ThreeSixtyGiving Data Quality

This repository contains tools and libraries for checking and assessing the quality of data published to the ThreeSixtyGiving data standard.

CoVE

CoVE is a web application to Convert, Validate and Explore data that adheres to an open data standard. This repository contains a CoVE configured for the ThreeSixtyGiving data standard.

A live instance of CoVE is available at: https://dataquality.threesixtygiving.org

Installing and running for development

Install the python dependences:

$ virtualenv .ve --python python3
$ source .ve/bin/activate
$ pip install -r requirements_cove.txt

Start the development server:

$ ./cove/manage.py runserver

Testing Cove

Install the python dependencies:

$ pip install -r requirements_cove_dev.txt
$ cd cove
$ py.test

Deploying to production

CoVE is based on Django and be deployed using the deployment mechanisms as defined in the Django docs

Deploying with dokku

There is incomplete and experimental support for deploying with dokku. (No persistent data yet, for example).

DO NOT use in production.

On the server:

app_name=dqt
dokku apps:create $app_name
dokku config:set --no-restart $app_name DEBUG=False
dokku config:set --no-restart $app_name ALLOWED_HOSTS=.example.org
dokku git:set $app_name keep-git-dir true

On the local machine:

app_name=dqt
git remote add dokku [email protected]:$app_name
git push dokku main

lib360dataquality

lib360dataquality is a library that contains useful functions and classes for checking ThreeSixtyGiving standard data.

It is used by both tools and CoVE to generate reports on the data quality.

Installing library

Install just the python library

$ virtualenv .ve --python python3
$ source .ve/bin/activate
$ pip install -e .

Using the tools

The tools directory contains a number of command line tools that can be run on TheeSixtyGiving standard data to generate various reports.

Running various tools example:

cove_checks.py ../a001p00000tuoBKAAY.ods

Using the python library

To use the python library import it into your python program, for example to use the additional checks:

from lib360dataquality.cove.threesixtygiving import common_checks_360
from lib360dataquality.cove.schema import Schema360

context = { "file_type": "json" }
working_dir = os.path.abspath(os.path.curdir)

data = '' # Your JSON data

common_checks_360(context, working_dir, data, Schema360())

print(context)

Code Linting

Code should pass flake8

New code should aim to pass python black