Flow is a website that lets you plan courses with friends.
Plan your courses
It might seem funny that this repository and much of the code references
RMC stands for "Rate My Courses", which was the prototype name for this project before it was given the (slightly) better name of Flow.
Because of the profileration of this 3 letter prefix throughout the code, and the unfortunate coupling of the repository name and our python namespace, we decided to leave it be.
The fastest way to get started is to use Docker. This will let you run Flow inside of a virtual machine to avoid dealing with package installation problems, avoid polluting your development environment, and should take less time.
To get started, install Docker.
Once you have docker installed, run:
$ make shell_in_docker
This will download and run a Docker image with all of Flow's dependencies already installed. Don't worry if you don't know what a Docker image is.
Flow will only need to download the image once, so booting up in the future should be much faster.
Once the Docker image is downloaded and running, you should find yourself in a
/bin/bashshell. This is running inside a Docker container (effectively a virtual machine). Inside this container, the
rmcrepository can be found in
/rmc, which you should be in right after running
To start running Flow locally, run the following inside this new shell.
$ make local
If you point your browser at http://localhost:5000/, you should now see the Flow homepage running on your computer!
Congratulations! You now have Flow running locally.
To do anything interesting in Flow, you need data in your database. This is where information about courses, professors, and scheduling information is stored.
To get started, open a new terminal (different from the one running
make local), and start a new shell inside Docker by running
$ make shell_in_docker
Inside the Docker container, run the following to get some basic course data into the DB
$ make init_data
It may be helpful to read this document before diving into the code. This isn't exhaustive, but it should be enough to get you started if you want to contribute.
config/: Configuration for frameworks, databases, or anything that might vary between the development environment and production.
data/: This is where we collect data and load it into the database
crawler.pydownloads data by scraping pages and hitting APIs
processor.pyprocesses the data grabbed by
crawler.pyand loads it into the DB
aggregator.pyis run on a regular schedule (daily for the most part) to keep our data up to date
models/: "Schema" definitions for our models backed by MongoEngine
server/: Request handlers, static assets, and templates
templates/: Jinja2 templates
course_page.html) are rendered directly by the Flask server with
render_templatecalls, with the exception of the
base_*_page.htmlfiles which other
_page.htmltemplates inherit from.
course.html) contain Underscore templates used to render stuff on the client-side
static: Static assets eventually ending up as files served directly by nginx when on production
server.py: The majority of the request handlers for the application, written in Flask
If you need a REPL to fool around with the database or test out some code, check out
To automatically load some imports and connect to a database, setup code can be found in
Here's what an example session might look like:
$ tools/devshell.py Python 2.7.1 (r271:86832, Jul 31 2011, 19:30:53) Type "copyright", "credits" or "license" for more information.
IPython 0.13.1 -- An enhanced Interactive Python. ? -> Introduction and overview of IPython's features. %quickref -> Quick reference. help -> Python's own help system. object? -> Details about 'object', use 'object??' for extra details.
In : m.User.objects(first_name__in=['Jamie', 'David', 'Sandy', 'Mack'], last_name__in=['Wong', 'Hu', 'Duan', 'Wu']) Out: [<user: david hu>, <user: mack duan>, <user: sandy wu>, <user: jamie wong>]
To run all the tests in the entire system:
To run all the tests except the really slow ones (namely Selenium tests):
To run all the tests under a specific directory tree or in a specific file:
PYTHONPATH=.. nosetests server/api PYTHONPATH=.. nosetests server/api/v1_test.py
If you'd prefer to avoid the docker route, you can install the dependencies directly on your own machine.
To set up your dev environment, run
We work inside a virtualenv, so remember to
source ~/.virtualenv/rmc/bin/activatewhenever you're working within the repo.
You should now be ready to boot the local server, with
Once it starts running, point your browser to http://localhost:5000/
If you are getting a connection refused error when trying to run
make localand are on Linux, it is most likely due to MongoDB taking too long to start the first time it's run. To fix this, run
mongod --config config/mongodb_local.confand let it warm up for about 30 seconds to 1 minute. Then end the process, and run
make localagain. It should work now.