np-validator

Quick overview

API

Generating validation steps

from np_validator import Processor, Validator, ValidationStep, run_validation

# make a basic filesize validator
fs_validator = Validator(
  name="meets_filesize_threshold",
  args={
    "threshold": 10,
  },
)

# add validator to a validation step
validation_step_0 = ValidationStep(
  path_suffix=".mapping.pkl",
  validators=[fs_validator, ],
)

# make a validation step with a processor
# processors convert the data source from it's basic state, a filepath, to an easier to use object

# unpickle, unpickles an arbitrary filepath into a python object
unpickler = Processor(
  name="unpickle",
)

# has_dict_key, checks if a dict-like interface has a key at path
session_uuid_validator = Validator(
  name="has_dict_key",
  args={
    "path": ["session_uuid", ],
  }
)

# assembling it all together as a validation step

validation_step_1 = ValidationStep(
  path_suffix=".behavior.pkl",
  processor=unpickler,
  validators=[session_uuid_validator, ],
)

# running a validation
filepaths = [
  "some/sort/of/path/prefix/uuid-maybe.mapping.pkl",
  "some/sort/of/path/prefix/uuid-maybe.behavior.pkl",
  "this/will/be/ignored/uuid-maybe.replay.pkl",
]
results = run_validation(
  filepaths,
  [validation_step_0, validation_step_1, ],
)

Command-line interface

Run a validation

$ np-validator validate file_list.json validation_steps.yml output.json

Get version

$ np-validator main --version

Documentation

For more detailed documentation on using this package please refer to the docs.

Contributing

Processor

To add a new processor, add the function to np_validator/processors.py. Ideally create a test for it in tests. Run the tests to help ensure that no regressions have been introduced. Each processor is expected to have one required argument which is expected to be a string filepath.

Validator

To add a new validator, add the function to np_validator/validators.py. Ideally create a test for it in tests. Run the tests to help ensure that no regressions have been introduced.

Very first steps

Initialize your code

1. Initialize git inside your repo:

cd np-validator && git init

2. If you don't have Poetry installed run:

make poetry-download

3. Initialize poetry and install pre-commit hooks:

make install
make pre-commit-install

4. Run the codestyle:

make codestyle

5. Upload initial code to GitHub:

git add .
git commit -m ":tada: Initial commit"
git branch -M main
git remote add origin https://github.com/np_validator/np-validator.git
git push -u origin main

Set up bots

• Set up Dependabot to ensure you have the latest dependencies.
• Set up Stale bot for automatic issue closing.

Poetry

Want to know more about Poetry? Check its documentation.

Building and releasing your package

Building a new version of the application contains steps:

• Bump the version of your package poetry version <version>. You can pass the new version explicitly, or a rule such as major, minor, or patch. For more details, refer to the Semantic Versions standard.
• Make a commit to GitHub.
• Create a GitHub release.
• And... publish 🙂 poetry publish --build

🎯 What's next

Well, that's up to you 💪🏻. I can only recommend the packages and articles that helped me.

• Typer is great for creating CLI applications.
• Rich makes it easy to add beautiful formatting in the terminal.
• Pydantic – data validation and settings management using Python type hinting.
• Loguru makes logging (stupidly) simple.
• tqdm – fast, extensible progress bar for Python and CLI.
• IceCream is a little library for sweet and creamy debugging.
• orjson – ultra fast JSON parsing library.
• Returns makes you function's output meaningful, typed, and safe!
• Hydra is a framework for elegantly configuring complex applications.
• FastAPI is a type-driven asynchronous web framework.

Articles:

• Open Source Guides.
• A handy guide to financial support for open source
• GitHub Actions Documentation.
• Maybe you would like to add gitmoji to commit names. This is really funny. 😄

🚀 Features

Development features

• Supports for Python 3.7 and higher.
• Poetry as the dependencies manager. See configuration in pyproject.toml and setup.cfg.
• Automatic codestyle with black, isort and pyupgrade.
• Ready-to-use pre-commit hooks with code-formatting.
• Type checks with mypy; docstring checks with darglint; security checks with safety and bandit
• Testing with pytest.
• Ready-to-use .editorconfig, .dockerignore, and .gitignore. You don't have to worry about those things.

Deployment features

• GitHub integration: issue and pr templates.
• Github Actions with predefined build workflow as the default CI/CD.
• Everything is already set up for security checks, codestyle checks, code formatting, testing, linting, docker builds, etc with Makefile. More details in makefile-usage.
• Dockerfile for your package.
• Always up-to-date dependencies with @dependabot. You will only enable it.
• Automatic drafts of new releases with Release Drafter. You may see the list of labels in release-drafter.yml. Works perfectly with Semantic Versions specification.

Open source community features

• Ready-to-use Pull Requests templates and several Issue templates.
• Files such as: LICENSE, CONTRIBUTING.md, CODE_OF_CONDUCT.md, and SECURITY.md are generated automatically.
• Stale bot that closes abandoned issues after a period of inactivity. (You will only need to setup free plan). Configuration is here.
• Semantic Versions specification with Release Drafter.

Installation

pip install -U np-validator

or install with Poetry

poetry add np-validator

Makefile usage

Makefile contains a lot of functions for faster development.

make poetry-download

make poetry-remove

make install

make pre-commit-install

make codestyle

# or use synonym
make formatting

make check-codestyle

make update-dev-deps

make check-safety

make check-safety

make mypy

make test

make lint

make test && make check-codestyle && make mypy && make check-safety

make docker-build

make docker-build VERSION=latest

make docker-remove

make pycache-remove

make build-remove

make dsstore-remove

make mypycache-remove

make cleanup

📈 Releases

You can see the list of available releases on the GitHub Releases page.

We follow Semantic Versions specification.

We use Release Drafter. As pull requests are merged, a draft release is kept up-to-date listing the changes, ready to publish when you’re ready. With the categories option, you can categorize pull requests in release notes using labels.

List of labels and corresponding titles

You can update it in release-drafter.yml.

GitHub creates the bug, enhancement, and documentation labels for you. Dependabot creates the dependencies label. Create the remaining labels on the Issues tab of your GitHub repository, when you need them.

🛡 License

This project is licensed under the terms of the MIT license. See LICENSE for more details.

📃 Citation

@misc{np-validator,
  author = {np-validator},
  title = {Awesome `np-validator` is a Python cli/package created with https://github.com/TezRomacH/python-package-template},
  year = {2022},
  publisher = {GitHub},
  journal = {GitHub repository},
  howpublished = {\url{https://github.com/np_validator/np-validator}}
}

Credits

This project was generated with python-package-template