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assign-reviews's Introduction

Python project template

This is a template repository for any Python project that comes with the following dev tools:

  • ruff: identifies many errors and style issues (flake8, isort, pyupgrade)
  • black: auto-formats code

Those checks are run as pre-commit hooks using the pre-commit library.

It includes pytest for testing plus the pytest-cov plugin to measure coverage.

The checks and tests are all run using Github actions on every pull request and merge to main.

This repository is setup for Python 3.11. To customize that, change the VARIANT argument in .devcontainer/devcontainer.json, change the config options in .precommit-config.yaml and change the version number in .github/workflows/python.yaml.

Assign Reviewers

First download the following files from Pretalx into the data/ directory:

  • scipy_reviewers.csv # people who signed up as reviewers
  • sessions.csv # all proposal exported from pretalx
  • speakers.csv # all speakers exported from pretalx
  • pretalx_reviewers.csv # all reviewers copy-pasted from pretalx
  • scipy_coi_export.csv # all responses to the coi form
  • coi_authors.csv # copy pasted values of author names from coi form
  • tracks.csv # manually entered track IDs

Then run the notebooks as Python files in the following order with pixi

$ pixi run pre-processing
$ pixi run assignments

or run the notebooks manually as Jupyter notebooks either by asking for a JupyterLab instance

$ pixi run jupyter lab

or just getting a shell

$ pixi shell

Development instructions

With devcontainer

This repository comes with a devcontainer (a Dockerized Python environment). If you open it in Codespaces, it should automatically initialize the devcontainer.

Locally, you can open it in VS Code with the Dev Containers extension installed.

Without devcontainer

If you can't or don't want to use the devcontainer, then use pixi to control the application. If you don't have pixi installed yet, follow the 1-liner install command for the Rust binary for your operating system.

Then to install the full environment from the multi platform lock file simply just run

pixi install

To execute a specific task defined in the task runner section just run

pixi run <task name>

So for example, to run all the tests run

pixi run test

or to lint

pixi run lint

If you would like to have interactive shell access (like a classic virtual environment) run

pixi shell

and you will be dropped into a new shell with the environment activated.

Updating the lock file

To regenerate the lock file from the project pixi.toml run

rm pixi.lock && pixi install

This will be very fast!

Adding code and tests

This repository starts with a very simple main.py and a test for it at tests/main_test.py. You'll want to replace that with your own code, and you'll probably want to add additional files as your code grows in complexity.

When you're ready to run tests, run:

python3 -m pytest

Using jupytext with Jupyter notebooks

jupytext allows for easier versioning of Jupyter notebooks by saving all of the information that exists in them in specially formatted .py files and then generating the notebook representation when you select them in a Jupyter interface. Version the .py files as you normally would with any other text file. To run the .py files as Jupyter notebooks, select them in the Jupyter file browser, right click, and then select Open With โ†’ Notebook. Any changes made in a Jupyter notebook will be automatically synced to the paired .py file.

File breakdown

Here's a short explanation of each file/folder in this template:

  • .devcontainer: Folder containing files used for setting up a devcontainer
    • devcontainer.json: File configuring the devcontainer, includes VS Code settings
    • Dockerfile: File with commands to build the devcontainer's Docker image
  • .github: Folder for Github-specific files and folders
    • workflows: Folder containing Github actions config files
      • python.yaml: File configuring Github action that runs tools and tests
  • tests: Folder containing Python tests
    • main_test.py: File with pytest-style tests of main.py
  • .gitignore: File describing what file patterns Git should never track
  • .pre-commit-config.yaml: File listing all the pre-commit hooks and args
  • main.py: The main (and currently only) Python file for the program
  • pyproject.toml: File configuring most of the Python dev tools
  • README.md: You're reading it!
  • requirements-dev.txt: File listing all PyPi packages required for development
  • requirements.txt: File listing all PyPi packages required for production

For a longer explanation, read this blog post.

๐Ÿ”Ž Found an issue or have an idea for improvement?

Help me make this template repository better by letting us know and opening an issue!

assign-reviews's People

Contributors

matthewfeickert avatar guenp avatar

Stargazers

Sean P. Myrick V19.1.7.2 avatar

Watchers

Rowan Cockett avatar Steve Purves avatar  avatar Meghann Agarwal avatar  avatar Madicken Munk avatar Amey Ambade avatar Hongsup Shin avatar Anna Haensch  avatar Sean P. Myrick V19.1.7.2 avatar

Forkers

seanpm2001

assign-reviews's Issues

Add application tests

Please provide us with the following information:

This issue is for a: (mark with an x)

- [ ] bug report -> please search issues before submitting
- [x] feature request
- [ ] documentation issue or request
- [ ] regression (a behavior that used to work and stopped in a new release)

It would be good to add pytest based tests for the applicaiton functionality. This will require some mock data similar in form to what is expected in

df_submissions = pd.read_csv("2023_submissions_to_assign.csv")
df_reviewers = pd.read_csv("2023_reviewers_to_assign.csv")

Then unit and application tests can be written that in addition to typical use target edge cases that have been encournted in the past.

Integrate API calls to facilitate PreTalx data analysis

I have figured out some tooling to grab data from submissions (proposals) and reviews out of PreTalx, which may be useful for assigning proposal reviews to volunteers.

First, both relevant PreTalx APIs authenticate using a fixed token already assigned to each user. Fetch it from the user profile page, scroll down to the API Access heading.

Second, both APIs are streaming: each invocation returns a subset of the sequence of either submissions or reviews, along with a URL whose GET fetches the next page. The following function articulates the logic to pull the whole stream associated to one of these two API endpoints:

from contextlib import closing
import requests
from tqdm.auto import tqdm

def fetch_sequence(url1, token, max_queries=50):
    sequence = []
    url = url1
    max_queries = 50
    num_queries = 0
    num_results_expected = None

    with closing(tqdm(total=max_queries)) as progress:
        while True:
            response = requests.get(url, headers={"Authorization": f"Token {token}"})
            assert response.ok
            data = response.json()
            progress.update()
            num_queries += 1

            assert "results" in data
            assert "next" in data

            if num_results_expected is None and "count" in data:
                num_results_expected = data["count"]
                max_queries = int(np.ceil(num_results_expected / len(data["results"])))
                progress.reset(max_queries)
                progress.update(num_queries)
            else:
                assert num_results_expected == data["count"]

            sequence += data["results"]
            url = data["next"]
            if not url:
                break

    return sequence

The endpoints in question:

  1. Submissions: https://cfp.scipy.org/api/events/2024/submissions/
  2. Reviews: https://cfp.scipy.org/api/events/2024/reviews/

Easy peasy!

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