Decko is a decorator-based library designed for the following purposes
- Simplify the creation of decorators and reduce boilerplate
- Change the behavior of existing classes and function with minimal modifications
- Manage and monitor the state of instantiated decorators
- Provide a rich set of pre-made and tested decorators
Decko is not dependent on any external libraries that are not included in the standard Python package. However, one may choose to extend with external libraries such as numba to improve its performance.
Self-contained decorators that can be used without creating a Decko instance is under development.
In the most recent release, support for default keyword args is supported.
Additionally, support for @classmethod, @staticmethod and class instance method is also added.
Install and update using pip:
pip install -U deckoUninstall using pip:
pip uninstall deckodeckorator creates function decorators that can be used to decorate both functions and
classes. Demo for creating class and function decorators is shown below.
from decko import deckorator
import time
import typing as t
def timer(func):
"""
An ordinary decorator.
Will be used to check the
performance of decorate function
"""
def inner(*args, **kwargs):
start_time = time.time()
output = func(*args, **kwargs)
elapsed = time.time() - start_time
print(f"Time elapsed: {elapsed}")
return output
return inner
# Create decorator called "time_it" that accepts the following args
# 1. Int or float value
# 2. A callable object or a List
@deckorator((int, float), (t.Callable, t.List))
def time_it(wrapped_function,
interval,
callback,
*args, **kwargs):
print(f"wrapped function: {wrapped_function.__name__}, interval: {interval},"
f" args: {args}")
# Check every 5 interval
i = time_it.called
if (i + 1) % interval == 0:
start_time = time.time()
output = wrapped_function(*args, **kwargs)
elapsed = time.time() - start_time
callback(elapsed, i + 1)
else:
output = wrapped_function(*args, **kwargs)
time_it.called += 1
return output
time_it.called = 0
@deckorator(is_class_decorator=True)
def immutable(wrapped_class,
*args,
**kwargs):
def do_freeze(slf, name, value):
msg = f"Class {type(slf)} is immutable. " \
f"Attempted to set attribute '{name}' to value: '{value}'"
raise AttributeError(msg)
class Immutable(wrapped_class):
"""
A basic immutable class
"""
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
setattr(Immutable, '__setattr__', do_freeze)
return Immutable(*args, **kwargs)
@immutable
class SampleClass:
def __init__(self, a, teemo):
self.a = a
@time_it(1, print)
@classmethod
def method(cls):
return "yee"
if __name__ == "__main__":
deco_cls = SampleClass(10, 20)
deco_cls.method()
test = SampleClass(20, 40)
try:
deco_cls.a = 50
except AttributeError:
print("class is immutable")
print(deco_cls.a)Decko is a decorated-based module for debugging. It also provides useful decorators to speed up programming and provides utility function for easier decorator usage. Here is an example
from src.decko import Decko
if __name__ == "__main__":
# Create decko instance
dk = Decko(__name__)
# Attach a profiling function
@dk.profile
def create_list(n):
return list(range(n))
for i in range(20):
create_list(100000)
# print profiled result
dk.print_profile()Decko also provides standalone decorators that can be applied immediately to your projects. It also has built-in decorator functions to help developers quickly build debuggable custom decorators. This allows developers to modify and extend code with minimal modifications to the existing codebase.
Decko detects and raises customized, informative errors such as DuplicateDecoratorError.
This helps in debugging and extending features with minimal modifications to the existing
codebase.
from decko import Decko
dk = Decko(__name__, debug=True)
def log_impurity(argument, before, after):
print(f"Argument: {argument} modified. Before: {before}, after: {after}")
def i_run_before(a, b, c, item):
print(f"Run before func: {a}, {b}, {c}, {item}")
@dk.run_before(i_run_before) # This should not be allowed since it is a duplicate
@dk.run_before(i_run_before)
@dk.pure(callback=log_impurity)
@dk.profile
def expensive_func(a,
b,
c=1000000,
item=[]):
for i in range(100):
temp_list = list(range(c))
item.append(temp_list)
a += 20
b += a
total = a + b
return total
class DummyClass:
def __init__(self, item):
self.item = item
# @dk.pure(log_impurity)
# @dk.profile
def set_item(self, item):
self.item = item
def __repr__(self):
return f'DummyClass: {self.item}'
test = DummyClass(10)
test.set_item(20)
# Error raised
output = expensive_func(10, 20, 40)Decko raises informative error messages to help debug issues. In later versions, features to define error callbacks with custom exceptions will be made.
Traceback (most recent call last):
File "path", line 17, in <module>
def expensive_func(a,
File "path", line 522, in wrapper
fn: t.Callable = self._decorate(self.run_before, fn)
File "path", line 334, in _decorate
self.add_decorator_rule(decorator_func, func)
File "path", line 241, in add_decorator_rule
self._add_function_decorator_rule(decorator_func,
File "path", line 213, in _add_function_decorator_rule
self._update_decoration_info(decorator_func, func, properties)
File "path", line 490, in _update_decoration_info
self.handle_error(f"Found duplicate decorator with identity: {func_name}",
File "path", line 325, in handle_error
raise error_type(msg)
src.decko.exceptions.DuplicateDecoratorError: Found duplicate decorator with identity: __main__.expensive_func- Documentation: https://github.com/JWLee89/decko/wiki
- Github: https://github.com/JWLee89/decko