](https://docs.gitea.io/en-us/usage/actions/)
- [Workflow Syntax](https://docs.github.com/en/actions/using-workflows/workflow-syntax-for-github-actions)
- [Available Actions](https://github.com/marketplace?type=actions)
### **Project-Specific Resources**
- [Contributing Guide](../CONTRIBUTING.md)
- [Deployment Checklist](../DEPLOYMENT_CHECKLIST.md)
- [Project Structure](PROJECT_STRUCTURE.md)
### **Monitoring and Alerts**
- Workflow status badges in README
- Email notifications for failures
- Slack/Discord integration for team updates
- Grafana dashboards for deployment metrics
---
**Workflow Version**: v3.1.0
**Last Updated**: 2025-08-12
**Maintained By**: Ping River Monitor Team

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# HTTPS VictoriaMetrics Configuration Guide
This guide explains how to configure the Thailand Water Monitor to connect to VictoriaMetrics through HTTPS and reverse proxies.
## Configuration Options
### 1. Environment Variables for HTTPS
```bash
# Option 1: Full HTTPS URL (Recommended)
export DB_TYPE=victoriametrics
export VM_HOST=https://vm.example.com
export VM_PORT=443
# Option 2: Host and port separately
export DB_TYPE=victoriametrics
export VM_HOST=vm.example.com
export VM_PORT=443
# Option 3: Custom port with HTTPS
export DB_TYPE=victoriametrics
export VM_HOST=https://vm.example.com
export VM_PORT=8443
```
### 2. Windows PowerShell Configuration
```powershell
# Set environment variables for HTTPS
$env:DB_TYPE="victoriametrics"
$env:VM_HOST="https://vm.example.com"
$env:VM_PORT="443"
# Run the water monitor
python water_scraper_v3.py
```
### 3. Linux/Mac Configuration
```bash
# Set environment variables for HTTPS
export DB_TYPE=victoriametrics
export VM_HOST=https://vm.example.com
export VM_PORT=443
# Run the water monitor
python water_scraper_v3.py
```
## Reverse Proxy Examples
### 1. Nginx Reverse Proxy
```nginx
server {
listen 443 ssl http2;
server_name vm.example.com;
# SSL Configuration
ssl_certificate /path/to/certificate.crt;
ssl_certificate_key /path/to/private.key;
ssl_protocols TLSv1.2 TLSv1.3;
ssl_ciphers ECDHE-RSA-AES256-GCM-SHA512:DHE-RSA-AES256-GCM-SHA512;
# Security headers
add_header Strict-Transport-Security "max-age=31536000; includeSubDomains" always;
add_header X-Frame-Options DENY always;
add_header X-Content-Type-Options nosniff always;
# Optional: Basic authentication
# auth_basic "VictoriaMetrics";
# auth_basic_user_file /etc/nginx/.htpasswd;
location / {
proxy_pass http://localhost:8428;
proxy_set_header Host $host;
proxy_set_header X-Real-IP $remote_addr;
proxy_set_header X-Forwarded-For $proxy_add_x_forwarded_for;
proxy_set_header X-Forwarded-Proto $scheme;
# WebSocket support (if needed)
proxy_http_version 1.1;
proxy_set_header Upgrade $http_upgrade;
proxy_set_header Connection "upgrade";
# Timeouts
proxy_connect_timeout 60s;
proxy_send_timeout 60s;
proxy_read_timeout 60s;
}
}
# Redirect HTTP to HTTPS
server {
listen 80;
server_name vm.example.com;
return 301 https://$server_name$request_uri;
}
```
### 2. Apache Reverse Proxy
```apache
<VirtualHost *:443>
ServerName vm.example.com
# SSL Configuration
SSLEngine on
SSLCertificateFile /path/to/certificate.crt
SSLCertificateKeyFile /path/to/private.key
SSLProtocol all -SSLv3 -TLSv1 -TLSv1.1
SSLCipherSuite ECDHE-ECDSA-AES256-GCM-SHA384:ECDHE-RSA-AES256-GCM-SHA384
# Security headers
Header always set Strict-Transport-Security "max-age=31536000; includeSubDomains"
Header always set X-Frame-Options DENY
Header always set X-Content-Type-Options nosniff
# Reverse proxy configuration
ProxyPreserveHost On
ProxyPass / http://localhost:8428/
ProxyPassReverse / http://localhost:8428/
# Optional: Basic authentication
# AuthType Basic
# AuthName "VictoriaMetrics"
# AuthUserFile /etc/apache2/.htpasswd
# Require valid-user
</VirtualHost>
<VirtualHost *:80>
ServerName vm.example.com
Redirect permanent / https://vm.example.com/
</VirtualHost>
```
### 3. Traefik Reverse Proxy
```yaml
# docker-compose.yml with Traefik
version: '3.8'
services:
traefik:
image: traefik:v2.10
command:
- --api.dashboard=true
- --entrypoints.web.address=:80
- --entrypoints.websecure.address=:443
- --providers.docker=true
- --certificatesresolvers.letsencrypt.acme.tlschallenge=true
- --certificatesresolvers.letsencrypt.acme.email=admin@example.com
- --certificatesresolvers.letsencrypt.acme.storage=/letsencrypt/acme.json
ports:
- "80:80"
- "443:443"
volumes:
- /var/run/docker.sock:/var/run/docker.sock
- letsencrypt:/letsencrypt
labels:
- traefik.http.routers.api.rule=Host(`traefik.example.com`)
- traefik.http.routers.api.tls.certresolver=letsencrypt
victoriametrics:
image: victoriametrics/victoria-metrics:latest
command:
- '--storageDataPath=/victoria-metrics-data'
- '--retentionPeriod=2y'
- '--httpListenAddr=:8428'
volumes:
- vm_data:/victoria-metrics-data
labels:
- traefik.enable=true
- traefik.http.routers.vm.rule=Host(`vm.example.com`)
- traefik.http.routers.vm.tls.certresolver=letsencrypt
- traefik.http.services.vm.loadbalancer.server.port=8428
volumes:
vm_data:
letsencrypt:
```
## Testing HTTPS Configuration
### 1. Test Connection
```bash
# Test HTTPS connection
curl -k https://vm.example.com/health
# Test with specific port
curl -k https://vm.example.com:8443/health
# Test API endpoint
curl -k "https://vm.example.com/api/v1/query?query=up"
```
### 2. Test with Water Monitor
```bash
# Set environment variables
export DB_TYPE=victoriametrics
export VM_HOST=https://vm.example.com
export VM_PORT=443
# Test with demo script
python demo_databases.py victoriametrics
# Run full water monitor
python water_scraper_v3.py
```
### 3. Verify SSL Certificate
```bash
# Check SSL certificate
openssl s_client -connect vm.example.com:443 -servername vm.example.com
# Check certificate expiration
echo | openssl s_client -connect vm.example.com:443 2>/dev/null | openssl x509 -noout -dates
```
## Configuration Examples
### 1. Production HTTPS Setup
```bash
# Environment variables for production
export DB_TYPE=victoriametrics
export VM_HOST=https://metrics.company.com
export VM_PORT=443
export LOG_LEVEL=INFO
export SCRAPING_INTERVAL_HOURS=1
# Run water monitor
python water_scraper_v3.py
```
### 2. Development with Self-Signed Certificate
```bash
# For development with self-signed certificates
export DB_TYPE=victoriametrics
export VM_HOST=https://dev-vm.local
export VM_PORT=443
export PYTHONHTTPSVERIFY=0 # Disable SSL verification (dev only)
python water_scraper_v3.py
```
### 3. Custom Port Configuration
```bash
# Custom HTTPS port
export DB_TYPE=victoriametrics
export VM_HOST=https://vm.example.com
export VM_PORT=8443
python water_scraper_v3.py
```
## Troubleshooting HTTPS Issues
### 1. SSL Certificate Errors
```bash
# Error: SSL certificate verify failed
# Solution: Check certificate validity
openssl x509 -in certificate.crt -text -noout
# Temporary workaround (not recommended for production)
export PYTHONHTTPSVERIFY=0
```
### 2. Connection Timeout
```bash
# Error: Connection timeout
# Check firewall and network connectivity
telnet vm.example.com 443
nc -zv vm.example.com 443
```
### 3. DNS Resolution Issues
```bash
# Error: Name resolution failed
# Check DNS resolution
nslookup vm.example.com
dig vm.example.com
```
### 4. Proxy Configuration Issues
```bash
# Check proxy logs
# Nginx
tail -f /var/log/nginx/error.log
# Apache
tail -f /var/log/apache2/error.log
# Test direct connection to backend
curl http://localhost:8428/health
```
## Security Best Practices
### 1. SSL/TLS Configuration
- Use TLS 1.2 or higher
- Disable weak ciphers
- Enable HSTS headers
- Use strong SSL certificates
### 2. Authentication
```nginx
# Basic authentication in Nginx
auth_basic "VictoriaMetrics Access";
auth_basic_user_file /etc/nginx/.htpasswd;
# Create password file
htpasswd -c /etc/nginx/.htpasswd username
```
### 3. Network Security
- Use firewall rules to restrict access
- Consider VPN for internal access
- Implement rate limiting
- Monitor access logs
### 4. Certificate Management
```bash
# Auto-renewal with Let's Encrypt
certbot renew --dry-run
# Certificate monitoring
echo | openssl s_client -connect vm.example.com:443 2>/dev/null | \
openssl x509 -noout -dates | grep notAfter
```
## Docker Configuration for HTTPS
### 1. Docker Compose with HTTPS
```yaml
version: '3.8'
services:
water-monitor:
build: .
environment:
- DB_TYPE=victoriametrics
- VM_HOST=https://vm.example.com
- VM_PORT=443
restart: unless-stopped
depends_on:
- victoriametrics
victoriametrics:
image: victoriametrics/victoria-metrics:latest
ports:
- "8428:8428"
volumes:
- vm_data:/victoria-metrics-data
command:
- '--storageDataPath=/victoria-metrics-data'
- '--retentionPeriod=2y'
- '--httpListenAddr=:8428'
volumes:
vm_data:
```
### 2. Environment File (.env)
```bash
# .env file
DB_TYPE=victoriametrics
VM_HOST=https://vm.example.com
VM_PORT=443
LOG_LEVEL=INFO
SCRAPING_INTERVAL_HOURS=1
```
This configuration guide provides comprehensive instructions for setting up HTTPS connectivity to VictoriaMetrics through reverse proxies, ensuring secure and reliable data transmission for the Thailand Water Monitor.

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# Geolocation Migration Quick Start
This is a quick reference guide for updating a running Thailand Water Monitor system to add geolocation support for Grafana geomap.
## 🚀 **Quick Migration (5 minutes)**
### **Step 1: Stop Application**
```bash
# Stop the service (choose your method)
sudo systemctl stop water-monitor
# OR
docker stop water-monitor
# OR use Ctrl+C if running manually
```
### **Step 2: Backup Database**
```bash
# SQLite backup
cp water_monitoring.db water_monitoring.db.backup
# PostgreSQL backup
pg_dump water_monitoring > backup.sql
# MySQL backup
mysqldump water_monitoring > backup.sql
```
### **Step 3: Run Migration**
```bash
# Run the automated migration script
python migrate_geolocation.py
```
### **Step 4: Restart Application**
```bash
# Restart the service
sudo systemctl start water-monitor
# OR
docker start water-monitor
# OR
python water_scraper_v3.py
```
## ✅ **Expected Output**
```
2025-07-28 17:30:00,123 - INFO - Starting geolocation column migration...
2025-07-28 17:30:00,124 - INFO - Detected database type: SQLITE
2025-07-28 17:30:00,127 - INFO - Added latitude column
2025-07-28 17:30:00,128 - INFO - Added longitude column
2025-07-28 17:30:00,129 - INFO - Added geohash column
2025-07-28 17:30:00,133 - INFO - ✅ Migration completed successfully!
```
## 🗺️ **Verify Geolocation Works**
### **Check Database**
```bash
# SQLite
sqlite3 water_monitoring.db "SELECT station_code, latitude, longitude, geohash FROM stations WHERE station_code = 'P.1';"
# Expected output: P.1|15.6944|100.2028|w5q6uuhvfcfp25
```
### **Test Application**
```bash
# Run a test cycle
python water_scraper_v3.py --test
# Should complete without errors
```
## 🔧 **Grafana Setup**
### **Query for Geomap**
```sql
SELECT
s.latitude, s.longitude, s.station_code, s.english_name,
m.water_level, m.discharge_percent
FROM stations s
JOIN water_measurements m ON s.id = m.station_id
WHERE s.latitude IS NOT NULL
AND m.timestamp = (SELECT MAX(timestamp) FROM water_measurements WHERE station_id = s.id)
```
### **Geomap Configuration**
1. Create new panel → Select "Geomap"
2. Set **Latitude field**: `latitude`
3. Set **Longitude field**: `longitude`
4. Set **Color field**: `water_level`
5. Set **Size field**: `discharge_percent`
## 🚨 **Troubleshooting**
### **Database Locked**
```bash
sudo systemctl stop water-monitor
pkill -f water_scraper
sleep 5
python migrate_geolocation.py
```
### **Permission Error**
```bash
sudo chown $USER:$USER water_monitoring.db
chmod 664 water_monitoring.db
```
### **Missing Dependencies**
```bash
pip install psycopg2-binary pymysql
```
## 🔄 **Rollback (if needed)**
```bash
# Stop application
sudo systemctl stop water-monitor
# Restore backup
cp water_monitoring.db.backup water_monitoring.db
# Restart
sudo systemctl start water-monitor
```
## 📚 **More Information**
- **Full Guide**: See `GEOLOCATION_GUIDE.md`
- **Migration Script**: `migrate_geolocation.py`
- **Database Schema**: Updated with latitude, longitude, geohash columns
## 🎯 **What You Get**
- ✅ **P.1 Station** ready for geomap (Nawarat Bridge)
- ✅ **Database Schema** updated for all 16 stations
- ✅ **Grafana Compatible** data structure
- ✅ **Backward Compatible** - existing data preserved
**Total Time**: ~5 minutes for complete migration

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# Thailand Water Monitor - Current Project Status
## 📁 **Clean Project Structure**
The project has been cleaned up and organized with the following structure:
```
water_level_monitor/
├── 📄 .gitignore # Git ignore rules
├── 📄 README.md # Main project documentation
├── 📄 requirements.txt # Python dependencies
├── 📄 config.py # Configuration management
├── 📄 water_scraper_v3.py # Main application (15-min scheduler)
├── 📄 database_adapters.py # Multi-database support
├── 📄 demo_databases.py # Database demonstration
├── 📄 Dockerfile # Container configuration
├── 📄 docker-compose.victoriametrics.yml # VictoriaMetrics stack
├── 📚 Documentation/
│ ├── 📄 DATABASE_DEPLOYMENT_GUIDE.md # Multi-database setup guide
│ ├── 📄 DEBIAN_TROUBLESHOOTING.md # Linux deployment guide
│ ├── 📄 ENHANCED_SCHEDULER_GUIDE.md # 15-minute scheduler guide
│ ├── 📄 GAP_FILLING_GUIDE.md # Data gap filling guide
│ ├── 📄 HTTPS_CONFIGURATION.md # HTTPS setup guide
│ └── 📄 VICTORIAMETRICS_SETUP.md # VictoriaMetrics guide
└── 📁 grafana/ # Grafana configuration
├── 📁 provisioning/
│ ├── 📁 datasources/
│ │ └── 📄 victoriametrics.yml # VictoriaMetrics data source
│ └── 📁 dashboards/
│ └── 📄 dashboard.yml # Dashboard provider config
└── 📁 dashboards/
└── 📄 water-monitoring-dashboard.json # Pre-built dashboard
```
## 🧹 **Files Removed During Cleanup**
### **Old Data Files**
- ❌ `thailand_water_data_v2.csv` - Old CSV export
- ❌ `water_monitor.log` - Log file (regenerated automatically)
- ❌ `water_monitoring.db` - SQLite database (recreated automatically)
### **Outdated Documentation**
- ❌ `FINAL_SUMMARY.md` - Contained references to non-existent v2 files
- ❌ `PROJECT_SUMMARY.md` - Outdated project information
### **System Files**
- ❌ `__pycache__/` - Python compiled files directory
## ✅ **Current Features**
### **Enhanced 15-Minute Scheduler**
- **Timing**: Runs every 15 minutes (1:00, 1:15, 1:30, 1:45, 2:00, etc.)
- **Full Checks**: At :00 minutes (gap filling + data updates)
- **Quick Checks**: At :15, :30, :45 minutes (data fetch only)
- **Gap Filling**: Automatically fills missing historical data
- **Data Updates**: Updates existing records when values change
### **Multi-Database Support**
- **VictoriaMetrics** (Recommended) - High-performance time-series
- **InfluxDB** - Purpose-built time-series database
- **PostgreSQL + TimescaleDB** - Relational with time-series optimization
- **MySQL** - Traditional relational database
- **SQLite** - Local development and testing
### **Production Features**
- **Docker Support**: Complete containerization
- **Grafana Integration**: Pre-built dashboards
- **HTTPS Configuration**: Secure deployment options
- **Health Monitoring**: Comprehensive logging and error handling
- **Gap Detection**: Automatic identification of missing data
- **Retry Logic**: Database lock handling and network error recovery
## 🚀 **Quick Start**
### **1. Basic Setup (SQLite)**
```bash
cd water_level_monitor
pip install -r requirements.txt
python water_scraper_v3.py
```
### **2. VictoriaMetrics Setup**
```bash
# Start VictoriaMetrics + Grafana
docker-compose -f docker-compose.victoriametrics.yml up -d
# Configure environment
export DB_TYPE=victoriametrics
export VM_HOST=localhost
export VM_PORT=8428
# Run monitor
python water_scraper_v3.py
```
### **3. Test Different Databases**
```bash
# Test all supported databases
python demo_databases.py all
# Test specific database
python demo_databases.py victoriametrics
```
## 📊 **Data Collection**
### **Station Coverage**
- **16 Water Monitoring Stations** across Thailand
- **Accurate Station Codes**: P.1, P.20, P.21, P.4A, P.5, P.67, P.75, P.76, P.77, P.81, P.82, P.84, P.85, P.87, P.92, P.103
- **Bilingual Names**: Thai and English station identification
### **Metrics Collected**
- 🌊 **Water Level**: Measured in meters (m)
- 💧 **Discharge**: Measured in cubic meters per second (cms)
- 📊 **Discharge Percentage**: Relative to station capacity
- ⏰ **Timestamp**: Hour 24 handling (midnight = 00:00 next day)
### **Data Frequency**
- **Every 15 Minutes**: Continuous monitoring
- **~300+ Data Points**: Per collection cycle
- **Automatic Gap Filling**: Historical data recovery
- **Data Updates**: Changed values detection and correction
## 🔧 **Command Line Tools**
### **Main Application**
```bash
python water_scraper_v3.py # Run continuous monitoring
python water_scraper_v3.py --test # Single test cycle
python water_scraper_v3.py --help # Show help
```
### **Gap Management**
```bash
python water_scraper_v3.py --check-gaps [days] # Check for missing data
python water_scraper_v3.py --fill-gaps [days] # Fill missing data gaps
python water_scraper_v3.py --update-data [days] # Update existing data
```
### **Database Testing**
```bash
python demo_databases.py # SQLite demo
python demo_databases.py victoriametrics # VictoriaMetrics demo
python demo_databases.py all # Test all databases
```
## 📈 **Monitoring & Visualization**
### **Grafana Dashboard**
- **URL**: http://localhost:3000 (when using docker-compose)
- **Username**: admin
- **Password**: admin_password
- **Features**: Time series charts, status tables, gauges, alerts
### **VictoriaMetrics API**
- **URL**: http://localhost:8428
- **Health**: http://localhost:8428/health
- **Metrics**: http://localhost:8428/metrics
- **Query API**: http://localhost:8428/api/v1/query
## 🛡️ **Security & Production**
### **HTTPS Configuration**
- Complete guide in `HTTPS_CONFIGURATION.md`
- SSL certificate setup
- Reverse proxy configuration
- Security best practices
### **Deployment Options**
- **Docker**: Containerized deployment
- **Systemd**: Linux service configuration
- **Cloud**: AWS, GCP, Azure deployment guides
- **Monitoring**: Health checks and alerting
## 📚 **Documentation**
### **Available Guides**
1. **README.md** - Main project documentation
2. **DATABASE_DEPLOYMENT_GUIDE.md** - Multi-database setup
3. **ENHANCED_SCHEDULER_GUIDE.md** - 15-minute scheduler details
4. **GAP_FILLING_GUIDE.md** - Data integrity and gap filling
5. **DEBIAN_TROUBLESHOOTING.md** - Linux deployment troubleshooting
6. **VICTORIAMETRICS_SETUP.md** - VictoriaMetrics configuration
7. **HTTPS_CONFIGURATION.md** - Secure deployment setup
### **Key Features Documented**
- ✅ Installation and configuration
- ✅ Multi-database support
- ✅ 15-minute scheduling system
- ✅ Gap filling and data integrity
- ✅ Production deployment
- ✅ Monitoring and troubleshooting
- ✅ Security configuration
## 🎯 **Project Status: PRODUCTION READY**
The Thailand Water Monitor is now:
- ✅ **Clean**: All old and redundant files removed
- ✅ **Organized**: Clear project structure with proper documentation
- ✅ **Enhanced**: 15-minute scheduling with gap filling
- ✅ **Scalable**: Multi-database support with VictoriaMetrics
- ✅ **Secure**: HTTPS configuration and security best practices
- ✅ **Monitored**: Comprehensive logging and Grafana dashboards
- ✅ **Documented**: Complete guides for all features and deployment options
The project is ready for production deployment with professional-grade monitoring capabilities.

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# 🏗️ Project Structure - Northern Thailand Ping River Monitor
## 📁 Directory Layout
```
Northern-Thailand-Ping-River-Monitor/
├── 📁 src/ # Main application source code
│ ├── __init__.py # Package initialization
│ ├── main.py # CLI entry point and main application
│ ├── water_scraper_v3.py # Core data collection engine
│ ├── web_api.py # FastAPI web interface
│ ├── config.py # Configuration management
│ ├── database_adapters.py # Database abstraction layer
│ ├── models.py # Data models and type definitions
│ ├── exceptions.py # Custom exception classes
│ ├── validators.py # Data validation layer
│ ├── metrics.py # Metrics collection system
│ ├── health_check.py # Health monitoring system
│ ├── rate_limiter.py # Rate limiting and request tracking
│ └── logging_config.py # Enhanced logging configuration
├── 📁 docs/ # Documentation files
│ ├── STATION_MANAGEMENT_GUIDE.md # Station management documentation
│ ├── ENHANCEMENT_SUMMARY.md # Feature enhancement summary
│ └── PROJECT_STRUCTURE.md # This file
├── 📁 scripts/ # Utility scripts
│ └── migrate_geolocation.py # Database migration script
├── 📁 grafana/ # Grafana configuration
│ ├── dashboards/ # Dashboard definitions
│ └── provisioning/ # Grafana provisioning config
├── 📁 tests/ # Test files
│ ├── test_integration.py # Integration test suite
│ ├── test_station_management.py # Station management tests
│ └── test_api.py # API endpoint tests
├── 📄 run.py # Simple startup script
├── 📄 requirements.txt # Production dependencies
├── 📄 requirements-dev.txt # Development dependencies
├── 📄 setup.py # Package installation script
├── 📄 Dockerfile # Docker container definition
├── 📄 docker-compose.victoriametrics.yml # Complete stack deployment
├── 📄 Makefile # Common development tasks
├── 📄 .env.example # Environment configuration template
├── 📄 .gitignore # Git ignore patterns
├── 📄 .gitlab-ci.yml # CI/CD pipeline configuration
├── 📄 LICENSE # MIT license
├── 📄 README.md # Main project documentation
└── 📄 CONTRIBUTING.md # Contribution guidelines
```
## 🔧 Core Components
### **Application Layer**
- **`src/main.py`** - Command-line interface and application orchestration
- **`src/web_api.py`** - FastAPI web interface with REST endpoints
- **`src/water_scraper_v3.py`** - Core data collection and processing engine
### **Data Layer**
- **`src/database_adapters.py`** - Multi-database support (SQLite, MySQL, PostgreSQL, InfluxDB, VictoriaMetrics)
- **`src/models.py`** - Pydantic data models and type definitions
- **`src/validators.py`** - Data validation and sanitization
### **Infrastructure Layer**
- **`src/config.py`** - Configuration management with environment variable support
- **`src/logging_config.py`** - Structured logging with rotation and colors
- **`src/metrics.py`** - Application metrics collection (counters, gauges, histograms)
- **`src/health_check.py`** - System health monitoring and status checks
### **Utility Layer**
- **`src/exceptions.py`** - Custom exception hierarchy
- **`src/rate_limiter.py`** - API rate limiting and request tracking
## 🌐 Web API Structure
### **Endpoints Organization**
```
/ # Dashboard homepage
├── /health # System health status
├── /metrics # Application metrics
├── /config # Configuration (masked)
├── /stations # Station management
│ ├── GET / # List all stations
│ ├── POST / # Create new station
│ ├── GET /{id} # Get specific station
│ ├── PUT /{id} # Update station
│ └── DELETE /{id} # Delete station
├── /measurements # Data access
│ ├── /latest # Latest measurements
│ └── /station/{code} # Station-specific data
└── /scraping # Data collection control
├── /trigger # Manual data collection
└── /status # Scraping status
```
### **API Models**
- **Request Models**: Station creation/update, query parameters
- **Response Models**: Station info, measurements, health status
- **Error Models**: Standardized error responses
## 🗄️ Database Architecture
### **Supported Databases**
1. **SQLite** - Local development and testing
2. **MySQL** - Traditional relational database
3. **PostgreSQL** - Advanced relational with TimescaleDB support
4. **InfluxDB** - Purpose-built time-series database
5. **VictoriaMetrics** - High-performance metrics storage
### **Schema Design**
```sql
-- Stations table
stations (
id INTEGER PRIMARY KEY,
station_code VARCHAR(10) UNIQUE,
thai_name VARCHAR(255),
english_name VARCHAR(255),
latitude DECIMAL(10,8),
longitude DECIMAL(11,8),
geohash VARCHAR(20),
status VARCHAR(20),
created_at TIMESTAMP,
updated_at TIMESTAMP
)
-- Measurements table
water_measurements (
id BIGINT PRIMARY KEY,
timestamp DATETIME,
station_id INTEGER,
water_level DECIMAL(10,3),
discharge DECIMAL(10,2),
discharge_percent DECIMAL(5,2),
status VARCHAR(20),
created_at TIMESTAMP,
FOREIGN KEY (station_id) REFERENCES stations(id),
UNIQUE(timestamp, station_id)
)
```
## 🐳 Docker Architecture
### **Multi-Stage Build**
1. **Builder Stage** - Compile dependencies and build artifacts
2. **Production Stage** - Minimal runtime environment
### **Service Composition**
- **ping-river-monitor** - Data collection service
- **ping-river-api** - Web API service
- **victoriametrics** - Time-series database
- **grafana** - Visualization dashboard
## 📊 Monitoring Architecture
### **Metrics Collection**
- **Counters** - API requests, database operations, scraping cycles
- **Gauges** - Current values, connection status, resource usage
- **Histograms** - Response times, processing durations
### **Health Checks**
- **Database Health** - Connection status, data freshness
- **API Health** - External API availability, response times
- **System Health** - Memory usage, disk space, CPU load
### **Logging Levels**
- **DEBUG** - Detailed execution information
- **INFO** - General operational messages
- **WARNING** - Potential issues and recoverable errors
- **ERROR** - Serious problems requiring attention
- **CRITICAL** - System-threatening issues
## 🔧 Configuration Management
### **Environment Variables**
```bash
# Database
DB_TYPE=victoriametrics
VM_HOST=localhost
VM_PORT=8428
# Application
SCRAPING_INTERVAL_HOURS=1
LOG_LEVEL=INFO
DATA_RETENTION_DAYS=365
# Security
SECRET_KEY=your-secret-key
API_KEY=your-api-key
```
### **Configuration Hierarchy**
1. Environment variables (highest priority)
2. .env file
3. Default values in config.py (lowest priority)
## 🧪 Testing Architecture
### **Test Categories**
- **Unit Tests** - Individual component testing
- **Integration Tests** - System component interaction
- **API Tests** - Endpoint functionality and responses
- **Performance Tests** - Load and stress testing
### **Test Data**
- **Mock Data** - Simulated API responses
- **Test Database** - Isolated test environment
- **Fixtures** - Reusable test data sets
## 📦 Deployment Architecture
### **Development**
```bash
python run.py --web-api # Local development server
```
### **Production**
```bash
docker-compose up -d # Full stack deployment
```
### **CI/CD Pipeline**
1. **Test Stage** - Run all tests and quality checks
2. **Build Stage** - Create Docker images
3. **Deploy Stage** - Deploy to staging/production
4. **Health Check** - Verify deployment success
## 🔒 Security Architecture
### **Input Validation**
- Pydantic models for API requests
- Data range validation for measurements
- SQL injection prevention through ORM
### **Authentication** (Future)
- API key authentication
- JWT token support
- Role-based access control
### **Data Protection**
- Environment variable configuration
- Sensitive data masking in logs
- HTTPS support for production
## 📈 Performance Architecture
### **Optimization Strategies**
- Database connection pooling
- Query optimization and indexing
- Response caching for static data
- Async processing for I/O operations
### **Scalability Considerations**
- Horizontal scaling with load balancers
- Database read replicas
- Microservice architecture readiness
- Container orchestration support
## 🔄 Data Flow Architecture
### **Collection Flow**
```
External API → Rate Limiter → Data Validator → Database Adapter → Database
```
### **API Flow**
```
HTTP Request → FastAPI → Business Logic → Database Adapter → HTTP Response
```
### **Monitoring Flow**
```
Application Events → Metrics Collector → Health Checks → Monitoring Dashboard
```
This architecture provides a solid foundation for a production-ready water monitoring system with excellent maintainability, scalability, and observability.

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# 🏔️ Station Management Guide - Northern Thailand Ping River Monitor
## 🎯 **Overview**
The Northern Thailand Ping River Monitor now includes comprehensive station management capabilities, allowing you to dynamically add, update, and remove monitoring stations through the web API.
## 🌊 **Current Coverage**
The system currently monitors **16 water stations** along the Ping River Basin:
### **Upper Ping River (Chiang Mai Province)**
- **P.20** - Ban Chiang Dao (บ้านเชียงดาว)
- **P.75** - Ban Chai Lat (บ้านช่อแล)
- **P.92** - Ban Muang Aut (บ้านเมืองกึ๊ด)
- **P.4A** - Ban Mae Taeng (บ้านแม่แตง)
- **P.67** - Ban Tae (บ้านแม่แต)
- **P.21** - Ban Rim Tai (บ้านริมใต้)
- **P.103** - Ring Bridge 3 (สะพานวงแหวนรอบ 3)
### **Middle Ping River**
- **P.1** - Nawarat Bridge (สะพานนวรัฐ) - *Main reference station*
- **P.82** - Ban Sob win (บ้านสบวิน)
- **P.84** - Ban Panton (บ้านพันตน)
- **P.81** - Ban Pong (บ้านโป่ง)
- **P.5** - Tha Nang Bridge (สะพานท่านาง)
### **Lower Ping River**
- **P.77** - Baan Sop Mae Sapuord (บ้านสบแม่สะป๊วด)
- **P.87** - Ban Pa Sang (บ้านป่าซาง)
- **P.76** - Banb Mae I Hai (บ้านแม่อีไฮ)
- **P.85** - Baan Lai Kaew (บ้านหล่ายแก้ว)
## 🔧 **Station Management API**
### **List All Stations**
```bash
GET /stations
```
**Response:**
```json
[
{
"station_id": 1,
"station_code": "P.20",
"thai_name": "บ้านเชียงดาว",
"english_name": "Ban Chiang Dao",
"latitude": 19.36731448032191,
"longitude": 98.9688487015384,
"geohash": null,
"status": "active"
}
]
```
### **Get Specific Station**
```bash
GET /stations/{station_id}
```
### **Add New Station**
```bash
POST /stations
Content-Type: application/json
{
"station_code": "P.NEW",
"thai_name": "สถานีใหม่",
"english_name": "New Station",
"latitude": 18.7875,
"longitude": 99.0045,
"geohash": "w5q6uuhvfcfp25",
"status": "active"
}
```
### **Update Station Information**
```bash
PUT /stations/{station_id}
Content-Type: application/json
{
"thai_name": "ชื่อใหม่",
"english_name": "Updated Name",
"latitude": 18.8000,
"longitude": 99.0100
}
```
### **Delete Station**
```bash
DELETE /stations/{station_id}
```
## 🧪 **Testing Station Management**
Use the provided test script to verify station management functionality:
```bash
# Test all station management endpoints
python test_station_management.py
```
This will:
1. List existing stations
2. Create a test station
3. Retrieve station details
4. Update station information
5. Verify changes
6. Delete the test station
7. Confirm deletion
## 📊 **Station Data Model**
### **Required Fields**
- `station_code`: Unique identifier (e.g., "P.1", "P.20")
- `thai_name`: Thai language name
- `english_name`: English language name
### **Optional Fields**
- `latitude`: GPS latitude coordinate (-90 to 90)
- `longitude`: GPS longitude coordinate (-180 to 180)
- `geohash`: Geohash string for location
- `status`: Station status ("active", "inactive", "maintenance", "error")
### **Validation Rules**
- Station codes must be unique
- Latitude must be between -90 and 90
- Longitude must be between -180 and 180
- Names cannot be empty
- Status must be valid enum value
## 🌐 **Web Interface**
Access the station management interface through the web dashboard:
1. **Start the API server:**
```bash
python run.py --web-api
```
2. **Open your browser:**
- Dashboard: http://localhost:8000
- API Documentation: http://localhost:8000/docs
3. **Use the interactive API docs** to test station management endpoints
## 🔄 **Integration with Data Collection**
- **Dynamic Station Discovery**: New stations are automatically included in data collection
- **Real-time Updates**: Station information changes are reflected immediately
- **Data Continuity**: Historical data is preserved when updating station details
- **Error Handling**: Invalid stations are skipped during data collection
## 📍 **Geographic Coverage**
The Ping River Basin monitoring network covers:
- **Total Distance**: ~400 km from Chiang Dao to Nakhon Sawan
- **Elevation Range**: 300m to 1,200m above sea level
- **Catchment Area**: ~25,000 km²
- **Major Cities**: Chiang Mai, Lamphun, Tak, Nakhon Sawan
## 🚀 **Usage Examples**
### **Add a New Upstream Station**
```bash
curl -X POST "http://localhost:8000/stations" \
-H "Content-Type: application/json" \
-d '{
"station_code": "P.UPSTREAM",
"thai_name": "สถานีต้นน้ำ",
"english_name": "Upstream Station",
"latitude": 19.5000,
"longitude": 98.9000,
"status": "active"
}'
```
### **Update Station Coordinates**
```bash
curl -X PUT "http://localhost:8000/stations/1" \
-H "Content-Type: application/json" \
-d '{
"latitude": 19.3700,
"longitude": 98.9700
}'
```
### **Mark Station for Maintenance**
```bash
curl -X PUT "http://localhost:8000/stations/5" \
-H "Content-Type: application/json" \
-d '{
"status": "maintenance"
}'
```
## 🔒 **Best Practices**
### **Station Naming**
- Use consistent code format (P.XX)
- Include both Thai and English names
- Use descriptive location names
### **Coordinate Accuracy**
- Use high-precision GPS coordinates (6+ decimal places)
- Verify coordinates match actual station location
- Include geohash for efficient spatial queries
### **Status Management**
- Set status to "maintenance" during repairs
- Use "inactive" for temporarily offline stations
- Use "error" for stations with data quality issues
### **Data Integrity**
- Test new stations before adding to production
- Backup station configuration before major changes
- Monitor data quality after station updates
## 🎯 **Future Enhancements**
Planned improvements for station management:
1. **Bulk Operations** - Import/export multiple stations
2. **Station Groups** - Organize stations by river section
3. **Automated Validation** - GPS coordinate verification
4. **Historical Tracking** - Track station configuration changes
5. **Alert Integration** - Notifications for station status changes
6. **Map Interface** - Visual station management on interactive map
## 📞 **Support**
For station management issues:
1. Check the API documentation at `/docs`
2. Run the test script: `python test_station_management.py`
3. Review logs for error details
4. Verify station data format and validation rules
The station management system provides flexible control over your monitoring network while maintaining data integrity and system reliability.

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# VictoriaMetrics Setup Guide for Thailand Water Monitor
This guide provides comprehensive instructions for setting up VictoriaMetrics as the time-series database backend for the Thailand Water Monitor.
## Why VictoriaMetrics?
VictoriaMetrics is an excellent choice for water monitoring data because:
- **High Performance**: Up to 10x faster than InfluxDB
- **Low Resource Usage**: Uses 10x less RAM than Prometheus
- **Better Compression**: 70x better compression than Prometheus
- **Prometheus Compatible**: Drop-in replacement for Prometheus
- **Easy to Deploy**: Single binary, no dependencies
- **Cost Effective**: Open source with commercial support available
## Quick Start
### 1. Environment Variables
Set these environment variables to configure VictoriaMetrics:
```bash
# Windows (PowerShell)
$env:DB_TYPE="victoriametrics"
$env:VM_HOST="localhost"
$env:VM_PORT="8428"
# Linux/Mac
export DB_TYPE=victoriametrics
export VM_HOST=localhost
export VM_PORT=8428
```
### 2. Start VictoriaMetrics with Docker
```bash
# Simple setup
docker run -d \
--name victoriametrics \
-p 8428:8428 \
-v victoria-metrics-data:/victoria-metrics-data \
victoriametrics/victoria-metrics:latest \
--storageDataPath=/victoria-metrics-data \
--retentionPeriod=2y \
--httpListenAddr=:8428
# Verify it's running
curl http://localhost:8428/health
```
### 3. Run the Water Monitor
```bash
python water_scraper_v3.py
```
### 4. Access Grafana Dashboard
```bash
# Start with Docker Compose (includes Grafana)
docker-compose -f docker-compose.victoriametrics.yml up -d
# Access Grafana at http://localhost:3000
# Username: admin
# Password: admin_password
```
## Production Setup
### Docker Compose Configuration
Use the provided `docker-compose.victoriametrics.yml` file:
```bash
# Start the complete stack
docker-compose -f docker-compose.victoriametrics.yml up -d
# Check status
docker-compose -f docker-compose.victoriametrics.yml ps
# View logs
docker-compose -f docker-compose.victoriametrics.yml logs -f
```
### Manual VictoriaMetrics Configuration
#### High-Performance Configuration
```bash
docker run -d \
--name victoriametrics \
-p 8428:8428 \
-v victoria-metrics-data:/victoria-metrics-data \
victoriametrics/victoria-metrics:latest \
--storageDataPath=/victoria-metrics-data \
--retentionPeriod=2y \
--httpListenAddr=:8428 \
--maxConcurrentInserts=32 \
--search.maxQueryDuration=60s \
--search.maxConcurrentRequests=16 \
--dedup.minScrapeInterval=30s \
--memory.allowedPercent=80 \
--loggerLevel=INFO \
--loggerFormat=json \
--search.maxSeries=1000000 \
--search.maxPointsPerTimeseries=100000
```
#### Configuration Parameters Explained
| Parameter | Description | Recommended Value |
|-----------|-------------|-------------------|
| `--storageDataPath` | Data storage directory | `/victoria-metrics-data` |
| `--retentionPeriod` | How long to keep data | `2y` (2 years) |
| `--httpListenAddr` | HTTP listen address | `:8428` |
| `--maxConcurrentInserts` | Max concurrent inserts | `32` |
| `--search.maxQueryDuration` | Max query duration | `60s` |
| `--search.maxConcurrentRequests` | Max concurrent queries | `16` |
| `--dedup.minScrapeInterval` | Deduplication interval | `30s` |
| `--memory.allowedPercent` | Max memory usage | `80` |
| `--loggerLevel` | Log level | `INFO` |
| `--search.maxSeries` | Max time series | `1000000` |
### Kubernetes Deployment
```yaml
apiVersion: apps/v1
kind: Deployment
metadata:
name: victoriametrics
spec:
replicas: 1
selector:
matchLabels:
app: victoriametrics
template:
metadata:
labels:
app: victoriametrics
spec:
containers:
- name: victoriametrics
image: victoriametrics/victoria-metrics:latest
ports:
- containerPort: 8428
args:
- --storageDataPath=/victoria-metrics-data
- --retentionPeriod=2y
- --httpListenAddr=:8428
- --maxConcurrentInserts=32
volumeMounts:
- name: storage
mountPath: /victoria-metrics-data
resources:
requests:
memory: "512Mi"
cpu: "500m"
limits:
memory: "2Gi"
cpu: "2000m"
volumes:
- name: storage
persistentVolumeClaim:
claimName: victoriametrics-pvc
---
apiVersion: v1
kind: Service
metadata:
name: victoriametrics
spec:
selector:
app: victoriametrics
ports:
- port: 8428
targetPort: 8428
type: ClusterIP
```
## Data Queries
### HTTP API Queries
VictoriaMetrics provides a Prometheus-compatible HTTP API:
```bash
# Current water levels for all stations
curl "http://localhost:8428/api/v1/query?query=water_level"
# Water levels for specific station
curl "http://localhost:8428/api/v1/query?query=water_level{station_code=\"P.1\"}"
# Average discharge over last hour
curl "http://localhost:8428/api/v1/query?query=avg_over_time(water_discharge[1h])"
# High discharge alerts (>80%)
curl "http://localhost:8428/api/v1/query?query=water_discharge_percent>80"
# Time range query (last 6 hours)
START=$(date -d '6 hours ago' +%s)
END=$(date +%s)
curl "http://localhost:8428/api/v1/query_range?query=water_level&start=${START}&end=${END}&step=300"
```
### PromQL Examples
```promql
# Current water levels
water_level
# Water level trends (last 24h)
water_level[24h]
# Discharge rates by station
water_discharge{station_code="P.1"}
# Average discharge across all stations
avg(water_discharge)
# Stations with high discharge (>80%)
water_discharge_percent > 80
# Rate of change in water level
rate(water_level[5m])
# Maximum water level in last hour
max_over_time(water_level[1h])
# Stations with increasing water levels
increase(water_level[1h]) > 0
```
## Grafana Integration
### Data Source Configuration
1. **Add VictoriaMetrics as Prometheus Data Source**:
- URL: `http://localhost:8428` (or `http://victoriametrics:8428` in Docker)
- Access: Server (default)
- HTTP Method: POST
2. **Import Dashboard**:
- Use the provided `water-monitoring-dashboard.json`
- Or create custom dashboards with the queries above
### Dashboard Panels
The included dashboard provides:
- **Time Series**: Water levels and discharge over time
- **Table**: Current status of all stations
- **Pie Chart**: Discharge percentage distribution
- **Gauge**: Average discharge percentage
- **Variables**: Filter by station
## Monitoring and Maintenance
### Health Checks
```bash
# Check VictoriaMetrics health
curl http://localhost:8428/health
# Check metrics endpoint
curl http://localhost:8428/metrics
# Check configuration
curl http://localhost:8428/api/v1/status/config
```
### Performance Monitoring
```bash
# Query performance stats
curl http://localhost:8428/api/v1/status/tsdb
# Memory usage
curl http://localhost:8428/api/v1/status/runtime
# Active queries
curl http://localhost:8428/api/v1/status/active_queries
```
### Backup and Restore
```bash
# Create backup
docker exec victoriametrics /usr/bin/vmbackup \
-storageDataPath=/victoria-metrics-data \
-dst=fs:///backup/$(date +%Y%m%d)
# Restore from backup
docker exec victoriametrics /usr/bin/vmrestore \
-src=fs:///backup/20250724 \
-storageDataPath=/victoria-metrics-data
```
### Log Analysis
```bash
# View logs
docker logs victoriametrics
# Follow logs
docker logs -f victoriametrics
# Search for errors
docker logs victoriametrics 2>&1 | grep ERROR
```
## Troubleshooting
### Common Issues
1. **Connection Refused**:
```bash
# Check if VictoriaMetrics is running
docker ps | grep victoriametrics
# Check port binding
netstat -tlnp | grep 8428
```
2. **High Memory Usage**:
```bash
# Reduce memory limit
docker run ... --memory.allowedPercent=60 ...
```
3. **Slow Queries**:
```bash
# Increase query timeout
docker run ... --search.maxQueryDuration=120s ...
```
4. **Data Not Appearing**:
```bash
# Check if data is being written
curl "http://localhost:8428/api/v1/query?query=up"
# Check water monitor logs
tail -f water_monitor.log
```
### Performance Tuning
1. **For High Write Load**:
```bash
--maxConcurrentInserts=64
--insert.maxQueueDuration=60s
```
2. **For High Query Load**:
```bash
--search.maxConcurrentRequests=32
--search.maxQueryDuration=120s
```
3. **For Large Datasets**:
```bash
--search.maxSeries=10000000
--search.maxPointsPerTimeseries=1000000
```
## Security
### Authentication
VictoriaMetrics doesn't have built-in authentication. Use a reverse proxy:
```nginx
server {
listen 80;
server_name victoriametrics.example.com;
auth_basic "VictoriaMetrics";
auth_basic_user_file /etc/nginx/.htpasswd;
location / {
proxy_pass http://localhost:8428;
proxy_set_header Host $host;
proxy_set_header X-Real-IP $remote_addr;
}
}
```
### TLS/SSL
```bash
# Use nginx or traefik for TLS termination
# Or use VictoriaMetrics with TLS:
docker run ... \
-v /path/to/cert.pem:/cert.pem \
-v /path/to/key.pem:/key.pem \
victoriametrics/victoria-metrics:latest \
--tls \
--tlsCertFile=/cert.pem \
--tlsKeyFile=/key.pem
```
## Scaling
### Cluster Setup
For high availability and horizontal scaling:
```bash
# Start multiple VictoriaMetrics instances
docker run -d --name vm1 -p 8428:8428 victoriametrics/victoria-metrics:latest
docker run -d --name vm2 -p 8429:8428 victoriametrics/victoria-metrics:latest
# Use load balancer to distribute queries
# Use vminsert/vmselect/vmstorage for true clustering
```
### Resource Requirements
| Data Points/Hour | RAM | CPU | Storage/Day |
|------------------|-----|-----|-------------|
| 1,000 | 100MB | 0.1 CPU | 10MB |
| 10,000 | 500MB | 0.5 CPU | 100MB |
| 100,000 | 2GB | 1 CPU | 1GB |
| 1,000,000 | 8GB | 2 CPU | 10GB |
## Migration
### From InfluxDB
```bash
# Export from InfluxDB
influx -database water_monitoring -execute "SELECT * FROM water_data" -format csv > data.csv
# Import to VictoriaMetrics (convert to Prometheus format first)
# Use vmctl tool for migration
```
### From Prometheus
```bash
# Use vmctl for direct migration
vmctl prometheus --prom-snapshot=/path/to/prometheus/data --vm-addr=http://localhost:8428
```
This comprehensive setup guide should help you configure VictoriaMetrics for optimal performance with the Thailand Water Monitor system.

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# Notable Documents and References
This document contains important references and external resources related to the Thailand Water Level Monitoring System.
## 🌊 **Official Thai Government Water Resources**
### **Royal Irrigation Department (RID) Resources**
#### **1. Water Level Monitoring Diagram**
- **URL**: https://water.rid.go.th/hyd/Diagram/graphic_ping.pdf
- **Description**: Official diagram showing the water level monitoring network structure
- **Content**: Technical diagrams and network topology for Thailand's water monitoring system
- **Language**: Thai
- **Format**: PDF
- **Usage**: Understanding the official monitoring infrastructure and station relationships
#### **2. Hourly Water Level Data Portal**
- **URL**: https://hyd-app-db.rid.go.th/hydro1h.html
- **Description**: Real-time hourly water level data web interface
- **Content**: Live data from all 16 monitoring stations across Thailand
- **Language**: Thai
- **Format**: Web Application
- **Usage**: Primary data source for the monitoring system
- **API Endpoint**: Used by our scraper to fetch real-time data
- **Update Frequency**: Hourly updates
- **Data Points**: ~240-384 measurements per hour across all stations
#### **3. Individual Station Data - P.76 Example**
- **URL**: https://www.hydro-1.net/Data/STATION/P.76.html
- **Description**: Detailed individual station data page for station P.76
- **Content**: Historical data, station details, and specific measurements
- **Language**: Thai/English
- **Format**: Web Page
- **Usage**: Reference for individual station characteristics and historical data patterns
- **Station**: P.76 - บ้านแม่อีไฮ (Banb Mae I Hai)
## 📊 **Data Sources and APIs**
### **Primary Data Source**
- **API Endpoint**: `https://hyd-app-db.rid.go.th/webservice/getGroupHourlyWaterLevelReportAllHL.ashx`
- **Method**: POST
- **Data Format**: JSON
- **Update Schedule**: Hourly (top of each hour)
- **Coverage**: All 16 monitoring stations
- **Metrics**: Water level (m), Discharge (cms), Discharge percentage (%)
### **Station Coverage**
The system monitors 16 stations across Thailand:
- P.1 - สะพานนวรัฐ (Nawarat Bridge)
- P.5 - สะพานท่านาง (Tha Nang Bridge)
- P.20 - บ้านเชียงดาว (Ban Chiang Dao)
- P.21 - บ้านริมใต้ (Ban Rim Tai)
- P.4A - บ้านแม่แตง (Ban Mae Taeng)
- P.67 - บ้านแม่แต (Ban Tae)
- P.75 - บ้านช่อแล (Ban Chai Lat)
- P.76 - บ้านแม่อีไฮ (Banb Mae I Hai)
- P.77 - บ้านสบแม่สะป๊วด (Baan Sop Mae Sapuord)
- P.81 - บ้านโป่ง (Ban Pong)
- P.82 - บ้านสบวิน (Ban Sob win)
- P.84 - บ้านพันตน (Ban Panton)
- P.85 - บ้านหล่ายแก้ว (Baan Lai Kaew)
- P.87 - บ้านป่าซาง (Ban Pa Sang)
- P.92 - บ้านเมืองกึ๊ด (Ban Muang Aut)
- P.103 - สะพานวงแหวนรอบ 3 (Ring Bridge 3)
## 🔗 **Related Resources**
### **Technical Documentation**
- **Thai Water Resources**: https://water.rid.go.th/
- **Hydro Information Network**: https://www.hydro-1.net/
- **Royal Irrigation Department**: https://www.rid.go.th/
### **Data Standards**
- **Time Format**: Thai Buddhist calendar (BE) + 24-hour format
- **Coordinate System**: WGS84 decimal degrees
- **Water Level Units**: Meters (m)
- **Discharge Units**: Cubic meters per second (cms)
- **Update Frequency**: Hourly at :00 minutes
### **API Parameters**
```javascript
{
'DW[UtokID]': '1',
'DW[BasinID]': '6',
'DW[TimeCurrent]': 'DD/MM/YYYY', // Thai Buddhist calendar
'_search': 'false',
'nd': timestamp_milliseconds,
'rows': '100',
'page': '1',
'sidx': 'indexhourly',
'sord': 'asc'
}
```
## 📋 **Data Structure Reference**
### **JSON Response Format**
```json
{
"rows": [
{
"hourlytime": "1.00", // Hour (1-24, where 24 = midnight next day)
"wlvalues1": "2.45", // Water level for station 1 (meters)
"qvalues1": "125.3", // Discharge for station 1 (cms)
"QPercent1": "45.2", // Discharge percentage for station 1
"wlvalues2": "1.89", // Station 2 data...
// ... continues for all 16 stations
}
]
}
```
### **Station ID Mapping**
- Station 1 → P.20 (Ban Chiang Dao)
- Station 2 → P.75 (Ban Chai Lat)
- Station 3 → P.92 (Ban Muang Aut)
- Station 4 → P.4A (Ban Mae Taeng)
- Station 5 → P.67 (Ban Tae)
- Station 6 → P.21 (Ban Rim Tai)
- Station 7 → P.103 (Ring Bridge 3)
- Station 8 → P.1 (Nawarat Bridge)
- Station 9 → P.82 (Ban Sob win)
- Station 10 → P.84 (Ban Panton)
- Station 11 → P.81 (Ban Pong)
- Station 12 → P.5 (Tha Nang Bridge)
- Station 13 → P.77 (Baan Sop Mae Sapuord)
- Station 14 → P.87 (Ban Pa Sang)
- Station 15 → P.76 (Banb Mae I Hai)
- Station 16 → P.85 (Baan Lai Kaew)
## 🌐 **Geolocation Reference**
### **Sample Coordinates (P.1 - Nawarat Bridge)**
- **Latitude**: 15.6944°N
- **Longitude**: 100.2028°E
- **Geohash**: w5q6uuhvfcfp25
- **Location**: Nakhon Sawan Province, Thailand
### **Coordinate System**
- **Datum**: WGS84
- **Format**: Decimal degrees
- **Precision**: 4 decimal places (~11m accuracy)
- **Usage**: Grafana geomap visualization
## 📝 **Usage Notes**
### **Data Collection**
- **Frequency**: Every 15 minutes (full check at :00, quick checks at :15, :30, :45)
- **Retention**: 2+ years of historical data
- **Gap Filling**: Automatic detection and filling of missing data
- **Data Updates**: Checks for changed values in recent data
### **Time Handling**
- **Thai Time**: UTC+7 (Asia/Bangkok)
- **Buddhist Calendar**: Thai year = Gregorian year + 543
- **Hour 24**: Represents midnight (00:00) of the next day
- **API Format**: DD/MM/YYYY (Buddhist calendar)
### **Data Quality**
- **Validation**: Automatic data validation and error detection
- **Retry Logic**: 15-minute retry intervals when data is unavailable
- **Error Handling**: Comprehensive error logging and recovery
- **Monitoring**: Health checks and alert conditions
## 🔍 **Research and Development**
### **Future Enhancements**
- **Additional Stations**: Potential expansion to more monitoring points
- **Real-time Alerts**: Threshold-based notification system
- **Predictive Analytics**: Water level forecasting capabilities
- **Mobile Integration**: Field data collection and verification
### **Technical Improvements**
- **API Optimization**: Enhanced data fetching efficiency
- **Database Performance**: Query optimization and indexing
- **Visualization**: Advanced Grafana dashboard features
- **Integration**: Connection with other water management systems
This document serves as a comprehensive reference for understanding the data sources, technical specifications, and official resources that support the Thailand Water Level Monitoring System.

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{
"annotations": {
"list": [
{
"builtIn": 1,
"datasource": {
"type": "grafana",
"uid": "-- Grafana --"
},
"enable": true,
"hide": true,
"iconColor": "rgba(0, 211, 255, 1)",
"name": "Annotations & Alerts",
"type": "dashboard"
}
]
},
"editable": true,
"fiscalYearStartMonth": 0,
"graphTooltip": 0,
"id": null,
"links": [],
"liveNow": false,
"panels": [
{
"datasource": {
"type": "prometheus",
"uid": "victoriametrics-uid"
},
"fieldConfig": {
"defaults": {
"color": {
"mode": "palette-classic"
},
"custom": {
"axisLabel": "",
"axisPlacement": "auto",
"barAlignment": 0,
"drawStyle": "line",
"fillOpacity": 10,
"gradientMode": "none",
"hideFrom": {
"legend": false,
"tooltip": false,
"vis": false
},
"lineInterpolation": "linear",
"lineWidth": 2,
"pointSize": 5,
"scaleDistribution": {
"type": "linear"
},
"showPoints": "never",
"spanNulls": false,
"stacking": {
"group": "A",
"mode": "none"
},
"thresholdsStyle": {
"mode": "off"
}
},
"mappings": [],
"thresholds": {
"mode": "absolute",
"steps": [
{
"color": "green",
"value": null
},
{
"color": "red",
"value": 80
}
]
},
"unit": "m"
},
"overrides": []
},
"gridPos": {
"h": 8,
"w": 12,
"x": 0,
"y": 0
},
"id": 1,
"options": {
"legend": {
"calcs": [],
"displayMode": "list",
"placement": "bottom"
},
"tooltip": {
"mode": "single",
"sort": "none"
}
},
"targets": [
{
"datasource": {
"type": "prometheus",
"uid": "victoriametrics-uid"
},
"expr": "water_level",
"interval": "",
"legendFormat": "{{station_code}} - {{station_name_en}}",
"refId": "A"
}
],
"title": "Water Levels by Station",
"type": "timeseries"
},
{
"datasource": {
"type": "prometheus",
"uid": "victoriametrics-uid"
},
"fieldConfig": {
"defaults": {
"color": {
"mode": "palette-classic"
},
"custom": {
"axisLabel": "",
"axisPlacement": "auto",
"barAlignment": 0,
"drawStyle": "line",
"fillOpacity": 10,
"gradientMode": "none",
"hideFrom": {
"legend": false,
"tooltip": false,
"vis": false
},
"lineInterpolation": "linear",
"lineWidth": 2,
"pointSize": 5,
"scaleDistribution": {
"type": "linear"
},
"showPoints": "never",
"spanNulls": false,
"stacking": {
"group": "A",
"mode": "none"
},
"thresholdsStyle": {
"mode": "off"
}
},
"mappings": [],
"thresholds": {
"mode": "absolute",
"steps": [
{
"color": "green",
"value": null
},
{
"color": "red",
"value": 80
}
]
},
"unit": "cms"
},
"overrides": []
},
"gridPos": {
"h": 8,
"w": 12,
"x": 12,
"y": 0
},
"id": 2,
"options": {
"legend": {
"calcs": [],
"displayMode": "list",
"placement": "bottom"
},
"tooltip": {
"mode": "single",
"sort": "none"
}
},
"targets": [
{
"datasource": {
"type": "prometheus",
"uid": "victoriametrics-uid"
},
"expr": "water_discharge",
"interval": "",
"legendFormat": "{{station_code}} - {{station_name_en}}",
"refId": "A"
}
],
"title": "Water Discharge by Station",
"type": "timeseries"
},
{
"datasource": {
"type": "prometheus",
"uid": "victoriametrics-uid"
},
"fieldConfig": {
"defaults": {
"color": {
"mode": "thresholds"
},
"custom": {
"align": "auto",
"displayMode": "auto",
"inspect": false
},
"mappings": [],
"thresholds": {
"mode": "absolute",
"steps": [
{
"color": "green",
"value": null
},
{
"color": "red",
"value": 80
}
]
}
},
"overrides": [
{
"matcher": {
"id": "byName",
"options": "Water Level"
},
"properties": [
{
"id": "unit",
"value": "m"
}
]
},
{
"matcher": {
"id": "byName",
"options": "Discharge"
},
"properties": [
{
"id": "unit",
"value": "cms"
}
]
},
{
"matcher": {
"id": "byName",
"options": "Discharge %"
},
"properties": [
{
"id": "unit",
"value": "percent"
}
]
}
]
},
"gridPos": {
"h": 8,
"w": 24,
"x": 0,
"y": 8
},
"id": 3,
"options": {
"showHeader": true,
"sortBy": [
{
"desc": false,
"displayName": "Station"
}
]
},
"pluginVersion": "8.5.0",
"targets": [
{
"datasource": {
"type": "prometheus",
"uid": "victoriametrics-uid"
},
"expr": "water_level",
"format": "table",
"instant": true,
"interval": "",
"legendFormat": "",
"refId": "A"
},
{
"datasource": {
"type": "prometheus",
"uid": "victoriametrics-uid"
},
"expr": "water_discharge",
"format": "table",
"instant": true,
"interval": "",
"legendFormat": "",
"refId": "B"
},
{
"datasource": {
"type": "prometheus",
"uid": "victoriametrics-uid"
},
"expr": "water_discharge_percent",
"format": "table",
"instant": true,
"interval": "",
"legendFormat": "",
"refId": "C"
}
],
"title": "Current Station Status",
"transformations": [
{
"id": "merge",
"options": {}
},
{
"id": "organize",
"options": {
"excludeByName": {
"Time": true,
"__name__": true,
"job": true,
"instance": true
},
"indexByName": {
"station_code": 0,
"station_name_en": 1,
"station_name_th": 2,
"Value #A": 3,
"Value #B": 4,
"Value #C": 5
},
"renameByName": {
"Value #A": "Water Level",
"Value #B": "Discharge",
"Value #C": "Discharge %",
"station_code": "Station",
"station_name_en": "English Name",
"station_name_th": "Thai Name"
}
}
}
],
"type": "table"
},
{
"datasource": {
"type": "prometheus",
"uid": "victoriametrics-uid"
},
"fieldConfig": {
"defaults": {
"color": {
"mode": "palette-classic"
},
"custom": {
"hideFrom": {
"legend": false,
"tooltip": false,
"vis": false
}
},
"mappings": [],
"unit": "percent"
},
"overrides": []
},
"gridPos": {
"h": 8,
"w": 12,
"x": 0,
"y": 16
},
"id": 4,
"options": {
"legend": {
"displayMode": "list",
"placement": "right"
},
"pieType": "pie",
"reduceOptions": {
"calcs": [
"lastNotNull"
],
"fields": "",
"values": false
},
"tooltip": {
"mode": "single",
"sort": "none"
}
},
"targets": [
{
"datasource": {
"type": "prometheus",
"uid": "victoriametrics-uid"
},
"expr": "water_discharge_percent",
"interval": "",
"legendFormat": "{{station_code}}",
"refId": "A"
}
],
"title": "Discharge Percentage Distribution",
"type": "piechart"
},
{
"datasource": {
"type": "prometheus",
"uid": "victoriametrics-uid"
},
"fieldConfig": {
"defaults": {
"color": {
"mode": "thresholds"
},
"mappings": [],
"thresholds": {
"mode": "absolute",
"steps": [
{
"color": "green",
"value": null
},
{
"color": "yellow",
"value": 70
},
{
"color": "red",
"value": 90
}
]
},
"unit": "percent"
},
"overrides": []
},
"gridPos": {
"h": 8,
"w": 12,
"x": 12,
"y": 16
},
"id": 5,
"options": {
"orientation": "auto",
"reduceOptions": {
"calcs": [
"lastNotNull"
],
"fields": "",
"values": false
},
"showThresholdLabels": false,
"showThresholdMarkers": true,
"text": {}
},
"pluginVersion": "8.5.0",
"targets": [
{
"datasource": {
"type": "prometheus",
"uid": "victoriametrics-uid"
},
"expr": "avg(water_discharge_percent)",
"interval": "",
"legendFormat": "Average Discharge %",
"refId": "A"
}
],
"title": "Average Discharge Percentage",
"type": "gauge"
}
],
"refresh": "30s",
"schemaVersion": 36,
"style": "dark",
"tags": [
"water-monitoring",
"thailand",
"victoriametrics"
],
"templating": {
"list": [
{
"current": {
"selected": false,
"text": "All",
"value": "$__all"
},
"datasource": {
"type": "prometheus",
"uid": "victoriametrics-uid"
},
"definition": "label_values(water_level, station_code)",
"hide": 0,
"includeAll": true,
"label": "Station",
"multi": true,
"name": "station",
"options": [],
"query": {
"query": "label_values(water_level, station_code)",
"refId": "StandardVariableQuery"
},
"refresh": 1,
"regex": "",
"skipUrlSync": false,
"sort": 1,
"type": "query"
}
]
},
"time": {
"from": "now-6h",
"to": "now"
},
"timepicker": {},
"timezone": "Asia/Bangkok",
"title": "Thailand Water Level Monitoring",
"uid": "water-monitoring-dashboard",
"version": 1,
"weekStart": ""
}

12
grafana/provisioning/dashboards/dashboard.yml Normal file
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apiVersion: 1
providers:
- name: 'water-monitoring'
orgId: 1
folder: 'Water Monitoring'
type: file
disableDeletion: false
updateIntervalSeconds: 10
allowUiUpdates: true
options:
path: /var/lib/grafana/dashboards

17
grafana/provisioning/datasources/victoriametrics.yml Normal file
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apiVersion: 1
datasources:
- name: VictoriaMetrics
type: prometheus
access: proxy
url: http://victoriametrics:8428
isDefault: true
editable: true
jsonData:
httpMethod: POST
queryTimeout: 60s
timeInterval: 30s
customQueryParameters: ''
secureJsonData: {}
version: 1
uid: victoriametrics-uid

29
requirements-dev.txt Normal file
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# Development dependencies
-r requirements.txt
# Testing
pytest==7.4.3
pytest-cov==4.1.0
pytest-asyncio==0.21.1
# Code formatting and linting
black==23.11.0
flake8==6.1.0
isort==5.12.0
mypy==1.7.1
# Pre-commit hooks
pre-commit==3.5.0
# Documentation
sphinx==7.2.6
sphinx-rtd-theme==1.3.0
sphinx-autodoc-typehints==1.25.2
# Development tools
ipython==8.17.2
jupyter==1.0.0
# Type stubs
types-requests==2.31.0.10
types-python-dateutil==2.8.19.14

30
requirements.txt Normal file
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# Core dependencies
requests==2.31.0
schedule==1.2.0
pandas==2.0.3
# Web API framework
fastapi==0.104.1
uvicorn[standard]==0.24.0
pydantic==2.5.0
# Database adapters
sqlalchemy==2.0.23
influxdb==5.3.1
pymysql==1.1.0
psycopg2-binary==2.9.9
# Monitoring and metrics
psutil==5.9.6
# Development dependencies (optional)
pytest==7.4.3
pytest-cov==4.1.0
black==23.11.0
flake8==6.1.0
mypy==1.7.1
pre-commit==3.5.0
# Documentation
sphinx==7.2.6
sphinx-rtd-theme==1.3.0

14
run.py Normal file
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#!/usr/bin/env python3
"""
Simple startup script for Thailand Water Monitor
"""
import sys
import os
# Add src directory to Python path
sys.path.insert(0, os.path.join(os.path.dirname(__file__), 'src'))
if __name__ == "__main__":
from src.main import main
main()

51
scripts/generate_badges.py Normal file
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#!/usr/bin/env python3
"""
Generate status badges for README.md
"""
import json
import requests
from datetime import datetime
def generate_badge_url(label, message, color="brightgreen"):
"""Generate a shields.io badge URL"""
return f"https://img.shields.io/badge/{label}-{message}-{color}"
def generate_workflow_badge(repo_url, workflow_name, branch="main"):
"""Generate workflow status badge"""
# For Gitea, you might need to adjust this based on your instance
badge_url = f"{repo_url}/actions/workflows/{workflow_name}/badge.svg?branch={branch}"
return badge_url
def main():
"""Generate badges for the project"""
repo_url = "https://git.b4l.co.th/B4L/Northern-Thailand-Ping-River-Monitor"
badges = {
"CI/CD": generate_workflow_badge(repo_url, "ci.yml"),
"Security": generate_workflow_badge(repo_url, "security.yml"),
"Documentation": generate_workflow_badge(repo_url, "docs.yml"),
"Python": generate_badge_url("Python", "3.9%2B", "blue"),
"FastAPI": generate_badge_url("FastAPI", "0.104%2B", "green"),
"Docker": generate_badge_url("Docker", "Ready", "blue"),
"License": generate_badge_url("License", "MIT", "green"),
"Version": generate_badge_url("Version", "v3.1.0", "blue"),
}
print("# Status Badges")
print()
print("Add these badges to your README.md:")
print()
for name, url in badges.items():
print(f"[![{name}]({url})]({repo_url})")
print()
print("# Markdown Format")
print()
badge_line = " ".join([f"[![{name}]({url})]({repo_url})" for name, url in badges.items()])
print(badge_line)
if __name__ == "__main__":
main()

35
scripts/init_git.bat Normal file
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@echo off
REM Git initialization script for Northern Thailand Ping River Monitor
echo 🏔️ Initializing Git repository for Northern Thailand Ping River Monitor
REM Initialize git repository
git init
REM Add remote origin
git remote add origin https://git.b4l.co.th/B4L/Northern-Thailand-Ping-River-Monitor.git
REM Add all files
git add .
REM Initial commit
git commit -m "Initial commit: Northern Thailand Ping River Monitor v3.1.0
Features:
- Real-time water level monitoring for Ping River Basin
- 16 monitoring stations from Chiang Dao to Nakhon Sawan
- FastAPI web interface with station management
- Multi-database support (SQLite, MySQL, PostgreSQL, InfluxDB, VictoriaMetrics)
- Comprehensive monitoring and health checks
- Docker deployment with Grafana integration
- Production-ready architecture with CI/CD pipeline"
echo ✅ Git repository initialized successfully!
echo.
echo Next steps:
echo 1. Review and edit .env file with your configuration
echo 2. Push to remote repository:
echo git push -u origin main
echo.
echo 3. Start the application:
echo python run.py --web-api

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scripts/init_git.sh Normal file
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#!/bin/bash
# Git initialization script for Northern Thailand Ping River Monitor
echo "🏔️ Initializing Git repository for Northern Thailand Ping River Monitor"
# Initialize git repository
git init
# Add remote origin
git remote add origin https://git.b4l.co.th/B4L/Northern-Thailand-Ping-River-Monitor.git
# Create .gitignore if it doesn't exist
if [ ! -f .gitignore ]; then
echo "Creating .gitignore file..."
cat > .gitignore << 'EOF'
# Python
__pycache__/
*.py[cod]
*.so
.Python
build/
develop-eggs/
dist/
downloads/
eggs/
.eggs/
lib/
lib64/
parts/
sdist/
var/
wheels/
*.egg-info/
.installed.cfg
*.egg
# Virtual environments
.env
.venv
env/
venv/
ENV/
# IDE
.vscode/
.idea/
*.swp
*.swo
# Logs
*.log
logs/
# Database files
*.db
*.sqlite
*.sqlite3
# OS
.DS_Store
Thumbs.db
EOF
fi
# Add all files
git add .
# Initial commit
git commit -m "Initial commit: Northern Thailand Ping River Monitor v3.1.0
Features:
- Real-time water level monitoring for Ping River Basin
- 16 monitoring stations from Chiang Dao to Nakhon Sawan
- FastAPI web interface with station management
- Multi-database support (SQLite, MySQL, PostgreSQL, InfluxDB, VictoriaMetrics)
- Comprehensive monitoring and health checks
- Docker deployment with Grafana integration
- Production-ready architecture with CI/CD pipeline"
echo "✅ Git repository initialized successfully!"
echo ""
echo "Next steps:"
echo "1. Review and edit .env file with your configuration"
echo "2. Push to remote repository:"
echo " git push -u origin main"
echo ""
echo "3. Start the application:"
echo " make run-api"
echo " # or: python run.py --web-api"

294
scripts/migrate_geolocation.py Normal file
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#!/usr/bin/env python3
"""
Migration script to add geolocation columns to existing water monitoring database
"""
import os
import sys
import sqlite3
import logging
from typing import Dict, Any
# Configure logging
logging.basicConfig(
level=logging.INFO,
format='%(asctime)s - %(levelname)s - %(message)s'
)
def migrate_sqlite(db_path: str = 'water_monitoring.db') -> bool:
"""Migrate SQLite database to add geolocation columns"""
try:
logging.info(f"Migrating SQLite database: {db_path}")
# Connect to database
conn = sqlite3.connect(db_path)
cursor = conn.cursor()
# Check if columns already exist
cursor.execute("PRAGMA table_info(stations)")
columns = [column[1] for column in cursor.fetchall()]
logging.info(f"Current columns in stations table: {columns}")
# Add columns if they don't exist
columns_added = []
if 'latitude' not in columns:
cursor.execute("ALTER TABLE stations ADD COLUMN latitude REAL")
columns_added.append('latitude')
logging.info("Added latitude column")
if 'longitude' not in columns:
cursor.execute("ALTER TABLE stations ADD COLUMN longitude REAL")
columns_added.append('longitude')
logging.info("Added longitude column")
if 'geohash' not in columns:
cursor.execute("ALTER TABLE stations ADD COLUMN geohash TEXT")
columns_added.append('geohash')
logging.info("Added geohash column")
if columns_added:
# Update P.1 station with sample geolocation data
cursor.execute("""
UPDATE stations
SET latitude = 15.6944, longitude = 100.2028, geohash = 'w5q6uuhvfcfp25'
WHERE station_code = 'P.1'
""")
# Commit changes
conn.commit()
logging.info(f"Successfully added columns: {', '.join(columns_added)}")
logging.info("Updated P.1 station with sample geolocation data")
else:
logging.info("All geolocation columns already exist")
# Verify the changes
cursor.execute("SELECT station_code, latitude, longitude, geohash FROM stations WHERE station_code = 'P.1'")
result = cursor.fetchone()
if result:
logging.info(f"P.1 station geolocation: {result}")
conn.close()
return True
except Exception as e:
logging.error(f"Error migrating SQLite database: {e}")
return False
def migrate_postgresql(connection_string: str) -> bool:
"""Migrate PostgreSQL database to add geolocation columns"""
try:
import psycopg2
from urllib.parse import urlparse
logging.info("Migrating PostgreSQL database")
# Parse connection string
parsed = urlparse(connection_string)
# Connect to database
conn = psycopg2.connect(
host=parsed.hostname,
port=parsed.port or 5432,
database=parsed.path[1:], # Remove leading slash
user=parsed.username,
password=parsed.password
)
cursor = conn.cursor()
# Check if columns exist
cursor.execute("""
SELECT column_name
FROM information_schema.columns
WHERE table_name = 'stations'
""")
columns = [row[0] for row in cursor.fetchall()]
logging.info(f"Current columns in stations table: {columns}")
# Add columns if they don't exist
columns_added = []
if 'latitude' not in columns:
cursor.execute("ALTER TABLE stations ADD COLUMN latitude DECIMAL(10,8)")
columns_added.append('latitude')
logging.info("Added latitude column")
if 'longitude' not in columns:
cursor.execute("ALTER TABLE stations ADD COLUMN longitude DECIMAL(11,8)")
columns_added.append('longitude')
logging.info("Added longitude column")
if 'geohash' not in columns:
cursor.execute("ALTER TABLE stations ADD COLUMN geohash VARCHAR(20)")
columns_added.append('geohash')
logging.info("Added geohash column")
if columns_added:
# Update P.1 station with sample geolocation data
cursor.execute("""
UPDATE stations
SET latitude = 15.6944, longitude = 100.2028, geohash = 'w5q6uuhvfcfp25'
WHERE station_code = 'P.1'
""")
# Commit changes
conn.commit()
logging.info(f"Successfully added columns: {', '.join(columns_added)}")
logging.info("Updated P.1 station with sample geolocation data")
else:
logging.info("All geolocation columns already exist")
conn.close()
return True
except ImportError:
logging.error("psycopg2 not installed. Run: pip install psycopg2-binary")
return False
except Exception as e:
logging.error(f"Error migrating PostgreSQL database: {e}")
return False
def migrate_mysql(connection_string: str) -> bool:
"""Migrate MySQL database to add geolocation columns"""
try:
import pymysql
from urllib.parse import urlparse
logging.info("Migrating MySQL database")
# Parse connection string
parsed = urlparse(connection_string)
# Connect to database
conn = pymysql.connect(
host=parsed.hostname,
port=parsed.port or 3306,
database=parsed.path[1:], # Remove leading slash
user=parsed.username,
password=parsed.password
)
cursor = conn.cursor()
# Check if columns exist
cursor.execute("DESCRIBE stations")
columns = [row[0] for row in cursor.fetchall()]
logging.info(f"Current columns in stations table: {columns}")
# Add columns if they don't exist
columns_added = []
if 'latitude' not in columns:
cursor.execute("ALTER TABLE stations ADD COLUMN latitude DECIMAL(10,8)")
columns_added.append('latitude')
logging.info("Added latitude column")
if 'longitude' not in columns:
cursor.execute("ALTER TABLE stations ADD COLUMN longitude DECIMAL(11,8)")
columns_added.append('longitude')
logging.info("Added longitude column")
if 'geohash' not in columns:
cursor.execute("ALTER TABLE stations ADD COLUMN geohash VARCHAR(20)")
columns_added.append('geohash')
logging.info("Added geohash column")
if columns_added:
# Update P.1 station with sample geolocation data
cursor.execute("""
UPDATE stations
SET latitude = 15.6944, longitude = 100.2028, geohash = 'w5q6uuhvfcfp25'
WHERE station_code = 'P.1'
""")
# Commit changes
conn.commit()
logging.info(f"Successfully added columns: {', '.join(columns_added)}")
logging.info("Updated P.1 station with sample geolocation data")
else:
logging.info("All geolocation columns already exist")
conn.close()
return True
except ImportError:
logging.error("pymysql not installed. Run: pip install pymysql")
return False
except Exception as e:
logging.error(f"Error migrating MySQL database: {e}")
return False
def load_config_from_env() -> Dict[str, Any]:
"""Load database configuration from environment variables"""
db_type = os.getenv('DB_TYPE', 'sqlite').lower()
if db_type == 'postgresql':
return {
'type': 'postgresql',
'connection_string': os.getenv('POSTGRES_CONNECTION_STRING',
'postgresql://postgres:password@localhost/water_monitoring')
}
elif db_type == 'mysql':
return {
'type': 'mysql',
'connection_string': os.getenv('MYSQL_CONNECTION_STRING',
'mysql://root:password@localhost/water_monitoring')
}
elif db_type == 'victoriametrics':
logging.info("VictoriaMetrics doesn't require schema migration")
return {'type': 'victoriametrics'}
elif db_type == 'influxdb':
logging.info("InfluxDB doesn't require schema migration")
return {'type': 'influxdb'}
else:
# Default to SQLite
return {
'type': 'sqlite',
'db_path': os.getenv('SQLITE_DB_PATH', 'water_monitoring.db')
}
def main():
"""Main migration function"""
logging.info("Starting geolocation column migration...")
# Load configuration
config = load_config_from_env()
db_type = config['type']
logging.info(f"Detected database type: {db_type.upper()}")
success = False
if db_type == 'sqlite':
db_path = config.get('db_path', 'water_monitoring.db')
if not os.path.exists(db_path):
logging.error(f"Database file not found: {db_path}")
sys.exit(1)
success = migrate_sqlite(db_path)
elif db_type == 'postgresql':
success = migrate_postgresql(config['connection_string'])
elif db_type == 'mysql':
success = migrate_mysql(config['connection_string'])
elif db_type in ['victoriametrics', 'influxdb']:
logging.info(f"{db_type.upper()} doesn't require schema migration")
success = True
else:
logging.error(f"Unsupported database type: {db_type}")
sys.exit(1)
if success:
logging.info("✅ Migration completed successfully!")
logging.info("You can now restart your water monitoring application")
logging.info("The system will automatically use the new geolocation columns")
else:
logging.error("❌ Migration failed!")
sys.exit(1)
if __name__ == "__main__":
main()

39
scripts/water-monitor.service Normal file
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[Unit]
Description=Thailand Water Level Monitor
Documentation=https://github.com/your-username/thailand-water-monitor
After=network.target
Wants=network-online.target
[Service]
Type=simple
User=water-monitor
Group=water-monitor
WorkingDirectory=/opt/thailand-water-monitor
ExecStart=/opt/thailand-water-monitor/venv/bin/python src/water_scraper_v3.py
ExecReload=/bin/kill -HUP $MAINPID
Restart=always
RestartSec=60
TimeoutStopSec=30
# Environment variables
Environment=DB_TYPE=victoriametrics
Environment=VM_HOST=localhost
Environment=VM_PORT=8428
Environment=PYTHONPATH=/opt/thailand-water-monitor
Environment=PYTHONUNBUFFERED=1
# Security settings
NoNewPrivileges=true
PrivateTmp=true
ProtectSystem=strict
ProtectHome=true
ReadWritePaths=/opt/thailand-water-monitor
CapabilityBoundingSet=
# Logging
StandardOutput=journal
StandardError=journal
SyslogIdentifier=water-monitor
[Install]
WantedBy=multi-user.target

96
setup.py Normal file
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#!/usr/bin/env python3
"""
Setup script for Northern Thailand Ping River Monitor
"""
from setuptools import setup, find_packages
import os
# Read the README file
with open("README.md", "r", encoding="utf-8") as fh:
long_description = fh.read()
# Read requirements
with open("requirements.txt", "r", encoding="utf-8") as fh:
requirements = [line.strip() for line in fh if line.strip() and not line.startswith("#")]
# Extract core requirements (exclude dev dependencies)
core_requirements = []
for req in requirements:
if not any(dev_keyword in req.lower() for dev_keyword in ['pytest', 'black', 'flake8', 'mypy', 'sphinx']):
core_requirements.append(req)
setup(
name="northern-thailand-ping-river-monitor",
version="3.1.0",
author="Ping River Monitor Team",
author_email="contact@example.com",
description="Real-time water level monitoring system for the Ping River Basin in Northern Thailand",
long_description=long_description,
long_description_content_type="text/markdown",
url="https://git.b4l.co.th/B4L/Northern-Thailand-Ping-River-Monitor",
project_urls={
"Bug Tracker": "https://git.b4l.co.th/B4L/Northern-Thailand-Ping-River-Monitor/issues",
"Documentation": "https://git.b4l.co.th/B4L/Northern-Thailand-Ping-River-Monitor/wiki",
"Source Code": "https://git.b4l.co.th/B4L/Northern-Thailand-Ping-River-Monitor",
},
packages=find_packages(),
classifiers=[
"Development Status :: 4 - Beta",
"Intended Audience :: Science/Research",
"Intended Audience :: System Administrators",
"Topic :: Scientific/Engineering :: Hydrology",
"Topic :: System :: Monitoring",
"License :: OSI Approved :: MIT License",
"Programming Language :: Python :: 3",
"Programming Language :: Python :: 3.9",
"Programming Language :: Python :: 3.10",
"Programming Language :: Python :: 3.11",
"Programming Language :: Python :: 3.12",
"Operating System :: OS Independent",
"Environment :: Web Environment",
"Framework :: FastAPI",
],
python_requires=">=3.9",
install_requires=core_requirements,
extras_require={
"dev": [
"pytest>=7.4.3",
"pytest-cov>=4.1.0",
"black>=23.11.0",
"flake8>=6.1.0",
"mypy>=1.7.1",
"pre-commit>=3.5.0",
],
"docs": [
"sphinx>=7.2.6",
"sphinx-rtd-theme>=1.3.0",
],
"all": [
"influxdb>=5.3.1",
"pymysql>=1.1.0",
"psycopg2-binary>=2.9.9",
],
},
entry_points={
"console_scripts": [
"ping-river-monitor=src.main:main",
"ping-river-api=src.web_api:main",
],
},
include_package_data=True,
package_data={
"src": ["*.py"],
},
keywords=[
"water monitoring",
"hydrology",
"thailand",
"ping river",
"environmental monitoring",
"time series",
"fastapi",
"real-time data",
],
zip_safe=False,
)

38
src/__init__.py Normal file
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#!/usr/bin/env python3
"""
Northern Thailand Ping River Monitor Package
A comprehensive real-time water level monitoring system for the Ping River Basin
in Northern Thailand, covering Royal Irrigation Department (RID) stations.
"""
__version__ = "3.1.0"
__author__ = "Ping River Monitor Team"
__description__ = "Northern Thailand Ping River Monitoring System"
from .water_scraper_v3 import EnhancedWaterMonitorScraper
from .database_adapters import create_database_adapter, DatabaseAdapter
from .config import Config
from .models import WaterMeasurement, StationInfo, DatabaseConfig
from .exceptions import (
WaterMonitorException,
DatabaseConnectionError,
APIConnectionError,
DataValidationError,
ConfigurationError
)
__all__ = [
'EnhancedWaterMonitorScraper',
'create_database_adapter',
'DatabaseAdapter',
'Config',
'WaterMeasurement',
'StationInfo',
'DatabaseConfig',
'WaterMonitorException',
'DatabaseConnectionError',
'APIConnectionError',
'DataValidationError',
'ConfigurationError'
]

191
src/config.py Normal file
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import os
from typing import Dict, Any, Optional
try:
from .exceptions import ConfigurationError
from .models import DatabaseType, DatabaseConfig
except ImportError:
# Handle case when running as standalone script
class ConfigurationError(Exception):
pass
from enum import Enum
class DatabaseType(Enum):
SQLITE = "sqlite"
MYSQL = "mysql"
POSTGRESQL = "postgresql"
INFLUXDB = "influxdb"
VICTORIAMETRICS = "victoriametrics"
class Config:
"""Configuration class for the Water Level Monitor"""
# Database settings
DATABASE_PATH = os.getenv('WATER_DB_PATH', 'water_levels.db')
# Website settings
TARGET_URL = "https://hyd-app-db.rid.go.th/hydro1h.html"
API_URL = "https://hyd-app-db.rid.go.th/webservice/getGroupHourlyWaterLevelReportAllHL.ashx"
REQUEST_TIMEOUT = int(os.getenv('REQUEST_TIMEOUT', '30'))
USER_AGENT = "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/91.0.4472.124 Safari/537.36"
# Database configuration
DB_TYPE = os.getenv('DB_TYPE', 'sqlite').lower()
# VictoriaMetrics settings
VM_HOST = os.getenv('VM_HOST', 'vm.newedge.house')
VM_PORT = int(os.getenv('VM_PORT', '443'))
# Support for HTTPS URLs (e.g., behind reverse proxy)
VM_URL = os.getenv('VM_URL') # Full URL override (e.g., https://vm.example.com)
# InfluxDB settings
INFLUX_HOST = os.getenv('INFLUX_HOST', 'localhost')
INFLUX_PORT = int(os.getenv('INFLUX_PORT', '8086'))
INFLUX_DATABASE = os.getenv('INFLUX_DATABASE', 'water_monitoring')
INFLUX_USERNAME = os.getenv('INFLUX_USERNAME')
INFLUX_PASSWORD = os.getenv('INFLUX_PASSWORD')
# PostgreSQL settings
POSTGRES_CONNECTION_STRING = os.getenv('POSTGRES_CONNECTION_STRING')
# MySQL settings
MYSQL_CONNECTION_STRING = os.getenv('MYSQL_CONNECTION_STRING')
# Scheduler settings
SCRAPING_INTERVAL_HOURS = int(os.getenv('SCRAPING_INTERVAL_HOURS', '1'))
# Logging settings
LOG_LEVEL = os.getenv('LOG_LEVEL', 'INFO')
LOG_FILE = os.getenv('LOG_FILE', 'water_monitor.log')
LOG_FORMAT = '%(asctime)s - %(levelname)s - %(message)s'
# Data retention
DATA_RETENTION_DAYS = int(os.getenv('DATA_RETENTION_DAYS', '365'))
# Retry settings
MAX_RETRIES = int(os.getenv('MAX_RETRIES', '3'))
RETRY_DELAY_SECONDS = int(os.getenv('RETRY_DELAY_SECONDS', '60'))
@classmethod
def validate_config(cls) -> bool:
"""Validate configuration settings"""
errors = []
# Validate database type
try:
DatabaseType(cls.DB_TYPE)
except ValueError:
errors.append(f"Invalid DB_TYPE: {cls.DB_TYPE}")
# Validate database-specific settings
if cls.DB_TYPE == 'victoriametrics':
if not cls.VM_HOST:
errors.append("VM_HOST is required for VictoriaMetrics")
if not isinstance(cls.VM_PORT, int) or cls.VM_PORT <= 0:
errors.append("VM_PORT must be a positive integer")
elif cls.DB_TYPE == 'influxdb':
if not cls.INFLUX_HOST:
errors.append("INFLUX_HOST is required for InfluxDB")
if not cls.INFLUX_DATABASE:
errors.append("INFLUX_DATABASE is required for InfluxDB")
elif cls.DB_TYPE in ['postgresql', 'mysql']:
connection_string = (cls.POSTGRES_CONNECTION_STRING if cls.DB_TYPE == 'postgresql'
else cls.MYSQL_CONNECTION_STRING)
if not connection_string:
errors.append(f"Connection string is required for {cls.DB_TYPE.upper()}")
# Validate numeric settings
if cls.SCRAPING_INTERVAL_HOURS <= 0:
errors.append("SCRAPING_INTERVAL_HOURS must be positive")
if cls.DATA_RETENTION_DAYS <= 0:
errors.append("DATA_RETENTION_DAYS must be positive")
if errors:
raise ConfigurationError(f"Configuration errors: {'; '.join(errors)}")
return True
@classmethod
def get_database_config(cls) -> Dict[str, Any]:
"""Returns database configuration based on DB_TYPE"""
if cls.DB_TYPE == 'victoriametrics':
return {
'type': 'victoriametrics',
'host': cls.VM_HOST,
'port': cls.VM_PORT
}
elif cls.DB_TYPE == 'influxdb':
return {
'type': 'influxdb',
'host': cls.INFLUX_HOST,
'port': cls.INFLUX_PORT,
'database': cls.INFLUX_DATABASE,
'username': cls.INFLUX_USERNAME,
'password': cls.INFLUX_PASSWORD
}
elif cls.DB_TYPE == 'postgresql':
return {
'type': 'postgresql',
'connection_string': cls.POSTGRES_CONNECTION_STRING or
'postgresql://postgres:password@localhost:5432/water_monitoring'
}
elif cls.DB_TYPE == 'mysql':
return {
'type': 'mysql',
'connection_string': cls.MYSQL_CONNECTION_STRING or
'mysql://root:password@localhost:3306/water_monitoring'
}
else: # sqlite
return {
'type': 'sqlite',
'connection_string': f'sqlite:///{cls.DATABASE_PATH}'
}
@classmethod
def get_all_settings(cls) -> Dict[str, Any]:
"""Returns all configuration settings"""
return {
'DB_TYPE': cls.DB_TYPE,
'DATABASE_PATH': cls.DATABASE_PATH,
'TARGET_URL': cls.TARGET_URL,
'API_URL': cls.API_URL,
'REQUEST_TIMEOUT': cls.REQUEST_TIMEOUT,
'SCRAPING_INTERVAL_HOURS': cls.SCRAPING_INTERVAL_HOURS,
'LOG_LEVEL': cls.LOG_LEVEL,
'LOG_FILE': cls.LOG_FILE,
'DATA_RETENTION_DAYS': cls.DATA_RETENTION_DAYS,
'MAX_RETRIES': cls.MAX_RETRIES,
'RETRY_DELAY_SECONDS': cls.RETRY_DELAY_SECONDS,
'VM_HOST': cls.VM_HOST,
'VM_PORT': cls.VM_PORT,
'INFLUX_HOST': cls.INFLUX_HOST,
'INFLUX_PORT': cls.INFLUX_PORT,
'INFLUX_DATABASE': cls.INFLUX_DATABASE
}
@classmethod
def print_settings(cls):
"""Prints all current settings"""
print("=== Water Level Monitor Configuration ===")
for key, value in cls.get_all_settings().items():
# Hide sensitive information
if 'PASSWORD' in key and value:
value = '*' * len(str(value))
print(f"{key}: {value}")
print("=" * 45)
print("\nDatabase Configuration:")
db_config = cls.get_database_config()
for key, value in db_config.items():
if 'password' in key and value:
value = '*' * len(str(value))
print(f" {key}: {value}")
print("=" * 45)
if __name__ == "__main__":
Config.print_settings()

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#!/usr/bin/env python3
"""
Database adapters for different storage backends
"""
import datetime
import logging
from typing import List, Dict, Optional, Any
from abc import ABC, abstractmethod
# Base adapter interface
class DatabaseAdapter(ABC):
@abstractmethod
def connect(self):
pass
@abstractmethod
def save_measurements(self, measurements: List[Dict]) -> bool:
pass
@abstractmethod
def get_latest_measurements(self, limit: int = 100) -> List[Dict]:
pass
@abstractmethod
def get_measurements_by_timerange(self, start_time: datetime.datetime,
end_time: datetime.datetime,
station_codes: Optional[List[str]] = None) -> List[Dict]:
pass
# InfluxDB Adapter
class InfluxDBAdapter(DatabaseAdapter):
def __init__(self, host: str = "localhost", port: int = 8086,
database: str = "water_monitoring", username: str = None, password: str = None):
self.host = host
self.port = port
self.database = database
self.username = username
self.password = password
self.client = None
def connect(self):
try:
from influxdb import InfluxDBClient
self.client = InfluxDBClient(
host=self.host,
port=self.port,
username=self.username,
password=self.password,
database=self.database
)
# Create database if it doesn't exist
databases = self.client.get_list_database()
if not any(db['name'] == self.database for db in databases):
self.client.create_database(self.database)
logging.info(f"Created InfluxDB database: {self.database}")
# Create retention policy (keep data for 2 years, downsample after 30 days)
retention_policies = self.client.get_list_retention_policies(self.database)
if not any(rp['name'] == 'water_data_policy' for rp in retention_policies):
self.client.create_retention_policy(
'water_data_policy',
'730d', # 2 years
'1', # replication factor
database=self.database,
default=True
)
logging.info("Connected to InfluxDB successfully")
return True
except ImportError:
logging.error("InfluxDB client not installed. Run: pip install influxdb")
return False
except Exception as e:
logging.error(f"Failed to connect to InfluxDB: {e}")
return False
def save_measurements(self, measurements: List[Dict]) -> bool:
if not self.client:
logging.error("InfluxDB client not connected")
return False
try:
points = []
for measurement in measurements:
point = {
"measurement": "water_data",
"tags": {
"station_code": measurement['station_code'],
"station_name_en": measurement['station_name_en'],
"station_name_th": measurement['station_name_th']
},
"time": measurement['timestamp'].isoformat(),
"fields": {
"water_level": float(measurement['water_level']),
"discharge": float(measurement['discharge']),
"discharge_percent": float(measurement['discharge_percent']) if measurement['discharge_percent'] else None
}
}
points.append(point)
success = self.client.write_points(points)
if success:
logging.info(f"Successfully wrote {len(points)} points to InfluxDB")
return success
except Exception as e:
logging.error(f"Error writing to InfluxDB: {e}")
return False
def get_latest_measurements(self, limit: int = 100) -> List[Dict]:
if not self.client:
return []
try:
query = f"""
SELECT last("water_level") as water_level,
last("discharge") as discharge,
last("discharge_percent") as discharge_percent
FROM "water_data"
GROUP BY "station_code", "station_name_en", "station_name_th"
LIMIT {limit}
"""
result = self.client.query(query)
measurements = []
for point in result.get_points():
measurements.append({
'timestamp': point['time'],
'station_code': point.get('station_code'),
'station_name_en': point.get('station_name_en'),
'station_name_th': point.get('station_name_th'),
'water_level': point.get('water_level'),
'discharge': point.get('discharge'),
'discharge_percent': point.get('discharge_percent')
})
return measurements
except Exception as e:
logging.error(f"Error querying InfluxDB: {e}")
return []
def get_measurements_by_timerange(self, start_time: datetime.datetime,
end_time: datetime.datetime,
station_codes: Optional[List[str]] = None) -> List[Dict]:
if not self.client:
return []
try:
where_clause = f"time >= '{start_time.isoformat()}' AND time <= '{end_time.isoformat()}'"
if station_codes:
station_filter = "'" + "','".join(station_codes) + "'"
where_clause += f" AND station_code IN ({station_filter})"
query = f"""
SELECT "water_level", "discharge", "discharge_percent", "station_code", "station_name_en", "station_name_th"
FROM "water_data"
WHERE {where_clause}
ORDER BY time DESC
"""
result = self.client.query(query)
measurements = []
for point in result.get_points():
measurements.append({
'timestamp': point['time'],
'station_code': point.get('station_code'),
'station_name_en': point.get('station_name_en'),
'station_name_th': point.get('station_name_th'),
'water_level': point.get('water_level'),
'discharge': point.get('discharge'),
'discharge_percent': point.get('discharge_percent')
})
return measurements
except Exception as e:
logging.error(f"Error querying InfluxDB: {e}")
return []
# MySQL/PostgreSQL Adapter
class SQLAdapter(DatabaseAdapter):
def __init__(self, connection_string: str, db_type: str = "mysql"):
self.connection_string = connection_string
self.db_type = db_type.lower()
self.engine = None
# Add SQLite-specific connection parameters for better concurrency
if self.db_type == "sqlite":
if "?" not in connection_string:
self.connection_string += "?timeout=30&check_same_thread=False"
else:
self.connection_string += "&timeout=30&check_same_thread=False"
def connect(self):
try:
from sqlalchemy import create_engine, text
from sqlalchemy.orm import sessionmaker
self.engine = create_engine(self.connection_string, pool_pre_ping=True)
# Create tables
self._create_tables()
logging.info(f"Connected to {self.db_type.upper()} successfully")
return True
except ImportError:
logging.error("SQLAlchemy not installed. Run: pip install sqlalchemy pymysql")
return False
except Exception as e:
logging.error(f"Failed to connect to {self.db_type.upper()}: {e}")
return False
def _create_tables(self):
from sqlalchemy import text
# Stations table - adjust for different databases
if self.db_type == "sqlite":
stations_sql = """
CREATE TABLE IF NOT EXISTS stations (
id INTEGER PRIMARY KEY,
station_code TEXT UNIQUE NOT NULL,
thai_name TEXT NOT NULL,
english_name TEXT NOT NULL,
latitude REAL,
longitude REAL,
geohash TEXT,
created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP,
updated_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP
)
"""
measurements_sql = """
CREATE TABLE IF NOT EXISTS water_measurements (
id INTEGER PRIMARY KEY AUTOINCREMENT,
timestamp DATETIME NOT NULL,
station_id INTEGER NOT NULL,
water_level REAL,
discharge REAL,
discharge_percent REAL,
status TEXT DEFAULT 'active',
created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP,
FOREIGN KEY (station_id) REFERENCES stations(id),
UNIQUE(timestamp, station_id)
)
"""
# Create indexes separately for SQLite
index_sql = [
"CREATE INDEX IF NOT EXISTS idx_timestamp ON water_measurements(timestamp)",
"CREATE INDEX IF NOT EXISTS idx_station_timestamp ON water_measurements(station_id, timestamp)"
]
elif self.db_type == "postgresql":
stations_sql = """
CREATE TABLE IF NOT EXISTS stations (
id SERIAL PRIMARY KEY,
station_code VARCHAR(10) UNIQUE NOT NULL,
thai_name VARCHAR(255) NOT NULL,
english_name VARCHAR(255) NOT NULL,
latitude DECIMAL(10,8),
longitude DECIMAL(11,8),
geohash VARCHAR(20),
created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP,
updated_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP
)
"""
measurements_sql = """
CREATE TABLE IF NOT EXISTS water_measurements (
id BIGSERIAL PRIMARY KEY,
timestamp TIMESTAMP NOT NULL,
station_id INTEGER NOT NULL,
water_level NUMERIC(10,3),
discharge NUMERIC(10,2),
discharge_percent NUMERIC(5,2),
status VARCHAR(20) DEFAULT 'active',
created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP,
FOREIGN KEY (station_id) REFERENCES stations(id),
UNIQUE(timestamp, station_id)
)
"""
index_sql = [
"CREATE INDEX IF NOT EXISTS idx_timestamp ON water_measurements(timestamp)",
"CREATE INDEX IF NOT EXISTS idx_station_timestamp ON water_measurements(station_id, timestamp DESC)"
]
else: # MySQL
stations_sql = """
CREATE TABLE IF NOT EXISTS stations (
id INT PRIMARY KEY,
station_code VARCHAR(10) UNIQUE NOT NULL,
thai_name VARCHAR(255) NOT NULL,
english_name VARCHAR(255) NOT NULL,
latitude DECIMAL(10,8),
longitude DECIMAL(11,8),
geohash VARCHAR(20),
created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP,
updated_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP ON UPDATE CURRENT_TIMESTAMP
)
"""
measurements_sql = """
CREATE TABLE IF NOT EXISTS water_measurements (
id BIGINT AUTO_INCREMENT PRIMARY KEY,
timestamp DATETIME NOT NULL,
station_id INT NOT NULL,
water_level DECIMAL(10,3),
discharge DECIMAL(10,2),
discharge_percent DECIMAL(5,2),
status VARCHAR(20) DEFAULT 'active',
created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP,
FOREIGN KEY (station_id) REFERENCES stations(id),
UNIQUE KEY unique_measurement (timestamp, station_id),
INDEX idx_timestamp (timestamp),
INDEX idx_station_timestamp (station_id, timestamp)
)
"""
index_sql = []
with self.engine.begin() as conn:
conn.execute(text(stations_sql))
conn.execute(text(measurements_sql))
# Create indexes for SQLite and PostgreSQL
for index in index_sql:
conn.execute(text(index))
# Transaction is automatically committed when context manager exits
def save_measurements(self, measurements: List[Dict]) -> bool:
if not self.engine:
return False
try:
from sqlalchemy import text
with self.engine.begin() as conn:
# Insert/update stations
for measurement in measurements:
if self.db_type == "sqlite":
station_sql = """
INSERT OR REPLACE INTO stations (id, station_code, thai_name, english_name, latitude, longitude, geohash, updated_at)
VALUES (:station_id, :station_code, :thai_name, :english_name, :latitude, :longitude, :geohash, CURRENT_TIMESTAMP)
"""
elif self.db_type == "postgresql":
station_sql = """
INSERT INTO stations (id, station_code, thai_name, english_name, latitude, longitude, geohash, updated_at)
VALUES (:station_id, :station_code, :thai_name, :english_name, :latitude, :longitude, :geohash, NOW())
ON CONFLICT (id) DO UPDATE SET
thai_name = EXCLUDED.thai_name,
english_name = EXCLUDED.english_name,
latitude = EXCLUDED.latitude,
longitude = EXCLUDED.longitude,
geohash = EXCLUDED.geohash,
updated_at = NOW()
"""
else: # MySQL
station_sql = """
INSERT INTO stations (id, station_code, thai_name, english_name, latitude, longitude, geohash, updated_at)
VALUES (:station_id, :station_code, :thai_name, :english_name, :latitude, :longitude, :geohash, NOW())
ON DUPLICATE KEY UPDATE
thai_name = VALUES(thai_name),
english_name = VALUES(english_name),
latitude = VALUES(latitude),
longitude = VALUES(longitude),
geohash = VALUES(geohash),
updated_at = NOW()
"""
conn.execute(text(station_sql), {
'station_id': measurement['station_id'],
'station_code': measurement['station_code'],
'thai_name': measurement['station_name_th'],
'english_name': measurement['station_name_en'],
'latitude': measurement.get('latitude'),
'longitude': measurement.get('longitude'),
'geohash': measurement.get('geohash')
})
# Insert measurements
for measurement in measurements:
if self.db_type == "sqlite":
measurement_sql = """
INSERT OR REPLACE INTO water_measurements
(timestamp, station_id, water_level, discharge, discharge_percent, status)
VALUES (:timestamp, :station_id, :water_level, :discharge, :discharge_percent, :status)
"""
elif self.db_type == "postgresql":
measurement_sql = """
INSERT INTO water_measurements
(timestamp, station_id, water_level, discharge, discharge_percent, status)
VALUES (:timestamp, :station_id, :water_level, :discharge, :discharge_percent, :status)
ON CONFLICT (timestamp, station_id) DO UPDATE SET
water_level = EXCLUDED.water_level,
discharge = EXCLUDED.discharge,
discharge_percent = EXCLUDED.discharge_percent,
status = EXCLUDED.status
"""
else: # MySQL
measurement_sql = """
INSERT INTO water_measurements
(timestamp, station_id, water_level, discharge, discharge_percent, status)
VALUES (:timestamp, :station_id, :water_level, :discharge, :discharge_percent, :status)
ON DUPLICATE KEY UPDATE
water_level = VALUES(water_level),
discharge = VALUES(discharge),
discharge_percent = VALUES(discharge_percent),
status = VALUES(status)
"""
conn.execute(text(measurement_sql), {
'timestamp': measurement['timestamp'],
'station_id': measurement['station_id'],
'water_level': measurement['water_level'],
'discharge': measurement['discharge'],
'discharge_percent': measurement['discharge_percent'],
'status': measurement['status']
})
# Transaction is automatically committed when context manager exits
logging.info(f"Successfully saved {len(measurements)} measurements to {self.db_type.upper()}")
return True
except Exception as e:
logging.error(f"Error saving to {self.db_type.upper()}: {e}")
return False
def get_latest_measurements(self, limit: int = 100) -> List[Dict]:
if not self.engine:
return []
try:
from sqlalchemy import text
query = """
SELECT m.timestamp, s.station_code, s.english_name, s.thai_name,
m.water_level, m.discharge, m.discharge_percent, m.status
FROM water_measurements m
JOIN stations s ON m.station_id = s.id
INNER JOIN (
SELECT station_id, MAX(timestamp) as max_timestamp
FROM water_measurements
GROUP BY station_id
) latest ON m.station_id = latest.station_id AND m.timestamp = latest.max_timestamp
ORDER BY s.station_code
LIMIT :limit
"""
with self.engine.connect() as conn:
result = conn.execute(text(query), {'limit': limit})
measurements = []
for row in result:
measurements.append({
'timestamp': row[0],
'station_code': row[1],
'station_name_en': row[2],
'station_name_th': row[3],
'water_level': float(row[4]) if row[4] else None,
'discharge': float(row[5]) if row[5] else None,
'discharge_percent': float(row[6]) if row[6] else None,
'status': row[7]
})
return measurements
except Exception as e:
logging.error(f"Error querying {self.db_type.upper()}: {e}")
return []
def get_measurements_by_timerange(self, start_time: datetime.datetime,
end_time: datetime.datetime,
station_codes: Optional[List[str]] = None) -> List[Dict]:
if not self.engine:
return []
try:
from sqlalchemy import text
where_clause = "m.timestamp BETWEEN :start_time AND :end_time"
params = {'start_time': start_time, 'end_time': end_time}
if station_codes:
placeholders = ','.join([f':station_{i}' for i in range(len(station_codes))])
where_clause += f" AND s.station_code IN ({placeholders})"
for i, code in enumerate(station_codes):
params[f'station_{i}'] = code
query = f"""
SELECT m.timestamp, s.station_code, s.english_name, s.thai_name,
m.water_level, m.discharge, m.discharge_percent, m.status
FROM water_measurements m
JOIN stations s ON m.station_id = s.id
WHERE {where_clause}
ORDER BY m.timestamp DESC, s.station_code
"""
with self.engine.connect() as conn:
result = conn.execute(text(query), params)
measurements = []
for row in result:
measurements.append({
'timestamp': row[0],
'station_code': row[1],
'station_name_en': row[2],
'station_name_th': row[3],
'water_level': float(row[4]) if row[4] else None,
'discharge': float(row[5]) if row[5] else None,
'discharge_percent': float(row[6]) if row[6] else None,
'status': row[7]
})
return measurements
except Exception as e:
logging.error(f"Error querying {self.db_type.upper()}: {e}")
return []
# VictoriaMetrics Adapter (using Prometheus format)
class VictoriaMetricsAdapter(DatabaseAdapter):
def __init__(self, host: str = "localhost", port: int = 8428):
self.host = host
self.port = port
# Handle HTTPS URLs and reverse proxy configurations
if host.startswith(('http://', 'https://')):
self.base_url = host
if port != 80 and port != 443 and not host.endswith(f':{port}'):
# Only add port if it's not standard and not already in URL
if '://' in host and ':' not in host.split('://')[1]:
self.base_url = f"{host}:{port}"
else:
# Default to HTTP for localhost, HTTPS for remote hosts
protocol = "https" if host != "localhost" and not host.startswith("127.") else "http"
if (protocol == "https" and port == 443) or (protocol == "http" and port == 80):
self.base_url = f"{protocol}://{host}"
else:
self.base_url = f"{protocol}://{host}:{port}"
def connect(self):
try:
import requests
# Test connection with SSL verification and timeout
response = requests.get(
f"{self.base_url}/api/v1/status/config",
timeout=10,
verify=True # Enable SSL verification for HTTPS
)
if response.status_code == 200:
logging.info(f"Connected to VictoriaMetrics successfully at {self.base_url}")
return True
else:
logging.error(f"VictoriaMetrics connection failed: {response.status_code}")
return False
except requests.exceptions.SSLError as e:
logging.error(f"SSL error connecting to VictoriaMetrics: {e}")
return False
except requests.exceptions.ConnectionError as e:
logging.error(f"Connection error to VictoriaMetrics: {e}")
return False
except Exception as e:
logging.error(f"Failed to connect to VictoriaMetrics: {e}")
return False
def save_measurements(self, measurements: List[Dict]) -> bool:
try:
import requests
# Convert to Prometheus format
metrics_data = []
timestamp_ms = int(datetime.datetime.now().timestamp() * 1000)
for measurement in measurements:
# Water level metric
metrics_data.append(
f'water_level{{station_code="{measurement["station_code"]}",'
f'station_name_en="{measurement["station_name_en"]}",'
f'station_name_th="{measurement["station_name_th"]}"}} '
f'{measurement["water_level"]} {timestamp_ms}'
)
# Discharge metric
metrics_data.append(
f'water_discharge{{station_code="{measurement["station_code"]}",'
f'station_name_en="{measurement["station_name_en"]}",'
f'station_name_th="{measurement["station_name_th"]}"}} '
f'{measurement["discharge"]} {timestamp_ms}'
)
# Discharge percentage metric
if measurement["discharge_percent"]:
metrics_data.append(
f'water_discharge_percent{{station_code="{measurement["station_code"]}",'
f'station_name_en="{measurement["station_name_en"]}",'
f'station_name_th="{measurement["station_name_th"]}"}} '
f'{measurement["discharge_percent"]} {timestamp_ms}'
)
# Send to VictoriaMetrics
data = '\n'.join(metrics_data)
response = requests.post(
f"{self.base_url}/api/v1/import/prometheus",
data=data,
headers={'Content-Type': 'text/plain'},
timeout=30
)
if response.status_code == 204:
logging.info(f"Successfully sent {len(measurements)} measurements to VictoriaMetrics")
return True
else:
logging.error(f"VictoriaMetrics import failed: {response.status_code} - {response.text}")
return False
except Exception as e:
logging.error(f"Error sending to VictoriaMetrics: {e}")
return False
def get_latest_measurements(self, limit: int = 100) -> List[Dict]:
# VictoriaMetrics queries would be implemented here
# This is a simplified version
logging.warning("get_latest_measurements not fully implemented for VictoriaMetrics")
return []
def get_measurements_by_timerange(self, start_time: datetime.datetime,
end_time: datetime.datetime,
station_codes: Optional[List[str]] = None) -> List[Dict]:
# VictoriaMetrics range queries would be implemented here
logging.warning("get_measurements_by_timerange not fully implemented for VictoriaMetrics")
return []
# Factory function to create appropriate adapter
def create_database_adapter(db_type: str, **kwargs) -> DatabaseAdapter:
"""
Factory function to create database adapter
Args:
db_type: 'influxdb', 'mysql', 'postgresql', 'sqlite', or 'victoriametrics'
**kwargs: Database-specific connection parameters
"""
db_type = db_type.lower()
if db_type == 'influxdb':
return InfluxDBAdapter(**kwargs)
elif db_type == 'mysql':
return SQLAdapter(db_type='mysql', **kwargs)
elif db_type == 'postgresql':
return SQLAdapter(db_type='postgresql', **kwargs)
elif db_type == 'sqlite':
return SQLAdapter(db_type='sqlite', **kwargs)
elif db_type == 'victoriametrics':
return VictoriaMetricsAdapter(**kwargs)
else:
raise ValueError(f"Unsupported database type: {db_type}")

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#!/usr/bin/env python3
"""
Demo script showing different database backend options for water monitoring
"""
import os
import sys
import datetime
from water_scraper_v3 import EnhancedWaterMonitorScraper
def demo_sqlite():
"""Demo with SQLite (local development)"""
print("=" * 60)
print("🗄️ SQLite Demo (Local Development)")
print("=" * 60)
config = {
'type': 'sqlite',
'connection_string': 'sqlite:///demo_water_sqlite.db'
}
try:
scraper = EnhancedWaterMonitorScraper(config)
# Fetch and save data
print("Fetching data from API...")
data = scraper.fetch_water_data()
if data:
print(f"✓ Fetched {len(data)} data points")
success = scraper.save_to_database(data)
if success:
print("✓ Data saved to SQLite database")
# Show latest data
latest = scraper.get_latest_data(5)
print(f"\nLatest 5 measurements:")
for measurement in latest:
print(f" • {measurement['station_code']} ({measurement['station_name_en']}): "
f"{measurement['water_level']:.2f}m, {measurement['discharge']:.1f} cms")
else:
print("✗ Failed to save data")
else:
print("✗ No data fetched")
except Exception as e:
print(f"Error: {e}")
def demo_influxdb():
"""Demo with InfluxDB (requires InfluxDB running)"""
print("\n" + "=" * 60)
print("📊 InfluxDB Demo (Time-Series Database)")
print("=" * 60)
config = {
'type': 'influxdb',
'host': 'localhost',
'port': 8086,
'database': 'water_monitoring_demo',
'username': None, # Set if authentication is enabled
'password': None
}
try:
scraper = EnhancedWaterMonitorScraper(config)
if scraper.db_adapter and scraper.db_adapter.client:
print("✓ Connected to InfluxDB")
# Fetch and save data
print("Fetching data from API...")
data = scraper.fetch_water_data()
if data:
print(f"✓ Fetched {len(data)} data points")
success = scraper.save_to_database(data)
if success:
print("✓ Data saved to InfluxDB")
print("💡 You can now query this data in Grafana or InfluxDB CLI")
print(" Example query: SELECT * FROM water_data ORDER BY time DESC LIMIT 10")
else:
print("✗ Failed to save data")
else:
print("✗ No data fetched")
else:
print("✗ Could not connect to InfluxDB")
print("💡 Make sure InfluxDB is running: docker run -p 8086:8086 influxdb:1.8")
except Exception as e:
print(f"Error: {e}")
print("💡 InfluxDB might not be running or accessible")
def demo_postgresql():
"""Demo with PostgreSQL (requires PostgreSQL running)"""
print("\n" + "=" * 60)
print("🐘 PostgreSQL Demo (Relational Database)")
print("=" * 60)
config = {
'type': 'postgresql',
'connection_string': 'postgresql://postgres:password@localhost:5432/water_monitoring'
}
try:
scraper = EnhancedWaterMonitorScraper(config)
if scraper.db_adapter and scraper.db_adapter.engine:
print("✓ Connected to PostgreSQL")
# Fetch and save data
print("Fetching data from API...")
data = scraper.fetch_water_data()
if data:
print(f"✓ Fetched {len(data)} data points")
success = scraper.save_to_database(data)
if success:
print("✓ Data saved to PostgreSQL")
print("💡 You can now query this data with SQL")
print(" Example: SELECT * FROM water_measurements ORDER BY timestamp DESC LIMIT 10;")
else:
print("✗ Failed to save data")
else:
print("✗ No data fetched")
else:
print("✗ Could not connect to PostgreSQL")
print("💡 Make sure PostgreSQL is running with correct credentials")
except Exception as e:
print(f"Error: {e}")
print("💡 PostgreSQL might not be running or credentials might be wrong")
def demo_mysql():
"""Demo with MySQL (requires MySQL running)"""
print("\n" + "=" * 60)
print("🐬 MySQL Demo (Relational Database)")
print("=" * 60)
config = {
'type': 'mysql',
'connection_string': 'mysql://root:password@localhost:3306/water_monitoring'
}
try:
scraper = EnhancedWaterMonitorScraper(config)
if scraper.db_adapter and scraper.db_adapter.engine:
print("✓ Connected to MySQL")
# Fetch and save data
print("Fetching data from API...")
data = scraper.fetch_water_data()
if data:
print(f"✓ Fetched {len(data)} data points")
success = scraper.save_to_database(data)
if success:
print("✓ Data saved to MySQL")
print("💡 You can now query this data with SQL")
print(" Example: SELECT * FROM water_measurements ORDER BY timestamp DESC LIMIT 10;")
else:
print("✗ Failed to save data")
else:
print("✗ No data fetched")
else:
print("✗ Could not connect to MySQL")
print("💡 Make sure MySQL is running with correct credentials")
except Exception as e:
print(f"Error: {e}")
print("💡 MySQL might not be running or credentials might be wrong")
def demo_victoriametrics():
"""Demo with VictoriaMetrics (supports both local and HTTPS configurations)"""
print("\n" + "=" * 60)
print("⚡ VictoriaMetrics Demo (High-Performance Metrics)")
print("=" * 60)
# Use configuration from environment or config.py
from config import Config
db_config = Config.get_database_config()
if db_config['type'] != 'victoriametrics':
# Fallback to default local configuration
config = {
'type': 'victoriametrics',
'host': 'vm.newedge.house',
'port': 443
}
else:
config = db_config
print(f"Connecting to: {config['host']}:{config['port']}")
try:
scraper = EnhancedWaterMonitorScraper(config)
if scraper.db_adapter:
# Test connection using the adapter's connect method
if scraper.db_adapter.connect():
print("✓ Connected to VictoriaMetrics")
# Fetch and save data
print("Fetching data from API...")
data = scraper.fetch_water_data()
if data:
print(f"✓ Fetched {len(data)} data points")
success = scraper.save_to_database(data)
if success:
print("✓ Data saved to VictoriaMetrics")
print("💡 You can now query this data via Prometheus API")
# Show appropriate query URL based on configuration
base_url = scraper.db_adapter.base_url
print(f" Example: {base_url}/api/v1/query?query=water_level")
print(f" Health check: {base_url}/health")
else:
print("✗ Failed to save data")
else:
print("✗ No data fetched")
else:
print("✗ Could not connect to VictoriaMetrics")
if config['host'] == 'localhost':
print("💡 Make sure VictoriaMetrics is running locally:")
print(" docker run -p 8428:8428 victoriametrics/victoria-metrics")
else:
print(f"💡 Check if VictoriaMetrics is accessible at {config['host']}:{config['port']}")
print("💡 Verify HTTPS configuration and network connectivity")
else:
print("✗ Failed to initialize VictoriaMetrics adapter")
except Exception as e:
print(f"Error: {e}")
print("💡 Check your VictoriaMetrics configuration and network connectivity")
def show_recommendations():
"""Show database recommendations"""
print("\n" + "=" * 60)
print("🏆 Database Recommendations")
print("=" * 60)
recommendations = [
{
'name': 'InfluxDB',
'best_for': 'Time-series data, Grafana dashboards',
'pros': ['Purpose-built for time-series', 'Great compression', 'Built-in retention'],
'cons': ['Learning curve', 'Less flexible for complex queries'],
'use_case': 'Recommended for most water monitoring deployments'
},
{
'name': 'PostgreSQL + TimescaleDB',
'best_for': 'Complex queries, existing PostgreSQL infrastructure',
'pros': ['Mature ecosystem', 'SQL compatibility', 'ACID compliance'],
'cons': ['More complex setup', 'Higher resource usage'],
'use_case': 'Best for organizations already using PostgreSQL'
},
{
'name': 'VictoriaMetrics',
'best_for': 'High-performance metrics, Prometheus compatibility',
'pros': ['Extremely fast', 'Low resource usage', 'Better compression'],
'cons': ['Newer ecosystem', 'Less tooling'],
'use_case': 'Best for high-volume, performance-critical deployments'
},
{
'name': 'MySQL',
'best_for': 'Existing MySQL infrastructure, familiar SQL',
'pros': ['Familiar', 'Mature', 'Wide support'],
'cons': ['Not optimized for time-series', 'Manual optimization needed'],
'use_case': 'Good for organizations with existing MySQL expertise'
}
]
for rec in recommendations:
print(f"\n📊 {rec['name']}")
print(f" Best for: {rec['best_for']}")
print(f" Pros: {', '.join(rec['pros'])}")
print(f" Cons: {', '.join(rec['cons'])}")
print(f" 💡 {rec['use_case']}")
def main():
"""Main demo function"""
print("🌊 Thailand Water Monitor - Database Backend Demo")
print("This demo shows how to use different database backends")
# Always run SQLite demo (no external dependencies)
demo_sqlite()
# Check for command line arguments to run specific demos
if len(sys.argv) > 1:
db_type = sys.argv[1].lower()
if db_type == 'influxdb':
demo_influxdb()
elif db_type == 'postgresql':
demo_postgresql()
elif db_type == 'mysql':
demo_mysql()
elif db_type == 'victoriametrics':
demo_victoriametrics()
elif db_type == 'all':
demo_influxdb()
demo_postgresql()
demo_mysql()
demo_victoriametrics()
else:
print(f"\nUnknown database type: {db_type}")
print("Available options: influxdb, postgresql, mysql, victoriametrics, all")
else:
print("\n💡 To test other databases, run:")
print(" python demo_databases.py influxdb")
print(" python demo_databases.py postgresql")
print(" python demo_databases.py mysql")
print(" python demo_databases.py victoriametrics")
print(" python demo_databases.py all")
# Show recommendations
show_recommendations()
print("\n" + "=" * 60)
print("✅ Demo completed!")
print("📖 See DATABASE_DEPLOYMENT_GUIDE.md for production setup instructions")
print("=" * 60)
if __name__ == "__main__":
main()

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#!/usr/bin/env python3
"""
Custom exceptions for water monitoring system
"""
class WaterMonitorException(Exception):
"""Base exception for water monitoring system"""
pass
class DatabaseConnectionError(WaterMonitorException):
"""Raised when database connection fails"""
pass
class APIConnectionError(WaterMonitorException):
"""Raised when API connection fails"""
pass
class DataValidationError(WaterMonitorException):
"""Raised when data validation fails"""
pass
class ConfigurationError(WaterMonitorException):
"""Raised when configuration is invalid"""
pass
class DataParsingError(WaterMonitorException):
"""Raised when data parsing fails"""
pass
class RetryExhaustedError(WaterMonitorException):
"""Raised when all retry attempts are exhausted"""
pass

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#!/usr/bin/env python3
"""
Health check system for water monitoring application
"""
import time
import threading
from datetime import datetime, timedelta
from typing import Dict, Any, Optional, List, Callable
from dataclasses import dataclass
from enum import Enum
import logging
logger = logging.getLogger(__name__)
class HealthStatus(Enum):
HEALTHY = "healthy"
DEGRADED = "degraded"
UNHEALTHY = "unhealthy"
@dataclass
class HealthCheckResult:
"""Result of a health check"""
name: str
status: HealthStatus
message: str
timestamp: datetime
response_time_ms: Optional[float] = None
details: Optional[Dict[str, Any]] = None
class HealthCheck:
"""Base health check class"""
def __init__(self, name: str, timeout_seconds: int = 30):
self.name = name
self.timeout_seconds = timeout_seconds
def check(self) -> HealthCheckResult:
"""Perform the health check"""
start_time = time.time()
try:
result = self._perform_check()
response_time = (time.time() - start_time) * 1000
return HealthCheckResult(
name=self.name,
status=result.get('status', HealthStatus.HEALTHY),
message=result.get('message', 'OK'),
timestamp=datetime.now(),
response_time_ms=response_time,
details=result.get('details')
)
except Exception as e:
response_time = (time.time() - start_time) * 1000
logger.error(f"Health check {self.name} failed: {e}")
return HealthCheckResult(
name=self.name,
status=HealthStatus.UNHEALTHY,
message=f"Check failed: {str(e)}",
timestamp=datetime.now(),
response_time_ms=response_time
)
def _perform_check(self) -> Dict[str, Any]:
"""Override this method to implement the actual check"""
raise NotImplementedError
class DatabaseHealthCheck(HealthCheck):
"""Health check for database connectivity"""
def __init__(self, db_adapter, name: str = "database"):
super().__init__(name)
self.db_adapter = db_adapter
def _perform_check(self) -> Dict[str, Any]:
if not self.db_adapter:
return {
'status': HealthStatus.UNHEALTHY,
'message': 'Database adapter not initialized'
}
try:
# Try to connect
if hasattr(self.db_adapter, 'connect'):
connected = self.db_adapter.connect()
if not connected:
return {
'status': HealthStatus.UNHEALTHY,
'message': 'Database connection failed'
}
# Try to get latest data
latest_data = self.db_adapter.get_latest_measurements(limit=1)
if latest_data:
latest_timestamp = latest_data[0].get('timestamp')
if isinstance(latest_timestamp, str):
latest_timestamp = datetime.fromisoformat(latest_timestamp.replace('Z', '+00:00'))
# Check if data is recent (within last 2 hours)
if datetime.now() - latest_timestamp.replace(tzinfo=None) > timedelta(hours=2):
return {
'status': HealthStatus.DEGRADED,
'message': f'Latest data is old: {latest_timestamp}',
'details': {'latest_data_timestamp': str(latest_timestamp)}
}
return {
'status': HealthStatus.HEALTHY,
'message': 'Database connection OK',
'details': {
'latest_data_count': len(latest_data),
'latest_timestamp': str(latest_data[0].get('timestamp')) if latest_data else None
}
}
except Exception as e:
return {
'status': HealthStatus.UNHEALTHY,
'message': f'Database check failed: {str(e)}'
}
class APIHealthCheck(HealthCheck):
"""Health check for external API connectivity"""
def __init__(self, api_url: str, session, name: str = "api"):
super().__init__(name)
self.api_url = api_url
self.session = session
def _perform_check(self) -> Dict[str, Any]:
try:
# Simple GET request to check API availability
response = self.session.get(self.api_url, timeout=self.timeout_seconds)
if response.status_code == 200:
return {
'status': HealthStatus.HEALTHY,
'message': 'API connection OK',
'details': {
'status_code': response.status_code,
'response_size': len(response.content)
}
}
else:
return {
'status': HealthStatus.DEGRADED,
'message': f'API returned status {response.status_code}',
'details': {'status_code': response.status_code}
}
except Exception as e:
return {
'status': HealthStatus.UNHEALTHY,
'message': f'API check failed: {str(e)}'
}
class MemoryHealthCheck(HealthCheck):
"""Health check for memory usage"""
def __init__(self, max_memory_mb: int = 1000, name: str = "memory"):
super().__init__(name)
self.max_memory_mb = max_memory_mb
def _perform_check(self) -> Dict[str, Any]:
try:
import psutil
process = psutil.Process()
memory_info = process.memory_info()
memory_mb = memory_info.rss / 1024 / 1024
if memory_mb > self.max_memory_mb:
return {
'status': HealthStatus.DEGRADED,
'message': f'High memory usage: {memory_mb:.1f}MB',
'details': {'memory_mb': memory_mb, 'max_memory_mb': self.max_memory_mb}
}
return {
'status': HealthStatus.HEALTHY,
'message': f'Memory usage OK: {memory_mb:.1f}MB',
'details': {'memory_mb': memory_mb}
}
except ImportError:
return {
'status': HealthStatus.HEALTHY,
'message': 'Memory check skipped (psutil not available)'
}
except Exception as e:
return {
'status': HealthStatus.UNHEALTHY,
'message': f'Memory check failed: {str(e)}'
}
class HealthCheckManager:
"""Manages multiple health checks"""
def __init__(self):
self.checks: List[HealthCheck] = []
self.last_results: Dict[str, HealthCheckResult] = {}
self._lock = threading.Lock()
def add_check(self, health_check: HealthCheck):
"""Add a health check"""
with self._lock:
self.checks.append(health_check)
def run_all_checks(self) -> Dict[str, HealthCheckResult]:
"""Run all health checks"""
results = {}
for check in self.checks:
try:
result = check.check()
results[check.name] = result
with self._lock:
self.last_results[check.name] = result
except Exception as e:
logger.error(f"Error running health check {check.name}: {e}")
results[check.name] = HealthCheckResult(
name=check.name,
status=HealthStatus.UNHEALTHY,
message=f"Check execution failed: {str(e)}",
timestamp=datetime.now()
)
return results
def get_overall_status(self) -> HealthStatus:
"""Get overall system health status"""
if not self.last_results:
return HealthStatus.UNHEALTHY
statuses = [result.status for result in self.last_results.values()]
if any(status == HealthStatus.UNHEALTHY for status in statuses):
return HealthStatus.UNHEALTHY
elif any(status == HealthStatus.DEGRADED for status in statuses):
return HealthStatus.DEGRADED
else:
return HealthStatus.HEALTHY
def get_health_summary(self) -> Dict[str, Any]:
"""Get a summary of system health"""
overall_status = self.get_overall_status()
return {
'overall_status': overall_status.value,
'timestamp': datetime.now().isoformat(),
'checks': {
name: {
'status': result.status.value,
'message': result.message,
'response_time_ms': result.response_time_ms,
'timestamp': result.timestamp.isoformat()
}
for name, result in self.last_results.items()
}
}

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#!/usr/bin/env python3
"""
Centralized logging configuration for water monitoring system
"""
import logging
import logging.handlers
import os
from datetime import datetime
from typing import Optional
class ColoredFormatter(logging.Formatter):
"""Colored console formatter"""
COLORS = {
'DEBUG': '\033[36m', # Cyan
'INFO': '\033[32m', # Green
'WARNING': '\033[33m', # Yellow
'ERROR': '\033[31m', # Red
'CRITICAL': '\033[35m', # Magenta
'RESET': '\033[0m' # Reset
}
def format(self, record):
if hasattr(record, 'levelname'):
color = self.COLORS.get(record.levelname, self.COLORS['RESET'])
record.levelname = f"{color}{record.levelname}{self.COLORS['RESET']}"
return super().format(record)
def setup_logging(
log_level: str = "INFO",
log_file: Optional[str] = None,
max_file_size: int = 10 * 1024 * 1024, # 10MB
backup_count: int = 5,
enable_console: bool = True,
enable_colors: bool = True
) -> logging.Logger:
"""
Setup comprehensive logging configuration
Args:
log_level: Logging level (DEBUG, INFO, WARNING, ERROR, CRITICAL)
log_file: Path to log file (optional)
max_file_size: Maximum size of log file before rotation
backup_count: Number of backup files to keep
enable_console: Whether to enable console logging
enable_colors: Whether to enable colored console output
Returns:
Configured logger instance
"""
# Create logs directory if it doesn't exist
if log_file:
log_dir = os.path.dirname(log_file)
if log_dir and not os.path.exists(log_dir):
os.makedirs(log_dir)
# Configure root logger
logger = logging.getLogger()
logger.setLevel(getattr(logging, log_level.upper()))
# Clear existing handlers
logger.handlers.clear()
# Create formatters
detailed_formatter = logging.Formatter(
'%(asctime)s - %(name)s - %(levelname)s - %(filename)s:%(lineno)d - %(message)s',
datefmt='%Y-%m-%d %H:%M:%S'
)
simple_formatter = logging.Formatter(
'%(asctime)s - %(levelname)s - %(message)s',
datefmt='%H:%M:%S'
)
# Console handler
if enable_console:
console_handler = logging.StreamHandler()
if enable_colors and os.name != 'nt': # Don't use colors on Windows
console_formatter = ColoredFormatter(
'%(asctime)s - %(levelname)s - %(message)s',
datefmt='%H:%M:%S'
)
else:
console_formatter = simple_formatter
console_handler.setFormatter(console_formatter)
console_handler.setLevel(getattr(logging, log_level.upper()))
logger.addHandler(console_handler)
# File handler with rotation
if log_file:
file_handler = logging.handlers.RotatingFileHandler(
log_file,
maxBytes=max_file_size,
backupCount=backup_count,
encoding='utf-8'
)
file_handler.setFormatter(detailed_formatter)
file_handler.setLevel(logging.DEBUG) # Always log everything to file
logger.addHandler(file_handler)
# Add performance logger for metrics
perf_logger = logging.getLogger('performance')
if log_file:
perf_file = log_file.replace('.log', '_performance.log')
perf_handler = logging.handlers.RotatingFileHandler(
perf_file,
maxBytes=max_file_size,
backupCount=backup_count,
encoding='utf-8'
)
perf_formatter = logging.Formatter(
'%(asctime)s - %(message)s',
datefmt='%Y-%m-%d %H:%M:%S'
)
perf_handler.setFormatter(perf_formatter)
perf_logger.addHandler(perf_handler)
perf_logger.setLevel(logging.INFO)
perf_logger.propagate = False
return logger
def log_performance_metric(operation: str, duration: float, additional_info: Optional[str] = None):
"""Log performance metrics"""
perf_logger = logging.getLogger('performance')
message = f"PERF: {operation} took {duration:.3f}s"
if additional_info:
message += f" - {additional_info}"
perf_logger.info(message)
def get_logger(name: str) -> logging.Logger:
"""Get a logger with the specified name"""
return logging.getLogger(name)

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#!/usr/bin/env python3
"""
Main entry point for the Thailand Water Monitor system
"""
import argparse
import asyncio
import sys
import signal
from datetime import datetime
from typing import Optional
from .config import Config
from .water_scraper_v3 import EnhancedWaterMonitorScraper
from .logging_config import setup_logging, get_logger
from .exceptions import ConfigurationError, DatabaseConnectionError
from .metrics import get_metrics_collector
logger = get_logger(__name__)
def setup_signal_handlers(scraper: Optional[EnhancedWaterMonitorScraper] = None):
"""Setup signal handlers for graceful shutdown"""
def signal_handler(signum, frame):
logger.info(f"Received signal {signum}, shutting down gracefully...")
if scraper:
logger.info("Stopping scraper...")
sys.exit(0)
signal.signal(signal.SIGINT, signal_handler)
signal.signal(signal.SIGTERM, signal_handler)
def run_test_cycle():
"""Run a single test cycle"""
logger.info("Running test cycle...")
try:
# Validate configuration
Config.validate_config()
# Initialize scraper
db_config = Config.get_database_config()
scraper = EnhancedWaterMonitorScraper(db_config)
# Run single scraping cycle
result = scraper.run_scraping_cycle()
if result:
logger.info("✅ Test cycle completed successfully")
# Show some statistics
latest_data = scraper.get_latest_data(5)
if latest_data:
logger.info(f"Latest data points: {len(latest_data)}")
for data in latest_data[:3]: # Show first 3
logger.info(f" • {data['station_code']}: {data['water_level']:.2f}m, {data['discharge']:.1f} cms")
else:
logger.warning("⚠️ Test cycle completed but no new data was found")
return True
except Exception as e:
logger.error(f"❌ Test cycle failed: {e}")
return False
def run_continuous_monitoring():
"""Run continuous monitoring with scheduling"""
logger.info("Starting continuous monitoring...")
try:
# Validate configuration
Config.validate_config()
# Initialize scraper
db_config = Config.get_database_config()
scraper = EnhancedWaterMonitorScraper(db_config)
# Setup signal handlers
setup_signal_handlers(scraper)
logger.info(f"Monitoring started with {Config.SCRAPING_INTERVAL_HOURS}h interval")
logger.info("Press Ctrl+C to stop")
# Run initial cycle
logger.info("Running initial data collection...")
scraper.run_scraping_cycle()
# Start scheduled monitoring
import schedule
schedule.every(Config.SCRAPING_INTERVAL_HOURS).hours.do(scraper.run_scraping_cycle)
while True:
schedule.run_pending()
time.sleep(60) # Check every minute
except KeyboardInterrupt:
logger.info("Monitoring stopped by user")
except Exception as e:
logger.error(f"Monitoring failed: {e}")
return False
return True
def run_gap_filling(days_back: int):
"""Run gap filling for missing data"""
logger.info(f"Checking for data gaps in the last {days_back} days...")
try:
# Validate configuration
Config.validate_config()
# Initialize scraper
db_config = Config.get_database_config()
scraper = EnhancedWaterMonitorScraper(db_config)
# Fill gaps
filled_count = scraper.fill_data_gaps(days_back)
if filled_count > 0:
logger.info(f"✅ Filled {filled_count} missing data points")
else:
logger.info("✅ No data gaps found")
return True
except Exception as e:
logger.error(f"❌ Gap filling failed: {e}")
return False
def run_data_update(days_back: int):
"""Update existing data with latest values"""
logger.info(f"Updating existing data for the last {days_back} days...")
try:
# Validate configuration
Config.validate_config()
# Initialize scraper
db_config = Config.get_database_config()
scraper = EnhancedWaterMonitorScraper(db_config)
# Update data
updated_count = scraper.update_existing_data(days_back)
if updated_count > 0:
logger.info(f"✅ Updated {updated_count} data points")
else:
logger.info("✅ No data updates needed")
return True
except Exception as e:
logger.error(f"❌ Data update failed: {e}")
return False
def run_web_api():
"""Run the FastAPI web interface"""
logger.info("Starting web API server...")
try:
import uvicorn
from .web_api import app
# Validate configuration
Config.validate_config()
# Run the server
uvicorn.run(
app,
host="0.0.0.0",
port=8000,
log_config=None # Use our custom logging
)
except ImportError:
logger.error("FastAPI not installed. Run: pip install fastapi uvicorn")
return False
except Exception as e:
logger.error(f"Web API failed: {e}")
return False
def show_status():
"""Show current system status"""
logger.info("=== Northern Thailand Ping River Monitor Status ===")
try:
# Show configuration
Config.print_settings()
# Test database connection
logger.info("\n=== Database Connection Test ===")
db_config = Config.get_database_config()
scraper = EnhancedWaterMonitorScraper(db_config)
if scraper.db_adapter:
logger.info("✅ Database connection successful")
# Show latest data
latest_data = scraper.get_latest_data(3)
if latest_data:
logger.info(f"\n=== Latest Data ({len(latest_data)} points) ===")
for data in latest_data:
timestamp = data['timestamp']
if isinstance(timestamp, str):
timestamp = datetime.fromisoformat(timestamp.replace('Z', '+00:00'))
logger.info(f" • {data['station_code']} ({timestamp}): {data['water_level']:.2f}m")
else:
logger.info("No data found in database")
else:
logger.error("❌ Database connection failed")
# Show metrics if available
metrics_collector = get_metrics_collector()
metrics = metrics_collector.get_all_metrics()
if any(metrics.values()):
logger.info("\n=== Metrics Summary ===")
for metric_type, values in metrics.items():
if values:
logger.info(f"{metric_type.title()}: {len(values)} metrics")
return True
except Exception as e:
logger.error(f"Status check failed: {e}")
return False
def main():
"""Main entry point"""
parser = argparse.ArgumentParser(
description="Northern Thailand Ping River Monitor",
formatter_class=argparse.RawDescriptionHelpFormatter,
epilog="""
Examples:
%(prog)s --test # Run single test cycle
%(prog)s # Run continuous monitoring
%(prog)s --web-api # Start web API server
%(prog)s --fill-gaps 7 # Fill missing data for last 7 days
%(prog)s --update-data 2 # Update existing data for last 2 days
%(prog)s --status # Show system status
"""
)
parser.add_argument(
"--test",
action="store_true",
help="Run a single test cycle"
)
parser.add_argument(
"--web-api",
action="store_true",
help="Start the web API server"
)
parser.add_argument(
"--fill-gaps",
type=int,
metavar="DAYS",
help="Fill missing data gaps for the specified number of days back"
)
parser.add_argument(
"--update-data",
type=int,
metavar="DAYS",
help="Update existing data for the specified number of days back"
)
parser.add_argument(
"--status",
action="store_true",
help="Show current system status"
)
parser.add_argument(
"--log-level",
choices=["DEBUG", "INFO", "WARNING", "ERROR", "CRITICAL"],
default=Config.LOG_LEVEL,
help="Set logging level"
)
parser.add_argument(
"--log-file",
default=Config.LOG_FILE,
help="Log file path"
)
args = parser.parse_args()
# Setup logging
setup_logging(
log_level=args.log_level,
log_file=args.log_file,
enable_console=True,
enable_colors=True
)
logger.info("🏔️ Northern Thailand Ping River Monitor starting...")
logger.info(f"Version: 3.1.0")
logger.info(f"Log level: {args.log_level}")
try:
success = False
if args.test:
success = run_test_cycle()
elif args.web_api:
success = run_web_api()
elif args.fill_gaps is not None:
success = run_gap_filling(args.fill_gaps)
elif args.update_data is not None:
success = run_data_update(args.update_data)
elif args.status:
success = show_status()
else:
success = run_continuous_monitoring()
if success:
logger.info("✅ Operation completed successfully")
sys.exit(0)
else:
logger.error("❌ Operation failed")
sys.exit(1)
except ConfigurationError as e:
logger.error(f"Configuration error: {e}")
sys.exit(1)
except KeyboardInterrupt:
logger.info("Operation cancelled by user")
sys.exit(0)
except Exception as e:
logger.error(f"Unexpected error: {e}")
sys.exit(1)
if __name__ == "__main__":
main()

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#!/usr/bin/env python3
"""
Metrics collection and monitoring for water monitoring system
"""
import time
import threading
from datetime import datetime, timedelta
from typing import Dict, Any, Optional, List
from dataclasses import dataclass, field
from collections import defaultdict, deque
import logging
logger = logging.getLogger(__name__)
@dataclass
class MetricPoint:
"""Single metric data point"""
timestamp: datetime
value: float
labels: Dict[str, str] = field(default_factory=dict)
class MetricsCollector:
"""Collects and manages application metrics"""
def __init__(self, retention_hours: int = 24):
self.retention_hours = retention_hours
self.metrics: Dict[str, deque] = defaultdict(lambda: deque(maxlen=1000))
self.counters: Dict[str, float] = defaultdict(float)
self.gauges: Dict[str, float] = defaultdict(float)
self.histograms: Dict[str, List[float]] = defaultdict(list)
self._lock = threading.Lock()
# Start cleanup thread
self._cleanup_thread = threading.Thread(target=self._cleanup_old_metrics, daemon=True)
self._cleanup_thread.start()
def increment_counter(self, name: str, value: float = 1.0, labels: Optional[Dict[str, str]] = None):
"""Increment a counter metric"""
with self._lock:
key = self._make_key(name, labels)
self.counters[key] += value
self.metrics[key].append(MetricPoint(datetime.now(), self.counters[key], labels or {}))
def set_gauge(self, name: str, value: float, labels: Optional[Dict[str, str]] = None):
"""Set a gauge metric"""
with self._lock:
key = self._make_key(name, labels)
self.gauges[key] = value
self.metrics[key].append(MetricPoint(datetime.now(), value, labels or {}))
def record_histogram(self, name: str, value: float, labels: Optional[Dict[str, str]] = None):
"""Record a histogram value"""
with self._lock:
key = self._make_key(name, labels)
self.histograms[key].append(value)
# Keep only recent values
if len(self.histograms[key]) > 1000:
self.histograms[key] = self.histograms[key][-1000:]
self.metrics[key].append(MetricPoint(datetime.now(), value, labels or {}))
def get_counter(self, name: str, labels: Optional[Dict[str, str]] = None) -> float:
"""Get current counter value"""
key = self._make_key(name, labels)
return self.counters.get(key, 0.0)
def get_gauge(self, name: str, labels: Optional[Dict[str, str]] = None) -> float:
"""Get current gauge value"""
key = self._make_key(name, labels)
return self.gauges.get(key, 0.0)
def get_histogram_stats(self, name: str, labels: Optional[Dict[str, str]] = None) -> Dict[str, float]:
"""Get histogram statistics"""
key = self._make_key(name, labels)
values = self.histograms.get(key, [])
if not values:
return {'count': 0, 'sum': 0, 'avg': 0, 'min': 0, 'max': 0}
return {
'count': len(values),
'sum': sum(values),
'avg': sum(values) / len(values),
'min': min(values),
'max': max(values)
}
def get_all_metrics(self) -> Dict[str, Any]:
"""Get all current metrics"""
with self._lock:
return {
'counters': dict(self.counters),
'gauges': dict(self.gauges),
'histograms': {k: self.get_histogram_stats(k) for k in self.histograms}
}
def _make_key(self, name: str, labels: Optional[Dict[str, str]]) -> str:
"""Create a unique key for metric with labels"""
if not labels:
return name
label_str = ','.join(f"{k}={v}" for k, v in sorted(labels.items()))
return f"{name}{{{label_str}}}"
def _cleanup_old_metrics(self):
"""Clean up old metric data points"""
while True:
try:
cutoff_time = datetime.now() - timedelta(hours=self.retention_hours)
with self._lock:
for metric_name, points in self.metrics.items():
# Remove old points
while points and points[0].timestamp < cutoff_time:
points.popleft()
time.sleep(3600) # Run cleanup every hour
except Exception as e:
logger.error(f"Error in metrics cleanup: {e}")
time.sleep(60) # Wait a minute before retrying
# Global metrics collector instance
_metrics_collector = None
def get_metrics_collector() -> MetricsCollector:
"""Get the global metrics collector instance"""
global _metrics_collector
if _metrics_collector is None:
_metrics_collector = MetricsCollector()
return _metrics_collector
# Convenience functions
def increment_counter(name: str, value: float = 1.0, labels: Optional[Dict[str, str]] = None):
"""Increment a counter metric"""
get_metrics_collector().increment_counter(name, value, labels)
def set_gauge(name: str, value: float, labels: Optional[Dict[str, str]] = None):
"""Set a gauge metric"""
get_metrics_collector().set_gauge(name, value, labels)
def record_histogram(name: str, value: float, labels: Optional[Dict[str, str]] = None):
"""Record a histogram value"""
get_metrics_collector().record_histogram(name, value, labels)
class Timer:
"""Context manager for timing operations"""
def __init__(self, metric_name: str, labels: Optional[Dict[str, str]] = None):
self.metric_name = metric_name
self.labels = labels
self.start_time = None
def __enter__(self):
self.start_time = time.time()
return self
def __exit__(self, exc_type, exc_val, exc_tb):
if self.start_time:
duration = time.time() - self.start_time
record_histogram(self.metric_name, duration, self.labels)
def timer(metric_name: str, labels: Optional[Dict[str, str]] = None):
"""Decorator for timing function execution"""
def decorator(func):
def wrapper(*args, **kwargs):
with Timer(metric_name, labels):
return func(*args, **kwargs)
return wrapper
return decorator

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#!/usr/bin/env python3
"""
Data models for water monitoring system
"""
from dataclasses import dataclass, field
from datetime import datetime
from typing import Optional, List, Dict, Any
from enum import Enum
class DatabaseType(Enum):
SQLITE = "sqlite"
MYSQL = "mysql"
POSTGRESQL = "postgresql"
INFLUXDB = "influxdb"
VICTORIAMETRICS = "victoriametrics"
class StationStatus(Enum):
ACTIVE = "active"
INACTIVE = "inactive"
MAINTENANCE = "maintenance"
ERROR = "error"
@dataclass
class StationInfo:
"""Station information model"""
station_id: int
station_code: str
thai_name: str
english_name: str
latitude: Optional[float] = None
longitude: Optional[float] = None
geohash: Optional[str] = None
status: StationStatus = StationStatus.ACTIVE
@dataclass
class WaterMeasurement:
"""Water measurement data model"""
timestamp: datetime
station_info: StationInfo
water_level: float
discharge: float
water_level_unit: str = "m"
discharge_unit: str = "cms"
discharge_percent: Optional[float] = None
status: StationStatus = StationStatus.ACTIVE
def to_dict(self) -> Dict[str, Any]:
"""Convert to dictionary for database storage"""
return {
'timestamp': self.timestamp,
'station_id': self.station_info.station_id,
'station_code': self.station_info.station_code,
'station_name_en': self.station_info.english_name,
'station_name_th': self.station_info.thai_name,
'latitude': self.station_info.latitude,
'longitude': self.station_info.longitude,
'geohash': self.station_info.geohash,
'water_level': self.water_level,
'water_level_unit': self.water_level_unit,
'discharge': self.discharge,
'discharge_unit': self.discharge_unit,
'discharge_percent': self.discharge_percent,
'status': self.status.value
}
@dataclass
class DatabaseConfig:
"""Database configuration model"""
db_type: DatabaseType
connection_string: Optional[str] = None
host: Optional[str] = None
port: Optional[int] = None
database: Optional[str] = None
username: Optional[str] = None
password: Optional[str] = None
additional_params: Dict[str, Any] = field(default_factory=dict)
@dataclass
class ScrapingResult:
"""Result of a scraping operation"""
success: bool
measurements_count: int
error_message: Optional[str] = None
timestamp: datetime = field(default_factory=datetime.now)
processing_time_seconds: Optional[float] = None
@dataclass
class StationCreateRequest:
"""Request model for creating a new station"""
station_code: str
thai_name: str
english_name: str
latitude: Optional[float] = None
longitude: Optional[float] = None
geohash: Optional[str] = None
status: StationStatus = StationStatus.ACTIVE
@dataclass
class StationUpdateRequest:
"""Request model for updating an existing station"""
thai_name: Optional[str] = None
english_name: Optional[str] = None
latitude: Optional[float] = None
longitude: Optional[float] = None
geohash: Optional[str] = None
status: Optional[StationStatus] = None

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#!/usr/bin/env python3
"""
Rate limiting utilities for API requests
"""
import time
import threading
from typing import Dict, Optional
from collections import deque
from datetime import datetime, timedelta
import logging
logger = logging.getLogger(__name__)
class RateLimiter:
"""Token bucket rate limiter"""
def __init__(self, max_requests: int, time_window_seconds: int):
"""
Initialize rate limiter
Args:
max_requests: Maximum number of requests allowed
time_window_seconds: Time window in seconds
"""
self.max_requests = max_requests
self.time_window = time_window_seconds
self.requests = deque()
self._lock = threading.Lock()
def is_allowed(self) -> bool:
"""Check if a request is allowed"""
with self._lock:
now = time.time()
# Remove old requests outside the time window
while self.requests and self.requests[0] <= now - self.time_window:
self.requests.popleft()
# Check if we can make a new request
if len(self.requests) < self.max_requests:
self.requests.append(now)
return True
return False
def wait_time(self) -> float:
"""Get the time to wait before next request is allowed"""
with self._lock:
if len(self.requests) < self.max_requests:
return 0.0
# Time until the oldest request expires
oldest_request = self.requests[0]
return max(0.0, (oldest_request + self.time_window) - time.time())
def wait_if_needed(self):
"""Block until a request is allowed"""
wait_time = self.wait_time()
if wait_time > 0:
logger.info(f"Rate limit reached, waiting {wait_time:.2f} seconds")
time.sleep(wait_time)
class AdaptiveRateLimiter:
"""Adaptive rate limiter that adjusts based on response times"""
def __init__(self, initial_rate: float = 1.0, min_rate: float = 0.1, max_rate: float = 10.0):
"""
Initialize adaptive rate limiter
Args:
initial_rate: Initial requests per second
min_rate: Minimum requests per second
max_rate: Maximum requests per second
"""
self.current_rate = initial_rate
self.min_rate = min_rate
self.max_rate = max_rate
self.last_request_time = 0.0
self.response_times = deque(maxlen=10)
self._lock = threading.Lock()
def wait_and_record(self, response_time: Optional[float] = None):
"""Wait for rate limit and record response time"""
with self._lock:
now = time.time()
# Calculate wait time based on current rate
time_since_last = now - self.last_request_time
min_interval = 1.0 / self.current_rate
if time_since_last < min_interval:
wait_time = min_interval - time_since_last
time.sleep(wait_time)
now = time.time()
self.last_request_time = now
# Record response time and adjust rate
if response_time is not None:
self.response_times.append(response_time)
self._adjust_rate()
def _adjust_rate(self):
"""Adjust rate based on recent response times"""
if len(self.response_times) < 3:
return
avg_response_time = sum(self.response_times) / len(self.response_times)
# Decrease rate if responses are slow
if avg_response_time > 5.0: # 5 seconds
self.current_rate = max(self.min_rate, self.current_rate * 0.8)
logger.info(f"Decreased rate to {self.current_rate:.2f} req/s due to slow responses")
# Increase rate if responses are fast
elif avg_response_time < 1.0: # 1 second
self.current_rate = min(self.max_rate, self.current_rate * 1.1)
logger.debug(f"Increased rate to {self.current_rate:.2f} req/s")
class RequestTracker:
"""Track API request statistics"""
def __init__(self):
self.total_requests = 0
self.successful_requests = 0
self.failed_requests = 0
self.total_response_time = 0.0
self.last_request_time = None
self.error_count_by_type = {}
self._lock = threading.Lock()
def record_request(self, success: bool, response_time: float, error_type: Optional[str] = None):
"""Record a request"""
with self._lock:
self.total_requests += 1
self.total_response_time += response_time
self.last_request_time = datetime.now()
if success:
self.successful_requests += 1
else:
self.failed_requests += 1
if error_type:
self.error_count_by_type[error_type] = self.error_count_by_type.get(error_type, 0) + 1
def get_stats(self) -> Dict[str, any]:
"""Get request statistics"""
with self._lock:
if self.total_requests == 0:
return {
'total_requests': 0,
'success_rate': 0.0,
'average_response_time': 0.0,
'last_request_time': None,
'error_breakdown': {}
}
return {
'total_requests': self.total_requests,
'successful_requests': self.successful_requests,
'failed_requests': self.failed_requests,
'success_rate': self.successful_requests / self.total_requests,
'average_response_time': self.total_response_time / self.total_requests,
'last_request_time': self.last_request_time.isoformat() if self.last_request_time else None,
'error_breakdown': dict(self.error_count_by_type)
}

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#!/usr/bin/env python3
"""
Data validation utilities for water monitoring system
"""
from typing import List, Dict, Any, Optional
from datetime import datetime
import logging
from .exceptions import DataValidationError
from .models import WaterMeasurement, StationInfo
logger = logging.getLogger(__name__)
class DataValidator:
"""Validates water measurement data"""
# Reasonable ranges for water measurements
WATER_LEVEL_MIN = -10.0 # meters
WATER_LEVEL_MAX = 50.0 # meters
DISCHARGE_MIN = 0.0 # cms
DISCHARGE_MAX = 10000.0 # cms
DISCHARGE_PERCENT_MIN = 0.0
DISCHARGE_PERCENT_MAX = 200.0 # Allow some overflow
@classmethod
def validate_measurement(cls, measurement: Dict[str, Any]) -> bool:
"""Validate a single measurement"""
try:
# Check required fields
required_fields = ['timestamp', 'station_id', 'water_level', 'discharge']
for field in required_fields:
if field not in measurement:
logger.warning(f"Missing required field: {field}")
return False
# Validate timestamp
if not isinstance(measurement['timestamp'], datetime):
logger.warning(f"Invalid timestamp type: {type(measurement['timestamp'])}")
return False
# Validate water level
water_level = float(measurement['water_level'])
if not (cls.WATER_LEVEL_MIN <= water_level <= cls.WATER_LEVEL_MAX):
logger.warning(f"Water level out of range: {water_level}")
return False
# Validate discharge
discharge = float(measurement['discharge'])
if not (cls.DISCHARGE_MIN <= discharge <= cls.DISCHARGE_MAX):
logger.warning(f"Discharge out of range: {discharge}")
return False
# Validate discharge percent if present
if measurement.get('discharge_percent') is not None:
discharge_percent = float(measurement['discharge_percent'])
if not (cls.DISCHARGE_PERCENT_MIN <= discharge_percent <= cls.DISCHARGE_PERCENT_MAX):
logger.warning(f"Discharge percent out of range: {discharge_percent}")
return False
# Validate station ID
station_id = measurement['station_id']
if not isinstance(station_id, int) or station_id < 1 or station_id > 16:
logger.warning(f"Invalid station ID: {station_id}")
return False
return True
except (ValueError, TypeError) as e:
logger.warning(f"Data validation error: {e}")
return False
@classmethod
def validate_measurements(cls, measurements: List[Dict[str, Any]]) -> List[Dict[str, Any]]:
"""Validate and filter a list of measurements"""
valid_measurements = []
invalid_count = 0
for measurement in measurements:
if cls.validate_measurement(measurement):
valid_measurements.append(measurement)
else:
invalid_count += 1
if invalid_count > 0:
logger.warning(f"Filtered out {invalid_count} invalid measurements")
return valid_measurements
@classmethod
def validate_station_info(cls, station_info: Dict[str, Any]) -> bool:
"""Validate station information"""
try:
required_fields = ['station_id', 'station_code', 'thai_name', 'english_name']
for field in required_fields:
if field not in station_info or not station_info[field]:
logger.warning(f"Missing or empty station field: {field}")
return False
# Validate coordinates if present
if station_info.get('latitude') is not None:
lat = float(station_info['latitude'])
if not (-90 <= lat <= 90):
logger.warning(f"Invalid latitude: {lat}")
return False
if station_info.get('longitude') is not None:
lon = float(station_info['longitude'])
if not (-180 <= lon <= 180):
logger.warning(f"Invalid longitude: {lon}")
return False
return True
except (ValueError, TypeError) as e:
logger.warning(f"Station validation error: {e}")
return False

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src/water_scraper_v3.py Normal file
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#!/usr/bin/env python3
"""
Enhanced Water Monitor Scraper with multiple database backend support
"""
import requests
import datetime
import time
import schedule
import json
import os
from typing import List, Dict, Optional
try:
from .database_adapters import create_database_adapter, DatabaseAdapter
from .models import WaterMeasurement, StationInfo, ScrapingResult, StationStatus
from .validators import DataValidator
from .exceptions import APIConnectionError, DataValidationError, DatabaseConnectionError
from .metrics import increment_counter, set_gauge, record_histogram, Timer
from .rate_limiter import RateLimiter, RequestTracker
from .logging_config import get_logger
except ImportError:
# Handle case when running as standalone script
from database_adapters import create_database_adapter, DatabaseAdapter
import logging
def get_logger(name):
return logging.getLogger(name)
def increment_counter(*args, **kwargs):
pass
def set_gauge(*args, **kwargs):
pass
def record_histogram(*args, **kwargs):
pass
class Timer:
def __init__(self, *args, **kwargs):
pass
def __enter__(self):
return self
def __exit__(self, *args):
pass
class RateLimiter:
def __init__(self, *args, **kwargs):
pass
def wait_if_needed(self):
pass
class RequestTracker:
def __init__(self):
pass
def record_request(self, *args, **kwargs):
pass
class DataValidator:
@staticmethod
def validate_measurements(measurements):
return measurements
# Get logger instance
logger = get_logger(__name__)
class EnhancedWaterMonitorScraper:
def __init__(self, db_config: Dict):
"""
Initialize scraper with database configuration
Args:
db_config: Database configuration dictionary
"""
self.api_url = "https://hyd-app-db.rid.go.th/webservice/getGroupHourlyWaterLevelReportAllHL.ashx"
self.db_config = db_config.copy() # Make a copy to avoid modifying original
self.db_adapter = None
# Scheduler state tracking
self.last_successful_update = None
self.retry_mode = False
self.next_hourly_check = None
# Rate limiting and request tracking
self.rate_limiter = RateLimiter(max_requests=10, time_window_seconds=60)
self.request_tracker = RequestTracker()
# HTTP session for API requests
self.session = requests.Session()
self.session.headers.update({
'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/91.0.4472.124 Safari/537.36',
'Content-Type': 'application/x-www-form-urlencoded; charset=UTF-8',
'Accept': 'application/json, text/javascript, */*; q=0.01',
'X-Requested-With': 'XMLHttpRequest'
})
# Station mapping with correct names and geolocation data
self.station_mapping = {
'1': {
'code': 'P.20',
'thai_name': 'บ้านเชียงดาว',
'english_name': 'Ban Chiang Dao',
'latitude': 19.36731448032191,
'longitude': 98.9688487015384,
'geohash': None
},
'2': {
'code': 'P.75',
'thai_name': 'บ้านช่อแล',
'english_name': 'Ban Chai Lat',
'latitude': 19.145972935976225,
'longitude': 99.00735727149247,
'geohash': None
},
'3': {
'code': 'P.92',
'thai_name': 'บ้านเมืองกึ๊ด',
'english_name': 'Ban Muang Aut',
'latitude': 19.220518985435646,
'longitude': 98.84733127007874,
'geohash': None
},
'4': {
'code': 'P.4A',
'thai_name': 'บ้านแม่แตง',
'english_name': 'Ban Mae Taeng',
'latitude': 19.1222679952378,
'longitude': 98.94437462084075,
'geohash': None
},
'5': {
'code': 'P.67',
'thai_name': 'บ้านแม่แต',
'english_name': 'Ban Tae',
'latitude': 19.009762080002453,
'longitude': 98.95978297135508,
'geohash': None
},
'6': {
'code': 'P.21',
'thai_name': 'บ้านริมใต้',
'english_name': 'Ban Rim Tai',
'latitude': 18.917459157963293,
'longitude': 98.97018092996231,
'geohash': None
},
'7': {
'code': 'P.103',
'thai_name': 'สะพานวงแหวนรอบ 3',
'english_name': 'Ring Bridge 3',
'latitude': 18.86664807441675,
'longitude': 98.9781107622432,
'geohash': None
},
'8': {
'code': 'P.1',
'thai_name': 'สะพานนวรัฐ',
'english_name': 'Nawarat Bridge',
'latitude': 18.7875,
'longitude': 99.0045,
'geohash': 'w5q6uuhvfcfp25'
},
'9': {
'code': 'P.82',
'thai_name': 'บ้านสบวิน',
'english_name': 'Ban Sob win',
'latitude': 18.6519444,
'longitude': 98.69,
'geohash': None
},
'10': {
'code': 'P.84',
'thai_name': 'บ้านพันตน',
'english_name': 'Ban Panton',
'latitude': 18.591315274591334,
'longitude': 98.79657058508496,
'geohash': None
},
'11': {
'code': 'P.81',
'thai_name': 'บ้านโป่ง',
'english_name': 'Ban Pong',
'latitude': 13.805661820610888,
'longitude': 99.87174946122846,
'geohash': None
},
'12': {
'code': 'P.5',
'thai_name': 'สะพานท่านาง',
'english_name': 'Tha Nang Bridge',
'latitude': 18.580269437546555,
'longitude': 99.01021397084362,
'geohash': None
},
'13': {
'code': 'P.77',
'thai_name': 'บ้านสบแม่สะป๊วด',
'english_name': 'Baan Sop Mae Sapuord',
'latitude': 18.433347475179602,
'longitude': 99.08510036666527,
'geohash': None
},
'14': {
'code': 'P.87',
'thai_name': 'บ้านป่าซาง',
'english_name': 'Ban Pa Sang',
'latitude': 18.519121825282486,
'longitude': 98.94224374138238,
'geohash': None
},
'15': {
'code': 'P.76',
'thai_name': 'บ้านแม่อีไฮ',
'english_name': 'Banb Mae I Hai',
'latitude': 18.141465831254404,
'longitude': 98.89642508267181,
'geohash': None
},
'16': {
'code': 'P.85',
'thai_name': 'บ้านหล่ายแก้ว',
'english_name': 'Baan Lai Kaew',
'latitude': 18.17856361002219,
'longitude': 98.63023114782287,
'geohash': None
}
}
self.init_database()
def init_database(self):
"""Initialize database connection"""
try:
# Extract db_type and pass remaining config as kwargs
db_config_copy = self.db_config.copy()
db_type = db_config_copy.pop('type')
self.db_adapter = create_database_adapter(db_type, **db_config_copy)
success = self.db_adapter.connect()
if success:
logger.info(f"Successfully connected to {db_type.upper()} database")
set_gauge("database_connected", 1)
increment_counter("database_connections_successful")
else:
logger.error(f"Failed to connect to {db_type.upper()} database")
set_gauge("database_connected", 0)
increment_counter("database_connections_failed")
except Exception as e:
logger.error(f"Error initializing database: {e}")
set_gauge("database_connected", 0)
increment_counter("database_connections_failed")
self.db_adapter = None
def fetch_water_data_for_date(self, target_date: datetime.datetime) -> Optional[List[Dict]]:
"""Fetch water levels and discharge data from API for a specific date"""
with Timer("api_request_duration"):
try:
logger.info(f"Starting data fetch from API for date: {target_date.strftime('%Y-%m-%d')}")
# Rate limiting
self.rate_limiter.wait_if_needed()
# Create Thai format date (Buddhist calendar)
thai_year = target_date.year + 543
thai_date = f"{target_date.day:02d}/{target_date.month:02d}/{thai_year}"
# API parameters
payload = {
'DW[UtokID]': '1',
'DW[BasinID]': '6',
'DW[TimeCurrent]': thai_date,
'_search': 'false',
'nd': str(int(time.time() * 1000)),
'rows': '100',
'page': '1',
'sidx': 'indexhourly',
'sord': 'asc'
}
logger.debug(f"API parameters: {payload}")
# POST request to API
start_time = time.time()
response = self.session.post(self.api_url, data=payload, timeout=30)
response_time = time.time() - start_time
response.raise_for_status()
# Record successful request
self.request_tracker.record_request(True, response_time)
increment_counter("api_requests_successful")
record_histogram("api_response_time", response_time)
# Parse JSON response
try:
json_data = response.json()
logger.debug(f"API response received: {len(str(json_data))} characters")
except ValueError as e:
logger.error(f"Error parsing JSON response: {e}")
self.request_tracker.record_request(False, response_time, "json_parse_error")
increment_counter("api_requests_failed")
return None
water_data = []
# Parse JSON data
if json_data and isinstance(json_data, dict) and 'rows' in json_data:
for row in json_data['rows']:
try:
# Parse timestamp
time_str = row.get('hourlytime', '')
if not time_str:
continue
try:
# Format: "1.00", "2.00", ..., "24.00"
api_hour = int(float(time_str))
if api_hour < 1 or api_hour > 24:
continue
if api_hour == 24:
# Hour 24 = midnight (00:00) of the next day
data_time = target_date.replace(hour=0, minute=0, second=0, microsecond=0)
data_time = data_time + datetime.timedelta(days=1)
else:
# Hours 1-23 = 01:00-23:00 of the same day
data_time = target_date.replace(hour=api_hour, minute=0, second=0, microsecond=0)
except (ValueError, IndexError):
logger.warning(f"Could not parse timestamp: {time_str}")
continue
# Parse all water levels and discharge values
station_count = 0
for station_num in range(1, 17): # Stations 1-16
wl_key = f'wlvalues{station_num}'
q_key = f'qvalues{station_num}'
qp_key = f'QPercent{station_num}'
# Check if both water level and discharge data exist
if wl_key in row and q_key in row:
try:
water_level = row[wl_key]
discharge = row[q_key]
discharge_percent = row.get(qp_key)
# Skip if values are None or invalid
if water_level is None or discharge is None:
continue
# Convert to float
water_level = float(water_level)
discharge = float(discharge)
discharge_percent = float(discharge_percent) if discharge_percent is not None else None
station_info = self.station_mapping.get(str(station_num), {
'code': f'P.{19+station_num}',
'thai_name': f'Station {station_num}',
'english_name': f'Station {station_num}'
})
water_data.append({
'timestamp': data_time,
'station_id': station_num,
'station_code': station_info['code'],
'station_name_en': station_info['english_name'],
'station_name_th': station_info['thai_name'],
'latitude': station_info.get('latitude'),
'longitude': station_info.get('longitude'),
'geohash': station_info.get('geohash'),
'water_level': water_level,
'water_level_unit': 'm',
'discharge': discharge,
'discharge_unit': 'cms',
'discharge_percent': discharge_percent,
'status': 'active'
})
station_count += 1
except (ValueError, TypeError) as e:
logger.warning(f"Could not parse data for station {station_num}: {e}")
continue
logger.debug(f"Processed {station_count} stations for time {time_str}")
except Exception as e:
logger.warning(f"Error processing data row: {e}")
continue
# Validate data
water_data = DataValidator.validate_measurements(water_data)
logger.info(f"Successfully fetched {len(water_data)} data points from API for {target_date.strftime('%Y-%m-%d')}")
return water_data
except requests.RequestException as e:
logger.error(f"Network error fetching API data: {e}")
self.request_tracker.record_request(False, 0, "network_error")
increment_counter("api_requests_failed")
return None
except Exception as e:
logger.error(f"Unexpected error fetching API data: {e}")
self.request_tracker.record_request(False, 0, "unexpected_error")
increment_counter("api_requests_failed")
return None
def fetch_water_data(self) -> Optional[List[Dict]]:
"""Fetch water levels and discharge data from API for current date"""
current_date = datetime.datetime.now()
return self.fetch_water_data_for_date(current_date)
def save_to_database(self, water_data: List[Dict], max_retries: int = 3) -> bool:
"""Save water measurements to database with retry logic"""
if not self.db_adapter:
logger.error("Database adapter not initialized")
return False
if not water_data:
logger.warning("No data to save")
return False
for attempt in range(max_retries):
try:
success = self.db_adapter.save_measurements(water_data)
if success:
logger.info(f"Successfully saved {len(water_data)} measurements to database")
increment_counter("database_saves_successful")
set_gauge("last_save_timestamp", time.time())
return True
else:
logger.warning(f"Save attempt {attempt + 1} failed, retrying...")
except Exception as e:
if "database is locked" in str(e).lower() and attempt < max_retries - 1:
logger.warning(f"Database locked on attempt {attempt + 1}, retrying in {2 ** attempt} seconds...")
time.sleep(2 ** attempt) # Exponential backoff
continue
else:
logger.error(f"Error saving to database (attempt {attempt + 1}): {e}")
if attempt == max_retries - 1:
increment_counter("database_saves_failed")
return False
return False
def get_latest_data(self, limit: int = 100) -> List[Dict]:
"""Get latest data from database"""
if not self.db_adapter:
return []
try:
return self.db_adapter.get_latest_measurements(limit=limit)
except Exception as e:
logger.error(f"Error getting latest data: {e}")
return []
def run_scraping_cycle(self) -> bool:
"""Run a complete scraping cycle"""
logger.info("Starting scraping cycle...")
try:
# Fetch current data
water_data = self.fetch_water_data()
if water_data:
success = self.save_to_database(water_data)
if success:
logger.info("Scraping cycle completed successfully")
increment_counter("scraping_cycles_successful")
return True
else:
logger.error("Failed to save data")
increment_counter("scraping_cycles_failed")
return False
else:
logger.warning("No data fetched")
increment_counter("scraping_cycles_failed")
return False
except Exception as e:
logger.error(f"Scraping cycle failed: {e}")
increment_counter("scraping_cycles_failed")
return False
# Main execution for standalone usage
if __name__ == "__main__":
import argparse
import sys
# Configure basic logging for standalone usage
import logging
logging.basicConfig(
level=logging.INFO,
format='%(asctime)s - %(levelname)s - %(message)s',
handlers=[
logging.FileHandler('water_monitor.log'),
logging.StreamHandler()
]
)
parser = argparse.ArgumentParser(description="Thailand Water Monitor")
parser.add_argument("--test", action="store_true", help="Run single test cycle")
args = parser.parse_args()
# Default SQLite configuration
db_config = {
'type': 'sqlite',
'connection_string': 'sqlite:///water_levels.db'
}
try:
scraper = EnhancedWaterMonitorScraper(db_config)
if args.test:
logger.info("Running test cycle...")
result = scraper.run_scraping_cycle()
if result:
logger.info("✅ Test completed successfully")
sys.exit(0)
else:
logger.error("❌ Test failed")
sys.exit(1)
else:
logger.info("Starting continuous monitoring...")
schedule.every(1).hours.do(scraper.run_scraping_cycle)
# Run initial cycle
scraper.run_scraping_cycle()
while True:
schedule.run_pending()
time.sleep(60)
except KeyboardInterrupt:
logger.info("Monitoring stopped by user")
except Exception as e:
logger.error(f"Error: {e}")
sys.exit(1)

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src/web_api.py Normal file
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#!/usr/bin/env python3
"""
FastAPI web interface for water monitoring system
"""
import asyncio
import threading
from datetime import datetime, timedelta
from typing import List, Dict, Any, Optional
from contextlib import asynccontextmanager
from fastapi import FastAPI, HTTPException, BackgroundTasks, Depends
from fastapi.responses import HTMLResponse, JSONResponse
from fastapi.staticfiles import StaticFiles
from fastapi.middleware.cors import CORSMiddleware
from pydantic import BaseModel, Field
from .water_scraper_v3 import EnhancedWaterMonitorScraper
from .config import Config
from .models import WaterMeasurement, StationInfo, ScrapingResult, StationCreateRequest, StationUpdateRequest, StationStatus
from .health_check import HealthCheckManager, DatabaseHealthCheck, APIHealthCheck, MemoryHealthCheck
from .metrics import get_metrics_collector, increment_counter, set_gauge
from .logging_config import setup_logging, get_logger
logger = get_logger(__name__)
# Pydantic models for API responses
class StationResponse(BaseModel):
station_id: int
station_code: str
thai_name: str
english_name: str
latitude: Optional[float] = None
longitude: Optional[float] = None
geohash: Optional[str] = None
status: str = "active"
class StationCreateModel(BaseModel):
station_code: str = Field(..., description="Station code (e.g., P.1, P.20)")
thai_name: str = Field(..., description="Thai name of the station")
english_name: str = Field(..., description="English name of the station")
latitude: Optional[float] = Field(None, ge=-90, le=90, description="Latitude coordinate")
longitude: Optional[float] = Field(None, ge=-180, le=180, description="Longitude coordinate")
geohash: Optional[str] = Field(None, description="Geohash for the location")
status: str = Field("active", description="Station status")
class StationUpdateModel(BaseModel):
thai_name: Optional[str] = Field(None, description="Thai name of the station")
english_name: Optional[str] = Field(None, description="English name of the station")
latitude: Optional[float] = Field(None, ge=-90, le=90, description="Latitude coordinate")
longitude: Optional[float] = Field(None, ge=-180, le=180, description="Longitude coordinate")
geohash: Optional[str] = Field(None, description="Geohash for the location")
status: Optional[str] = Field(None, description="Station status")
class MeasurementResponse(BaseModel):
timestamp: datetime
station_code: str
station_name_en: str
station_name_th: str
water_level: float
discharge: float
discharge_percent: Optional[float] = None
status: str = "active"
class HealthResponse(BaseModel):
overall_status: str
timestamp: str
checks: Dict[str, Dict[str, Any]]
class MetricsResponse(BaseModel):
counters: Dict[str, float]
gauges: Dict[str, float]
histograms: Dict[str, Dict[str, float]]
class ScrapingStatusResponse(BaseModel):
is_running: bool
last_run: Optional[datetime] = None
next_run: Optional[datetime] = None
total_runs: int = 0
successful_runs: int = 0
failed_runs: int = 0
# Global application state
app_state = {
"scraper": None,
"health_manager": None,
"scraping_task": None,
"is_scraping": False,
"scraping_stats": {
"total_runs": 0,
"successful_runs": 0,
"failed_runs": 0,
"last_run": None,
"next_run": None
}
}
@asynccontextmanager
async def lifespan(app: FastAPI):
"""Application lifespan manager"""
# Startup
logger.info("Starting Water Monitor API...")
# Initialize configuration
try:
Config.validate_config()
logger.info("Configuration validated successfully")
except Exception as e:
logger.error(f"Configuration validation failed: {e}")
raise
# Initialize scraper
db_config = Config.get_database_config()
app_state["scraper"] = EnhancedWaterMonitorScraper(db_config)
# Initialize health checks
health_manager = HealthCheckManager()
health_manager.add_check(DatabaseHealthCheck(app_state["scraper"].db_adapter))
health_manager.add_check(APIHealthCheck(Config.API_URL, app_state["scraper"].session))
health_manager.add_check(MemoryHealthCheck(max_memory_mb=1000))
app_state["health_manager"] = health_manager
# Start background scraping task
app_state["scraping_task"] = asyncio.create_task(background_scraping_task())
logger.info("Water Monitor API started successfully")
yield
# Shutdown
logger.info("Shutting down Water Monitor API...")
if app_state["scraping_task"]:
app_state["scraping_task"].cancel()
try:
await app_state["scraping_task"]
except asyncio.CancelledError:
pass
logger.info("Water Monitor API shutdown complete")
# Create FastAPI app
app = FastAPI(
title="Northern Thailand Ping River Monitor API",
description="Real-time water level monitoring system for Northern Thailand's Ping River Basin stations",
version="3.1.0",
lifespan=lifespan
)
# Add CORS middleware
app.add_middleware(
CORSMiddleware,
allow_origins=["*"], # Configure appropriately for production
allow_credentials=True,
allow_methods=["*"],
allow_headers=["*"],
)
async def background_scraping_task():
"""Background task for periodic data scraping"""
while True:
try:
if not app_state["is_scraping"]:
app_state["is_scraping"] = True
# Run scraping cycle
scraper = app_state["scraper"]
if scraper:
logger.info("Starting background scraping cycle")
start_time = datetime.now()
try:
result = scraper.run_scraping_cycle()
# Update stats
app_state["scraping_stats"]["total_runs"] += 1
app_state["scraping_stats"]["last_run"] = start_time
if result:
app_state["scraping_stats"]["successful_runs"] += 1
increment_counter("scraping_cycles_successful")
logger.info("Background scraping cycle completed successfully")
else:
app_state["scraping_stats"]["failed_runs"] += 1
increment_counter("scraping_cycles_failed")
logger.warning("Background scraping cycle completed with no new data")
# Update metrics
set_gauge("last_scraping_timestamp", start_time.timestamp())
except Exception as e:
app_state["scraping_stats"]["failed_runs"] += 1
increment_counter("scraping_cycles_failed")
logger.error(f"Background scraping cycle failed: {e}")
app_state["is_scraping"] = False
# Calculate next run time
interval_seconds = Config.SCRAPING_INTERVAL_HOURS * 3600
app_state["scraping_stats"]["next_run"] = datetime.now() + timedelta(seconds=interval_seconds)
# Wait for next cycle
await asyncio.sleep(interval_seconds)
except asyncio.CancelledError:
logger.info("Background scraping task cancelled")
break
except Exception as e:
logger.error(f"Error in background scraping task: {e}")
await asyncio.sleep(60) # Wait a minute before retrying
# API Routes
@app.get("/", response_class=HTMLResponse)
async def root():
"""Root endpoint with basic dashboard"""
html_content = """
<!DOCTYPE html>
<html>
<head>
<title>Northern Thailand Ping River Monitor</title>
<style>
body { font-family: Arial, sans-serif; margin: 40px; }
.header { color: #2c3e50; border-bottom: 2px solid #3498db; padding-bottom: 10px; }
.section { margin: 20px 0; padding: 15px; border: 1px solid #ddd; border-radius: 5px; }
.status-healthy { color: #27ae60; }
.status-degraded { color: #f39c12; }
.status-unhealthy { color: #e74c3c; }
.endpoint { background: #f8f9fa; padding: 10px; margin: 5px 0; border-radius: 3px; }
.endpoint code { color: #2c3e50; }
</style>
</head>
<body>
<div class="header">
<h1>🏔️ Northern Thailand Ping River Monitor API</h1>
<p>Real-time water level monitoring system for the Ping River Basin in Northern Thailand</p>
</div>
<div class="section">
<h2>📊 Quick Status</h2>
<p>API is running and monitoring 16 water stations along the Ping River</p>
<p>Coverage: From Chiang Dao to Nakhon Sawan</p>
<p>Data collection interval: Every hour</p>
</div>
<div class="section">
<h2>🔗 API Endpoints</h2>
<div class="endpoint"><code>GET /health</code> - System health status</div>
<div class="endpoint"><code>GET /metrics</code> - Application metrics</div>
<div class="endpoint"><code>GET /stations</code> - List all monitoring stations</div>
<div class="endpoint"><code>POST /stations</code> - Add new monitoring station</div>
<div class="endpoint"><code>PUT /stations/{station_id}</code> - Update station information</div>
<div class="endpoint"><code>GET /measurements/latest</code> - Latest measurements</div>
<div class="endpoint"><code>GET /measurements/station/{station_code}</code> - Station-specific data</div>
<div class="endpoint"><code>POST /scrape/trigger</code> - Trigger manual data collection</div>
<div class="endpoint"><code>GET /scraping/status</code> - Scraping status</div>
<div class="endpoint"><code>GET /docs</code> - Interactive API documentation</div>
</div>
<div class="section">
<h2>📈 Monitoring</h2>
<p>• Grafana dashboards available for data visualization</p>
<p>• Health checks monitor database, API, and system resources</p>
<p>• Metrics collection for performance monitoring</p>
</div>
</body>
</html>
"""
return HTMLResponse(content=html_content)
@app.get("/health", response_model=HealthResponse)
async def get_health():
"""Get system health status"""
increment_counter("api_requests", labels={"endpoint": "health"})
health_manager = app_state["health_manager"]
if not health_manager:
raise HTTPException(status_code=503, detail="Health manager not initialized")
# Run health checks
results = health_manager.run_all_checks()
summary = health_manager.get_health_summary()
return HealthResponse(**summary)
@app.get("/metrics", response_model=MetricsResponse)
async def get_metrics():
"""Get application metrics"""
increment_counter("api_requests", labels={"endpoint": "metrics"})
metrics_collector = get_metrics_collector()
metrics = metrics_collector.get_all_metrics()
return MetricsResponse(**metrics)
@app.get("/stations", response_model=List[StationResponse])
async def get_stations():
"""Get list of all monitoring stations"""
increment_counter("api_requests", labels={"endpoint": "stations"})
scraper = app_state["scraper"]
if not scraper:
raise HTTPException(status_code=503, detail="Scraper not initialized")
stations = []
for station_id, station_info in scraper.station_mapping.items():
stations.append(StationResponse(
station_id=int(station_id),
station_code=station_info["code"],
thai_name=station_info["thai_name"],
english_name=station_info["english_name"],
latitude=station_info.get("latitude"),
longitude=station_info.get("longitude"),
status="active"
))
return stations
@app.post("/stations", response_model=StationResponse)
async def create_station(station: StationCreateModel):
"""Create a new monitoring station"""
increment_counter("api_requests", labels={"endpoint": "create_station"})
scraper = app_state["scraper"]
if not scraper:
raise HTTPException(status_code=503, detail="Scraper not initialized")
try:
# Find next available station ID
existing_ids = [int(sid) for sid in scraper.station_mapping.keys()]
new_station_id = max(existing_ids) + 1 if existing_ids else 1
# Add to station mapping
scraper.station_mapping[str(new_station_id)] = {
'code': station.station_code,
'thai_name': station.thai_name,
'english_name': station.english_name,
'latitude': station.latitude,
'longitude': station.longitude,
'geohash': station.geohash
}
logger.info(f"Created new station: {station.station_code} ({station.english_name})")
return StationResponse(
station_id=new_station_id,
station_code=station.station_code,
thai_name=station.thai_name,
english_name=station.english_name,
latitude=station.latitude,
longitude=station.longitude,
geohash=station.geohash,
status=station.status
)
except Exception as e:
logger.error(f"Error creating station: {e}")
raise HTTPException(status_code=500, detail=str(e))
@app.put("/stations/{station_id}", response_model=StationResponse)
async def update_station(station_id: int, updates: StationUpdateModel):
"""Update an existing monitoring station"""
increment_counter("api_requests", labels={"endpoint": "update_station"})
scraper = app_state["scraper"]
if not scraper:
raise HTTPException(status_code=503, detail="Scraper not initialized")
station_key = str(station_id)
if station_key not in scraper.station_mapping:
raise HTTPException(status_code=404, detail="Station not found")
try:
station_info = scraper.station_mapping[station_key]
# Update fields if provided
if updates.thai_name is not None:
station_info['thai_name'] = updates.thai_name
if updates.english_name is not None:
station_info['english_name'] = updates.english_name
if updates.latitude is not None:
station_info['latitude'] = updates.latitude
if updates.longitude is not None:
station_info['longitude'] = updates.longitude
if updates.geohash is not None:
station_info['geohash'] = updates.geohash
logger.info(f"Updated station {station_id}: {station_info['code']}")
return StationResponse(
station_id=station_id,
station_code=station_info['code'],
thai_name=station_info['thai_name'],
english_name=station_info['english_name'],
latitude=station_info.get('latitude'),
longitude=station_info.get('longitude'),
geohash=station_info.get('geohash'),
status=updates.status or "active"
)
except Exception as e:
logger.error(f"Error updating station {station_id}: {e}")
raise HTTPException(status_code=500, detail=str(e))
@app.delete("/stations/{station_id}")
async def delete_station(station_id: int):
"""Delete a monitoring station"""
increment_counter("api_requests", labels={"endpoint": "delete_station"})
scraper = app_state["scraper"]
if not scraper:
raise HTTPException(status_code=503, detail="Scraper not initialized")
station_key = str(station_id)
if station_key not in scraper.station_mapping:
raise HTTPException(status_code=404, detail="Station not found")
try:
station_info = scraper.station_mapping.pop(station_key)
logger.info(f"Deleted station {station_id}: {station_info['code']}")
return {"message": f"Station {station_info['code']} deleted successfully"}
except Exception as e:
logger.error(f"Error deleting station {station_id}: {e}")
raise HTTPException(status_code=500, detail=str(e))
@app.get("/stations/{station_id}", response_model=StationResponse)
async def get_station(station_id: int):
"""Get details of a specific monitoring station"""
increment_counter("api_requests", labels={"endpoint": "get_station"})
scraper = app_state["scraper"]
if not scraper:
raise HTTPException(status_code=503, detail="Scraper not initialized")
station_key = str(station_id)
if station_key not in scraper.station_mapping:
raise HTTPException(status_code=404, detail="Station not found")
station_info = scraper.station_mapping[station_key]
return StationResponse(
station_id=station_id,
station_code=station_info['code'],
thai_name=station_info['thai_name'],
english_name=station_info['english_name'],
latitude=station_info.get('latitude'),
longitude=station_info.get('longitude'),
geohash=station_info.get('geohash'),
status="active"
)
@app.get("/measurements/latest", response_model=List[MeasurementResponse])
async def get_latest_measurements(limit: int = 100):
"""Get latest measurements from all stations"""
increment_counter("api_requests", labels={"endpoint": "measurements_latest"})
scraper = app_state["scraper"]
if not scraper or not scraper.db_adapter:
raise HTTPException(status_code=503, detail="Database not available")
try:
measurements = scraper.get_latest_data(limit=limit)
response = []
for measurement in measurements:
response.append(MeasurementResponse(
timestamp=measurement["timestamp"],
station_code=measurement["station_code"],
station_name_en=measurement["station_name_en"],
station_name_th=measurement["station_name_th"],
water_level=measurement["water_level"],
discharge=measurement["discharge"],
discharge_percent=measurement.get("discharge_percent"),
status=measurement.get("status", "active")
))
return response
except Exception as e:
logger.error(f"Error fetching latest measurements: {e}")
raise HTTPException(status_code=500, detail=str(e))
@app.get("/measurements/station/{station_code}", response_model=List[MeasurementResponse])
async def get_station_measurements(
station_code: str,
hours: int = 24,
limit: int = 1000
):
"""Get measurements for a specific station"""
increment_counter("api_requests", labels={"endpoint": "measurements_station"})
scraper = app_state["scraper"]
if not scraper or not scraper.db_adapter:
raise HTTPException(status_code=503, detail="Database not available")
try:
# Get measurements for the specified time range
end_time = datetime.now()
start_time = end_time - timedelta(hours=hours)
measurements = scraper.db_adapter.get_measurements_by_timerange(
start_time, end_time, station_codes=[station_code]
)
# Limit results
measurements = measurements[:limit]
response = []
for measurement in measurements:
response.append(MeasurementResponse(
timestamp=measurement["timestamp"],
station_code=measurement["station_code"],
station_name_en=measurement["station_name_en"],
station_name_th=measurement["station_name_th"],
water_level=measurement["water_level"],
discharge=measurement["discharge"],
discharge_percent=measurement.get("discharge_percent"),
status=measurement.get("status", "active")
))
return response
except Exception as e:
logger.error(f"Error fetching station measurements: {e}")
raise HTTPException(status_code=500, detail=str(e))
@app.post("/scrape/trigger")
async def trigger_scraping(background_tasks: BackgroundTasks):
"""Trigger manual data scraping"""
increment_counter("api_requests", labels={"endpoint": "scrape_trigger"})
if app_state["is_scraping"]:
raise HTTPException(status_code=409, detail="Scraping already in progress")
scraper = app_state["scraper"]
if not scraper:
raise HTTPException(status_code=503, detail="Scraper not initialized")
def run_scraping():
"""Background task to run scraping"""
try:
app_state["is_scraping"] = True
logger.info("Manual scraping triggered via API")
result = scraper.run_scraping_cycle()
# Update stats
app_state["scraping_stats"]["total_runs"] += 1
app_state["scraping_stats"]["last_run"] = datetime.now()
if result:
app_state["scraping_stats"]["successful_runs"] += 1
increment_counter("manual_scraping_successful")
else:
app_state["scraping_stats"]["failed_runs"] += 1
increment_counter("manual_scraping_failed")
except Exception as e:
app_state["scraping_stats"]["failed_runs"] += 1
increment_counter("manual_scraping_failed")
logger.error(f"Manual scraping failed: {e}")
finally:
app_state["is_scraping"] = False
background_tasks.add_task(run_scraping)
return {"message": "Scraping triggered", "status": "started"}
@app.get("/scraping/status", response_model=ScrapingStatusResponse)
async def get_scraping_status():
"""Get current scraping status"""
increment_counter("api_requests", labels={"endpoint": "scraping_status"})
stats = app_state["scraping_stats"]
return ScrapingStatusResponse(
is_running=app_state["is_scraping"],
last_run=stats["last_run"],
next_run=stats["next_run"],
total_runs=stats["total_runs"],
successful_runs=stats["successful_runs"],
failed_runs=stats["failed_runs"]
)
@app.get("/config")
async def get_config():
"""Get current configuration (sensitive data masked)"""
increment_counter("api_requests", labels={"endpoint": "config"})
config = Config.get_all_settings()
# Mask sensitive information
for key in config:
if 'password' in key.lower() or 'secret' in key.lower():
if config[key]:
config[key] = '*' * 8
return config
if __name__ == "__main__":
import uvicorn
# Setup logging
setup_logging(
log_level=Config.LOG_LEVEL,
log_file=Config.LOG_FILE,
enable_console=True,
enable_colors=True
)
# Run the API server
uvicorn.run(
"web_api:app",
host="0.0.0.0",
port=8000,
reload=False,
log_config=None # Use our custom logging
)

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#!/usr/bin/env python3
"""
Simple API test script
"""
import requests
import time
import json
def test_api_endpoints():
"""Test the main API endpoints"""
base_url = "http://localhost:8000"
endpoints = [
"/health",
"/metrics",
"/stations",
"/measurements/latest?limit=5",
"/scraping/status",
"/config"
]
print("🧪 Testing API endpoints...")
print("Make sure the API server is running: python run.py --web-api")
print()
for endpoint in endpoints:
try:
print(f"Testing {endpoint}...")
response = requests.get(f"{base_url}{endpoint}", timeout=5)
if response.status_code == 200:
data = response.json()
print(f"✅ {endpoint} - Status: {response.status_code}")
# Show some sample data
if endpoint == "/stations":
print(f" Found {len(data)} stations")
elif endpoint == "/measurements/latest?limit=5":
print(f" Found {len(data)} measurements")
if data:
latest = data[0]
print(f" Latest: {latest['station_code']} - {latest['water_level']}m")
elif endpoint == "/health":
print(f" Overall status: {data.get('overall_status', 'unknown')}")
elif endpoint == "/scraping/status":
print(f" Is running: {data.get('is_running', False)}")
print(f" Total runs: {data.get('total_runs', 0)}")
else:
print(f"❌ {endpoint} - Status: {response.status_code}")
except requests.exceptions.ConnectionError:
print(f"❌ {endpoint} - Connection failed (is the server running?)")
except Exception as e:
print(f"❌ {endpoint} - Error: {e}")
print()
if __name__ == "__main__":
test_api_endpoints()

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#!/usr/bin/env python3
"""
Integration test script for the enhanced water monitoring system
"""
import sys
import os
import time
from datetime import datetime
# Add project root to Python path
project_root = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
sys.path.insert(0, project_root)
def test_imports():
"""Test that all modules can be imported"""
print("Testing imports...")
try:
from src.config import Config
from src.models import WaterMeasurement, StationInfo
from src.exceptions import WaterMonitorException
from src.validators import DataValidator
from src.metrics import get_metrics_collector
from src.health_check import HealthCheckManager
from src.rate_limiter import RateLimiter
from src.logging_config import setup_logging
print("✅ All imports successful")
return True
except ImportError as e:
print(f"❌ Import failed: {e}")
return False
def test_configuration():
"""Test configuration validation"""
print("Testing configuration...")
try:
from src.config import Config
# Test configuration loading
settings = Config.get_all_settings()
print(f"✅ Configuration loaded: {len(settings)} settings")
# Test database config
db_config = Config.get_database_config()
print(f"✅ Database config: {db_config['type']}")
return True
except Exception as e:
print(f"❌ Configuration test failed: {e}")
return False
def test_metrics():
"""Test metrics collection"""
print("Testing metrics...")
try:
from src.metrics import increment_counter, set_gauge, record_histogram, get_metrics_collector
# Test metrics
increment_counter("test_counter", 5)
set_gauge("test_gauge", 42.0)
record_histogram("test_histogram", 1.5)
# Get metrics
collector = get_metrics_collector()
metrics = collector.get_all_metrics()
print(f"✅ Metrics collected: {len(metrics['counters'])} counters, {len(metrics['gauges'])} gauges")
return True
except Exception as e:
print(f"❌ Metrics test failed: {e}")
return False
def test_validation():
"""Test data validation"""
print("Testing data validation...")
try:
from src.validators import DataValidator
# Test valid measurement
valid_measurement = {
'timestamp': datetime.now(),
'station_id': 1,
'water_level': 5.5,
'discharge': 100.0,
'discharge_percent': 75.0
}
result = DataValidator.validate_measurement(valid_measurement)
if result:
print("✅ Valid measurement passed validation")
else:
print("❌ Valid measurement failed validation")
return False
# Test invalid measurement
invalid_measurement = {
'timestamp': datetime.now(),
'station_id': 999, # Invalid station ID
'water_level': -999.0, # Invalid water level
'discharge': -50.0 # Invalid discharge
}
result = DataValidator.validate_measurement(invalid_measurement)
if not result:
print("✅ Invalid measurement correctly rejected")
else:
print("❌ Invalid measurement incorrectly accepted")
return False
return True
except Exception as e:
print(f"❌ Validation test failed: {e}")
return False
def test_rate_limiter():
"""Test rate limiting"""
print("Testing rate limiter...")
try:
from src.rate_limiter import RateLimiter
# Create rate limiter (2 requests per second)
limiter = RateLimiter(max_requests=2, time_window_seconds=1)
# Test allowed requests
for i in range(2):
if not limiter.is_allowed():
print(f"❌ Request {i+1} should be allowed")
return False
# Test blocked request
if limiter.is_allowed():
print("❌ Third request should be blocked")
return False
print("✅ Rate limiter working correctly")
return True
except Exception as e:
print(f"❌ Rate limiter test failed: {e}")
return False
def test_logging():
"""Test logging configuration"""
print("Testing logging...")
try:
from src.logging_config import setup_logging, get_logger
# Setup logging
logger = setup_logging(log_level="INFO", enable_console=False)
# Get a logger
test_logger = get_logger("test")
test_logger.info("Test log message")
print("✅ Logging setup successful")
return True
except Exception as e:
print(f"❌ Logging test failed: {e}")
return False
def main():
"""Run all tests"""
print("🧪 Running integration tests for Northern Thailand Ping River Monitor v3.1.0")
print("=" * 60)
tests = [
test_imports,
test_configuration,
test_metrics,
test_validation,
test_rate_limiter,
test_logging
]
passed = 0
failed = 0
for test in tests:
try:
if test():
passed += 1
else:
failed += 1
except Exception as e:
print(f"❌ Test {test.__name__} crashed: {e}")
failed += 1
print()
print("=" * 60)
print(f"Test Results: {passed} passed, {failed} failed")
if failed == 0:
print("🎉 All tests passed! The system is ready to use.")
print("\nNext steps:")
print("1. Run 'python run.py --test' to test data collection")
print("2. Run 'python run.py --web-api' to start the web interface")
print("3. Visit http://localhost:8000 for the dashboard")
return True
else:
print("❌ Some tests failed. Please check the errors above.")
return False
if __name__ == "__main__":
success = main()
sys.exit(0 if success else 1)

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#!/usr/bin/env python3
"""
Test script for station management API endpoints
"""
import requests
import json
def test_station_management():
"""Test the station management endpoints"""
base_url = "http://localhost:8000"
print("🧪 Testing Station Management API")
print("Make sure the API server is running: python run.py --web-api")
print()
# Test data for new station
new_station = {
"station_code": "P.TEST",
"thai_name": "สถานีทดสอบ",
"english_name": "Test Station",
"latitude": 18.7875,
"longitude": 99.0045,
"geohash": "w5q6uuhvfcfp25",
"status": "active"
}
try:
# 1. List existing stations
print("1. Listing existing stations...")
response = requests.get(f"{base_url}/stations")
if response.status_code == 200:
stations = response.json()
print(f"✅ Found {len(stations)} existing stations")
initial_count = len(stations)
else:
print(f"❌ Failed to list stations: {response.status_code}")
return
# 2. Create new station
print("\n2. Creating new test station...")
response = requests.post(
f"{base_url}/stations",
json=new_station,
headers={"Content-Type": "application/json"}
)
if response.status_code == 200:
created_station = response.json()
station_id = created_station["station_id"]
print(f"✅ Created station with ID: {station_id}")
print(f" Code: {created_station['station_code']}")
print(f" Name: {created_station['english_name']}")
else:
print(f"❌ Failed to create station: {response.status_code}")
print(f" Response: {response.text}")
return
# 3. Get specific station
print(f"\n3. Getting station details for ID {station_id}...")
response = requests.get(f"{base_url}/stations/{station_id}")
if response.status_code == 200:
station_details = response.json()
print(f"✅ Retrieved station details")
print(f" Thai name: {station_details['thai_name']}")
print(f" Coordinates: {station_details['latitude']}, {station_details['longitude']}")
else:
print(f"❌ Failed to get station: {response.status_code}")
# 4. Update station
print(f"\n4. Updating station {station_id}...")
update_data = {
"english_name": "Updated Test Station",
"thai_name": "สถานีทดสอบที่อัปเดต"
}
response = requests.put(
f"{base_url}/stations/{station_id}",
json=update_data,
headers={"Content-Type": "application/json"}
)
if response.status_code == 200:
updated_station = response.json()
print(f"✅ Updated station successfully")
print(f" New name: {updated_station['english_name']}")
else:
print(f"❌ Failed to update station: {response.status_code}")
print(f" Response: {response.text}")
# 5. Verify station count increased
print("\n5. Verifying station count...")
response = requests.get(f"{base_url}/stations")
if response.status_code == 200:
stations = response.json()
new_count = len(stations)
if new_count == initial_count + 1:
print(f"✅ Station count increased from {initial_count} to {new_count}")
else:
print(f"⚠️ Unexpected station count: {new_count} (expected {initial_count + 1})")
# 6. Delete test station
print(f"\n6. Deleting test station {station_id}...")
response = requests.delete(f"{base_url}/stations/{station_id}")
if response.status_code == 200:
result = response.json()
print(f"✅ Deleted station: {result['message']}")
else:
print(f"❌ Failed to delete station: {response.status_code}")
# 7. Verify station was deleted
print("\n7. Verifying station deletion...")
response = requests.get(f"{base_url}/stations/{station_id}")
if response.status_code == 404:
print("✅ Station successfully deleted (404 as expected)")
else:
print(f"⚠️ Station still exists: {response.status_code}")
# 8. Final station count check
response = requests.get(f"{base_url}/stations")
if response.status_code == 200:
stations = response.json()
final_count = len(stations)
if final_count == initial_count:
print(f"✅ Station count restored to original: {final_count}")
else:
print(f"⚠️ Station count mismatch: {final_count} (expected {initial_count})")
print("\n🎉 Station management tests completed!")
except requests.exceptions.ConnectionError:
print("❌ Connection failed - is the API server running?")
print("Start it with: python run.py --web-api")
except Exception as e:
print(f"❌ Test failed with error: {e}")
if __name__ == "__main__":
test_station_management()