Skip to content
Snippets Groups Projects
Commit 6a23db48 authored by Paul McCarthy's avatar Paul McCarthy :mountain_bicyclist:
Browse files

Finished main part of decorator practical - some appendices to go

parent 66930a2f
No related branches found
No related tags found
No related merge requests found
Hide whitespace changes
Inline Side-by-side
advanced_topics/decorators.ipynb
+ 330
− 70
View file @ 6a23db48
%% Cell type:markdown id: tags:
# Decorators
Remember that in Python, everything is an object, including functions. This
means that we can do things like:
- Pass a function as an argument to another function.
- Create/define a function inside another function.
- Write a function which returns another function.
These abilities mean that we can do some neat things with functions in Python.
## Overview
Let's say that we want a way to calculate the execution time of any function
(this example might feel familiar to you if you have gone through the
practical on operator overloading).
Our first attempt at writing such a function might look like this:
%% Cell type:code id: tags:
```
import time
def timeFunc(func, *args, **kwargs):
start = time.time()
retval = func(*args, **kwargs)
end = time.time()
print('Ran {} in {:0.2f} seconds'.format(func.__name__, end - start))
return retval
```
%% Cell type:markdown id: tags:
Ok, this looks like it might do the trick:
The `timeFunc` function accepts another function, `func`, as its first
argument. It calls `func`, passing it all of the other arguments, and then
prints the time taken for `func` to complete:
%% Cell type:code id: tags:
```
import numpy as np
import numpy.linalg as npla
def inverse(a):
return npla.inv(a)
data = np.random.random((2000, 2000))
invdata = timeFunc(inverse, data)
```
%% Cell type:markdown id: tags:
But this means that whenever we want to time something, we have to call the
`timeFunc` function directly. Let's take advantage of the fact that we can
define a function inside another funciton. Look at the next block of code
carefully, and make sure you understand what our new `timeFunc` implementation
is doing.
%% Cell type:code id: tags:
```
import time
def timeFunc(func):
def wrapperFunc(*args, **kwargs):
start = time.time()
retval = func(*args, **kwargs)
end = time.time()
print('Ran {} in {:0.2f} seconds'.format(func.__name__, end - start))
return retval
return wrapperFunc
```
%% Cell type:markdown id: tags:
The `timeFunc` function is given a function as its sole argument. It then
creates and returns a new function, `wrapperFunc`. This `wrapperFunc` function
calls and times the function that was passed to `timeFunc`<sup>1</sup>. But
note that when `timeFunc` is called, `wrapperFunc` is _not_ called - it is
only created and returned.
> <sup>1</sup> If you are wondering how the `wrapperFunc` is able to access
> the `func` argument, refer to the [appendix on
> closures](#appendix-closures).
This new `timeFunc` function is again passed a function `func`, but this time
as its sole argument. It then creates and returns a new function,
`wrapperFunc`. This `wrapperFunc` function calls and times the function that
was passed to `timeFunc`. But note that when `timeFunc` is called,
`wrapperFunc` is _not_ called - it is only created and returned.
Let's use our new `timeFunc` implementation:
%% Cell type:code id: tags:
```
import numpy as np
import numpy.linalg as npla
def inverse(a):
return npla.inv(a)
data = np.random.random((2000, 2000))
inverse = timeFunc(inverse)
invdata = inverse(data)
```
%% Cell type:markdown id: tags:
Here, we did the following:
1. We defined a function called `inverse`,
2. We passed the `inverse` function to the `timeFunc` function
3. We re-assigned the return value of `timeFunc` back to `inverse`.
So now the `inverse` variable refers to an instantiation of `wrapperFunc`,
which holds a reference to the original definition of `inverse`.
> If this is not clear, take a break now and read through the appendix on how
> [functions are not special](#appendix-functions-are-not-special).
Guess what? We have just created a __decorator__. A decorator is simply a
function which accepts a function as its input, and returns another function
as its output. In the example above, we have _decorated_ the `inverse`
function with the `timeFunc` decorator.
Python provides an alternative syntax for decorating one function with
another, using the `@` character. The approach that we used to decorate
`inverse` above:
%% Cell type:code id: tags:
```
def inverse(a):
return npla.inv(a)
inverse = timeFunc(inverse)
invdata = inverse(data)
```
%% Cell type:markdown id: tags:
is semantically equivalent to this:
%% Cell type:code id: tags:
```
@timeFunc
def inverse(a):
return npla.inv(a)
invdata = inverse(data)
```
%% Cell type:markdown id: tags:
## Decorators on methods
Applying a decorator to the methods of a class works in the same way:
%% Cell type:code id: tags:
```
import numpy.linalg as npla
class MiscMaths(object):
@timeFunc
def inverse(self, a):
return npla.inv(a)
```
%% Cell type:markdown id: tags:
Now, the `inverse` method of all `MiscMaths` instances will be timed:
%% Cell type:code id: tags:
```
mm1 = MiscMaths()
mm2 = MiscMaths()
i1 = mm1.inverse(np.random.random((1000, 1000)))
i2 = mm2.inverse(np.random.random((1500, 1500)))
```
%% Cell type:markdown id: tags:
Note that only one `timeFunc` decorator was created here - the `timeFunc`
function was only called once - when the `MiscMaths` class was defined. This
might be clearer if we re-write the above code in the following (equivalent)
manner:
%% Cell type:code id: tags:
```
class MiscMaths(object):
def inverse(self, a):
return npla.inv(a)
MiscMaths.inverse = timeFunc(MiscMaths.inverse)
```
%% Cell type:markdown id: tags:
So only one `wrapperFunc` function exists, and this function is _shared_ by
all instances of the `MiscMaths` class - (such as the `mm1` and `mm2`
instances in the example above). In many cases this is not a problem, but
there can be situations where you need each instance of your class to have its
own unique decorator.
If you are interested in solutions to this problem, read the next section on
[memoization](#example-memoization), and then take a look at the appendix on
[per-instance decorators](#appendix-per-instance-decorators).
## Example - memoization
Let's move onto another example.
[Meowmoization](https://en.wikipedia.org/wiki/Memoization) is a common
performance optimisation technique used in cats. I mean software. Essentially,
memoization refers to the process of maintaining a cache for a function which
performs some expensive calculation. When the function is executed with a set
of inputs, the calculation is performed, and then a copy of the inputs and the
result are cached. If the function is called again with the same inputs, the
cached result can be returned.
This is a perfect problem to tackle with decorators:
%% Cell type:code id: tags:
```
def memoize(func):
cache = {}
def wrapper(*args):
# is there a value in the cache
# for this set of inputs?
cached = cache.get(args, None)
# If not, call the function,
# and cache the result.
if cached is None:
cached = func(*args)
cache[args] = cached
else:
print('Cached {}({}): {}'.format(func.__name__, args, cached))
return cached
return wrapper
```
%% Cell type:markdown id: tags:
We can now use our `memoize` decorator to add a memoization cache to any
function. Let's memoize a function which generates the $n^{th}$ number in the
[Fibonacci series](https://en.wikipedia.org/wiki/Fibonacci_number):
%% Cell type:code id: tags:
```
@memoize
def fib(n):
if n in (1, 2):
print('fib({}) = 1'.format(n))
if n in (0, 1):
print('fib({}) = {}'.format(n, n))
return 1
twoback = 1
oneback = 1
val = 0
for _ in range(2, n):
val = oneback + twoback
twoback = oneback
oneback = val
print('fib({}) = {}'.format(n, val))
return val
```
%% Cell type:markdown id: tags:
For a given input, when `fib` is called the first time, it will calculate the
$n^{th}$ Fibonacci number:
%% Cell type:code id: tags:
```
for i in range(10):
fib(i)
```
%% Cell type:markdown id: tags:
However, on repeated calls with the same input, the calculation is skipped,
and instead the result is retrieved from the memoization cache:
%% Cell type:code id: tags:
```
for i in range(10):
fib(i)
```
%% Cell type:markdown id: tags:
> If you are wondering how the `wrapper` function is able to access the
> `cache` variable, refer to the [appendix on closures](#appendix-closures).
## Decorators with arguments
Continuing with our memoization example, let's say that we want to place a
limit on the maximum size that our cache can grow to. For example, the output
of our function might have large memory requirements, so we can only afford to
store a handful of pre-calculated results. It would be nice to be able to
specify the maximum cache size when we define our function to be memoized,
like so:
> ```
> # cache at most 10 results
> @limitedMemoize(10):
> def fib(n):
> ...
> ```
In order to support this, our `memoize` decorator function needs to be
modified - it is currently written to accept a function as its sole argument,
but we need it to accept a cache size limit.
%% Cell type:code id: tags:
```
from collections import OrderedDict
def limitedMemoize(maxSize):
cache = OrderedDict()
def decorator(func):
def wrapper(*args):
# is there a value in the cache
# for this set of inputs?
cached = cache.get(args, None)
# If not, call the function,
# and cache the result.
if cached is None:
cached = func(*args)
# If the cache has grown too big,
# remove the oldest item. In practice
# it would make more sense to remove
# the item with the oldest access
# time, but this is good enough for
# an introduction!
if len(cache) >= maxSize:
cache.popitem(last=False)
cache[args] = cached
else:
print('Cached {}({}): {}'.format(func.__name__, args, cached))
return cached
return wrapper
return decorator
```
%% Cell type:markdown id: tags:
> We used the handy
> [`collections.OrderedDict`](https://docs.python.org/3.5/library/collections.html#collections.OrderedDict)
> class here which preserves the insertion order of key-value pairs.
This is starting to look a little complicated - we now have _three_ layers of
functions. This is necessary when you wish to write a decorator which accepts
arguments (refer to the
[appendix](appendix-decorators-without-arguments-versus-decorators-with-arguments)
for more details).
But this `limitedMemoize` decorator is used in essentially the same way as our
earlier `memoize` decorator:
%% Cell type:code id: tags:
```
@limitedMemoize(5)
def fib(n):
if n in (1, 2):
print('fib({}) = 1'.format(n))
return 1
twoback = 1
oneback = 1
val = 0
for _ in range(2, n):
val = oneback + twoback
twoback = oneback
oneback = val
print('fib({}) = {}'.format(n, val))
return val
```
%% Cell type:markdown id: tags:
Except that now, the `fib` function will only cache up to 5 values.
%% Cell type:code id: tags:
```
fib(10)
fib(11)
fib(12)
fib(13)
fib(14)
print('The result for 10 should come from the cache')
fib(10)
fib(15)
print('The result for 10 should no longer be cached')
fib(10)
```
%% Cell type:markdown id: tags:
## Chaining decorators
Decorators can easily be chained, or nested:
%% Cell type:code id: tags:
```
import time
@timeFunc
@memoize
def expensiveFunc(n):
time.sleep(n)
return n
```
%% Cell type:markdown id: tags:
> Remember that this is semantically equivalent to the following:
>
> ```
> def expensiveFunc(n):
> time.sleep(n)
> return n
>
> expensiveFunc = timeFunc(memoize(expensiveFunc))
> ```
Now we can see the effect of our memoization layer on performance:
%% Cell type:code id: tags:
```
expensiveFunc(1)
expensiveFunc(2)
expensiveFunc(2)
```
%% Cell type:markdown id: tags:
## Decorator classes
> not to be confused with _class decorators_ - see the
> [appendix](#appendix-class-decorators)
By now, you will have gained the impression that a decorator is a function
which _decorates_ another function. But if you went through the practical on
operator overloading, you might remember the special `__call__` method, that
allows an object to be called as if it were a function.
This feature allows us to write our decorators as classes, instead of
functions. This can be handy if you are writing a decorator that has
complicated behaviour, and/or needs to maintain some sort of state which
cannot be easily or elegantly written using nested functions.
As an example, let's say we are writing a framework for unit testing. We want
to be able to "mark" our test functions like so, so they can be easily
identified and executed:
> ```
> @unitTest
> def testblerk():
> """tests the blerk algorithm."""
> ...
> ```
With a decorator like this, we wouldn't need to worry about where our tests
are located - they will all be detected because we have marked them as test
functions. What does this `unitTest` decorator look like?
%% Cell type:code id: tags:
```
class TestRegistry(object):
def __init__(self):
self.testFuncs = []
def __call__(self, func):
self.testFuncs.append(func)
def listTests(self):
print('All registered tests:')
for test in self.testFuncs:
print(' ', test.__name__)
def runTests(self):
for test in self.testFuncs:
print('Running test {:10s} ...'.format(test.__name__), end='')
try:
test()
print('passed!')
except Exception as e:
print('failed!')
# Create a test registry, and
# alias it to "unitTest" so
# that we can register tests
# with a "@unitTest" decorator.
registry = TestRegistry()
unitTest = registry
```
%% Cell type:markdown id: tags:
So we've defined a class, `TestRegistry`, which will manage all of our unit
tests. Now, in order to "mark" any function as being a unit test, we just need
to use the `unitTest` decorator:
%% Cell type:code id: tags:
```
@unitTest
def testFoo():
assert 'a' in 'bcde'
@unitTest
def testBar():
assert 1 > 0
@unitTest
def testBlerk():
assert 9 % 2 == 0
```
%% Cell type:markdown id: tags:
Now that these functions have been registered with our `TestRegistry`
instance, we can run them all:
%% Cell type:code id: tags:
```
registry.listTests()
registry.runTests()
```
%% Cell type:markdown id: tags:
> Unit testing is something which you must do! This is __especially__
> important in an interpreted language such as Python, where there is no
> compiler to catch all of your mistakes.
>
> Python has a built-in
> [`unittest`](https://docs.python.org/3.5/library/unittest.html) module,
> however the third-party [`pytest`](https://docs.pytest.org/en/latest/) and
> [`nose`](http://nose2.readthedocs.io/en/latest/) are popular. It is also
> wise to combine your unit tests with
> [`coverage`](https://coverage.readthedocs.io/en/coverage-4.5.1/), which
> tells you how much of your code was executed, or _covered_ when your
> tests were run.
## Appendix: Functions are not special
When we write a statement like this:
%% Cell type:code id: tags:
```
a = [1, 2, 3]
```
%% Cell type:markdown id: tags:
the variable `a` is a reference to a `list`. We can create a new reference to
the same list, and delete `a`:
%% Cell type:code id: tags:
```
b = a
del a
```
%% Cell type:markdown id: tags:
Deleting `a` doesn't affect the list at all - the list still exists, and is
now referred to by a variable called `b`.
%% Cell type:code id: tags:
```
print('b: ', b)
```
%% Cell type:markdown id: tags:
`a` has, however, been deleted:
%% Cell type:code id: tags:
```
print('a: ', a)
```
%% Cell type:markdown id: tags:
The variables `a` and `b` are just references to a list that is sitting in
memory somewhere - renaming or removing a reference does not have any effect
upon the list<sup>2</sup>.
If you are familiar with C or C++, you can think of a variable in Python as
like a `void *` pointer - it is just a pointer of an undefined type, which is
pointing to some item in memory (which does have a specific type). Deleting
like a `void *` pointer - it is just a pointer of an unspecified type, which
is pointing to some item in memory (which does have a specific type). Deleting
the pointer does not have any effect upon the item to which it was pointing.
> <sup>2</sup> Until no more references to the list exist, at which point it
> will be
> [garbage-collected](https://www.quora.com/How-does-garbage-collection-in-Python-work-What-are-the-pros-and-cons).
Now, functions in Python work in _exactly_ the same way as variables. When we
define a function like this:
%% Cell type:code id: tags:
```
def inverse(a):
return npla.inv(a)
print(inverse)
```
%% Cell type:markdown id: tags:
there is nothing special about the name `inverse` - `inverse` is just a
reference to a function that resides somewhere in memory. We can create a new
reference to this function:
%% Cell type:code id: tags:
```
inv2 = inverse
```
%% Cell type:markdown id: tags:
And delete the old reference:
%% Cell type:code id: tags:
```
del inverse
```
%% Cell type:markdown id: tags:
But the function still exists, and is still callable, via our second
reference:
%% Cell type:code id: tags:
```
print(inv2)
data = np.random.random((10, 10))
invdata = inv2(data)
```
%% Cell type:markdown id: tags:
So there is nothing special about functions in Python - they are just items
that reside somewhere in memory, and to which we can create as many references
as we like.
> If it bothers you that `print(inv2)` resulted in
> `<function inverse at ...>`, and not `<function inv2 at ...>`, then keep
> going straight through to the
> [next appendix](#appendix-preserving-function-metadata).
## Appendix: Preserving function metadata
TODO `functools.wraps`
> `<function inverse at ...>`, and not `<function inv2 at ...>`, then refer to
> the appendix on
> [preserving function metdata](#appendix-preserving-function-metadata).
## Appendix: Closures
Whenever we define or use a decorator, we are taking advantage of a concept
called a [_closure_](https://www.geeksforgeeks.org/python-closures/). Take a
second to re-familiarise yourself with our `timeFunc` decorator function from
earlier - when `timeFunc` is called, it creates and returns `wrapperFunc`:
called a [_closure_][wiki-closure]. Take a second to re-familiarise yourself
with our `memoize` decorator function from earlier - when `memoize` is called,
it creates and returns a function called `wrapper`:
[wiki-closure]: https://en.wikipedia.org/wiki/Closure_(computer_programming)
%% Cell type:code id: tags:
```
import time
def timeFunc(func):
def memoize(func):
def wrapperFunc(*args, **kwargs):
cache = {}
start = time.time()
retval = func(*args, **kwargs)
end = time.time()
def wrapper(*args):
print('Ran {} in {:0.2f} seconds'.format(func.__name__, end - start))
# is there a value in the cache
# for this set of inputs?
cached = cache.get(args, None)
return retval
# If not, call the function,
# and cache the result.
if cached is None:
cached = func(*args)
cache[args] = cached
else:
print('Cached {}({}): {}'.format(func.__name__, args, cached))
return wrapperFunc
return cached
return wrapper
```
%% Cell type:markdown id: tags:
Then `wrapperFunc` is executed at some arbitrary point in the future. But how
does it have access to `func`, defined within the scope of the `timeFunc`
function, after the execution of `timeFunc` has ended?
Then `wrapper` is executed at some arbitrary point in the future. But how does
it have access to `cache`, defined within the scope of the `memoize` function,
after the execution of `memoize` has ended?
%% Cell type:code id: tags:
```
def pause(secs):
print('Pausing for {} seconds ...'.format(secs))
time.sleep(secs)
print('Finished pausing')
def nby2(n):
return n * 2
# wrapperFunc is created here
pause = timeFunc(pause)
# wrapper function is created here (and
# assigned back to the nby2 reference)
nby2 = memoize(nby2)
# wrapperFunc is executed here
pause(2)
# wrapper function is executed here
print('nby2(2): ', nby2(2))
print('nby2(2): ', nby2(2))
```
%% Cell type:markdown id: tags:
The trick is that whenever a nested function is defined in Python, the scope
in which it is defined is preserved for that function's lifetime. So
`wrapperFunc` has access to all of the variables within the `timeFunc`
function's scope, that were defined at the time that `wrapperFunc` was created
(which was when we called `timeFunc`). This is why `wrapperFunc` is able to
access `func`, even though at the time that `wrapperFunc` is called, the
execution of `timeFunc` has long since finished.
in which it is defined is preserved for that function's lifetime. So `wrapper`
has access to all of the variables within the `memoize` function's scope, that
were defined at the time that `wrapper` was created (which was when we called
`memoize`). This is why `wrapper` is able to access `cache`, even though at
the time that `wrapper` is called, the execution of `memoize` has long since
finished.
This is what is known as a _closure_. Closures are a fundamental, and
extremely powerful, aspect of Python and other high level languages such as
Javascript. So there's your answer,
This is what is known as a
[_closure_](https://www.geeksforgeeks.org/python-closures/). Closures are a
fundamental, and extremely powerful, aspect of Python and other high level
languages. So there's your answer,
[fishbulb](https://www.youtube.com/watch?v=CiAaEPcnlOg).
## Appendix: Preserving function metadata
TODO `functools.wraps`
## Appendix: per-instance decorators
Below, we have defined a class called `Multiplier`, which has a method
`multiply` that multiplies some number by a fixed (but changeable) constant,
and is memoized via the `@memoize` decorator we [defined
earlier](#example-memoization).
Can you spot the problem with this implementation?
%% Cell type:code id: tags:
```
class Multiplier(object):
def __init__(self):
self.__multiplier = 1
@property
def multiplier(self):
return self.multiplier
@multiplier.setter
def multiplier(self, m):
self.multiplier = m
@memoize
def multiply(self, x):
return x * self.multiplier
```
%% Cell type:markdown id: tags:
TODO Instanecify
## Appendix: class decorators
TODO
## Appendix: Decorators without arguments versus decorators with arguments
There are three ways to invoke a decorator with the `@` notation:
1. Naming it (e.g. `@mydecorator`)
2. Calling it (e.g. `@mydecorator()`)
3. Calling it, and passing it arguments (e.g. `@mydecorator(1, 2, 3)`)
1. Naming it, e.g. `@mydecorator`
2. Calling it, e.g. `@mydecorator()`
3. Calling it, and passing it arguments, e.g. `@mydecorator(1, 2, 3)`
Python expects a decorator function to behave differently in the second and
third scenarios, when compared to the first:
%% Cell type:code id: tags:
```
def decorator(*args):
print(' decorator({})'.format(args))
def wrapper(*args):
print(' wrapper({})'.format(args))
return wrapper
print('Scenario #1: @decorator')
@decorator
def noop():
pass
print('\nScenario #2: @decorator()')
@decorator()
def noop():
pass
print('\nScenario #3: @decorator(1, 2, 3)')
@decorator(1, 2, 3)
def noop():
pass
```
%% Cell type:markdown id: tags:
So if a decorator is "named" (scenario 1), only the decorator function
(`decorator` in the example above) is called, and is passed the decorated
function.
But if a decorator function is "called" (scenarios 2 or 3), both the decorator
function (`decorator`), __and its return value__ (`wrapper`) are called - the
decorator function is passed the arguments that were provided, and its return
value is passed the decorated function.
This is why, if you are writing a decorator function which expects arguments,
you must use three layers of functions, like so:
%% Cell type:code id: tags:
```
def decorator(*args):
def realDecorator(func):
def wrapper(*args, **kwargs):
return func(*args, **kwargs)
return wrapper
return realDecorator
```
%% Cell type:markdown id: tags:
> The author of this practical is angry about this, as he does not understand
> why the Python language designers couldn't allow a decorator function to be
> passed both the decorated function, and any arguments that were passed when
> the decorator was invoked, like so:
>
> ```
> def decorator(func, *args, **kwargs): # args/kwargs here contain
> # whatever is passed to the
> # decorator
>
> def wrapper(*args, **kwargs): # args/kwargs here contain
> # whatever is passed to the
> # decorated function
> return func(*args, **kwargs)
>
> return wrapper
> ```
## Appendix: class decorators
TODO
## Useful references
* [Decorator tutorial](http://blog.thedigitalcatonline.com/blog/2015/04/23/python-decorators-metaprogramming-with-style/)
* [Another decorator tutorial](https://realpython.com/blog/python/primer-on-python-decorators/)
* [The decorators they won't tell you about](https://github.com/hchasestevens/hchasestevens.github.io/blob/master/notebooks/the-decorators-they-wont-tell-you-about.ipynb)
* [Closures - Wikipedia][wiki-closure]
* [Closures in Python](https://www.geeksforgeeks.org/python-closures/)
* [Garbage collection in Python](https://www.quora.com/How-does-garbage-collection-in-Python-work-What-are-the-pros-and-cons)
[wiki-closure]: https://en.wikipedia.org/wiki/Closure_(computer_programming)
......
advanced_topics/decorators.md
+ 270
− 67
View file @ 6a23db48
......@@ -38,7 +38,9 @@ def timeFunc(func, *args, **kwargs):
```
Ok, this looks like it might do the trick:
The `timeFunc` function accepts another function, `func`, as its first
argument. It calls `func`, passing it all of the other arguments, and then
prints the time taken for `func` to complete:
```
......@@ -78,16 +80,11 @@ def timeFunc(func):
```
The `timeFunc` function is given a function as its sole argument. It then
creates and returns a new function, `wrapperFunc`. This `wrapperFunc` function
calls and times the function that was passed to `timeFunc`<sup>1</sup>. But
note that when `timeFunc` is called, `wrapperFunc` is _not_ called - it is
only created and returned.
> <sup>1</sup> If you are wondering how the `wrapperFunc` is able to access
> the `func` argument, refer to the [appendix on
> closures](#appendix-closures).
This new `timeFunc` function is again passed a function `func`, but this time
as its sole argument. It then creates and returns a new function,
`wrapperFunc`. This `wrapperFunc` function calls and times the function that
was passed to `timeFunc`. But note that when `timeFunc` is called,
`wrapperFunc` is _not_ called - it is only created and returned.
Let's use our new `timeFunc` implementation:
......@@ -153,6 +150,62 @@ invdata = inverse(data)
```
## Decorators on methods
Applying a decorator to the methods of a class works in the same way:
```
import numpy.linalg as npla
class MiscMaths(object):
@timeFunc
def inverse(self, a):
return npla.inv(a)
```
Now, the `inverse` method of all `MiscMaths` instances will be timed:
```
mm1 = MiscMaths()
mm2 = MiscMaths()
i1 = mm1.inverse(np.random.random((1000, 1000)))
i2 = mm2.inverse(np.random.random((1500, 1500)))
```
Note that only one `timeFunc` decorator was created here - the `timeFunc`
function was only called once - when the `MiscMaths` class was defined. This
might be clearer if we re-write the above code in the following (equivalent)
manner:
```
class MiscMaths(object):
def inverse(self, a):
return npla.inv(a)
MiscMaths.inverse = timeFunc(MiscMaths.inverse)
```
So only one `wrapperFunc` function exists, and this function is _shared_ by
all instances of the `MiscMaths` class - (such as the `mm1` and `mm2`
instances in the example above). In many cases this is not a problem, but
there can be situations where you need each instance of your class to have its
own unique decorator.
If you are interested in solutions to this problem, read the next section on
[memoization](#example-memoization), and then take a look at the appendix on
[per-instance decorators](#appendix-per-instance-decorators).
## Example - memoization
......@@ -203,8 +256,8 @@ function. Let's memoize a function which generates the $n^{th}$ number in the
@memoize
def fib(n):
if n in (1, 2):
print('fib({}) = 1'.format(n))
if n in (0, 1):
print('fib({}) = {}'.format(n, n))
return 1
twoback = 1
......@@ -222,6 +275,7 @@ def fib(n):
return val
```
For a given input, when `fib` is called the first time, it will calculate the
$n^{th}$ Fibonacci number:
......@@ -242,6 +296,10 @@ for i in range(10):
```
> If you are wondering how the `wrapper` function is able to access the
> `cache` variable, refer to the [appendix on closures](#appendix-closures).
## Decorators with arguments
......@@ -400,12 +458,109 @@ expensiveFunc(2)
## Decorator classes
> not to be confused with _class decorators_ - see the
> [appendix](#appendix-class-decorators)
By now, you will have gained the impression that a decorator is a function
which _decorates_ another function. But if you went through the practical on
operator overloading, you might remember the special `__call__` method, that
allows an object to be called as if it were a function.
This feature allows us to write our decorators as classes, instead of
functions. This can be handy if you are writing a decorator that has
complicated behaviour, and/or needs to maintain some sort of state which
cannot be easily or elegantly written using nested functions.
As an example, let's say we are writing a framework for unit testing. We want
to be able to "mark" our test functions like so, so they can be easily
identified and executed:
> ```
> @unitTest
> def testblerk():
> """tests the blerk algorithm."""
> ...
> ```
With a decorator like this, we wouldn't need to worry about where our tests
are located - they will all be detected because we have marked them as test
functions. What does this `unitTest` decorator look like?
```
class TestRegistry(object):
def __init__(self):
self.testFuncs = []
def __call__(self, func):
self.testFuncs.append(func)
def listTests(self):
print('All registered tests:')
for test in self.testFuncs:
print(' ', test.__name__)
def runTests(self):
for test in self.testFuncs:
print('Running test {:10s} ...'.format(test.__name__), end='')
try:
test()
print('passed!')
except Exception as e:
print('failed!')
# Create a test registry, and
# alias it to "unitTest" so
# that we can register tests
# with a "@unitTest" decorator.
registry = TestRegistry()
unitTest = registry
```
So we've defined a class, `TestRegistry`, which will manage all of our unit
tests. Now, in order to "mark" any function as being a unit test, we just need
to use the `unitTest` decorator:
```
@unitTest
def testFoo():
assert 'a' in 'bcde'
@unitTest
def testBar():
assert 1 > 0
@unitTest
def testBlerk():
assert 9 % 2 == 0
```
Now that these functions have been registered with our `TestRegistry`
instance, we can run them all:
```
registry.listTests()
registry.runTests()
```
> Unit testing is something which you must do! This is __especially__
> important in an interpreted language such as Python, where there is no
> compiler to catch all of your mistakes.
>
> Python has a built-in
> [`unittest`](https://docs.python.org/3.5/library/unittest.html) module,
> however the third-party [`pytest`](https://docs.pytest.org/en/latest/) and
> [`nose`](http://nose2.readthedocs.io/en/latest/) are popular. It is also
> wise to combine your unit tests with
> [`coverage`](https://coverage.readthedocs.io/en/coverage-4.5.1/), which
> tells you how much of your code was executed, or _covered_ when your
> tests were run.
## Appendix: Functions are not special
......@@ -451,8 +606,8 @@ upon the list<sup>2</sup>.
If you are familiar with C or C++, you can think of a variable in Python as
like a `void *` pointer - it is just a pointer of an undefined type, which is
pointing to some item in memory (which does have a specific type). Deleting
like a `void *` pointer - it is just a pointer of an unspecified type, which
is pointing to some item in memory (which does have a specific type). Deleting
the pointer does not have any effect upon the item to which it was pointing.
......@@ -508,77 +663,127 @@ as we like.
> If it bothers you that `print(inv2)` resulted in
> `<function inverse at ...>`, and not `<function inv2 at ...>`, then keep
> going straight through to the
> [next appendix](#appendix-preserving-function-metadata).
> `<function inverse at ...>`, and not `<function inv2 at ...>`, then refer to
> the appendix on
> [preserving function metdata](#appendix-preserving-function-metadata).
## Appendix: Preserving function metadata
## Appendix: Closures
TODO `functools.wraps`
Whenever we define or use a decorator, we are taking advantage of a concept
called a [_closure_][wiki-closure]. Take a second to re-familiarise yourself
with our `memoize` decorator function from earlier - when `memoize` is called,
it creates and returns a function called `wrapper`:
## Appendix: Closures
[wiki-closure]: https://en.wikipedia.org/wiki/Closure_(computer_programming)
```
def memoize(func):
Whenever we define or use a decorator, we are taking advantage of a concept
called a [_closure_](https://www.geeksforgeeks.org/python-closures/). Take a
second to re-familiarise yourself with our `timeFunc` decorator function from
earlier - when `timeFunc` is called, it creates and returns `wrapperFunc`:
cache = {}
def wrapper(*args):
# is there a value in the cache
# for this set of inputs?
cached = cache.get(args, None)
# If not, call the function,
# and cache the result.
if cached is None:
cached = func(*args)
cache[args] = cached
else:
print('Cached {}({}): {}'.format(func.__name__, args, cached))
return cached
return wrapper
```
import time
def timeFunc(func):
def wrapperFunc(*args, **kwargs):
start = time.time()
retval = func(*args, **kwargs)
end = time.time()
Then `wrapper` is executed at some arbitrary point in the future. But how does
it have access to `cache`, defined within the scope of the `memoize` function,
after the execution of `memoize` has ended?
print('Ran {} in {:0.2f} seconds'.format(func.__name__, end - start))
return retval
```
def nby2(n):
return n * 2
return wrapperFunc
# wrapper function is created here (and
# assigned back to the nby2 reference)
nby2 = memoize(nby2)
# wrapper function is executed here
print('nby2(2): ', nby2(2))
print('nby2(2): ', nby2(2))
```
Then `wrapperFunc` is executed at some arbitrary point in the future. But how
does it have access to `func`, defined within the scope of the `timeFunc`
function, after the execution of `timeFunc` has ended?
The trick is that whenever a nested function is defined in Python, the scope
in which it is defined is preserved for that function's lifetime. So `wrapper`
has access to all of the variables within the `memoize` function's scope, that
were defined at the time that `wrapper` was created (which was when we called
`memoize`). This is why `wrapper` is able to access `cache`, even though at
the time that `wrapper` is called, the execution of `memoize` has long since
finished.
This is what is known as a
[_closure_](https://www.geeksforgeeks.org/python-closures/). Closures are a
fundamental, and extremely powerful, aspect of Python and other high level
languages. So there's your answer,
[fishbulb](https://www.youtube.com/watch?v=CiAaEPcnlOg).
## Appendix: Preserving function metadata
TODO `functools.wraps`
## Appendix: per-instance decorators
Below, we have defined a class called `Multiplier`, which has a method
`multiply` that multiplies some number by a fixed (but changeable) constant,
and is memoized via the `@memoize` decorator we [defined
earlier](#example-memoization).
Can you spot the problem with this implementation?
```
def pause(secs):
print('Pausing for {} seconds ...'.format(secs))
time.sleep(secs)
print('Finished pausing')
class Multiplier(object):
def __init__(self):
self.__multiplier = 1
# wrapperFunc is created here
pause = timeFunc(pause)
@property
def multiplier(self):
return self.multiplier
# wrapperFunc is executed here
pause(2)
@multiplier.setter
def multiplier(self, m):
self.multiplier = m
@memoize
def multiply(self, x):
return x * self.multiplier
```
The trick is that whenever a nested function is defined in Python, the scope
in which it is defined is preserved for that function's lifetime. So
`wrapperFunc` has access to all of the variables within the `timeFunc`
function's scope, that were defined at the time that `wrapperFunc` was created
(which was when we called `timeFunc`). This is why `wrapperFunc` is able to
access `func`, even though at the time that `wrapperFunc` is called, the
execution of `timeFunc` has long since finished.
TODO Instanecify
This is what is known as a _closure_. Closures are a fundamental, and
extremely powerful, aspect of Python and other high level languages such as
Javascript. So there's your answer,
[fishbulb](https://www.youtube.com/watch?v=CiAaEPcnlOg).
## Appendix: class decorators
TODO
## Appendix: Decorators without arguments versus decorators with arguments
......@@ -587,9 +792,9 @@ Javascript. So there's your answer,
There are three ways to invoke a decorator with the `@` notation:
1. Naming it (e.g. `@mydecorator`)
2. Calling it (e.g. `@mydecorator()`)
3. Calling it, and passing it arguments (e.g. `@mydecorator(1, 2, 3)`)
1. Naming it, e.g. `@mydecorator`
2. Calling it, e.g. `@mydecorator()`
3. Calling it, and passing it arguments, e.g. `@mydecorator(1, 2, 3)`
Python expects a decorator function to behave differently in the second and
......@@ -663,16 +868,14 @@ def decorator(*args):
> ```
## Appendix: class decorators
TODO
## Useful references
* [Decorator tutorial](http://blog.thedigitalcatonline.com/blog/2015/04/23/python-decorators-metaprogramming-with-style/)
* [Another decorator tutorial](https://realpython.com/blog/python/primer-on-python-decorators/)
* [The decorators they won't tell you about](https://github.com/hchasestevens/hchasestevens.github.io/blob/master/notebooks/the-decorators-they-wont-tell-you-about.ipynb)
* [Closures - Wikipedia][wiki-closure]
* [Closures in Python](https://www.geeksforgeeks.org/python-closures/)
* [Garbage collection in Python](https://www.quora.com/How-does-garbage-collection-in-Python-work-What-are-the-pros-and-cons)
[wiki-closure]: https://en.wikipedia.org/wiki/Closure_(computer_programming)
0% or .
You are about to add 0 people to the discussion. Proceed with caution.
Finish editing this message first!
Please register or to comment