Beerxml

Description

The beerxml python package is a powerful tool designed to streamline the process of working with BeerXML files, ensuring that specific beer recipes adhere to the BeerXML standard. BeerXML is a widely adopted XML-based standard for representing brewing recipes, allowing brewers to share and exchange their recipes seamlessly.

Installation

To install your package, use the following command:

pip install beerxml

Code Generation

The models in this beerxml package have been generated from this beerxml json schema with this command:

 datamodel-codegen  --input .\schemas\Recipe.json --input-file-type jsonschema --output output.py --snake-case-field --capitalise-enum-members --use-annotated  --use-non-positive-negative-number-constrained-types --output-model-type pydantic_v2.BaseModel

Usage

Import the BeerxmlParser class and inject the file content into the class like in the following example:

from beerxml import BeerxmlParser

# Example usage
parser: BeerxmlParser = BeerxmlParser()
with open("path/to/xml.file", "r") as file_content:
    recipe = parser.parse(file_content.read())

print(recipe.name)
print(recipe.hops)
print(recipe.fermentables)
print(recipe.yeasts)

Contributing

We welcome contributions from the community! If you would like to contribute to this project, please follow these guidelines:

Reporting Issues

If you encounter any issues or have suggestions, please open an issue on our GitHub repository.

Pull Requests

1. Fork the repository and create a new branch for your contribution.
2. Write clear and concise commit messages.
3. Include unit tests for your changes
4. Submit a pull request, clearly describing the changes you've made and providing any necessary context.

Linting and Code Style

We use the following linters to maintain code quality:

• Ruff: for formatting.
• Bandit: for security checks.
• Mypy: for type checking.

Before submitting a pull request, ensure that your code passes these checks.

Unit Testing

All contributions must be accompanied by unit tests using pytest. Test coverage is crucial to ensure the reliability of the codebase.

To run tests locally:

pytest .