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README.rst

SmartElect

https://travis-ci.org/SmartElect/SmartElect.svg?branch=develop

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

Getting Started

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

mkvirtualenv -p `which python2.7` smartelect
$VIRTUAL_ENV/bin/pip install -U -r $PWD/requirements/dev.txt

Then create a local settings file and set your DJANGO_SETTINGS_MODULE to use it:

cp libya_elections/settings/local.example.py libya_elections/settings/local.py
echo "export DJANGO_SETTINGS_MODULE=libya_elections.settings.local" >> $VIRTUAL_ENV/bin/postactivate
echo "unset DJANGO_SETTINGS_MODULE" >> $VIRTUAL_ENV/bin/postdeactivate

Exit the virtualenv and reactivate it to activate the settings just changed:

deactivate
workon smartelect

Create the Postgres database and run the initial migrate:

createdb -E UTF-8 smartelect
python manage.py migrate

Create a superuser:

python manage.py createsuperuser

Redis is required. Install the server with brew install redis (OS X) or sudo apt-get install redis-server (some Linux), or something else. If Redis is listening on a non-standard port or not accessible over localhost, use the REPORTING_REDIS_SETTINGS in base.py to configure it.

You should now be able to run the development server:

python manage.py runserver

To run the test suite (including flake8 and a coverage report):

./run_tests.sh

We use the django-nose test runner which offers some extra features. You can set the env var REUSE_DB=1 to have Django reuse the test database between test runs, saving many seconds in test startup time. If you do, be aware that test runs will not automatically pick up new migrations anymore, so if you create a new migration, you'll have to unset REUSE_DB for one test run to pick up the new migrations:

REUSE_DB=1 ./run_tests.sh

You can also tell django-nose to stop at the first test failure, and to only rerun failed tests. This is very handy when you're focused on a new feature and don't want to run the whole test suite with each change, but do want to run the whole test suite once you have your test working (to make sure that your working code didn't break something else). Put this in $HOME/.noserc:

[nosetests]
# failed=1 means only run tests that failed on a previous run.
# Note though that failed tests are only recorded when failed=1
# is set, which means it's not very useful unless you keep it
# set.
failed=1

# stop=1 means stop on the first failure. This sometimes breaks tests by leaving
# the database in an unclean state. If you hit weird test failures after the
# first one, try adding FORCE_DB=1 (just one time); it's the anti-REUSE_DB=1
# and will ensure running the tests with a fresh new test database.
stop=1

Next, we'll discuss setting up celery and celerybeat. This may not be necessary for all purposes, so see below for a simpler alternative mechanism. If that simpler mechanism is not sufficient, then you will need to setup celery. In a separate shell, run the celery workers:

python manage.py celery worker
  • If this fails due to an error connecting to RabbitMQ, ensure that RabbitMQ is installed and running. (Ubuntu: sudo apt-get install rabbitmq-server)

In a separate shell, run the celerybeat process:

python manage.py celerybeat

The celerybeat and celery worker processes will need to be manually recycled to pick up code changes.

An alternative mechanism for running Celery tasks in a development environment without RabbitMQ is to run tasks synchronously in a separate process. In order to accomplish this, set CELERY_ALWAYS_EAGER = True and CELERY_EAGER_ALWAYS_PROPAGATES_EXCEPTIONS = True in local.py. You'll still need the celerybeat process if you want to test recurring tasks, but if you only want to generate data for the dashboard, then run these commands:

python manage.py create_reporting_api_test_data --yes-delete-my-data --num-registration-dates=30
python manage.py generate_reporting_api_reports

Reporting API and VR Dashboard

The integrated VR Dashboard accesses the reports directly, not over HTTP. If you need to enable access to reporting API reports over HTTP, such as for testing or for access from the legacy vr-dashboard application:

  • Configure the Basic auth user and password by setting REPORTING_API_USERNAME and REPORTING_API_PASSWORD in the environment or in local.py.

The reports are generated by Celery tasks, with the normal schedule defined by REPORT_GENERATION_INTERVALS in base.py. When testing, smaller intervals will likely be needed in local.py, such as in the following example:

from datetime import timedelta
REPORT_GENERATION_INTERVALS = {
    'default': timedelta(minutes=1),  # used for reports that don't have an overridden schedule below
    # 'election_day': timedelta(minutes=5),
    # 'registrations': timedelta(minutes=7)
}

(And remember to start the Celery process(es), which you might not normally need.)

Source

Developed for the Libya High National Elections Commission by Caktus Consulting Group.