gomex/jarbas-elm repository overview

⁠Jarbas — a tool for Serenata de Amor⁠

Jarbas⁠ is part of Serenata de Amor⁠ — we fight corruption with data science.

Jarbas is in charge of making data from CEAP⁠ more accessible. In the near future Jarbas will show what Rosie⁠ thinks of each reimbursement made for our congresspeople.

⁠Table of Contents

1. JSON API endpoints⁠
   1. Reimbursement⁠
   2. Subquota⁠
   3. Applicant⁠
   4. Company⁠
   5. Tapioca Jarbas⁠
2. Installing⁠
   1. Using Docker⁠
   2. Local install⁠

⁠JSON API endpoints

⁠Reimbursement

Each Reimbursement object is a reimbursement claim made by a congressperson. Each reimbursement isidentified by an unique combination of year, applicant_id and document_id.

⁠Retrieving a specific reimbursement

⁠GET /api/reimbursement/<year>/<applicant_id>/<document_id>/

Details from a specific reimbursement. If receipt_url wasn't fecthed yet, the server won't try to fetche it.

⁠GET /api/reimbursement/<year>/<applicant_id>/<document_id>/receipt/

URL of the digitalized version of the receipt of this specific reimbursement.

If receipt_url wasn't fecthed yet, the server will try to fetch it.

If you append the parameter force (i.e. GET /api/reimbursement/<year>/<applicant_id>/<document_id>/receipt/?force) the server will re-fetch the receipt URL.

Not all receipts are available, so this URL can be null.

⁠Listing reimbursements

⁠GET /api/reimbursement/

Lists all reimbursements.

⁠GET /api/reimbursement/<year>/

Lists all reimbursements from a specific year.

⁠GET /api/reimbursement/<year>/<applicant_id>/

Lists all reimbursements from a specific year and applicant_id.

⁠Filtering

All these endpoints accepts any combination of the following parameters:

• applicant_id
• cnpj_cpf
• document_id
• issue_date_start (inclusive)
• issue_date_end (exclusive)
• month
• subquota_id
• year
• order_by: issue_date (default) or probability (both descending)

For example:

GET /api/reimbursement/2016/?cnpj_cpf=11111111111111&subquota_id=42&order_by=probability

This request will list:

• all 2016 reimbursements
• made in the supplier with the CNPJ 11.111.111/1111-11
• made according to the subquota with the ID 42
• sorted by the highest probability

Also you can pass more than one value per field (e.g. document_id=111111,222222).

⁠Subquota

Subqoutas are categories of expenses that can be reimbursed by congresspeople.

⁠Listing subquotas

⁠GET /api/subquota/

Lists all subquotas names and IDs.

⁠Filtering

Accepts a case-insensitve LIKE filter in as the q URL parameter (e.g. GET /api/subquota/?q=meal list all applicant that have meal in their names.

⁠Applicant

An applicant is the person (congressperson or theleadership of aparty or government) who claimed the reimbursemement.

⁠List applicants

⁠GET /api/applicant/

Lists all names of applicants together with their IDs.

⁠Filtering

Accepts a case-insensitve LIKE filter in as the q URL parameter (e.g. GET /api/applicant/?q=lideranca list all applicant that have lideranca in their names.

⁠Company

A company is a Brazilian company in which congressperson have made expenses and claimed for reimbursement.

⁠Retrieving a specific company

⁠GET /api/company/<cnpj>/

This endpoit gets the info we have for a specific company. The endpoint expects a cnpj (i.e. the CNPJ of a Company object, digits only). It returns 404 if the company is not found.

⁠Tapioca Jarbas

There is also a tapioca-wrapper⁠ for the API. The tapioca-jarbas⁠ can be installed with pip install tapioca-jarbas and can be used to access the API in any Python script.

⁠Installing

⁠Using Docker

If you have Docker⁠ (with Docker Compose⁠) and make, just run:

make run.devel

or

docker-compose up -d --build
docker-compose run --rm jarbas python manage.py migrate
docker-compose run --rm jarbas python manage.py ceapdatasets
docker-compose run --rm jarbas python manage.py collectstatic --no-input

You can access it at localhost:80⁠. However your database starts empty, but you can use sample data to development using this command:

make seed.sample

or

docker-compose run --rm jarbas python manage.py reimbursements contrib/sample-data/reimbursements_sample.xz
docker-compose run --rm jarbas python manage.py companies contrib/sample-data/companies_sample.xz
docker-compose run --rm jarbas python manage.py irregularities contrib/sample-data/irregularities_sample.xz

You can get the datasets running Rosie⁠ or directly with the toolbox⁠.

If you have some issues with settings, maybe this section can be helpful⁠.

⁠Local install

⁠Requirements

Jarbas requires Python 3.5⁠, Node.js 6⁠ with Yarn⁠, and PostgreSQL 9.4+⁠.

Once you have pip and yarn available install the dependencies:

yarn install
python -m pip install -r requirements.txt

⁠Python's lzma module

In some Linux distros lzma is not installed by default. You can check whether you have it or not with $ python -m lzma. In Debian based systems you can fix that with $ apt-get install liblzma-dev or in macOS with $ brew install xz — but you mihght have to re-compile your Python.

⁠Settings

Copy contrib/.env.sample as .env in the project's root folder and adjust your settings. These are the main variables:

⁠Django settings

• DEBUG (bool) enable or disable Django debug mode⁠
• SECRET_KEY (str) Django's secret key⁠
• ALLOWED_HOSTS (str) Django's allowed hosts⁠
• USE_X_FORWARDED_HOST (bool) Whether to use the X-Forwarded-Host header⁠
• CACHE_BACKEND (str) Cache backend⁠ (e.g. django.core.cache.backends.memcached.MemcachedCache)
• CACHE_LOCATION (str) Cache location⁠ (e.g. localhost:11211)
• SECURE_PROXY_SSL_HEADER (str) Django secure proxy SSL header⁠ (e.g. HTTP_X_FORWARDED_PROTO,https transforms in tuple ('HTTP_X_FORWARDED_PROTO', 'https'))

⁠Database

• DATABASE_URL (string) Database URL⁠, must be PostgreSQL⁠ since Jarbas uses JSONField⁠.

⁠Amazon S3 settings

• AMAZON_S3_BUCKET (str) Name of the Amazon S3 bucket to look for datasets (e.g. serenata-de-amor-data)
• AMAZON_S3_REGION (str) Region of the Amazon S3 (e.g. s3-sa-east-1)
• AMAZON_S3_CEAPTRANSLATION_DATE (str) File name prefix for dataset guide (e.g. 2016-08-08 for 2016-08-08-ceap-datasets.md)

⁠Google settings

• GOOGLE_ANALYTICS (str) Google Analytics tracking code (e.g. UA-123456-7)
• GOOGLE_STREET_VIEW_API_KEY (str) Google Street View Image API key

⁠Migrations

Once you're done with requirements, dependencies and settings, create the basic database structure:

$ python manage.py migrate

⁠Load data

Now you can load the data from our datasets and get some other data as static files:

$ python manage.py reimbursements <path to reimbursements.xz>
$ python manage.py irregularities <path to irregularities.xz file>
$ python manage.py companies <path to companies.xz>
$ python manage.py ceapdatasets

You can get the datasets running Rosie⁠ or directly with the toolbox⁠.

⁠Generate static files

We generate assets through NodeJS, so run it before Django collecting static files:

$ yarn assets
$ python manage.py collectstatic

⁠Ready?

Not sure? Test it!

$ python manage.py check
$ python manage.py test
$ yarn test

⁠Ready!

Run the server with $ python manage.py runserver and load localhost:8000⁠ in your favorite browser.