pedantic 1.4.9

Some useful Python decorators for cleaner software development.

pedantic-python-decorators

These decorators will make you write cleaner and well-documented Python code.

Getting Started

This package requires Python 3.6.1 or later. There are multiple options for installing this package.

Option 1: Installing with pip from Pypi

Run pip install pedantic.

Option 2: Installing with pip and git

1. Install Git if you don't have it already.
2. Run pip install git+https://github.com/LostInDarkMath/pedantic-python-decorators.git@master

Option 3: Offline installation using wheel

1. Download the latest release here by clicking on pedantic-python-decorators-x.y.z-py-none-any.whl.
2. Execute pip install pedantic-python-decorators-x.y.z-py3-none-any.whl.

The @pedantic decorator

The @pedantic decorator does the following things:

• The decorated function can only be called by using keyword arguments. Positional arguments are not accepted.
• The decorated function must have Type annotations.
• Each time the decorated function is called, pedantic checks that the passed arguments and the return value of the function matches the given type annotations. As a consequence, the arguments are also checked for None, because None is only a valid argument, if it is annotated via typing.Optional.
• If the decorated function has a docstring which lists the arguments, the docstring is parsed and compared with the type annotations. In other words, pedantic ensures that the docstring is everytime up-to-date. Currently, only docstrings in the Google style are supported.

In a nutshell: @pedantic raises an PedanticException if one of the following happened:

• The decorated function is called with positional arguments.
• The function has no type annotation for their return type or one or more parameters do not have type annotations.
• A type annotation is incorrect.
• A type annotation misses type arguments, e.g. typing.List instead of typing.List[int].
• The documented arguments do not match the argument list or their type annotations.

Minimal example

from typing import Union, List
from pedantic import pedantic, pedantic_class

@pedantic
def get_sum_of(values: List[Union[int, float]]) -> Union[int, float]:
    return sum(values)

@pedantic_class
class MyClass:
    def __init__(self, x: float, y: int) -> None:
        self.x = x
        self.y = y

    def print_sum(self) -> None:
        print(get_sum_of(values=[self.x, self.y]))

m = MyClass(x=3.14, y=2)
m.print_sum()

The @validate decorator

As the name suggests, with @validate you are able to validate the values that are passed to your function. That is done in a highly customizable way. But the highest benefit of this decorator the opportunity to decouple your code easily and make easy testable, maintainable and scalable. The following example shows the decoupled implementation of a configurable algorithm with the help of @decorate:

import os
from dataclasses import dataclass

from pedantic import validate, ExternalParameter, overrides, Validator, ValidationError


@dataclass(frozen=True)
class Configuration:
    iterations: int
    max_error: float


class ConfigurationValidator(Validator):
    @overrides(Validator)
    def validate(self, value: Configuration) -> Configuration:
        if value.iterations < 1 or value.max_error < 0:
            raise ValidationError(f'Invalid configuration: {value}')

        return value


class ConfigFromEnvVar(ExternalParameter):
    """ Reads the configuration from environment variables. """

    @overrides(ExternalParameter)
    def load_value(self) -> Configuration:
        return Configuration(
            iterations=int(os.environ['iterations']),
            max_error=float(os.environ['max_error']),
        )


class ConfigFromFile(ExternalParameter):
    """ Reads the configuration from a config file. """

    @overrides(ExternalParameter)
    def load_value(self) -> Configuration:
        with open(file='config.csv', mode='r') as file:
            content = file.readlines()
            return Configuration(
                iterations=int(content[0].strip('\n')),
                max_error=float(content[1]),
            )


# choose your configuration source here:
@validate(ConfigFromEnvVar(name='config', validators=[ConfigurationValidator()]), strict=False)
# @validate(ConfigFromFile(name='config', validators=[ConfigurationValidator()]), strict=False)

# with strict_mode = True (which is the default) 
# you need to pass a Parameter for each parameter of the decorated function 
# @validate(Parameter(name='value') ConfigFromFile(name='config', validators=[ConfigurationValidator()]))
def my_algorithm(value: float, config: Configuration) -> float:
    """
        This method calculates something that depends on the given value with considering the configuration.
        Note how well this small piece of code is designed:
            - Fhe function my_algorithm() need a Configuration but has no knowledge where this come from.
            - Furthermore, it need does not care about parameter validation.
            - The ConfigurationValidator doesn't now anything about the creation of the data.
            - The @validate decorator is the only you need to change, if you want a different configuration source.
    """
    print(value)
    print(config)
    return value


if __name__ == '__main__':
    # we can call the function with a config like there is no decorator.
    # This makes testing extremely easy: no config files, no environment variables or stuff like that
    print(my_algorithm(value=2, config=Configuration(iterations=3, max_error=4.4)))

    os.environ['iterations'] = '12'
    os.environ['max_error'] = '3.1415'

    # but we also can omit the config and load it implicitly by our custom Parameters
    print(my_algorithm(value=42.0))

List of all decorators in this package

• @count_calls
• @deprecated
• @does_same_as_function
• @for_all_methods
• @mock
• @overrides
• @pedantic
• @pedantic_require_docstring
• @pedantic_class
• @pedantic_class_require_docstring
• @rename_kwargs
• @require_kwargs
• @timer
• @timer_class
• @trace
• @trace_class
• @trace_if_returns
• @unimplemented
• @validate

Dependencies

Outside the Python standard library, the following dependencies are used:

• Docstring-Parser

To use the FlaskParameter class or subclasses, you obviously need to have Flask installed.

Contributing

Feel free to contribute by submitting a pull request :)

Acknowledgments

• Rathaustreppe
• Aran-Fey
• user395760

Risks and side effects

The usage of decorators may affect the performance of your application. For this reason, I would highly recommend you to disable the decorators if your code runs in a productive environment. You can disable pedantic by set an environment variable:

export ENABLE_PEDANTIC=0

You can also disable or enable the environment variables in your project by calling a method:

from pedantic import enable_pedantic, disable_pedantic
enable_pedantic()

Don't forget to check out the documentation. Happy coding!