ilgi

Life, routines, mental & physical health tracker.

Quickstart

1. Set up a Python virtual environment and install the required Python dependencies:

    pipenv install
   

2. Create .env configuration file based on env.sample:

    cp env.sample .env
    vim .env
   

3. Set up the database

   You'll need to create the database and set DATABASE_URL in the configuration file before you can run migrations and use the code.

   To use SQLite (supported out of the box), set the DATABASE_URL to the location of database file (it will be created on the first run), either relative to the project directory:

    DATABASE_URL=sqlite:///sqlite.db
   

   Or absolutely positioned in the file system:

    DATABASE_URL=sqlite:////full/path/to/sqlite.db
   

   (Note the three or four dashes in the URL, respectively).

   To use PostgreSQL or MariaDB databases, install the appropriate driver and create database and user as needed. Example for PostgreSQL (this assumes you already have PostgreSQL installed on your system via package manager such as apt, rpm, or brew):

   1. Connect to the database as admin and create a new user and database

      CREATE USER 'appuser' WITH PASSWORD 'secretpassword'; CREATE DATABASE 'dbname' WITH OWNER 'appuser';

   2. Install Python database driver for PostgreSQL

      pipenv install psycopg2

   3. Set up DATABASE_URL in your .env:

      DATABASE_URL=postgres://appuser:secretpassword@localhost/dbname

4. Run migrations:

    pipenv run python manage.py makemigrations
    pipenv run python manage.py migrate
   

5. Run the server:

    pipenv run python manage.py runserver
   

6. Visit the browsable API at http://localhost:8000/api/v1/

7. Access the Django admin at http://localhost:8000/admin/

Creating superuser

A superuser account can be created using the Django management command:

pipenv run python manage.py createsuperuser

Tests, linters and code coverage

Activate your pipenv environment with pipenv shell so you don't need to prefix every command with pipenv run.

To run the test suite:

python manage.py test

To run the test suite and get code coverage statistics:

coverage run manage.py test
coverage report

To generate HTML reports, run this and open htmlcov/index.html afterwards:

coverage html

To format the code automatically using black, run it from the project root directory:

black .

To check for common programming errors or style problems, run ruff linter in the project root directory:

ruff --fix .

To automatically run black (formatting), ruff (linter) and isort (sort/format package imports) on every git commit, set up a git pre-commit hook:

pre-commit install

Note that you'll need to have initialized your git repository for the git pre-commit hook to be available. To test it without installation, you can run:

pre-commit run --all-files

Continuous integration with GitHub Actions

This project comes with a GitHub Actions workflow that runs the test suite and linters on every push to the repository.

See the .github/workflows/django.yml file for details.

Background tasks using Celery

Use CELERY_BROKER_URL and CELERY_BACKEND environment variables to configure broker and optional results backend to use for the background jobs, see env.sample for details.

Tasks are defined in tasks.py in the appropriate app module.

To run the worker, in the project root, run:

    celery -A project worker

Logging from the tasks will be shown/hidden based on the celery worker log level (default is WARNING, can be changed with -l LEVEL worker option), not based on Django logging configuration.

To show INFO or higher-priority messages, use:

    celery -A project worker -l INFO

To run periodic tasks using Celery Beat, specify beat entries in settings/base.py and run celery beat:

    celery -A project beat -l INFO

Docker support

Build the docker image with:

    docker build -t ilgi .

The default command is to start the web server (gunicorn). Run the image with -P docker option to expose the internal port and check the exposed port with docker ps:

    docker run --env-file .env --P ilgi
    docker ps

Make sure you provide the correct path to the env file (this example assumes it's located in the local directory).

To run a custom command using the image (for example, db migrations):

    docker run --env-file .env ilgi python manage.py migrate

To run a Django shell inside the container:

    docker run --env-file .env -t ilgi

Note that any changes inside the container will be lost. For that reason, running collectstatic or using a SQLite database within a container will have no effect. If you want to use SQLite with docker, mount a docker volume and place the SQLite database inside it.

For more information on the docker build process, see the included Dockerfile.