Metadata-Version: 2.1
Name: decoripy
Version: 0.0.3
Summary: Decoripy helps you making better decorators.
Home-page: https://github.com/gioelecrispo/decoripy.git
Author: Gioele Crispo
Author-email: crispogioele@gmail.com
License: MIT
Description: # Decoripy
        decoripy provides a well-structured template class for creating Python decorators. It uses inheritance to be efficient 
        and simple.
        
        ![Python 2.7, 3.5, 3.6, 3.7](https://img.shields.io/badge/python-%203.3%2C%203.4%2C%203.5%2C%203.6%2C%203.7-blue.svg)
        [![PyPI version](https://badge.fury.io/py/decoripy.svg)](https://badge.fury.io/py/decoripy)
        [![Build Status](https://travis-ci.org/gioelecrispo/decoripy.svg?branch=master)](https://travis-ci.org/gioelecrispo/decoripy)
        
        ## Table of contents
        1. Motivation
        2. Usage
        
        ## 1. Motivation
        With decoripy, writing a decorator becomes very easy. It aims to improve the Python language expressiveness by 
        enhancing a very powerful Python mechanism.
        
        Decoripy provides a template built upon the basic wrapping of a function, hiding the implementation details, and
        providing some useful advantages:
         - no distinction between decorator with or without arguments has to be done;
         - a temporal based execution is provided. 
        
        ### Decorator arguments
        With decoripy you could create decorator with or without arguments with no pain.
        In standard Python you should handle the arguments passed to the decorator, because, in this case, the wrapper 
        function does not take the function as a the first argument.
        So you could do something like this:
        ```python
        @MyDecorator
        def function_to_decorate(var):
            pass
        ```
        or 
        ```python
        @MyDecorator(True)
        def function_to_decorate(var):
            pass
        ```
        or 
        ```python
        @MyDecorator(timeout=3000, num_retries=3)
        def function_to_decorate(var):
            pass
        ```
        or 
        ```python
        @MyDecorator(True, timeout=3000, num_retries=3)
        def function_to_decorate(var):
            pass
        ```
        and you have not to change your code. 
        The unnamed arguments (```*args```) passed to the decorator can be accessed by using the positional order (For example, 
        the first parameters could be taken in this way: ```first_arg = args[0]```, see Usage).
        The named arguments (```**kwargs```) passed to the decorator are parsed and can be accessed by their name (For example, 
        timeout could be used in the implementation code in this way: ```self.timeout```, see Usage).
        
        
        ### Temporal based execution
        The decoripy template is built to provide temporal based execution:
         - you could execute a pre-operation **before** the decorated function is executed;
         - you could do some operation **while** the decorated function is executed;
         - you could execute a post-operation **after** the decorated function is executed.
        
        In this way you can control the execution flow of the decorated function.
        
        ### Nested decorator
        You could nest more decorator. The order respects the writing order, so:
         ```python
        @First(timeout=3000)
        @Second
        def function_to_decorate(var):
            pass
        ```
        @First is executed before; 
         
        ## 2. Usage
        In order to create a new decorator, you have only to write a new class inheriting from the
        abstract class AbstractDecorator, and overriding the following (optional) methods:
         - ```__do_before__```: 
         - ```__do__```: it is mandatory doing the ```self.function(*args, **kwargs)``` call here to trigger the
         decorated function execution.
         - ```__do_after__```:
        
        
        The overriding of the three functions is optional. Clearly, no overriding means no
        operations done upon the decorated function.
        Summarizing, you have only to handle the temporal phases you are interested on.
         
        ---
         
        Example 1 - No decorator arguments:
        
        ```python
        from decoripy import AbstractDecorator
        
        
        class DecoratorWithoutArguments(AbstractDecorator):
            
            def __do_before__(self, *args, **kwargs):
                print("Executing: __do_before__")
                return "Executed: __do_before__"
        
            def __do__(self, *args, **kwargs):
                print(self.before_result, ", Executing: __do__")
                function_result = self.function(*args, **kwargs)
                return function_result + ", Executed: __do__"
        
            def __do_after__(self, *args, **kwargs):
                print(self.execution_result, ", Executing: __do_after__")
                return "Executed: __do_after__"
        
        
        @DecoratorWithoutArguments
        def function_to_decorate(var1, var2, dict_var1, dict_var2):
            print("Executing: function: ", var1, var2, dict_var1, dict_var2)
            return "Executed: function"
        
        
        function_to_decorate(1, "var2", dict_var1=[1, 2, 3], dict_var2={"key": "value"})
        ```
        
        
Platform: UNKNOWN
Classifier: Development Status :: 5 - Production/Stable
Classifier: Intended Audience :: Developers
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.4
Classifier: Programming Language :: Python :: 3.5
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: Implementation :: PyPy
Classifier: Topic :: Software Development :: Libraries :: Python Modules
Requires-Python: >=2.7
Description-Content-Type: text/markdown
