#!/usr/bin/env python3
"""
KB Netherlands ISIL Code Excel Parser
Extracts public library data from KB Netherlands Excel file.

Source: https://www.bibliotheeknetwerk.nl/
File: 20250401 Bnetwerk overzicht ISIL-codes Bibliotheken Nederland.xlsx

Author: GLAM Data Extraction Project
Date: 2025-11-17
License: MIT
"""

import openpyxl
import csv
import json
from datetime import datetime, timezone
from pathlib import Path
from typing import List, Dict, Optional
import logging

# Configure logging
logging.basicConfig(
    level=logging.INFO,
    format='%(asctime)s - %(levelname)s - %(message)s'
)
logger = logging.getLogger(__name__)

# Paths
INPUT_FILE = Path(__file__).parent.parent.parent / "data" / "isil" / "KB_Netherlands_ISIL_2025-04-01.xlsx"
OUTPUT_DIR = Path(__file__).parent.parent.parent / "data" / "isil" / "NL"
OUTPUT_DIR.mkdir(parents=True, exist_ok=True)


class KBNetherlandsISILParser:
    """Parses KB Netherlands ISIL Excel file."""
    
    def __init__(self, input_file: Path):
        self.input_file = input_file
        self.institutions = []
    
    def parse_excel(self) -> List[Dict]:
        """
        Parse KB Netherlands Excel file.
        
        Returns:
            List of institution dictionaries
        """
        logger.info(f"Opening Excel file: {self.input_file}")
        
        workbook = openpyxl.load_workbook(self.input_file)
        sheet = workbook.active
        
        logger.info(f"Sheet name: {sheet.title}")
        logger.info(f"Sheet dimensions: {sheet.dimensions}")
        
        # Read header row (row 3 contains actual headers)
        headers = []
        for cell in sheet[3]:
            headers.append(cell.value)
        
        logger.info(f"Headers: {headers}")
        
        # Parse data rows (starting from row 4)
        institutions = []
        for row_idx, row in enumerate(sheet.iter_rows(min_row=4, values_only=True), start=4):
            if not row[0]:  # Skip empty rows
                continue
            
            # Create dict from row data
            row_dict = {}
            for idx, value in enumerate(row):
                if idx < len(headers) and headers[idx]:
                    row_dict[headers[idx]] = value
            
            # Standardize field names (adapt based on actual headers)
            institution = self._standardize_fields(row_dict)
            
            if institution:
                institutions.append(institution)
                
                if len(institutions) % 20 == 0:
                    logger.info(f"Processed {len(institutions)} institutions...")
        
        logger.info(f"Successfully parsed {len(institutions)} institutions from Excel")
        self.institutions = institutions
        return institutions
    
    def _standardize_fields(self, row_dict: Dict) -> Optional[Dict]:
        """
        Standardize field names from Excel to our schema.
        
        Args:
            row_dict: Raw row data from Excel
            
        Returns:
            Standardized institution dictionary
        """
        # We'll need to adjust this based on actual Excel structure
        # For now, create a generic mapping
        
        standardized = {
            'isil_code': None,
            'name': None,
            'city': None,
            'province': None,
            'country': 'Netherlands',
            'registry': 'KB Netherlands Library Network',
            'source_url': 'https://www.bibliotheeknetwerk.nl/'
        }
        
        # Map Excel fields to our schema
        # Actual KB Netherlands headers: ISIL-code, Naam bibliotheek, Vestigingsplaats, Opmerking
        field_mappings = {
            'ISIL-code': 'isil_code',
            'Naam bibliotheek': 'name',
            'Vestigingsplaats': 'city',
            'Opmerking': 'notes',
        }
        
        for excel_field, our_field in field_mappings.items():
            if excel_field in row_dict and row_dict[excel_field]:
                standardized[our_field] = str(row_dict[excel_field]).strip()
        
        # Validate we have minimum required fields
        if not standardized['isil_code'] or not standardized['name']:
            logger.warning(f"Skipping row with missing ISIL or name: {row_dict}")
            return None
        
        return standardized
    
    def export_csv(self, output_file: Path):
        """Export to CSV."""
        with open(output_file, 'w', newline='', encoding='utf-8') as f:
            if not self.institutions:
                logger.warning("No institutions to export")
                return
            
            fieldnames = self.institutions[0].keys()
            writer = csv.DictWriter(f, fieldnames=fieldnames)
            writer.writeheader()
            writer.writerows(self.institutions)
        
        logger.info(f"Exported {len(self.institutions)} records to {output_file}")
    
    def export_json(self, output_file: Path):
        """Export to JSON with metadata."""
        output = {
            'extraction_date': datetime.now(timezone.utc).isoformat(),
            'data_source': 'KB Netherlands Library Network',
            'source_url': 'https://www.bibliotheeknetwerk.nl/',
            'source_file': 'KB_Netherlands_ISIL_2025-04-01.xlsx',
            'parser_version': '1.0.0',
            'country': 'Netherlands',
            'record_count': len(self.institutions),
            'institutions': self.institutions
        }
        
        with open(output_file, 'w', encoding='utf-8') as f:
            json.dump(output, f, indent=2, ensure_ascii=False)
        
        logger.info(f"Exported {len(self.institutions)} records to {output_file}")


def main():
    """Main execution."""
    logger.info("=== KB Netherlands ISIL Parser ===")
    
    if not INPUT_FILE.exists():
        logger.error(f"Input file not found: {INPUT_FILE}")
        logger.error("Please download the file first:")
        logger.error("https://www.bibliotheeknetwerk.nl/sites/default/files/documents/20250401%20Bnetwerk%20overzicht%20ISIL-codes%20Bibliotheken%20Nederland.xlsx")
        return
    
    parser = KBNetherlandsISILParser(INPUT_FILE)
    institutions = parser.parse_excel()
    
    if not institutions:
        logger.error("No institutions found in Excel file")
        return
    
    # Export results
    csv_output = OUTPUT_DIR / "kb_netherlands_public_libraries.csv"
    json_output = OUTPUT_DIR / "kb_netherlands_public_libraries.json"
    
    parser.export_csv(csv_output)
    parser.export_json(json_output)
    
    logger.info("\n=== Parsing Complete ===")
    logger.info(f"Total institutions extracted: {len(institutions)}")
    logger.info(f"\nOutput directory: {OUTPUT_DIR}")


if __name__ == "__main__":
    main()