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Initial commit: Complete Paw Burn Risk Assessment Tool with WeatherAPI integration, comprehensive risk scoring, SQLite storage, rich visualizations, configurable parameters, and full documentation with examples
This commit is contained in:
Chris Sewell
238
main.py
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238
main.py
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"""Main application for paw burn risk assessment."""
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import logging
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import argparse
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from datetime import datetime, timedelta
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from typing import Optional
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import json
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from config import get_config, AppConfig
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from models import DatabaseManager
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from weather_api import create_weather_client
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from risk_calculator import create_risk_calculator
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from plotting import create_plotter
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# Set up logging
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logging.basicConfig(
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level=logging.INFO,
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format='%(asctime)s - %(name)s - %(levelname)s - %(message)s'
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)
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logger = logging.getLogger(__name__)
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class PawRiskApp:
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"""Main application class for paw burn risk assessment."""
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def __init__(self, config: Optional[AppConfig] = None):
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self.config = config or get_config()
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self.db_manager = DatabaseManager(self.config.database_path)
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self.weather_client = create_weather_client()
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self.risk_calculator = create_risk_calculator()
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self.plotter = create_plotter()
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def fetch_and_analyze_today(self, location: Optional[str] = None) -> dict:
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"""Fetch weather data and analyze risk for today."""
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location = location or self.config.default_location
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print(f"🌤️ Fetching weather data for {location}...")
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try:
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# Fetch complete day weather data
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weather_hours = self.weather_client.get_full_day_weather(location)
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if not weather_hours:
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return {"error": "No weather data available"}
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print(f"📊 Retrieved {len(weather_hours)} hours of weather data")
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# Save weather data to database
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self.db_manager.save_weather_data(weather_hours)
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# Calculate risk scores
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print("🧮 Calculating paw burn risk scores...")
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risk_scores = self.risk_calculator.calculate_risk_scores(weather_hours)
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if not risk_scores:
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return {"error": "Unable to calculate risk scores"}
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# Save risk scores to database
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self.db_manager.save_risk_scores(risk_scores)
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# Generate recommendations
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recommendations = self.risk_calculator.generate_recommendations(risk_scores)
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return {
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"weather_hours": weather_hours,
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"risk_scores": risk_scores,
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"recommendations": recommendations,
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"location": location
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}
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except Exception as e:
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logger.error(f"Error during analysis: {e}")
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return {"error": str(e)}
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def print_summary(self, analysis_result: dict):
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"""Print a formatted summary of the analysis."""
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if "error" in analysis_result:
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print(f"❌ Error: {analysis_result['error']}")
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return
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recommendations = analysis_result["recommendations"]
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location = analysis_result["location"]
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print("\n" + "="*60)
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print(f"🐕 PAW BURN RISK ASSESSMENT - {location.upper()}")
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print("="*60)
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# Summary statistics
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summary = recommendations["summary"]
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print(f"\n📈 DAILY SUMMARY:")
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print(f" • Total Hours Analyzed: {summary['total_hours_analyzed']}")
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print(f" • High Risk Hours: {summary['high_risk_hours']}")
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print(f" • Maximum Risk Score: {summary['max_risk_score']}/10")
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print(f" • Average Risk Score: {summary['average_risk_score']}/10")
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print(f" • Peak Risk Time: {summary['peak_risk_time']}")
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print(f" • Continuous Risk Periods: {summary['continuous_risk_blocks']}")
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# Risk periods
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if recommendations["risk_periods"]:
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print(f"\n⏰ HIGH RISK TIME PERIODS:")
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for period in recommendations["risk_periods"]:
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print(f" • {period['start']} - {period['end']} ({period['duration_hours']} hours)")
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# Recommendations
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print(f"\n💡 RECOMMENDATIONS:")
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for rec in recommendations["recommendations"]:
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print(f" {rec}")
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print("\n" + "="*60)
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def print_detailed_hourly(self, analysis_result: dict):
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"""Print detailed hourly breakdown."""
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if "error" in analysis_result:
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return
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weather_hours = analysis_result["weather_hours"]
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risk_scores = analysis_result["risk_scores"]
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print("\n🕐 HOURLY BREAKDOWN:")
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print("-" * 80)
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print(f"{'Time':>6} {'Temp':>6} {'UV':>4} {'Condition':>12} {'Risk':>6} {'Shoes':>7}")
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print("-" * 80)
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for weather, risk in zip(weather_hours, risk_scores):
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time_str = weather.datetime.strftime("%H:%M")
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temp_str = f"{weather.temperature_f:.0f}°F"
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uv_str = f"{weather.uv_index:.1f}" if weather.uv_index else "N/A"
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condition_short = weather.condition[:12]
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risk_str = f"{risk.total_score:.1f}"
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shoes_str = "YES" if risk.recommend_shoes else "no"
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shoes_color = "⚠️ " if risk.recommend_shoes else "✅ "
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print(f"{time_str:>6} {temp_str:>6} {uv_str:>4} {condition_short:>12} "
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f"{risk_str:>6} {shoes_color}{shoes_str:>5}")
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def create_plots(self, analysis_result: dict, save_plots: bool = False):
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"""Create and display plots."""
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if "error" in analysis_result:
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return
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weather_hours = analysis_result["weather_hours"]
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risk_scores = analysis_result["risk_scores"]
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recommendations = analysis_result["recommendations"]
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location = analysis_result["location"]
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if save_plots:
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print("\n📊 Generating and saving visualizations...")
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else:
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print("\n📊 Generating visualizations...")
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# Create timestamp for file names
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timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
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location_safe = location.replace(" ", "_").replace(",", "")
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try:
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# Main risk timeline
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save_path = f"risk_timeline_{location_safe}_{timestamp}.png" if save_plots else None
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self.plotter.plot_risk_timeline(
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risk_scores, weather_hours,
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threshold=self.config.risk_config.risk_threshold_shoes,
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save_path=save_path,
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show=not save_plots
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)
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# Component breakdown
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save_path = f"risk_components_{location_safe}_{timestamp}.png" if save_plots else None
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self.plotter.plot_risk_components(
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risk_scores,
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save_path=save_path,
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show=not save_plots
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)
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# Summary dashboard
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save_path = f"risk_dashboard_{location_safe}_{timestamp}.png" if save_plots else None
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self.plotter.create_summary_dashboard(
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risk_scores, weather_hours, recommendations,
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save_path=save_path,
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show=not save_plots
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)
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except Exception as e:
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logger.error(f"Error creating plots: {e}")
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print(f"⚠️ Error creating plots: {e}")
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def main():
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"""Main entry point for the application."""
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parser = argparse.ArgumentParser(description="Paw Burn Risk Assessment Tool")
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parser.add_argument("--location", "-l", type=str, help="Location for weather data (city name, zip code, or coordinates)")
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parser.add_argument("--detailed", "-d", action="store_true", help="Show detailed hourly breakdown")
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parser.add_argument("--plot", "-p", action="store_true", help="Show plots")
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parser.add_argument("--save-plots", "-s", action="store_true", help="Save plots to files")
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parser.add_argument("--no-recommendations", action="store_true", help="Skip recommendations")
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parser.add_argument("--config-check", action="store_true", help="Check configuration and exit")
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args = parser.parse_args()
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try:
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# Initialize application
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print("🐾 Paw Burn Risk Assessment Tool")
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print("=" * 40)
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# Check configuration if requested
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if args.config_check:
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try:
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config = get_config()
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print("✅ Configuration loaded successfully")
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print(f"API Key: {'Set' if config.weather_api_key else 'NOT SET'}")
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print(f"Default Location: {config.default_location}")
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print(f"Database Path: {config.database_path}")
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print(f"Risk Threshold: {config.risk_config.risk_threshold_shoes}")
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return
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except Exception as e:
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print(f"❌ Configuration error: {e}")
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return
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app = PawRiskApp()
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# Run analysis
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analysis_result = app.fetch_and_analyze_today(args.location)
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# Display results
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if args.detailed:
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app.print_detailed_hourly(analysis_result)
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if args.plot or args.save_plots:
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app.create_plots(analysis_result, save_plots=args.save_plots)
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# Show summary unless explicitly disabled
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if not args.no_recommendations:
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app.print_summary(analysis_result)
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except KeyboardInterrupt:
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print("\n👋 Goodbye!")
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except Exception as e:
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logger.error(f"Application error: {e}")
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print(f"❌ Application error: {e}")
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if __name__ == "__main__":
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main()
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