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Powering Penn Course Review, Penn Course Plan and Penn Course Alert

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Penn Courses

Workflow Coverage Status

This is the unified home of all Penn Courses products.

Installation & Setup

  1. Install the backend
  2. If you need to, install the frontend

Note that you need the backend to run the frontend.

Using Dev Containers

This repository has a built-in dev container that you can use to develop the project. To use it, you need to have Docker and VSCode installed.

Press the remote container button in the bottom left of the window and select "Reopen in Container". This will build the dev container and open the project in a new window.

This container is designed to work with both backend and frontend development. Some additional steps are required, like installing pipenv dependencies and setting up the database and running the frontend server. Please read and follow the instructions in the backend and frontend READMEs.

For more information, visit dev container documentation.

Pushing Code

You can use ssh-add to make your SSH keys available to the dev container. This will allow you to push code to GitHub.

ssh-add ~/.ssh/id_rsa

API Documentation

API Docs can be found at /api/documentation on the back-end server. Also check out the code for more explanations and documentation! We've tried to keep it up-to-date.

Runbook

This section is to collect thoughts/learnings from the codebase that have been hard-won, so we don't lose a record of it if and when the information proves useful again

Derived fields

Normally, derived fields on models are represented as @property-decorated functions. However, there are a few in the codebase that need to be accessed on the database layer without JOINs. So that they can be indexed. Specifically, these are the full_code fields on Course and Section models, which are derived from fields on related models.

These are updated every time the save() method is called. However, it's possible to get into a state (such as with db migrations) where full_code isn't set properly.

Open a shell in production, and run this small script:

from tqdm import tqdm
from courses.models import Section, Course

for c in tqdm(Course.objects.all().select_related("department")):
    c.save()

for s in tqdm(Section.objects.all().select_related("course")):
    s.save()

tqdm will give you a nice progress bar as the script completes. The select_related clause speeds up the query, avoiding what would be a pretty nasty N+1 scenario.

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