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"""
Multi-Term NEU Course Scraper - Merges data from multiple terms
Fixes: Missing courses by scraping Fall/Spring/Summer catalogs
"""
import requests
import pickle
import networkx as nx
import time
import logging
from typing import List, Dict, Set, Any
from datetime import datetime
from collections import defaultdict
logging.basicConfig(level=logging.INFO, format="%(asctime)s %(levelname)s %(message)s")
logger = logging.getLogger(__name__)
class MultiTermScraper:
def __init__(self, term_ids: List[str], api_url: str = "https://searchneu.com/graphql"):
self.term_ids = term_ids
self.api_url = api_url
self.headers = {"Content-Type": "application/json"}
self.merged_courses: Dict[str, Dict] = {} # cid -> course data
self.graph = nx.DiGraph()
def get_all_courses_by_subject(self, term_id: str, subject: str, batch_size: int = 100) -> List[Dict]:
"""Fetch ALL courses for a specific subject/term via pagination."""
all_courses = []
offset = 0
page = 1
while True:
query = """
query searchQuery($termId: String!, $query: String!, $first: Int, $offset: Int) {
search(termId: $termId, query: $query, first: $first, offset: $offset) {
totalCount
nodes {
__typename
... on ClassOccurrence {
subject
classId
name
desc
prereqs
coreqs
minCredits
maxCredits
}
}
}
}
"""
variables = {
"termId": term_id,
"query": subject,
"first": batch_size,
"offset": offset
}
try:
resp = requests.post(self.api_url, json={"query": query, "variables": variables}, headers=self.headers, timeout=10)
resp.raise_for_status()
data = resp.json()
if "errors" in data:
logger.error(f"GraphQL errors for {term_id}/{subject}: {data['errors']}")
break
search_data = data.get("data", {}).get("search", {})
nodes = search_data.get("nodes", [])
page_courses = [c for c in nodes if c.get("__typename") == "ClassOccurrence"]
all_courses.extend(page_courses)
logger.info(f"[{term_id}] {subject} Page {page}: {len(page_courses)} courses (Total: {len(all_courses)})")
if len(page_courses) < batch_size:
break
offset += batch_size
page += 1
time.sleep(0.1)
except Exception as e:
logger.error(f"Error fetching {term_id}/{subject} page {page}: {e}")
break
logger.info(f"[{term_id}] {subject}: {len(all_courses)} total courses")
return all_courses
def _recursive_parse_prereqs(self, prereq_obj: Any) -> Set[str]:
"""Extract course IDs from nested prereq structures."""
ids = set()
if not isinstance(prereq_obj, dict):
return ids
if "classId" in prereq_obj and "subject" in prereq_obj:
ids.add(f"{prereq_obj['subject']}{prereq_obj['classId']}")
return ids
if prereq_obj.get("type") in ["and", "or"]:
for val in prereq_obj.get("values", []):
ids |= self._recursive_parse_prereqs(val)
elif "values" in prereq_obj:
for val in prereq_obj.get("values", []):
ids |= self._recursive_parse_prereqs(val)
return ids
def scrape_all_terms(self, subjects: List[str]):
"""Scrape courses from all terms and merge by course ID."""
term_data = defaultdict(lambda: defaultdict(list)) # term_id -> subject -> courses
for term_id in self.term_ids:
logger.info(f"\n{'='*70}")
logger.info(f"SCRAPING TERM: {term_id}")
logger.info(f"{'='*70}")
for subject in subjects:
courses = self.get_all_courses_by_subject(term_id, subject)
term_data[term_id][subject] = courses
time.sleep(0.5)
# Merge courses across terms (prefer most recent data for duplicates)
for term_id in self.term_ids:
for subject in subjects:
for course in term_data[term_id][subject]:
cid = f"{course['subject']}{course['classId']}"
# Only update if we don't have this course OR this term is newer
if cid not in self.merged_courses:
self.merged_courses[cid] = course
logger.debug(f"Added {cid} from {term_id}")
else:
# Update if current course has more complete data
existing = self.merged_courses[cid]
if not existing.get('desc') and course.get('desc'):
self.merged_courses[cid] = course
logger.debug(f"Updated {cid} from {term_id} (better description)")
logger.info(f"\n{'='*70}")
logger.info(f"MERGE COMPLETE: {len(self.merged_courses)} unique courses")
logger.info(f"{'='*70}")
# Log subject breakdown
subject_counts = defaultdict(int)
for cid in self.merged_courses:
subject = self.merged_courses[cid].get('subject', 'UNKNOWN')
subject_counts[subject] += 1
logger.info("\nSubject breakdown:")
for subject in sorted(subject_counts.keys()):
logger.info(f" {subject}: {subject_counts[subject]} courses")
def build_graph(self):
"""Build NetworkX graph from merged course data."""
logger.info("\nBuilding course dependency graph...")
# Add all courses as nodes
for cid, cdata in self.merged_courses.items():
self.graph.add_node(cid, **{
"name": cdata.get("name", ""),
"subject": cdata.get("subject", ""),
"classId": cdata.get("classId", ""),
"description": cdata.get("desc", ""),
"minCredits": cdata.get("minCredits", 0),
"maxCredits": cdata.get("maxCredits", 0)
})
# Add prerequisite edges
edge_count = 0
for cid, cdata in self.merged_courses.items():
prereqs = cdata.get("prereqs", {})
if prereqs:
prereq_ids = self._recursive_parse_prereqs(prereqs)
for pid in prereq_ids:
if pid in self.graph:
self.graph.add_edge(pid, cid, relationship="prerequisite")
edge_count += 1
else:
logger.warning(f"Prerequisite {pid} for {cid} not in graph")
logger.info(f"Graph built: {self.graph.number_of_nodes()} nodes, {edge_count} edges")
def save_data(self, prefix: str):
"""Save merged graph and courses."""
ts = datetime.now().strftime("%Y%m%d_%H%M%S")
gfile = f"{prefix}_graph_{ts}.pkl"
cfile = f"{prefix}_courses_{ts}.pkl"
with open(gfile, "wb") as gf:
pickle.dump(self.graph, gf)
with open(cfile, "wb") as cf:
pickle.dump(self.merged_courses, cf)
logger.info(f"\nData saved:")
logger.info(f" Graph: {gfile}")
logger.info(f" Courses: {cfile}")
# Save merge report
report_file = f"{prefix}_merge_report_{ts}.txt"
with open(report_file, "w") as rf:
rf.write(f"Multi-Term Scrape Report\n")
rf.write(f"{'='*70}\n\n")
rf.write(f"Terms scraped: {', '.join(self.term_ids)}\n")
rf.write(f"Total unique courses: {len(self.merged_courses)}\n")
rf.write(f"Total edges: {self.graph.number_of_edges()}\n\n")
rf.write("Subject breakdown:\n")
subject_counts = defaultdict(int)
for cid in self.merged_courses:
subject = self.merged_courses[cid].get('subject', 'UNKNOWN')
subject_counts[subject] += 1
for subject in sorted(subject_counts.keys()):
rf.write(f" {subject}: {subject_counts[subject]}\n")
logger.info(f" Report: {report_file}")
def main():
import argparse
parser = argparse.ArgumentParser(description="Multi-Term NEU Catalog Scraper")
parser.add_argument("--terms", nargs="+", required=True, help="Term IDs (e.g., 202510 202520 202530)")
parser.add_argument("--subjects", nargs="+", required=True, help="Subjects (e.g., CS DS STAT)")
parser.add_argument("--prefix", default="neu_merged", help="Output prefix")
parser.add_argument("--batch-size", type=int, default=100, help="Courses per page")
args = parser.parse_args()
scraper = MultiTermScraper(term_ids=args.terms)
scraper.scrape_all_terms(args.subjects)
scraper.build_graph()
scraper.save_data(args.prefix)
logger.info("\n✅ Multi-term scraping complete!")
if __name__ == "__main__":
main() |