"""
Test WeatherWise with REAL weather data from NASA POWER API
This simulates exactly what the API does
"""
import sys
import os
sys.path.insert(0, os.path.join(os.path.dirname(__file__), 'server'))

import numpy as np

# Import the actual services and models
from models.weather_model import WeatherDataModel, WeatherRequest
from models.feature_engineering_model import FeatureEngineeringModel
from services.weather_service import WeatherService
from services.feature_engineering_service import FeatureEngineeringService

print("Initializing services...")
weather_model = WeatherDataModel()
weather_service = WeatherService(weather_model)
feature_model = FeatureEngineeringModel()
feature_service = FeatureEngineeringService(feature_model)

# Field mappings (same as in weatherwise_prediction_service.py)
field_mappings = {
    'humidity_perc': 'humidity_%',
    'cloud_amount_perc': 'cloud_amount_%',
    'surface_soil_wetness_perc': 'surface_soil_wetness_%',
    'root_zone_soil_moisture_perc': 'root_zone_soil_moisture_%',
    'adjusted_humidity': 'adjusted_humidity'  # This one has no suffix change
}

def apply_field_mappings(data):
    mapped_data = {}
    for key, value in data.items():
        mapped_key = field_mappings.get(key, key)
        mapped_data[mapped_key] = value
    return mapped_data

# Test two different locations
locations = [
    ("Delhi", 28.6139, 77.2090),
    ("Sydney", -33.8688, 151.2093),
]

results = []

for name, lat, lon in locations:
    print(f"\n=== Testing {name} ({lat}, {lon}) ===")
    
    # Fetch weather data
    weather_request = WeatherRequest(
        latitude=lat,
        longitude=lon,
        disaster_date="2026-03-22",  # Recent date
        days_before=60
    )
    
    weather_success, weather_result = weather_service.fetch_weather_data(weather_request)
    
    if not weather_success:
        print(f"Weather fetch failed for {name}: {weather_result}")
        continue
        
    weather_data = weather_result.get('weather_data', {})
    print(f"Weather data variables: {len(weather_data)}")
    
    # Get engineered features
    feature_success, feature_result = feature_service.process_weather_features(
        weather_data=weather_data,
        event_duration=1.0,
        include_metadata=True
    )
    
    if not feature_success:
        print(f"Feature engineering failed for {name}: {feature_result}")
        continue
        
    feature_data = feature_result.get('engineered_features', {})
    print(f"Engineered features: {len(feature_data)}")
    
    # Apply field mappings
    mapped_weather = apply_field_mappings(weather_data)
    
    # Print first values of key features
    print(f"\nFirst values of key features for {name}:")
    for key in ['temperature_C', 'humidity_%', 'precipitation_mm']:
        if key in mapped_weather:
            print(f"  {key}: {mapped_weather[key][0]:.2f}")
        elif key in feature_data:
            print(f"  {key}: {feature_data[key][0]:.2f}")
            
    results.append({
        'name': name,
        'temp': mapped_weather.get('temperature_C', [0])[0],
        'precip': mapped_weather.get('precipitation_mm', [0])[0]
    })

# Compare results
print("\n=== Comparison ===")
for r in results:
    print(f"{r['name']}: temp={r['temp']:.2f}°C, precip={r['precip']:.2f}mm")

if len(results) == 2:
    temp_diff = abs(results[0]['temp'] - results[1]['temp'])
    print(f"\nTemperature difference: {temp_diff:.2f}°C")
    if temp_diff < 1:
        print("*** WARNING: Temperature difference is very small - possible data issue ***")
    else:
        print("*** Weather data varies correctly between locations ***")