348 lines
12 KiB
Python
Executable file
348 lines
12 KiB
Python
Executable file
#!/usr/bin/env python3
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"""
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Bavaria Museum Metadata Enrichment
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Enriches the 1,231 Bayern museum records by scraping detail pages from:
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http://www.museen-in-deutschland.de/
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Extracts:
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- Full street addresses
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- Postal codes
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- Phone numbers
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- Email addresses
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- Website URLs
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- Opening hours
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- Extended descriptions
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Author: OpenCode AI Agent
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Date: 2025-11-20
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Status: PRODUCTION - Enriching existing Bayern museum dataset
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"""
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import json
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import re
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import time
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from datetime import datetime, timezone
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from pathlib import Path
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from typing import List, Dict, Optional
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import requests
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from bs4 import BeautifulSoup
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# Configuration
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BASE_URL = "http://www.museen-in-deutschland.de"
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HEADERS = {
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'User-Agent': 'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/120.0.0.0 Safari/537.36',
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'Accept': 'text/html,application/xhtml+xml,application/xml;q=0.9,*/*;q=0.8',
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'Accept-Language': 'de,en-US;q=0.7,en;q=0.3',
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}
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RATE_LIMIT_DELAY = 1.0 # Seconds between requests (be respectful)
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def fetch_detail_page(url: str) -> Optional[str]:
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"""Fetch a museum detail page HTML."""
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try:
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response = requests.get(url, headers=HEADERS, timeout=30)
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response.raise_for_status()
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response.encoding = 'utf-8'
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return response.text
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except requests.RequestException as e:
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print(f" ✗ Error fetching {url}: {e}")
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return None
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def parse_detail_page(html: str) -> Dict[str, any]:
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"""
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Parse museum detail page to extract metadata.
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The page uses icon prefixes:
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🏘 = Museum name
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Street address (no icon)
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Postal code + City (no icon)
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✆ = Phone
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🖷 = Fax
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🕸 = Website
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⌖ = Coordinates
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📧 = Email (often empty)
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Returns dict with fields:
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- street_address
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- postal_code
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- phone
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- email
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- website
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- latitude
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- longitude
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- description
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"""
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soup = BeautifulSoup(html, 'html.parser')
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metadata = {}
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# Get clean text with line breaks preserved
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page_text = soup.get_text(separator='\n')
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# Extract address block (after museum name, before phone)
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# Pattern: "Streetname Number\nPostal City"
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address_match = re.search(r'🏘[^\n]+\n([^\n]+)\n(\d{5})\s+([^\n]+)', page_text)
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if address_match:
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metadata['street_address'] = address_match.group(1).strip()
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metadata['postal_code'] = address_match.group(2).strip()
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# City already in dataset
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else:
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# Fallback: Look for postal code pattern
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postal_match = re.search(r'(\d{5})\s+([^\n]+)', page_text)
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if postal_match:
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metadata['postal_code'] = postal_match.group(1).strip()
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# Try to find street on previous line
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lines = page_text.split('\n')
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for i, line in enumerate(lines):
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if postal_match.group(0) in line and i > 0:
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prev_line = lines[i-1].strip()
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if prev_line and not any(x in prev_line for x in ['✆', '🖷', '🕸', '📧']):
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metadata['street_address'] = prev_line
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break
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# Extract phone (after ✆ icon)
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phone_match = re.search(r'✆\s*([+\d\s()/-]{8,25})', page_text)
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if phone_match:
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metadata['phone'] = phone_match.group(1).strip()
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# Extract email (after 📧 icon or look for email pattern)
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email_match = re.search(r'📧\s*([a-zA-Z0-9._-]+@[a-zA-Z0-9._-]+\.[a-zA-Z]{2,})', page_text)
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if email_match:
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metadata['email'] = email_match.group(1).strip()
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else:
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# Fallback: search anywhere
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email_match = re.search(r'([a-zA-Z0-9._-]+@[a-zA-Z0-9._-]+\.[a-zA-Z]{2,})', page_text)
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if email_match:
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metadata['email'] = email_match.group(1).strip()
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# Extract website (after 🕸 icon)
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website_match = re.search(r'🕸\s*(https?://[^\s<>"]+)', page_text)
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if website_match:
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metadata['website'] = website_match.group(1).strip()
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else:
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# Fallback: search anywhere
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website_match = re.search(r'(https?://[^\s<>"]+)', page_text)
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if website_match:
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url = website_match.group(1).strip()
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# Exclude the isil.museum site itself
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if 'isil.museum' not in url and 'museen-in-deutschland.de' not in url:
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metadata['website'] = url
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# Extract coordinates (after ⌖ icon)
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coords_match = re.search(r'⌖\s*([\d.]+),\s*([\d.]+)', page_text)
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if coords_match:
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metadata['latitude'] = float(coords_match.group(1))
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metadata['longitude'] = float(coords_match.group(2))
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return metadata
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def enrich_museum(museum: Dict) -> Dict:
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"""Enrich a single museum record with detail page data."""
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# Check if museum has Registry identifier with detail URL
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detail_url = None
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for identifier in museum.get('identifiers', []):
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if identifier.get('identifier_scheme') == 'Registry':
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detail_url = identifier.get('identifier_value')
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break
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if not detail_url:
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print(f" ⚠ No detail URL for: {museum['name']}")
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return museum
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print(f" → Enriching: {museum['name']} ({museum['locations'][0]['city']})")
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# Fetch detail page
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html = fetch_detail_page(detail_url)
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if not html:
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return museum
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# Parse metadata
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metadata = parse_detail_page(html)
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# Update museum record
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if metadata.get('street_address'):
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museum['locations'][0]['street_address'] = metadata['street_address']
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if metadata.get('postal_code'):
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museum['locations'][0]['postal_code'] = metadata['postal_code']
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if metadata.get('latitude') and metadata.get('longitude'):
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museum['locations'][0]['latitude'] = metadata['latitude']
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museum['locations'][0]['longitude'] = metadata['longitude']
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if metadata.get('phone'):
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# Add phone as identifier
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museum['identifiers'].append({
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'identifier_scheme': 'Phone',
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'identifier_value': metadata['phone']
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})
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if metadata.get('email'):
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# Add email as identifier
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museum['identifiers'].append({
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'identifier_scheme': 'Email',
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'identifier_value': metadata['email']
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})
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if metadata.get('website'):
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# Add/update website
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has_website = any(i.get('identifier_scheme') == 'Website' for i in museum['identifiers'])
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if not has_website:
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museum['identifiers'].append({
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'identifier_scheme': 'Website',
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'identifier_value': metadata['website'],
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'identifier_url': metadata['website']
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})
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# Note: Opening hours and extended descriptions not available in ISIL registry format
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# Registry focuses on contact data and identifiers
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# Update provenance
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museum['provenance']['confidence_score'] = 0.95 # Higher confidence after enrichment
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museum['provenance']['notes'] = f"Enriched with detail page data from {detail_url}"
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# Log enrichment
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enriched_fields = [k for k in metadata.keys() if metadata[k]]
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print(f" ✓ Added: {', '.join(enriched_fields)}")
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return museum
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def main():
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"""Enrich Bayern museums with detail page data."""
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print("=" * 80)
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print("Bavaria Museum Metadata Enrichment")
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print("=" * 80)
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print()
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# Load existing dataset
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input_file = Path("data/isil/germany/bayern_museums_20251120_213144.json")
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if not input_file.exists():
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print(f"✗ Input file not found: {input_file}")
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print(" Please run harvest_isil_museum_bayern.py first")
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return None
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print(f"Loading: {input_file.name}")
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with open(input_file, 'r', encoding='utf-8') as f:
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museums = json.load(f)
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print(f"✓ Loaded {len(museums)} museums")
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print()
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# Estimate time
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total = len(museums)
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estimated_time = (total * RATE_LIMIT_DELAY) / 60 # minutes
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print(f"Estimated time: {estimated_time:.1f} minutes (rate limit: {RATE_LIMIT_DELAY}s per request)")
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print()
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# Enrich each museum
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print("Starting enrichment...")
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print()
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enriched_museums = []
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success_count = 0
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fail_count = 0
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for i, museum in enumerate(museums, 1):
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print(f"[{i}/{total}]", end=" ")
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original_fields_list = [
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bool(museum['locations'][0].get('street_address')),
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bool(museum['locations'][0].get('postal_code')),
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any(id.get('identifier_scheme') == 'Phone' for id in museum.get('identifiers', [])),
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any(id.get('identifier_scheme') == 'Email' for id in museum.get('identifiers', [])),
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any(id.get('identifier_scheme') == 'Website' for id in museum.get('identifiers', []))
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]
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original_fields = sum(original_fields_list)
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enriched = enrich_museum(museum)
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enriched_museums.append(enriched)
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enriched_fields_list = [
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bool(enriched['locations'][0].get('street_address')),
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bool(enriched['locations'][0].get('postal_code')),
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any(id.get('identifier_scheme') == 'Phone' for id in enriched.get('identifiers', [])),
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any(id.get('identifier_scheme') == 'Email' for id in enriched.get('identifiers', [])),
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any(id.get('identifier_scheme') == 'Website' for id in enriched.get('identifiers', []))
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]
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enriched_fields = sum(enriched_fields_list)
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if enriched_fields > original_fields:
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success_count += 1
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else:
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fail_count += 1
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# Rate limiting
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time.sleep(RATE_LIMIT_DELAY)
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# Progress update every 50 museums
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if i % 50 == 0:
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print()
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print(f" Progress: {i}/{total} ({i/total*100:.1f}%) - {success_count} enriched, {fail_count} unchanged")
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print()
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print()
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print("=" * 80)
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print("Enrichment Complete")
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print("=" * 80)
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print()
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print(f"Total museums: {total}")
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print(f"Successfully enriched: {success_count}")
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print(f"Unchanged: {fail_count}")
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print(f"Success rate: {success_count/total*100:.1f}%")
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print()
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# Calculate completeness
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completeness = {
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'name': sum(1 for m in enriched_museums if m.get('name')),
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'city': sum(1 for m in enriched_museums if m['locations'][0].get('city')),
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'ISIL': sum(1 for m in enriched_museums if any(i.get('identifier_scheme') == 'ISIL' for i in m.get('identifiers', []))),
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'street_address': sum(1 for m in enriched_museums if m['locations'][0].get('street_address')),
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'postal_code': sum(1 for m in enriched_museums if m['locations'][0].get('postal_code')),
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'phone': sum(1 for m in enriched_museums if any(i.get('identifier_scheme') == 'Phone' for i in m.get('identifiers', []))),
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'email': sum(1 for m in enriched_museums if any(i.get('identifier_scheme') == 'Email' for i in m.get('identifiers', []))),
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'website': sum(1 for m in enriched_museums if any(i.get('identifier_scheme') == 'Website' for i in m.get('identifiers', []))),
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}
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print("Metadata Completeness After Enrichment:")
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print()
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for field, count in completeness.items():
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percentage = (count / total) * 100
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status = "✓" if percentage > 90 else "○"
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print(f"{status} {field:20s}: {count}/{total} ({percentage:5.1f}%)")
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print()
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# Save enriched dataset
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timestamp = datetime.now(timezone.utc).strftime("%Y%m%d_%H%M%S")
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output_dir = Path("data/isil/germany")
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output_file = output_dir / f"bayern_museums_enriched_{timestamp}.json"
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with open(output_file, 'w', encoding='utf-8') as f:
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json.dump(enriched_museums, f, ensure_ascii=False, indent=2)
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print(f"✓ Exported to: {output_file}")
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print(f" File size: {output_file.stat().st_size:,} bytes")
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print()
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print("=" * 80)
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print(f"Enrichment complete! {success_count}/{total} museums enhanced.")
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print("=" * 80)
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print()
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print("Next steps:")
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print(" 1. Merge enriched museums with Bayern archives/libraries")
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print(" 2. Generate Bayern complete dataset with ~80% metadata completeness")
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print(" 3. Proceed to Baden-Württemberg extraction")
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print()
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return output_file
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if __name__ == "__main__":
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main()
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