324 lines
10 KiB
Python
Executable file
324 lines
10 KiB
Python
Executable file
#!/usr/bin/env python3
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"""
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Saxony Museum Harvester from isil.museum Registry
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Extracts Saxony museums from the official German museum ISIL registry at:
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http://www.museen-in-deutschland.de/?t=liste&mode=land&suchbegriff=Bayern
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This is the Institut für Museumsforschung (Institute for Museum Research)
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official database containing ~6,304 German museums with ISIL codes.
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Author: OpenCode AI Agent
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Date: 2025-11-20
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Status: PRODUCTION - Extracting from authoritative German museum registry
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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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BAYERN_URL = f"{BASE_URL}/?t=liste&mode=land&suchbegriff=Bayern"
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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
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def fetch_museum_list(url: str) -> str:
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"""Fetch the Saxony museum list HTML."""
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print(f"Fetching museum list from: {url}")
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print()
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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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print(f"✓ HTTP {response.status_code} - {len(response.text):,} bytes")
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print()
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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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raise
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def parse_museum_table(html: str) -> List[Dict[str, str]]:
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"""
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Parse the HTML table to extract museum records.
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Expected structure:
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<tr>
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<td><a href="...">DE-MUS-907015</a></td> <!-- ISIL code -->
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<td>Adorf/Vogtl.</td> <!-- City -->
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<td><a href="...">Museum Name</a></td> <!-- Name + detail link -->
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</tr>
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"""
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soup = BeautifulSoup(html, 'html.parser')
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museums = []
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# Find the main table (assuming it's the largest <table> element)
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tables = soup.find_all('table')
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if not tables:
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print("✗ No tables found in HTML")
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return []
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# Try to find table with museum data (contains ISIL codes like DE-MUS-*)
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target_table = None
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for table in tables:
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# Check if this table contains ISIL codes
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sample_text = table.get_text()
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if 'DE-MUS-' in sample_text:
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target_table = table
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break
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if not target_table:
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print("✗ Could not find museum data table")
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return []
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# Parse table rows
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rows = target_table.find_all('tr')
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print(f"Found {len(rows)} table rows")
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print()
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for row in rows:
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cells = row.find_all('td')
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# Expected: 3 cells (ISIL, City, Name)
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if len(cells) < 3:
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continue
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# Extract ISIL code from first cell
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isil_cell = cells[0]
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isil_link = isil_cell.find('a')
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if not isil_link:
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continue
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isil_code = isil_link.get_text(strip=True)
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if not isil_code.startswith('DE-MUS-'):
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continue
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# Extract city from second cell
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city = cells[1].get_text(strip=True)
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# Extract museum name and detail link from third cell
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name_cell = cells[2]
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name_link = name_cell.find('a')
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if name_link:
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museum_name = name_link.get_text(strip=True)
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detail_path = name_link.get('href', '')
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detail_url = f"{BASE_URL}/{detail_path}" if detail_path else None
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else:
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museum_name = name_cell.get_text(strip=True)
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detail_url = None
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# Skip if essential fields are missing
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if not museum_name or not city:
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continue
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museums.append({
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'isil_code': isil_code,
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'city': city,
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'name': museum_name,
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'detail_url': detail_url
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})
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return museums
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def convert_to_linkml(museum_data: Dict[str, str]) -> Dict:
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"""Convert raw museum data to LinkML-compliant HeritageCustodian format."""
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# Generate clean ID from city and museum name
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city_slug = re.sub(r'[^a-z0-9]+', '-', museum_data['city'].lower())
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name_slug = re.sub(r'[^a-z0-9]+', '-', museum_data['name'][:50].lower())
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custodian = {
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"id": f"https://w3id.org/heritage/custodian/de/{city_slug}-{name_slug}",
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"name": museum_data["name"],
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"institution_type": "MUSEUM",
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"alternative_names": [],
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"description": f"Museum in {museum_data['city']}, Bayern. Part of the official German museum registry (Institut für Museumsforschung).",
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"locations": [
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{
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"city": museum_data["city"],
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"region": "Bayern",
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"country": "DE"
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}
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],
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"identifiers": [
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{
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"identifier_scheme": "ISIL",
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"identifier_value": museum_data["isil_code"],
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"identifier_url": f"https://sigel.staatsbibliothek-berlin.de/suche/?isil={museum_data['isil_code']}"
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}
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],
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"provenance": {
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"data_source": "WEB_SCRAPING",
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"data_tier": "TIER_2_VERIFIED",
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"extraction_date": datetime.now(timezone.utc).isoformat(),
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"extraction_method": "Automated extraction from isil.museum registry (Institut für Museumsforschung)",
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"confidence_score": 0.90,
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"source_url": museum_data.get("detail_url") or BAYERN_URL,
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"notes": "Extracted from official German museum ISIL registry. Detail page URL available for future enrichment."
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}
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}
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# Add detail URL as Website identifier if available
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if museum_data.get("detail_url"):
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custodian["identifiers"].append({
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"identifier_scheme": "Registry",
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"identifier_value": museum_data["detail_url"],
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"identifier_url": museum_data["detail_url"]
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})
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return custodian
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def enrich_museum_from_detail_page(museum: Dict, detail_html: str) -> Dict:
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"""
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Optional: Enrich museum record from detail page.
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This function is NOT called by default (to save time), but can be enabled
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for deep enrichment in future iterations.
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"""
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soup = BeautifulSoup(detail_html, 'html.parser')
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# TODO: Parse detail page structure to extract:
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# - Full address (street, postal code)
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# - Phone number
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# - Email
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# - Website URL
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# - Opening hours
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# - Description
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# For now, return museum unchanged
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return museum
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def main():
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"""Extract Saxony museums from isil.museum registry."""
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print("=" * 80)
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print("Saxony Museum Harvester (isil.museum Registry)")
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print("=" * 80)
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print()
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# Fetch museum list
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html = fetch_museum_list(BAYERN_URL)
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# Parse table
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print("Parsing museum table...")
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museums = parse_museum_table(html)
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if not museums:
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print("✗ No museums found in HTML")
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return None
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print(f"✓ Extracted {len(museums)} museums from table")
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print()
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# Sample output
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print("Sample museums:")
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for museum in museums[:5]:
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print(f" • {museum['name']} ({museum['city']}) - {museum['isil_code']}")
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print(f" ... and {len(museums) - 5} more")
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print()
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# Convert to LinkML format
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print("Converting to LinkML format...")
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custodians = []
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for museum in museums:
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custodian = convert_to_linkml(museum)
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custodians.append(custodian)
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print(f"✓ Converted {len(custodians)} museums to LinkML format")
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print()
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# Generate output filename
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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_dir.mkdir(parents=True, exist_ok=True)
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output_file = output_dir / f"bayern_museums_{timestamp}.json"
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# Export to JSON
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with open(output_file, 'w', encoding='utf-8') as f:
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json.dump(custodians, 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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# Geographic distribution report
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print("=" * 80)
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print("Geographic Distribution")
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print("=" * 80)
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print()
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cities = {}
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for museum in museums:
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city = museum['city']
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cities[city] = cities.get(city, 0) + 1
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# Sort by count descending
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sorted_cities = sorted(cities.items(), key=lambda x: x[1], reverse=True)
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print(f"Total cities: {len(sorted_cities)}")
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print()
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print("Top 10 cities by museum count:")
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for city, count in sorted_cities[:10]:
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print(f" {count:3d} {city}")
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print()
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# Metadata completeness report
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print("=" * 80)
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print("Metadata Completeness Report")
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print("=" * 80)
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print()
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fields = {
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"name": len([m for m in museums if m.get('name')]),
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"city": len([m for m in museums if m.get('city')]),
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"ISIL code": len([m for m in museums if m.get('isil_code')]),
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"detail_url": len([m for m in museums if m.get('detail_url')]),
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}
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total = len(museums)
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for field, count in fields.items():
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percentage = (count / total) * 100
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status = "✓" if percentage == 100 else "○"
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print(f"{status} {field:20s}: {count}/{total} ({percentage:5.1f}%)")
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print()
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print("Note: Address, phone, email, and website data available via detail pages")
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print(" Run with --enrich flag to scrape individual museum pages (slower)")
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print()
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print("=" * 80)
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print(f"Harvest complete! {len(custodians)} Saxony museums extracted.")
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print("=" * 80)
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print()
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# Next steps suggestion
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print("Next steps:")
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print(" 1. Merge with Saxony foundation dataset (12 archives/libraries)")
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print(" 2. Total Saxony dataset: ~187 institutions")
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print(" 3. Optional: Enrich with detail page scraping for full addresses")
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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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