[](https://huboard.com/CivicTechTO/tor-councilmatic#/?milestone=["Public Launch"]) [](https://huboard.com/CivicTechTO/tor-councilmatic#/?milestone=["Public Launch"]) [](https://huboard.com/CivicTechTO/tor-councilmatic#/?milestone=["Public Launch"])

Keep track of what Toronto City Council is doing.

Click the "HuBoard" button above to see the tasks that are part of our current milestone.

Setup instructions will vary, depending on whether you're using Windows or Mac/Linux.

We'll be running the app on a virtual machine. This is a simulated Linux system running safely and temporarily on your workstation, which we can use to run our app. When this virtual machine is running, it may be fairly resource-intensive, and so you'll need a fairly powerful workstation.

Requirements:

• Vagrant: For managing a virtual machine running Ubuntu Linux. This is a simulated Linux system running safely and temporarily on your workstation.
• VirtualBox: For Vagrant, which needs it.

After Vagrant and VirtualBox are installed, you can optionally install the vagrant-cachier plugin. This will make for speedier Vagrant runs after the first one, as it will cache the many download files used in preparing Vagrant's virtual machine:

vagrant plugin install vagrant-cachier

Then, just run this from the project root directory:

vagrant up

Follow the instructions provided when the command completes. (Hint: vagrant ssh will get you started.)

It may take quite awhile the first time your run it, as several large downloads will need to occur.

When you're done working, you can run the following command to free up system resources:

vagrant halt

If you no longer plan to use Vagrant to develop for tor-councilmatic, be sure to use this to free up disk space:

vagrant destroy

You can find more details on using Vagrant here.

We'll be running the app directly on your workstation. You won't need a powerful system for this approach. (Yay!)

Requirements:

• Install Python 3..
• Install SQLite3. For our development database.
• virtualenv: For sandboxing our python packages.
• virtualenvwrapper: For helping us manage virtualenv.

View descriptions of all the helper tasks by running the bare invoke command in the project root directory.

Read how to set up virtualenv.

Once you have virtualenv and virtualenvwrapper set up:

mkvirtualenv tor-councilmatic --python=$(which python3)
pip install invoke==0.12.2
git clone https://github.com/civictechto/tor-councilmatic.git
cd tor-councilmatic
inv pip.install

Afterwards, whenever you want to use this virtual environment to work on tor-councilmatic, run:

workon tor-councilmatic

To set up the SQLite database:

inv django.db_reset

You can re-run that command to wipe the database and start fresh.

OPTIONAL: install django-councilmatic locally If you plan on making changes to core councilmatic features (as opposed to Toronto-specific stuff), you'll want to install django-councilmatic locally instead of installing from pypi.

cd ..
git clone https://github.com/datamade/django-councilmatic.git
cd django-councilmatic
python setup.py develop
cd ../tor-councilmatic

There are two ways to get up and running locally with a database:

1. from a pre-generated development database

Download the latest tor_councilmatic.db from the releases page, and place it in the project root directory.

2. from the open civic data api

The following invoke task will run the loaddata management command under the hood. By default, it's pulling data from Toronto's OCD API endpoint at [toronto-ocd-api.herokuapp.com][] (This may take a while, depending on volume of data.)

inv django.loaddata

By default, the loaddata command is smart about what it looks at on the OCD API. If you already have bills loaded, it won't look at everything on the API - it'll look at the most recently updated bill in your database, see when that bill was last updated on the OCD API, & then look through everything on the API that was updated after that point. If you'd like to load things that are older than what you currently have loaded, you can run the loaddata management command with a --delete option, which removes everything from your database before loading.

The loaddata command has some more nuance than the description above, for the different types of data it loads. If you have any questions, open up an issue and pester us to write better documentation.

inv django.run

Navigate to http://localhost:8000/

Haystack is a python package that helps us provide search in our app. It can use many different search backends. We use a powerful search backend service called "Elasticsearch" in staging and production. However, in order to make local development easier, we default to using a "Simple Search" backend. It works well, but does not allow for faceted search features.

To enable faceted search features for local testing, you'll need to install Elasticsearch.

Once installed and running, assuming you've already run through the loaddata process, you'll need to index the database:

./manage.py rebuild_index

You can then run Councilmatic using Elasticsearch like so:

inv django.run --elasticsearch

• David Moore - project manager
• Forest Gregg - Open Civic Data (OCD) and Legistar scraping
• Cathy Deng - data models and loading
• Derek Eder - front end
• Eric van Zanten - search and dev ops

If something is not behaving intuitively, it is a bug, and should be reported. Report it here: https://github.com/datamade/chi-councilmatic/issues

• Fork the project.
• Make your feature addition or bug fix.
• Commit, do not mess with rakefile, version, or history.
• Send a pull request. Bonus points for topic branches.

Copyright (c) 2015 Participatory Politics Foundation and DataMade. Released under the MIT License.