#!/usr/bin/env python3
"""
Analyze geographic data quality for Mexican heritage institutions.
Identifies missing cities, suspicious coordinates, and non-Mexican institutions.
"""

import yaml
from collections import defaultdict
from typing import Dict, List, Tuple

def analyze_mexican_geography(filepath: str) -> Dict:
    """Analyze geographic data for Mexican institutions."""
    
    with open(filepath, 'r', encoding='utf-8') as f:
        data = yaml.safe_load(f)
    
    results = {
        'total_institutions': len(data),
        'mexican_institutions': [],
        'missing_city': [],
        'suspicious_coordinates': [],
        'non_mexican_in_dataset': [],
        'region_distribution': defaultdict(int),
        'city_distribution': defaultdict(int)
    }
    
    # Known US-based institutions that might appear in Mexican dataset
    us_institutions = [
        'Library of Congress',
        'Getty Research Institute',
        'FAMSI',  # Foundation for Advancement of Mesoamerican Studies (USA-based)
    ]
    
    for inst in data:
        if not inst.get('locations'):
            continue
            
        for loc in inst['locations']:
            country = loc.get('country')
            
            # Check for Mexican institutions
            if country == 'MX':
                results['mexican_institutions'].append(inst['name'])
                
                # Check for missing city
                if not loc.get('city'):
                    results['missing_city'].append({
                        'name': inst['name'],
                        'id': inst.get('id'),
                        'region': loc.get('region', 'N/A'),
                        'latitude': loc.get('latitude'),
                        'longitude': loc.get('longitude'),
                        'extraction_date': inst.get('provenance', {}).get('extraction_date'),
                        'conversation_id': inst.get('provenance', {}).get('conversation_id')
                    })
                
                # Track region distribution
                region = loc.get('region', 'Unknown')
                results['region_distribution'][region] += 1
                
                # Track city distribution
                city = loc.get('city', 'No City')
                results['city_distribution'][city] += 1
                
                # Check for suspicious coordinates (state-level centroids)
                lat = loc.get('latitude')
                lon = loc.get('longitude')
                
                # Known problematic coordinates (Zacatecas state centroid)
                if lat and lon:
                    # Check if coordinates match state centroids (simplified check)
                    if abs(lat - 23.0916177) < 0.01 and abs(lon - (-102.9333954)) < 0.01:
                        results['suspicious_coordinates'].append({
                            'name': inst['name'],
                            'region': loc.get('region'),
                            'lat': lat,
                            'lon': lon,
                            'note': 'Matches Zacatecas state centroid'
                        })
            
            # Check for non-Mexican institutions
            elif country != 'MX':
                # Check if institution name suggests it's US-based but related to Mexico
                for us_inst in us_institutions:
                    if us_inst.lower() in inst['name'].lower():
                        results['non_mexican_in_dataset'].append({
                            'name': inst['name'],
                            'country': country,
                            'city': loc.get('city', 'N/A'),
                            'region': loc.get('region', 'N/A'),
                            'note': 'US-based institution with Mexican content'
                        })
    
    return results

def print_report(results: Dict):
    """Print analysis report."""
    
    print("=" * 80)
    print("MEXICAN HERITAGE INSTITUTIONS - GEOGRAPHIC DATA ANALYSIS")
    print("=" * 80)
    print()
    
    print(f"Total institutions in dataset: {results['total_institutions']:,}")
    print(f"Mexican institutions (country: MX): {len(results['mexican_institutions'])}")
    print()
    
    print("-" * 80)
    print("ISSUE 1: Missing City Field")
    print("-" * 80)
    print(f"Institutions without city field: {len(results['missing_city'])}")
    print()
    
    if results['missing_city']:
        # Group by region
        by_region = defaultdict(list)
        for item in results['missing_city']:
            by_region[item['region']].append(item)
        
        for region, institutions in sorted(by_region.items()):
            print(f"\n{region} ({len(institutions)} institutions):")
            for inst in institutions[:5]:  # Show first 5
                print(f"  - {inst['name']}")
                if inst['latitude'] and inst['longitude']:
                    print(f"    Coords: {inst['latitude']}, {inst['longitude']}")
            if len(institutions) > 5:
                print(f"  ... and {len(institutions) - 5} more")
    
    print()
    print("-" * 80)
    print("ISSUE 2: Suspicious Coordinates")
    print("-" * 80)
    print(f"Institutions with suspicious coordinates: {len(results['suspicious_coordinates'])}")
    print()
    
    if results['suspicious_coordinates']:
        for item in results['suspicious_coordinates']:
            print(f"  - {item['name']}")
            print(f"    Region: {item['region']}, Coords: {item['lat']}, {item['lon']}")
            print(f"    Note: {item['note']}")
    
    print()
    print("-" * 80)
    print("ISSUE 3: Non-Mexican Institutions in Dataset")
    print("-" * 80)
    print(f"US-based institutions with Mexican content: {len(results['non_mexican_in_dataset'])}")
    print()
    
    if results['non_mexican_in_dataset']:
        for item in results['non_mexican_in_dataset']:
            print(f"  - {item['name']}")
            print(f"    Location: {item['city']}, {item['region']}, {item['country']}")
            print(f"    Note: {item['note']}")
    
    print()
    print("-" * 80)
    print("CITY DISTRIBUTION (Top 15)")
    print("-" * 80)
    
    city_dist = sorted(results['city_distribution'].items(), key=lambda x: x[1], reverse=True)
    for city, count in city_dist[:15]:
        print(f"  {city:30s} : {count:3d}")
    
    print()
    print("-" * 80)
    print("REGION DISTRIBUTION")
    print("-" * 80)
    
    region_dist = sorted(results['region_distribution'].items(), key=lambda x: x[1], reverse=True)
    for region, count in region_dist:
        print(f"  {region:30s} : {count:3d}")
    
    print()
    print("=" * 80)

def main():
    filepath = 'data/instances/all/globalglam-20251113-mexico-deduplicated.yaml'
    
    print("Analyzing Mexican geographic data...")
    results = analyze_mexican_geography(filepath)
    
    print_report(results)
    
    # Save detailed results to file
    output_file = 'data/mexican_geography_analysis.yaml'
    with open(output_file, 'w', encoding='utf-8') as f:
        yaml.dump(results, f, allow_unicode=True, sort_keys=False)
    
    print(f"\nDetailed results saved to: {output_file}")

if __name__ == '__main__':
    main()