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DP Wizard

pypi

Building on what we've learned from DP Creator, DP Wizard offers:

  • Easy installation with pip install dp_wizard
  • Simplified single-user application design
  • Streamlined workflow that doesn't assume familiarity with differential privacy
  • Interactive visualization of privacy budget choices
  • UI development in Python with Shiny

Usage

usage: dp-wizard [-h] [--csv CSV_PATH] [--contrib CONTRIB] [--demo]

options:
  -h, --help         show this help message and exit
  --csv CSV_PATH     Path to CSV containing private data
  --contrib CONTRIB  How many rows can an individual contribute?
  --demo             Use generated fake CSV for a quick demo

Development

This is the first project we've developed with Python Shiny, so let's remember what we learned along the way.

Getting Started

To get started, clone the repo and install dev dependencies in a virtual environment:

$ git clone https://github.com/opendp/dp-wizard.git
$ cd dp-wizard
$ python3 -m venv .venv
$ source .venv/bin/activate
$ pip install -r requirements-dev.txt
$ pre-commit install
$ playwright install

Now install the application itself and run it:

$ pip install --editable .
$ dp-wizard

Your browser should open and connect you to the application.

Testing

Tests should pass, and code coverage should be complete (except blocks we explicitly ignore):

$ ./ci.sh

We're using Playwright for end-to-end tests. You can use it to generate test code just by interacting with the app in a browser:

$ dp-wizard # The server will continue to run, so open a new terminal to continue.
$ playwright codegen http://127.0.0.1:8000/

You can also step through these tests and see what the browser sees:

$ PWDEBUG=1 pytest -k test_app

If Playwright fails in CI, we can still see what went wrong:

  • Scroll to the end of the CI log, to actions/upload-artifact.
  • Download the zipped artifact locally.
  • Inside the zipped artifact will be another zip: trace.zip.
  • Don't unzip it! Instead, open it with trace.playwright.dev.

Conventions

Branch names should be of the form NNNN-short-description, where NNNN is the issue number being addressed.

Dependencies should be pinned for development, but not pinned when the package is installed. New dev dependencies can be added to requirements-dev.in, and then run pip-compile requirements-dev.in to update requirements-dev.txt

A Github project board provides an overview of the issues and PRs.

graph TD
    subgraph Pending
        %% We only get one auto-add workflow with the free plan.
        %% https://docs.github.com/en/issues/planning-and-tracking-with-projects/automating-your-project/adding-items-automatically
        Issue-New
        PR-New-or-Changes
    end
    %% subgraph In Progress
        %% How should this be used?
        %% Can it be automated
    %% end
    subgraph Ready for Review
        PR-for-Review
    end
    subgraph In Review
        PR-in-Review --> PR-Approved
    end
    subgraph Done
        Issue-Closed
        PR-Merged
        PR-Closed
    end
    PR-New-or-Changes -->|manual| PR-for-Review
    PR-for-Review -->|manual| PR-in-Review
    Issue-New -->|auto| Issue-Closed
    PR-New-or-Changes -->|auto| PR-Closed
    PR-for-Review -->|auto| PR-Closed
    PR-in-Review -->|auto| PR-Closed
    PR-for-Review -->|manual| PR-New-or-Changes
    PR-in-Review -->|auto| PR-New-or-Changes
    PR-Approved -->|auto| PR-Merged
Loading
  • For manual transitions, the status of the issue or PR will need to be updated by hand, either on the issue, or by dragging between columns on the board.
  • For auto transitions, some other action (for example, approving a PR) should trigger a workflow.
  • These are the only the states that matter. Whether PR is a draft or has assignees does not matter.
  • If we need anything more than this, we should consider a paid plan, so that we have access to more workflows.