#!/usr/bin/env python3
"""
Merge Sachsen-Anhalt Datasets
Combines archives + museums for comprehensive regional coverage
"""

import json
from pathlib import Path
from datetime import datetime, timezone
from typing import List, Dict, Any

def load_json(file_path: Path) -> List[Dict[str, Any]]:
    """Load JSON file."""
    with open(file_path, 'r', encoding='utf-8') as f:
        data = json.load(f)
        # Handle wrapped format (with metadata.archives)
        if isinstance(data, dict) and 'archives' in data:
            return data['archives']
        # Handle list format
        elif isinstance(data, list):
            return data
        # Handle single institution
        elif isinstance(data, dict):
            return [data]
        return []

def merge_datasets() -> List[Dict[str, Any]]:
    """Merge all Sachsen-Anhalt datasets."""
    
    data_dir = Path('data/isil/germany')
    
    # Load archives (4 institutions)
    archives_file = data_dir / 'sachsen_anhalt_archives_20251120_131330.json'
    archives_raw = load_json(archives_file) if archives_file.exists() else []
    
    # Normalize archives to LinkML format
    archives = []
    for arch in archives_raw:
        normalized = {
            'name': arch.get('name', 'Unknown'),
            'institution_type': 'ARCHIVE',
            'description': arch.get('description', ''),
            'locations': [{
                'city': arch.get('city', ''),
                'region': arch.get('region', 'Sachsen-Anhalt'),
                'country': arch.get('country', 'DE')
            }],
            'identifiers': [],
            'provenance': {
                'data_source': 'WEBSITE_SCRAPING',
                'data_tier': 'TIER_2_VERIFIED',
                'extraction_date': datetime.now(timezone.utc).isoformat(),
                'extraction_method': 'Landesarchiv Sachsen-Anhalt website',
                'confidence_score': 0.95,
                'source_url': arch.get('url', '')
            }
        }
        
        if arch.get('url'):
            normalized['identifiers'].append({
                'identifier_scheme': 'Website',
                'identifier_value': arch['url'],
                'identifier_url': arch['url']
            })
        
        archives.append(normalized)
    
    print(f"Loaded {len(archives)} archives")
    
    # Load museums (162 institutions)
    museum_files = sorted(data_dir.glob('sachsen_anhalt_museums_*.json'), reverse=True)
    museums = load_json(museum_files[0]) if museum_files else []
    print(f"Loaded {len(museums)} museums")
    
    # Combine all institutions
    all_institutions = archives + museums
    
    print(f"\nTotal: {len(all_institutions)} institutions")
    
    return all_institutions

def generate_statistics(institutions: List[Dict[str, Any]]):
    """Generate dataset statistics."""
    
    print("\n" + "=" * 80)
    print("DATASET STATISTICS")
    print("=" * 80)
    
    # Institution types
    type_counts = {}
    for inst in institutions:
        inst_type = inst['institution_type']
        type_counts[inst_type] = type_counts.get(inst_type, 0) + 1
    
    print("\nInstitution Types:")
    for inst_type, count in sorted(type_counts.items(), key=lambda x: -x[1]):
        print(f"  {inst_type}: {count}")
    
    # Data completeness
    has_city = sum(1 for i in institutions if i['locations'][0].get('city'))
    has_address = sum(1 for i in institutions if i['locations'][0].get('street_address'))
    has_description = sum(1 for i in institutions if i.get('description'))
    has_website = sum(1 for i in institutions if i.get('identifiers'))
    
    print("\nData Completeness:")
    print(f"  Name: {len(institutions)}/{len(institutions)} (100%)")
    print(f"  City: {has_city}/{len(institutions)} ({has_city/len(institutions)*100:.1f}%)")
    print(f"  Street Address: {has_address}/{len(institutions)} ({has_address/len(institutions)*100:.1f}%)")
    print(f"  Description: {has_description}/{len(institutions)} ({has_description/len(institutions)*100:.1f}%)")
    print(f"  Website: {has_website}/{len(institutions)} ({has_website/len(institutions)*100:.1f}%)")
    
    # Cities with coverage
    city_counts = {}
    for inst in institutions:
        city = inst['locations'][0].get('city', 'Unknown')
        if city:
            city_counts[city] = city_counts.get(city, 0) + 1
    
    print(f"\nGeographic Coverage:")
    print(f"  {len(city_counts)} cities/towns")
    print("\n  Top 10 Cities:")
    for city, count in sorted(city_counts.items(), key=lambda x: -x[1])[:10]:
        print(f"    {city}: {count}")

def main():
    """Main execution."""
    
    print("=" * 80)
    print("Merge Sachsen-Anhalt Datasets")
    print("=" * 80)
    print()
    
    # Merge datasets
    merged_institutions = merge_datasets()
    
    # Generate statistics
    generate_statistics(merged_institutions)
    
    # Save merged dataset
    timestamp = datetime.now().strftime('%Y%m%d_%H%M%S')
    output_path = Path('data/isil/germany') / f'sachsen_anhalt_merged_{timestamp}.json'
    
    with open(output_path, 'w', encoding='utf-8') as f:
        json.dump(merged_institutions, f, ensure_ascii=False, indent=2)
    
    file_size_kb = output_path.stat().st_size / 1024
    
    print()
    print("=" * 80)
    print(f"✅ Saved to: {output_path}")
    print(f"   File size: {file_size_kb:.1f} KB")
    print(f"   Total institutions: {len(merged_institutions)}")
    print("=" * 80)

if __name__ == '__main__':
    main()