![whotracks.me](https://raw.githubusercontent.com/cliqz-oss/whotracks.me/master/static/img/who-tracksme-logo.png)
Bringing Transparency to online tracking - built by Cliqz and Ghostery.
This repository contains:
- data on trackers and websites as shown on whotracks.me (WTM)
- database mapping tracker domains to companies
- code to render the whotracks.me site
Python 3.6 is needed to build the site. We recommend creating a
virtualenv (or pipenv
) to install the dependencies.
$ pip install whotracksme
$ pip install -r requirements.txt
$ pip install -e .
That’s all you need to get started!
To get started with the data, everything you need can be found in
whotracksme.data
:
from whotracksme.data.loader import DataSource
data = DataSource()
# available entities
data.trackers
data.companies
data.sites
For examples of scripts, have a look in the contrib folder!
Building the site requires a few extra dependencies, not installed by
default to not make the installation heavier than it needs to be. You
will need to install whotracksme
from the repository, because not
all assets are packaged with whotracksme
released on pypi:
$ pip install -r requirements-dev.txt
$ pip install -e '.[dev]'
Once this is done, you will have access to a whotracksme
entry point
that can be used this way:
$ whotracksme website [serve]
The serve
part is optional and can be used while making changes on
the website.
All generated artifacts can be found in the _site/
folder.
To run tests, you will need pytest
, or simply install whotacksme
with the dev
extra:
$ pip install -e '.[dev]'
$ pytest
$ pip install twine
$ python setup.py sdist bdist_wheel
$ twine upload --username cliqz-oss dist/*
We are happy to take contributions on: * Guest articles for our blog in the topics of tracking, privacy and security. Feel free to use the data in this repository if you need inspiration. * Feature requests that are doable using the WTM database. * Curating our database of tracker profiles. Open an issue if you spot anything odd.
Please read our Guideline for 3rd parties wanting to suggest corrections to their data.
The content of this project itself is licensed under the Creative Commons Attribution 4.0 license, and the underlying source code used to generate and display that content is licensed under the MIT license.