#!/usr/bin/env python3
"""
Analyze Japan Prefecture Coverage

Maps prefecture codes to ISO 3166-2 codes and analyzes geographic distribution.
"""

import yaml
from pathlib import Path
from collections import Counter
import json

# ISO 3166-2:JP Prefecture Code Mapping
# Source: https://en.wikipedia.org/wiki/ISO_3166-2:JP
PREFECTURE_CODES = {
    # Hokkaido
    'HO': {'iso': 'JP-01', 'name': 'Hokkaido', 'region': 'Hokkaido'},
    
    # Tohoku
    'AO': {'iso': 'JP-02', 'name': 'Aomori', 'region': 'Tohoku'},
    'IW': {'iso': 'JP-03', 'name': 'Iwate', 'region': 'Tohoku'},
    'MI': {'iso': 'JP-04', 'name': 'Miyagi', 'region': 'Tohoku'},
    'AK': {'iso': 'JP-05', 'name': 'Akita', 'region': 'Tohoku'},
    'YA': {'iso': 'JP-06', 'name': 'Yamagata', 'region': 'Tohoku'},
    'FU': {'iso': 'JP-07', 'name': 'Fukushima', 'region': 'Tohoku'},
    
    # Kanto
    'IB': {'iso': 'JP-08', 'name': 'Ibaraki', 'region': 'Kanto'},
    'TC': {'iso': 'JP-09', 'name': 'Tochigi', 'region': 'Kanto'},
    'GU': {'iso': 'JP-10', 'name': 'Gunma', 'region': 'Kanto'},
    'SA': {'iso': 'JP-11', 'name': 'Saitama', 'region': 'Kanto'},
    'CH': {'iso': 'JP-12', 'name': 'Chiba', 'region': 'Kanto'},
    'TO': {'iso': 'JP-13', 'name': 'Tokyo', 'region': 'Kanto'},
    'KN': {'iso': 'JP-14', 'name': 'Kanagawa', 'region': 'Kanto'},
    
    # Chubu
    'NI': {'iso': 'JP-15', 'name': 'Niigata', 'region': 'Chubu'},
    'TY': {'iso': 'JP-16', 'name': 'Toyama', 'region': 'Chubu'},
    'IS': {'iso': 'JP-17', 'name': 'Ishikawa', 'region': 'Chubu'},
    'FK': {'iso': 'JP-18', 'name': 'Fukui', 'region': 'Chubu'},
    'YM': {'iso': 'JP-19', 'name': 'Yamanashi', 'region': 'Chubu'},
    'NA': {'iso': 'JP-20', 'name': 'Nagano', 'region': 'Chubu'},
    'GI': {'iso': 'JP-21', 'name': 'Gifu', 'region': 'Chubu'},
    'SZ': {'iso': 'JP-22', 'name': 'Shizuoka', 'region': 'Chubu'},
    'AI': {'iso': 'JP-23', 'name': 'Aichi', 'region': 'Chubu'},
    
    # Kansai
    'ME': {'iso': 'JP-24', 'name': 'Mie', 'region': 'Kansai'},
    'SH': {'iso': 'JP-25', 'name': 'Shiga', 'region': 'Kansai'},
    'KY': {'iso': 'JP-26', 'name': 'Kyoto', 'region': 'Kansai'},
    'OS': {'iso': 'JP-27', 'name': 'Osaka', 'region': 'Kansai'},
    'HY': {'iso': 'JP-28', 'name': 'Hyogo', 'region': 'Kansai'},
    'NR': {'iso': 'JP-29', 'name': 'Nara', 'region': 'Kansai'},
    'WA': {'iso': 'JP-30', 'name': 'Wakayama', 'region': 'Kansai'},
    
    # Chugoku
    'TT': {'iso': 'JP-31', 'name': 'Tottori', 'region': 'Chugoku'},
    'SM': {'iso': 'JP-32', 'name': 'Shimane', 'region': 'Chugoku'},
    'OK': {'iso': 'JP-33', 'name': 'Okayama', 'region': 'Chugoku'},
    'HI': {'iso': 'JP-34', 'name': 'Hiroshima', 'region': 'Chugoku'},
    'YG': {'iso': 'JP-35', 'name': 'Yamaguchi', 'region': 'Chugoku'},
    
    # Shikoku
    'TK': {'iso': 'JP-36', 'name': 'Tokushima', 'region': 'Shikoku'},
    'KA': {'iso': 'JP-37', 'name': 'Kagawa', 'region': 'Shikoku'},
    'EH': {'iso': 'JP-38', 'name': 'Ehime', 'region': 'Shikoku'},
    'KO': {'iso': 'JP-39', 'name': 'Kochi', 'region': 'Shikoku'},
    
    # Kyushu
    'FO': {'iso': 'JP-40', 'name': 'Fukuoka', 'region': 'Kyushu'},
    'SG': {'iso': 'JP-41', 'name': 'Saga', 'region': 'Kyushu'},
    'NS': {'iso': 'JP-42', 'name': 'Nagasaki', 'region': 'Kyushu'},
    'KM': {'iso': 'JP-43', 'name': 'Kumamoto', 'region': 'Kyushu'},
    'OI': {'iso': 'JP-44', 'name': 'Oita', 'region': 'Kyushu'},
    'MZ': {'iso': 'JP-45', 'name': 'Miyazaki', 'region': 'Kyushu'},
    'KS': {'iso': 'JP-46', 'name': 'Kagoshima', 'region': 'Kyushu'},
    'ON': {'iso': 'JP-47', 'name': 'Okinawa', 'region': 'Kyushu'},
}


def analyze_prefecture_coverage():
    """Analyze prefecture coverage in Japan dataset."""
    
    # Load dataset
    data_path = Path(__file__).parent.parent / 'data' / 'instances' / 'japan' / 'jp_institutions.yaml'
    with open(data_path, 'r', encoding='utf-8') as f:
        records = yaml.safe_load(f)
    
    print(f"Analyzing {len(records)} institutions...")
    print()
    
    # Extract prefecture codes from GHCIDs
    prefecture_counts = Counter()
    region_counts = Counter()
    missing_prefectures = []
    
    for record in records:
        ghcid = record.get('ghcid', '')
        if ghcid:
            parts = ghcid.split('-')
            if len(parts) >= 2:
                pref_code = parts[1]
                prefecture_counts[pref_code] += 1
                
                # Map to region
                if pref_code in PREFECTURE_CODES:
                    region = PREFECTURE_CODES[pref_code]['region']
                    region_counts[region] += 1
    
    # Find missing prefectures
    represented_prefs = set(prefecture_counts.keys())
    all_prefs = set(PREFECTURE_CODES.keys())
    missing_prefs = all_prefs - represented_prefs
    
    # Print results
    print("=" * 80)
    print("PREFECTURE COVERAGE ANALYSIS")
    print("=" * 80)
    print()
    print(f"Total Prefectures in Dataset: {len(represented_prefs)}/47")
    print(f"Missing Prefectures: {len(missing_prefs)}")
    print()
    
    # By region
    print("INSTITUTIONS BY REGION")
    print("-" * 80)
    for region, count in sorted(region_counts.items(), key=lambda x: -x[1]):
        percentage = (count / len(records) * 100)
        print(f"  {region:15s} {count:6,} ({percentage:5.2f}%)")
    print()
    
    # Top 20 prefectures
    print("TOP 20 PREFECTURES")
    print("-" * 80)
    for pref_code, count in prefecture_counts.most_common(20):
        pref_info = PREFECTURE_CODES.get(pref_code, {'name': 'Unknown', 'iso': 'N/A'})
        percentage = (count / len(records) * 100)
        print(f"  {pref_code} ({pref_info['iso']}) {pref_info['name']:15s} {count:6,} ({percentage:5.2f}%)")
    print()
    
    # Missing prefectures
    if missing_prefs:
        print("MISSING PREFECTURES")
        print("-" * 80)
        for pref_code in sorted(missing_prefs):
            pref_info = PREFECTURE_CODES[pref_code]
            print(f"  {pref_code} ({pref_info['iso']}) {pref_info['name']:15s} - {pref_info['region']}")
        print()
        print("Note: These prefectures have no institutions in the ISIL registry dataset.")
        print("This may indicate:")
        print("  - True absence of ISIL-registered institutions")
        print("  - Institutions not yet registered with National Diet Library")
        print("  - Data quality or coverage issues")
        print()
    
    # Export mapping
    export_path = Path(__file__).parent.parent / 'data' / 'instances' / 'japan' / 'prefecture_analysis.json'
    export_data = {
        'total_institutions': len(records),
        'represented_prefectures': len(represented_prefs),
        'missing_prefectures': len(missing_prefs),
        'prefecture_counts': dict(prefecture_counts),
        'region_counts': dict(region_counts),
        'prefecture_mapping': PREFECTURE_CODES,
        'missing_prefecture_codes': list(missing_prefs)
    }
    
    with open(export_path, 'w', encoding='utf-8') as f:
        json.dump(export_data, f, indent=2, ensure_ascii=False)
    
    print(f"✅ Prefecture analysis exported to: {export_path}")


if __name__ == '__main__':
    analyze_prefecture_coverage()