Taytay Quick Start

Below you will find basic setup and deployment instructions for the taytay project. To begin you should have the following applications installed on your local development system:

• Python >= 3.4
• pip >= 1.5
• virtualenv >= 1.10
• virtualenvwrapper >= 3.0
• Postgres >= 9.3
• git >= 1.7

Getting Started

First clone the repository from Github and switch to the new directory:

$ git clone [email protected]:[ORGANIZATION]/taytay.git
$ cd taytay

To setup your local environment you should create a virtualenv and install the necessary requirements:

# Check that you have python3.4 installed
$ which python3.4
$ mkvirtualenv taytay -p `which python3.4`
(taytay)$ make dev

Configurable settings are managed with django-dotenv. It reads environment variables located in a file name .env in the top level directory of the project. The previous command make dev creates new .env file with a new SECRET_KEY value set.

Create the Postgres database and run the initial migrate:

(taytay)$ createdb -E UTF-8 taytay
(taytay)$ python manage.py migrate

You should now be able to run the development server:

(taytay)$ python manage.py runserver

Connecting to the API

To connect to the API at baelor.io, you'll need to create a user using the following curl command

$ curl -H "Content-Type: application/json" -X POST -d
'{"username": "yourusername","email_address": "youremail",
"password": "yourpassword","password_confirm": "yourpassword"}'
http://baelor.io/api/v0/users

You should receive an API key in the response. To make use of this key, you should include it in your .env file:

$ echo 'BAELOR_API_KEY="<api key>"' >> .env

Where <api key> would be replaced with your actual API key value.

Get song data

To get song data run the fetch_song_data command

$ python manage.py fetch_song_data

Testing

The Makefile for this project has a number of helpful commands for testing and checking code quality. Below is a brief description of the commands

• make test - Runs the full test suite and reports test coverage
• make lint - Runs a set of subcommands to check code quality

• make lint-py - Runs the code through flake8 for static analysis
• make lint-migrations - Runs Django's checks for model changes without migrations
• make lint-django - Runs Django's system checks with the base settings
• make lint-deploy - Runs Django's system checks for deployment