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A collection of Python decorators and utilities to abstract away common/tedious Python patterns.


This package is more about providing interesting abstractions and trying to flesh out the possibilities of Python code organization. I am in no way saying that using these functions will provide "good" code and I am in no way condoning their use for creating evil Python code ;).

Some of the functions in here may incentivize less understandable code, but that's okay. I want to give them space to exist and hopefully we can develop them further to where they will be more understandable and provide more intuitive abstractions.

This package is about experimentation and trying to create basic, interesting, natural feeling, and convenient abstractions while sidelining the scrutiny of stuck-up Python purists :stuck_out_tongue_closed_eyes:.

So, I guess the motto of this package is to develop freely, but use responsibly. <3

One other thing to note though, some of these don't play nice with linters 😢


import wrappingpaper as wp

def doing_something(a, b):

# por que no los dos?

# you can do this
with doing_something(4, 5):
# prints 4 1 5

# as well as this
@doing_something(4, 5)
def something():
# prints 4 1 5



pip install wrappingpaper


import wrappingpaper as wp


NOTE: I haven't put in the work to mock logging objects for testing so beware that in their current form they are untested and most likely have 1 or 2 bugs in there.

I was working on a project that was full of error suppression and logging. There would be functions wrapped in try except blocks, logging calls, and a lot of redundancy in the scaffolding needed.

So I did work to factor that out and perform many of the common patterns in decorators.

The logging decorators here are primarily for functions that can be permitted to fail and return a default/empty value without the rest of the program breaking.

It also has utilities for pulling information from tracebacks. I haven't done anything about the logging Handlers and Formatters so that's a TODO.

import logging
log = logging.getLogger(__name__)

# handle and log error

@wp.log_error_as_warning(log, default=dict)
def get_stats(x=None):
    if x is True:
        raise ValueError() # some error happens
    return {'a': 5, 'b': 6}

assert get_stats() == {'a': 5, 'b': 6}
assert get_stats(True) == {}
Roughly equivalent to:
def get_stats(x=None):
        if x is True:
            raise ValueError() # some error happens
        return {'a': 5, 'b': 6}
    except ValueError as e:
        log.warning('Exception in get_stats: %s', e)
        return {}

Context Managers

Two common patterns in Python are context managers and decorators. Often, they have the same basic structure: do some initialization, run a function, and do some cleanup.

And both can be useful in different contexts to give you clean code, but to use both, I often find myself writing an additional wrapper function around the context manager, and then you have to give it a slightly different name and it can get confusing.

So, in comes contextdecorator which works the same as contextlib.contextmanager, but it also doubles as a function decorator. When used as a decorator, it will call the function inside the context manager.

def doing_something(a, b):

# por que no los dos?

# you can do this
with doing_something(4, 5):

# as well as this
@doing_something(4, 5)
def something():

Sometimes, your decorator isn't as simple and you need to do things a bit differently in the decorator (e.g. you need the name of the wrapped function).

@doing_something.caller # override default decorator
def doing_something(func, a, b): # wrapped function, decorator arguments
    # change arguments
    name = func.__name__
    a = 'calling {}: {}'.format(name, a)
    b = 'calling {}: {}'.format(name, b)

    # return the wrapped function
    def inner(*args, **kw):
        with doing_something(a, b):
            return func(*args, **kw)
    return inner
Roughly equivalent to:
import functools
from contextlib import contextmanager

def doing_something(a, b):

def doing_something2(a, b):
    def outer(func):
        def inner(*a, **kw):
            with doing_something(a, b):
                return func(*a, **kw)
        return inner
    return outer

# used like:
with doing_something(4, 5):

@doing_something2(4, 5)
def something():


Python property objects are incredibly useful as they allow you to create natural feeling objects with some complex stuff all bundled up in a nice unsuspecting interface.

But using them, there are often times where I find myself writing the same classes stored many times over in utility files.

One use-case is caching. There are different levels of caching that you can provide.

  • cachedproperty: cached on the instance object - runs once per instance
  • onceproperty: cached on the class object - runs once per class/baseclass
  • overridable_property: works as a normal property (calls the wrapped function), until the property is assigned to. Then it returns the assigned value.
  • overridable_method: works as a normal method (calls the wrapped function), until the function is called as a decorator. Then it calls the wrapped function. Works on an instance level.
import time

class SomeClass:
    def instance_prop(self):
        '''This is run once per object instance.'''
        return time.time()

    def class_prop(self):
        '''This is run once. It is cached in the property
        object itself.'''
        return time.time()

    def overridable(self):
        '''This property is run normally, until another value is assigned on top.'''
        return time.time()

    def __init__(self, overridable=None):
        if overridable: # override the property value
            # stores at self._overridable
            self.overridable = overridable
        # otherwise it just uses the property function like usual

a = SomeClass()
b = SomeClass()

assert a.instance_prop != b.instance_prop # prop runs once per object
assert a.class_prop == b.class_prop # prop runs only once
assert a.overridable != a.overridable # gets called twice, shouldn't be the same
a.overridable = 5
assert a.overridable == 5 # now the value is overridden

assert SomeClass(5).overridable == 5 # overriding inside class

Function Signature

This is something that I'm looking for constantly.

Personally, I like the idea of config files that wrap up a bunch of function arguments into a file.

I also hate having to duplicate arguments when passing variables down 5 levels of nested function calls.

I like to just pass keyword arguments (**kw) down to the next function.

But there are cases, where there are extra config values in your keyword dict and you only want to pass the values that your function takes.

# dynamic function defaults

def asdf(a=5, b=6, c=7):
    return a + b + c

assert asdf() == 5+6+7 # normal behavior
assert asdf() == 1+6+7 # updated default
assert asdf(3) == 3+6+7 # automatically resolves kwargs and posargs
assert asdf() == 5+6+7 # back to normal behavior

# filter out kwargs not in the signature (if **kw, it's a no-op).

def asdf(a=5, b=6, c=7):
    return a + b + c

assert asdf(b=10, d=1234) == 5+10+7


class Blah:
    def asdf(self):
        return 10

b = Blah()

def asdf():
    return 11

assert asdf() == 11
asdf.reset() # remove patch
assert asdf() == 10
asdf.repatch() # re-place the patch
assert asdf() == 11


# make sure that a for loop doesn't go too fast.
# limit the time one iteration takes.

# limiting the number of iterations to 10.
# by default it loops infinitely.

for dt, time_asleep in wp.limit(wp.throttled(1), 10):
    print('Iteration took {}s. Had to sleep for {}s.'.format(dt, time_asleep))

# check the first n items in an iterable, without removing them.

it = iter(range(6))
items, it = wp.pre_check_iter(it, 3)
assert items == [0, 1, 2]
assert list(it) == [0, 1, 2, 3, 4, 5, 6]

# repeat and chain iterables infinitely

import random

def get_numbers():
    return [random.random() for _ in range(10)]

numbers = wp.run_iter_forever(get_numbers)
# repeat get_numbers() and chain iterable outputs together
all_numbers = list(wp.limit(numbers, 100))
assert all(isinstance(x, float) for x in all_numbers)

# repeat and chain infinitely. If no items are returned by a call,
# instead of the iterable hanging indefinitely waiting for an item,
# return None.

def get_numbers():
    if random.random() > 0.8: # make random breaks
        return # returns empty
    return [random.random() for _ in range(10)]

numbers = wp.run_iter_forever(get_numbers, none_if_empty=True)
# this SHOULD contain sporadic None's at a multiple of 10
all_numbers = list(wp.limit(numbers, 5000))
assert None in all_numbers

# A wrappable alternative to `while True:`

for _ in wp.infinite():
    print('this is gonna be a while...')

Import Mechanics

This is probably the most dangerous thing to be playing with in here.

Python exposes a lot of its internal mechanics including its import system.

So we can take advantage of that to provide import wrappers that modify module behavior.

A basic example - lazy loading:

# lazyimport/
import wrappingpaper as wp

from lazyimport import sklearn.model_selection

# sklearn is not currently loaded

sklearn.model_selection.train_test_split() # now it's loaded.

I'll try to think up another simple example with the actual implementation shown.


Some other miscellaneous stuff that I have yet to organize.

import random

# retry a function if an exception is raised

def asdf():
    x = random.random()
    if x < 0.5:
        raise ValueError
    return x

# will either return a number that is definitely > 0.5
# or every number in the first 10 tries were below 0.5
    assert asdf() > 0.5
except ValueError:
    print("Couldn't get a number :/")

# ignore error

with wp.ignore():
    a, b = 5, 0
    c = a / b # throws divide by zero
    a = 10 # never run
assert a == 5

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