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Easy peasy Python decorators

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.. image::

Easy-peasy Python decorators!

* GitHub:
* PyPI:
* Docs:
* Contact: ``msplanchard`` ``@`` ``gmail`` or @msplanchard on Twitter


Decorators are great, but they're hard to write, especially if you want
to include arguments to your decorators, or use your decorators on
class methods as well as functions. I know that, no matter how many I write,
I still find myself looking up the syntax every time. And that's just for
simple function decorators. Getting decorators to work consistently at the
class and method level is a whole 'nother barrel of worms.

PyDecor aims to make function decoration easy and straightforward, so that
developers can stop worrying about closures and syntax in triply nested
functions and instead get down to decorating!

IMPORTANT: Upcoming Backwards Incompatible Changes

Version 2.0.0 will make some changes to the call signatures for functions
passed to ``@before``, ``@after``, ``@instead``, ``@decorate``, and
``construct_decorator``, as well as to the call signatures to the
decorators themselves.

Specifically, rather than defaulting the call signature to some subset
of decorated function args, kwargs, result, and the decorated function
itself and allowing overrides with keyword arguments
to the decorator like ``pass_params``, all functions passed to ``@before``,
``@after``, and ``@instead`` will receive an immutable ``Decorated``
object, which will have ``args``, ``kwargs``, ``wrapped``, and ``result``
attributes, and which will support direct calls as though it were the
decorated function/method/class. The aim of this is to make writing functions
to pass to the decorators more intuitive, but it will require some minor
re-writing of passed functions.

You can experiment with this syntax and prepare for the cut-over right away
by passing ``_use_future_syntax=True`` to any of your generic decorators
(``@after``, ``@before``, etc.) or to ``construct_decorator``. See the below
snippet to illustrate basic use of the new ``Decorated`` object:

.. code:: python

from pydecor import after, Decorated

def after_func(decorated: Decorated, extra_kwarg=None):
"""A function to be called after the decorated function"""
assert decorated.args == ('foo', )
assert decorated.kwargs == {'bar': 'bar'}
assert decorated.result == 'baz'
assert extra_kwarg == 'extra_kwarg'

@after(after_func, extra_kwarg='extra_kwarg')
def some_function('foo', bar='bar'):
"""A function that returns 'baz'"""
return 'baz'

All of the builtin non-generic decorators (``@memoize``, ``@intercept``,
and ``@log_call``) are already using the future syntax, so feel free
to look at those for more examples.

See the API docs for more information.

.. contents:: Table of Contents


Install ``pydecor``::

pip install pydecor

Use one of the ready-to-wear decorators:

.. code:: python

# Memoize a function

from pydecor import memoize

def fibonacci(n):
"""Compute the given number of the fibonacci sequence"""
if n < 2:
return n
return fibonacci(n - 2) + fibonacci(n - 1)


.. code:: python

# Intercept an error and raise a different one

from flask import Flask
from pydecor import intercept
from werkzeug.exceptions import InternalServerError

app = Flask(__name__)

@intercept(catch=Exception, reraise=InternalServerError,
err_msg='The server encountered an error rendering "some_view"')
def some_view():
"""The root view"""
assert False
return 'Asserted False successfully!'

client = app.test_client()
response = client.get('/')

assert response.status_code == 500
assert 'some_view'.encode() in

Use a generic decorator to run your own functions ``@before``, ``@after``,
or ``@instead`` of another function, like in the following example,
which sets a User-Agent header on a Flask response:

.. code:: python

from flask import Flask, make_response
from pydecor import after

app = Flask(__name__)

def set_user_agent(view_result):
"""Sets the user-agent header on a result from a view"""
resp = make_response(view_result)
resp.headers.set('User-Agent', 'my_applicatoin')
return resp

def index_view():
return 'Hello, world!'

client = app.test_client()
response = client.get('/')
assert response.headers.get('User-Agent') == 'my_application'

Or make your own decorator with ``construct_decorator``

.. code:: python

from flask import request
from pydecor import construct_decorator
from werkzeug.exceptions import Unauthorized

def check_auth(request):
"""Theoretically checks auth

It goes without saying, but this is example code. You should
not actually check auth this way!
if != 'localhost':
raise Unauthorized('locals only!')

authed = construct_decorator(before=check_auth)

app = Flask(__name__)

def some_view():
"""An authenticated view"""
return 'This is sensitive data!'

Why PyDecor?

* **It's easy!**

With PyDecor, you can go from this:

.. code:: python

from functools import wraps
from flask import request
from werkzeug.exceptions import Unauthorized
from my_pkg.auth import authorize_request

def auth_decorator(request=None):
"""Check the passed request for authentication"""

def decorator(decorated):

def wrapper(*args, **kwargs):
if not authorize_request(request):
raise Unauthorized('Not authorized!')
return decorated(*args, **kwargs)
return wrapper

return decorated

def some_view():
return 'Hello, World!'

to this:

.. code:: python

from flask import request
from pydecor import before
from werkzeug.exceptions import Unauthorized
from my_pkg.auth import authorize_request

def check_auth(request=request):
"""Ensure the request is authorized"""
if not authorize_request(request):
raise Unauthorized('Not authorized!')

@before(check_auth, request=request)
def some_view():
return 'Hello, world!'

Not only is it less code, but you don't have to remember decorator
syntax or mess with nested functions. Full disclosure, I had to look
up a decorator sample to be sure I got the first example's syntax right,
and I just spent two weeks writing a decorator library.

* **It's fast!**

PyDecor aims to make your life easier, not slower. The decoration machinery
is designed to be as efficient as is reasonable, and contributions to
speed things up are always welcome.

* **Implicit Method Decoration!**

Getting a decorator to "roll down" to methods when applied to a class is
a complicated business, but all of PyDecor's decorators provide it for
free, so rather than writing:

.. code:: python

from pydecor import log_call

class FullyLoggedClass(object):

def some_function(self, *args, **kwargs):
return args, kwargs

def another_function(self, *args, **kwargs):
return None


You can just write:

.. code:: python

from pydecor import log_call

class FullyLoggedClass(object):

def some_function(self, *args, **kwargs):
return args, kwargs

def another_function(self, *args, **kwargs):
return None


PyDecor ignores special methods (like ``__init__``) so as not to interfere
with deep Python magic. By default, it works on any methods of a class,
including instance, class and static methods. It also ensures that class
attributes are preserved after decoration, so your class references
continue to behave as expected.

* **Consistent Method Decoration!**

Whether you're decorating a class, an instance method, a class method, or
a static method, you can use the same passed function. ``self`` and ``cls``
variables are stripped out of the method parameters passed to the provided
callable, so your functions don't need to care about where they're used.

* **Lots of Tests!**

Seriously. Don't believe me? Just look. We've got the best tests. Just


Supported Python versions are 2.7 and 3.4+

To install `pydecor`, simply run::

pip install -U pydecor

To install the current development release::

pip install --pre -U pydecor

You can also install from source to get the absolute most recent
code, which may or may not be functional::

git clone
pip install ./pydecor


Provided Decorators

This package provides generic decorators, which can be used with any
function to provide extra utility to decorated resources, as well
as prête-à-porter (ready-to-wear) decorators for immediate use.

While the information below is enough to get you started, I highly
recommend checking out the `decorator module docs`_ to see all the
options and details for the various decorators!


* ``@before`` - run a callable before the decorated function executes

* by default called with no arguments other than extras

* ``@after`` - run a callable after the decorated function executes

* by default called with the result of the decorated function and any

* ``@instead`` - run a callable in place of the decorated function

* by default called with the args and kwargs to the decorated function,
along with a reference to the function itself

* ``@decorate`` - specify multiple callables to be run before, after, and/or
instead of the decorated function

* callables passed to ``decorate``'s ``before``, ``after``, or ``instead``
keyword arguments will be called with the same default function signature
as described for the individual decorators, above. Extras will be
passed to all provided callables

* ``construct_decorator`` - specify functions to be run ``before``, ``after``,
or ``instead``. Returns a reusable generator.

* in addition to ``before``, ``after``, and ``instead``, which receive
callables, ``before_opts``, ``after_opts``, and ``instead_opts`` dicts
may be passed to ``construct_decorator``, and they will apply in the same
way as their respective decorator parameters

Every generic decorator takes any number of keyword arguments, which will be
passed directly into the provided callable, unless ``unpack_extras`` is False
(see below), so, running the code below prints "red":

.. code:: python

from pydecor import before

def before_func(label=None):

@before(before_func, label='red')
def red_function():


Every generic decorator takes the following keyword arguments:

* ``pass_params`` - if True, passes the args and kwargs, as a tuple and
a dict, respectively, from the decorated function to the provided callable
* ``pass_decorated`` - if True, passes a reference to the decorated function
to the provided callable
* ``implicit_method_decoration`` - if True, decorating a class implies
decorating all of its methods. **Caution:** you should probably leave this
on unless you know what you are doing.
* ``instance_methods_only`` - if True, only instance methods (not class or
static methods) will be automatically decorated when
``implicit_method_decoration`` is True
* ``unpack_extras`` - if True, extras are unpacked into the provided callable.
If False, extras are placed into a dictionary on ``extras_key``, which
is passed into the provided callable.
* ``extras_key`` - the keyword to use when passing extras into the provided
callable if ``unpack_extras`` is False
* ``_use_future_syntax`` - See the note at the top on backwards incompatible
changes in version 2.0.0.

The ``construct_decorator`` function can be used to combine ``@before``,
``@after``, and ``@instead`` calls into one decorator, without having to
worry about unintended stacking effects. Let's make a
decorator that announces when we're starting an exiting a function:

.. code:: python

from pydecor import construct_decorator

def before_func(decorated_func):
print('Starting decorated function '

def after_func(decorated_result, decorated_func):
print('"{}" gave result "{}"'.format(
decorated_func.__name__, decorated_result

my_decorator = construct_decorator(
before_opts={'pass_decorated': True},
after_opts={'pass_decorated': True},

def this_function_returns_nothing():
return 'nothing'

And the output?

.. code::

Starting decorated function "this_function_returns_nothing"
"this_function_returns_nothing" gave result "nothing"

Maybe a more realistic example would be useful. Let's say we want to add
headers to a Flask response.

.. python::

from flask import Flask, Response, make_response
from pydecor import construct_decorator

def _set_app_json_header(response):
# Ensure the response is a Response object, even if a tuple was
# returned by the view function.
response = make_response(response)
response.headers.set('Content-Type', 'application/json')
return response

application_json = construct_decorator(after=_set_app_json_header)

# Now you can decorate any Flask view, and your headers will be set.

app = Flask(__name__)

# Note that you must decorate "before" (closer to) the function than the
# app.route() decoration, because the route decorator must be called on
# the "finalized" version of your function

def root_view():
return 'Hello, world!'

client = app.test_client()
response = app.get('/')


The output?


Content-Type: application/json
Content-Length: 13

Prête-à-porter (ready-to-wear)

* ``intercept`` - catch the specified exception and optionally re-raise and/or
call a provided callback to handle the exception
* ``log_call`` - automatically log the decorated function's call signature and
* ``memoize`` - memoize a function's call and return values for re-use. Can
use any cache in ``pydecor.caches``, which all have options for automatic
pruning to keep the memoization cache from growing too large.

**More to come!!** See Roadmap_ for more details on upcoming features


Three caches are provided with ``pydecor``. These are designed to be passed
to the ``@memoization`` decorator if you want to use something other than
the default ``LRUCache``, but they are perfectly functional for use elesewhere.

All caches implement the standard dictionary interface.


A least-recently-used cache. Both getting and setting of key/value pairs
results in their having been considered most-recently-used. When the cache
reaches the specified ``max_size``, least-recently-used items are discarded.


A first-in, first-out cache. When the cache reaches the specified ``max_size``,
the first item that was inserted is discarded, then the second, and so on.


A cache whose entries expire. If a ``max_age`` is specified, any entries older
than the ``max_age`` (in seconds) will be considered invalid, and will be
removed upon access.


Generic and convenience decorators may be stacked! You can stack multiple
of the same decorator, or you can mix and match. Some gotchas are listed

Generally, staciking works just as you might expect, but some care must be
taken when using the ``@instead`` decorator, or ``@intercept``, which
uses ``@instead`` under the hood.

Just remember that ``@instead`` replaces everything that comes before. So,
if long as ``@instead`` calls the decorated function, it's okay to stack it.
In these cases, it will be called *before* any decorators specified below
it, and those decorators will be executed when it calls the decorated function.
``@intercept`` behaves this way.

If an ``@instead`` decorator does *not* call the decorated function and
instead replaces it entirely, it **must** be specified first (at the bottom
of the stacked decorator pile), otherwise the decorators below it will not

For ``@before`` and ``@after``, it doesn't matter in what order the decorators
are specified. ``@before`` is always called first, and then ``@after``.

Class Decoration

Class decoration is difficult, but PyDecor aims to make it as easy and
intuitive as possible!

By default, decorating a class applies that decorator to all of that class'
methods (instance, class, and static). The decoration applies to class and
static methods whether they are referenced via an instance or via a class
reference. "Extras" specified at the class level persist across calls to
different methods, allowing for things like a class level memoization
dictionary (there's a very basic test in the test suite
that demonstrates this pattern, and a convenient memoization decorator
is scheduled for the next release!).

If you'd prefer that the decorator not apply to class and static methods,
set the ``instance_methods_only=True`` when decorating the class.

If you want to decorate the class itself, and *not* its methods, keep in
mind that the decorator will be triggered when the class is instantiated,
and that, if the decorator replaces or alters the return, that return will
replace the instantiated class. With those caveats in mind, setting
``implicit_method_decoration=False`` when decorating a class enables that

.. note::

Class decoration, and in particular the decoration of class and static
methods, is accomplished through some pretty deep, complicated magic.
The test suite has a lot of tests trying to make sure that everything
works as expected, but please report any bugs you find so that I
can resolve them!

Method Decoration

Decorators can be applied to static, class, or instance methods directly, as
well. If combined with ``@staticmethod`` or ``@classmethod`` decorators,
those decorators should always be at the "top" of the decorator stack
(furthest from the function).

When decorating instance methods, ``self`` is removed from the parameters
passed to the provided callable.

When decorating class methods, ``cls`` is removed from the parameters passed
to the provided callable.

Currently, the class and instance references *do not* have to be named
``"cls"`` and ``"self"``, respectively, in order to be removed. However,
this is not guaranteed for future releases, so try to keep your naming
standard if you can (just FYI, ``"self"`` is the more likely of the two to
wind up being required).


Below are some examples for the generic and standard decorators. Please
check out the API Docs for more information, and also check out the
convenience decorators, which are all implemented using the
``before``, ``after``, and ``instead`` decorators from this library.

Update a Function's Args or Kwargs

Functions passed to ``@before`` can either return None, in which case nothing
happens to the decorated functions parameters, or they can return a tuple
of args (as a tuple) and kwargs (as a dict), in which case those parameters
are used in the decorated function. In this example, we sillify a very
serious function.

.. note::
Because kwargs are mutable, they can be updated even if the function
passed to before doesn't return anything.

.. code:: python

from pydecor import before

def spamify_func(args, kwargs):
"""Mess with the function arguments"""
args = tuple(['spam' for _ in args])
kwargs = {k: 'spam' for k in kwargs}
return args, kwargs

@before(spamify_func, pass_params=True)
def serious_function(serious_string, serious_kwarg='serious'):
"""A very serious function"""
print('A serious arg: {}'.format(serious_string))
print('A serious kwarg: {}'.format(serious_kwarg))

serious_function('Politics', serious_kwarg='Religion')

The output?

.. code::

A serious arg: spam
A serious kwarg: spam

Do Something with a Function's Return Value

Functions passed to ``@after`` receive the decorated function's return value
by default. If ``@after`` returns None, the return value is sent back
unchanged. However, if ``@after`` returns something, its return value is
sent back as the return value of the function.

In this example, we ensure that a function's return value has been thoroughly

.. code:: python

from pydecor import after

def spamify_return(result):
"""Spamify the result of a function"""
return 'spam-spam-spam-spam-{}-spam-spam-spam-spam'.format(result)

def unspammed_function():
"""Return a non-spammy value"""
return 'beef'


The output?

.. code::


Do Something Instead of a Function

Functions passed to ``@instead`` by default receive the args and kwargs of
the decorated function, along with a reference to that function. But, they
don't *have* to receive anything. Maybe you want to skip a function when
a certain condition is True, but you don't want to use ``pytest.skipif``,
because ``pytest`` can't be a dependency of your production code for
whatever reason.

.. code:: python

from pydecor import instead

def skip(args, kwargs, decorated, when=False):
if when:
return decorated(*args, **kwargs)

@instead(skip, when=True)
def uncalled_function():
print("You won't see me (you won't see me)")


The output?

(There is no output, because the function was skipped)

Automatically Log Function Calls and Results

Maybe you want to make sure your functions get logged without having to
bother with the logging boilerplate each time. ``@log_call`` tries to
automatically get a logging instance corresponding to the module
in which the decoration occurs (in the same way as if you made a call
to ``logging.getLogger(__name__)``, or you can pass it your own, fancy,
custom, spoiler-bedecked logger instance.

.. code:: python

from logging import getLogger, StreamHandler
from sys import stdout

from pydecor import log_call

# We're just getting a logger here so we can see the output. This isn't
# actually necessary for @log_call to work!
log = getLogger(__name__)

def get_schwifty(*args, **kwargs):
"""Get schwifty in heeeeere"""
return "Gettin' Schwifty"

get_schwifty('wubba', 'lubba', dub='dub')

And the output?

.. code::

get_schwifty(*('wubba', 'lubba'), **{'dub': 'dub'}) -> Gettin' Schwifty

Intercept an Exception and Re-raise a Custom One

Are you a put-upon library developer tired of constantly having to re-raise
custom exceptions so that users of your library can have one nice try/except
looking for your base exception? Let's make that easier:

.. code:: python

from pydecor import intercept

class BetterException(Exception):
"""Much better than all those other exceptions"""

@intercept(catch=RuntimeError, reraise=BetterException)
def sometimes_i_error(val):
"""Sometimes, this function raises an exception"""
if val > 5:
raise RuntimeError('This value is too big!')

for i in range(7):

The output?

.. code::

Traceback (most recent call last):
File "/Users/Nautilus/Library/Preferences/PyCharm2017.1/scratches/", line 88, in <module>
File "/Users/Nautilus/Documents/Programming/pydecor/pydecor/", line 389, in wrapper
return fn(**fkwargs)
File "/Users/Nautilus/Documents/Programming/pydecor/pydecor/", line 58, in intercept
raise_from(new_exc, context)
File "<string>", line 2, in raise_from
__main__.BetterException: This value is too big!

Intercept an Exception, Do Something, and Re-raise the Original

Maybe you don't *want* to raise a custom exception. Maybe the original
one was just fine. All you want to do is print a special message before
re-raising the original exception. PyDecor has you covered:

.. code:: python

from pydecor import intercept

def print_exception(exc):
"""Make sure stdout knows about our exceptions"""
print('Houston, we have a problem: {}'.format(exc))

@intercept(catch=Exception, handler=print_exception, reraise=True)
def assert_false():
"""All I do is assert that False is True"""
assert False, 'Turns out, False is not True'


And the output:

.. code::

Houston, we have a problem: Turns out, False is not True
Traceback (most recent call last):
File "/Users/Nautilus/Library/Preferences/PyCharm2017.1/scratches/", line 105, in <module>
File "/Users/Nautilus/Documents/Programming/pydecor/pydecor/", line 389, in wrapper
return fn(**fkwargs)
File "/Users/Nautilus/Documents/Programming/pydecor/pydecor/", line 49, in intercept
return decorated(*decorated_args, **decorated_kwargs)
File "/Users/Nautilus/Library/Preferences/PyCharm2017.1/scratches/", line 102, in assert_false
assert False, 'Turns out, False is not True'
AssertionError: Turns out, False is not True

Intercept an Exception, Handle, and Be Done with It

Sometimes an exception isn't the end of the world, and it doesn't need to
bubble up to the top of your application. In these cases, maybe just handle
it and don't re-raise:

.. code:: python

from pydecor import intercept

def let_us_know_it_happened(exc):
"""Just let us know an exception happened (if we are reading stdout)"""
print('This non-critical exception happened: {}'.format(exc))

@intercept(catch=ValueError, handler=let_us_know_it_happened)
def resilient_function(val):
"""I am so resilient!"""
val = int(val)
print('If I get here, I have an integer: {}'.format(val))



.. code::

If I get here, I have an integer: 50
This non-critical exception happened: invalid literal for int() with base 10: 'foo'

Note that the function does *not* continue running after the exception is
handled. Use this for short-circuiting under certain conditions rather
than for instituting a ``try/except:pass`` block. Maybe one day I'll figure
out how to make this work like that, but as it stands, the decorator surrounds
the entire function, so it does not provide that fine-grained level of control.



More Prête-à-porter Decorators

* ``export`` - add the decorated item to ``__all__``
* ``skipif`` - similar to py.test's decorator, skip the function if a
provided condition is True

Let me know if you've got any idea for other decorators that would
be nice to have!

Typing Stubfiles

Right now type hints are provided via rst-style docstring specification.
Although this format is supported by PyCharm, it does not conform to the
type-hinting standard defined in `PEP 484`_.

In order to better conform with the new standard (and to remain compatible
with Python 2.7), stubfiles will be added for the ``1.1.0`` release,
and docstring hints will be removed so that contributors don't have
to adjust type specifications in two places.

Build-process Updates

A more automated build process, because remembering all the steps to push a
new version is a pain. This is marked as scheduled for a patch release,
because it does not affect users at all, so a minor version bump would
lead people on to thinking that some new functionality had been added, when
it hadn't.


* Use of immutable ``Decorated`` object to pass information about the
deprecated function
* Deprecation of ``pass_params``, ``pass_kwargs``, ``pass_decorated``,
``pass_result``, ``unapck_extras``, and ``extras_key`` keyword
arguments to all decorators.
* Better organization of documentation


Contributions are welcome! If you find a bug or if something doesn't
work the way you think it should, please `raise an issue <issues_>`_.
If you know how to fix the bug, please `open a PR! <prs_>`_

I absolutely welcome any level of contribution. If you think the docs
could be better, or if you've found a typo, please open up a PR to improve
and/or fix them.

Contributor Conduct

There is a ```` file with details, based on one of GitHub's
templates, but the upshot is that I expect everyone who contributes to this
project to do their best to be helpful, friendly, and patient. Discrimination
of any kind will not be tolerated and will be promptly reported to GitHub.

On a personal note, Open Source survives because of people who are willing to
contribute their time and effort for free. The least we can do is treat them
with respect.


Tests are fairly easy to run, with few dependencies. You'll need Python 2.7,
3.4, and 3.6 installed on your system to run the full suite, as well as tox_
in whatever environment or virtual environment you're using. From there, you
should just be able to run ``tox``. The underlying test suite is `py.test`_,
and any extra arguments passed to tox get sent along. For example, to
send stdout/stderr to the console and stop on the first failure,
``tox -- -sx``. You can also run `py.test`_ directly. If you do, make sure
the deps specified in ``tox.ini`` are installed to your virtualenv, and
install the package in development mode with ``pip install -e .``.

PRs that cause tests to fail will not be merged until tests pass.

Any new functionality is expected to come with appropriate tests. That being
said, the test suite is fairly complex, with lots of mocking and
parametrization. Don't feel as though you have to follow this pattern when
writing new tests! A bunch of simpler tests are just as good. If you have any
questions, feel free to reach out to me via email at ``msplanchard`` ``@``
``gmail`` or on Twitter as @msplanchard.

Credits and Links

* This project was started using my generic `project template`_
* Tests are run with pytest_ and tox_
* Mocking in Python 2.7 tests uses the `mock backport`_
* Python 2/3 compatible exception raising via six_
* The `typing backport`_ is used for Python2.7-3.4-compatible type definitions
* Documentation built with sphinx_
* Coverage information collected with coverage_
* Pickling of objects provided via dill_

.. _`project template`:
.. _pytest:
.. _`py.test`:
.. _tox:
.. _sphinx:
.. _coverage:
.. _`mock backport`:
.. _`pep 484`:
.. _six:
.. _`typing backport`:
.. _docs:
.. _`decorator module docs`:
.. _issues:
.. _PRs:
.. _dill:

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