This is a tool for crowdsourcing data, initially designed for gathering data about local authority climate actions for https://councilclimatescorecards.uk/
You will need Docker installed.
Clone the repository:
git clone git@github.com:mysociety/ceuk-marking.git
cd ceuk-marking
Create and edit a .env file using .env-example file and then
update SECRET_KEY and MAPIT_API_KEY. You can get the latter from https://mapit.mysociety.org/account/signup/
Start the Docker environment:
docker-compose up
Docker-compose will automatically install dependencies and start the development web server at https://localhost:8000 when the container is started.
(If Python complains about missing libraries, chances are the Python requirements have changed since your Docker image was last built. You can rebuild it with, eg: docker-compose build web.)
If you’re running Docker manually (recommended) you will need to enter a Bash shell inside the container, in order to run any of the data import commands:
docker-compose exec web bash
If you’re running Docker via Visual Studio Code, instead, you’ll want to run the commands via the built-in terminal.
You will likely want to create a Django superuser, by running this inside the container:
script/createsuperuser
The superuser will be created with the details specified in the DJANGO_SUPERUSER_* environment variables. Read more about how Docker handles environment variables.
Currently the create_initial_data management command relies on data
that is no longer freely available (see issue #128) but normally running
this would populate the list of councils and categories.
Then the import_questions management command will import the list of
questions and assign them to the relevant councils. The questions data
should be downloaded as an Excel sheet from the Google sheet populated
by CEUK.
Details on file strucures can be found in DATA.md.
Form more details on managing an instance see ADMIN.md.
See the separate ARCHITECTURE.md file for full details on the underlying architecture. For admin purposes the following should suffice.
As a brief overview of the operation volunteers are usually assigned to mark the questions in a section for a set of councils. Councils can then respond to the marking by agreeing or disagreeing with the mark, and then a final set of volunteers can then audit the initial responses and council feedback to provide a final mark. The audit volunteers are again assigned a section and a set of councils within that section.
The set of questions displayed for a council in a section depends on the council type to allow for not all councils having the same responsibilities and hence not all questions applying to all councils.
Questions also have a type so not all questions are visible to volunteers - e.g. questions that use data from national statistics. These are instead filled in by management commands.
In more detail Questions are split into Sections, and are also part of QuestionGroups. The QuestionGroup controls which councils the question applies to.
Councils are also assigned a QuestionGroup which again controls which questions apply to a Council.
There are Responses which are associated with a ResponseType such as Audit.
Volunteers are assigned to a council and a section with Assignements. This can be done in bulk using management commands or in the django admin. An assigment has a ResponseType as well as a Section and Council.
There is also a Marker object associated with a Volunteer which determines what stage they are marking (e.g. Audit)
First start the Docker environment:
docker-compose up
Then run the tests from inside or outside the docker container:
script/test
The first time you run script/test, it will ask whether you want the tests to run natively or inside the docker container. Type docker to run them inside the docker container. Your preference will be saved to .env for future runs.