vval 2.1.8

Input validation in Python

vval: value validation

The vval module provides functions for input validation in python.

Table of Contents

• Installation
• Usage
  • Basic Type Validation
  • Iterable Validation
  • Option Validation
  • Filter Validation
• Functionality Examples
• Notes
• Development and Publishing
  • Testing
  • CI/CD
  • Publishing a New Version

Installation

pip install vval

Usage

Import and use the validate, validate_iterable, validate_option, and validate_filter functions from the vval module.

Basic Type Validation

from typing import Union
from vval import validate

def f(x: int | str) -> None:
    validate(x, (int, str))
    ...

def g(x: Union[int, float]) -> None:
    validate(x, Union[int, float])
    ...

def h(x: Union[list, tuple] | dict | set) -> None:
    validate(x, (Union[list, tuple], dict, set))
    ...

Iterable Validation

from typing import Callable, Union
from vval import validate_iterable

def i(x: list[int | str | dict]) -> None:
    validate_iterable(x, (int, str, dict))
    ...

def j(x: list[int | str | dict | Union[float, Callable]]) -> None:
    validate_iterable(x, (int, str, dict, Union[float, Callable]))
    ...

Option Validation

from vval import validate_option

def k(x: str) -> None:
    validate_option(x, ["apple", "banana", "cherry"])
    ...

Filter Validation

from vval import validate_filter

def positive_filter(value):
    return value > 0

def l(x: int) -> None:
    validate_filter(x, positive_filter)
    ...

Functionality Examples

from vval import validate

y: list = [1, 2, 3]
validate(y, list) # True

x: list = [1, 2, 3]
validate(x, int) # Raises TypeError: Expected 'int' for `x`, got: 'list'.

Notes

• The API is still experimental and subject to changes.
• Will not validate beyond a certain depth.
• Will not validate generic types (except Union).

Currently provided functions:

• validate: Validate that an element is of a specified type.
• validate_iterable: Validate that all elements in an iterable are of a specified type.
• validate_option: Validates that a value is among a set of options.
• validate_filter: Validates that a value passes a specified filter function.

Development and Publishing

Testing

Run tests locally:

pip install .[dev]
pytest

CI/CD

This project uses GitHub Actions for Continuous Integration and Continuous Deployment:

1. Pull Request Checks: Automatically run tests on all pull requests to the main branch.
2. Automated Publishing: Triggers package build and publication to PyPI when a new version tag is pushed.

Publishing a New Version

1. Create a new branch for the version bump:

   git checkout -b bump-vx.y.z
   

2. Update the version in setup.py following Semantic Versioning.

3. Commit changes:

   git add setup.py
   git commit -m "pack: bump version to x.y.z"
   

4. Create and push a new tag on the bump branch:

   git tag vx.y.z
   git push origin bump-vx.y.z --tags
   

   Replace x.y.z with the new version number.

5. Push the branch and create a pull request:

   git push origin bump-vx.y.z
   

   Then create a pull request on GitHub from this branch to main.

6. After the pull request is approved and merged, the tag will be part of the main branch.

7. To publish the new version to PyPI:

   • Go to the "Actions" tab in your GitHub repository
   • Select the "Publish Python distribution to PyPI" workflow
   • Click "Run workflow"
   • Enter the version tag you created (e.g., v1.2.3) and click "Run workflow"

8. The GitHub Action will build, test, and publish the new version to PyPI based on the specified tag.

Note: This process allows you to control exactly when the package is published. You can create and push tags on feature branches without triggering the publish process, and you can choose to publish specific tags at any time using the manual workflow trigger. The tag becomes part of the main branch history when the pull request is merged.