Models-and-Data: First working version

Models-and-Data/fakedata.py

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+"""Generate fake data for the Conference Review System."""
+
+__author__ = "Nicholas Tamassia"
+
+
+import random
+
+from faker import Faker
+from sqlalchemy import create_engine
+from sqlalchemy.orm import Session, sessionmaker
+
+from models import (
+    Affiliation,
+    Base,
+    Conference,
+    History,
+    Paper,
+    PaperAuthor,
+    Person,
+    PersonAffiliation,
+    Review,
+    Reviewer,
+    Topic,
+    t_expertise,
+    t_paper_topic,
+)
+
+DB_URL = "postgresql+psycopg://tamassno:113880616@localhost/sec2"
+
+fake = Faker()
+
+
+def add_data(session: Session):
+    affiliations: list[Affiliation] = []
+    for _ in range(4):
+        aff = Affiliation(
+            org_name=fake.company(), website=fake.url(), country=fake.country()
+        )
+        affiliations.append(aff)
+    session.add_all(affiliations)
+    session.commit()
+
+    persons: list[Person] = []
+    reviewers: list[Reviewer] = []
+
+    for _ in range(5):
+        p = Person(
+            email=fake.unique.email(),
+            first_name=fake.first_name(),
+            last_name=fake.last_name(),
+        )
+        persons.append(p)
+
+    for _ in range(5):
+        r = Reviewer(
+            email=fake.unique.email(),
+            first_name=fake.first_name(),
+            last_name=fake.last_name(),
+            phone=fake.phone_number(),
+        )
+        reviewers.append(r)
+
+    session.add_all(persons + reviewers)
+    session.commit()
+
+    person_affiliations: list[PersonAffiliation] = []
+    people_pool = random.sample(persons + reviewers, 8)
+    for person in people_pool:
+        aff = random.choice(affiliations)
+        pa = PersonAffiliation(
+            email=person.email,
+            org_name=aff.org_name,
+            from_date=fake.date_between(start_date="-5y", end_date="-1y"),
+            to_date=fake.date_between(start_date="-1y", end_date="today"),
+        )
+        person_affiliations.append(pa)
+    session.add_all(person_affiliations)
+    session.commit()
+
+    conference = Conference(year=2025, location=fake.city())
+    session.add(conference)
+    session.commit()
+
+    topics: list[Topic] = []
+    for _ in range(20):
+        t = Topic(topic_name=fake.bs().title())
+        topics.append(t)
+    session.add_all(topics)
+    session.commit()
+
+    papers: list[Paper] = []
+    for _ in range(3):
+        contact = random.choice(persons + reviewers)
+        p = Paper(
+            title=fake.sentence(nb_words=6),
+            abstract=fake.paragraph(nb_sentences=3),
+            filename=f"{fake.word()}.pdf",
+            contact_email=contact.email,
+            year=conference.year,
+        )
+        papers.append(p)
+    session.add_all(papers)
+    session.commit()
+
+    paper_authors: list[PaperAuthor] = []
+    for _ in range(8):
+        paper = random.choice(papers)
+        author = random.choice(persons + reviewers)
+        pa = PaperAuthor(
+            paper_id=paper.paper_id, email=author.email, rank=random.randint(1, 5)
+        )
+        paper_authors.append(pa)
+    session.add_all(paper_authors)
+    session.commit()
+
+    paper_topic_data: list[dict[str, int]] = []
+    used_pairs: set[tuple[int, int]] = set()
+    while len(paper_topic_data) < 9:
+        paper = random.choice(papers)
+        topic = random.choice(topics)
+        if (paper.paper_id, topic.topic_id) not in used_pairs:
+            used_pairs.add((paper.paper_id, topic.topic_id))
+            paper_topic_data.append(
+                {"paper_id": paper.paper_id, "topic_id": topic.topic_id}
+            )
+    _ = session.execute(t_paper_topic.insert(), paper_topic_data)
+    session.commit()
+
+    expertise_data: list[dict[str, str | int]] = []
+    used_expertise: set[tuple[str, int]] = set()
+    while len(expertise_data) < 10:
+        reviewer = random.choice(reviewers)
+        topic = random.choice(topics)
+        if (reviewer.email, topic.topic_id) not in used_expertise:
+            used_expertise.add((reviewer.email, topic.topic_id))
+            expertise_data.append({"email": reviewer.email, "topic_id": topic.topic_id})
+    _ = session.execute(t_expertise.insert(), expertise_data)
+    session.commit()
+
+    reviews: list[Review] = []
+    posible_reviews = [
+        (paper.paper_id, reviewer.email) for paper in papers for reviewer in reviewers
+    ]
+    for _ in range(3):
+        paper_id, email = random.choice(posible_reviews)
+        rv = Review(
+            paper_id=paper_id,
+            email=email,
+            merit=random.randint(1, 5),
+            relevance=random.randint(1, 5),
+            readability=random.randint(1, 5),
+            originality=random.randint(1, 5),
+            author_comments=fake.sentence(),
+            committee_comments=fake.sentence(),
+        )
+        reviews.append(rv)
+    session.add_all(reviews)
+    session.commit()
+
+    statuses = ["SUBMITTED", "UNDER_REVIEW", "REVISION", "ACCEPTED", "PUBLISHED"]
+    histories: list[History] = []
+    for _ in range(5):
+        paper = random.choice(papers)
+        h = History(
+            paper_id=paper.paper_id,
+            timestamp=fake.date_time_between(start_date="-1y", end_date="now"),
+            paper_status=random.choice(statuses),
+            notes=fake.sentence(),
+        )
+        histories.append(h)
+    session.add_all(histories)
+    session.commit()
+
+
+def main():
+    engine = create_engine(DB_URL)
+    Base.metadata.drop_all(engine)
+    Base.metadata.create_all(engine)
+
+    Session = sessionmaker(bind=engine)
+    session = Session()
+
+    add_data(session)
+
+    session.close()
+
+
+if __name__ == "__main__":
+    main()