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FQN Decorators

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Installation

At the command line:

$ pip install fqn-decorators

Usage

Introduction

By extending the ~Decorator class you can create simple decorators. Implement the ~Decorator.before and/or ~Decorator.after methods to perform actions before or after execution of the decorated item. The ~Decorator.before method can access the arguments of the decorated item by changing the ~Decorator.args and ~Decorator.kwargs attributes. The ~Decorator.after method can access or change the result using the ~Decorator.result attribute. The ~Decorator.exception method can be used for do something with an Exception that has been raised. In all three methods the ~Decorator.fqn and ~Decorator.func attributes are available.

Simple decorator

Create a simple decorator:

import fqn_decorators
import time

class time_it(fqn_decorators.Decorator):

    def before(self):
        self.start = time.time()

    def after(self):
        duration = time.time() - self.start
        print("{0} took {1} seconds".format(self.fqn, duration))


@time_it
def my_function():
    time.sleep(1)

>>>my_function()
__main__.my_function took 1.00293397903 seconds

Decorator with arguments

It is also very easy to create a decorator with arguments.

Note

It is not possible to create decorators with non-keyworded arguments. To create a decorator that supports non-keyworded arguments see the Advanced Usage <usage_advanced_non_keyword_decorators> section.

Example:

import fqn_decorators
import time

class threshold(fqn_decorators.Decorator):

    def before(self):
        self.start = time.time()

    def after(self):
        duration = time.time() - self.start
        treshold = self.params.get('threshold')
        if threshold and duration > threshold:
            raise Exception('Execution took longer than the threshold')


@threshold(threshold=2)
def my_function():
    time.sleep(3)

>>> my_function()
Exception: Execution took longer than the threshold

About

Easily create multi-purpose decorators that have access to the FQN of the original function.

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  • Python 92.0%
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