### Exercise E0 [warmup]

Execute the two following implementations of `rm_change`. Which one is faster? Why is there a difference?

```from time import time

def rm_change(change):
if change in COMMITS:
COMMITS.remove(change)

COMMITS = range(10**7)
t = time()
rm_change(10**7); rm_change(10**7-1); rm_change(10**7-2)
print(time()-t)```
```def rm_change(change):
try:
COMMITS.remove(change)
except ValueError:
pass

COMMITS = range(10**7)
t = time()
rm_change(10**7); rm_change(10**7-1); rm_change(10**7-2)
print(time()-t)```

The first version goes over the list twice: first time to check if the value is in the list, and second time to remove it. The second version goes over the list just once, and is faster. The difference is almost exactly 2×.

### Exercise D1 (30 min)

Write a decorator which wraps functions to log function arguments and the return value on each call. Provide support for both positional and named arguments (your wrapper function should take both `*args` and **kwargs and print them both):

```>>> @logged
... def func(*args):
...     return 3 + len(args)
>>> func(4, 4, 4)
you called func(4, 4, 4)
it returned 6
6```
##### Solution (class)
```class logged(object):
def __init__(self, func):
self.func = func

def __call__(self, *args, **kwargs):
print('you called {.__name__}({}{}{})'.format(
func,
str(list(args))[1:-1], # cast to list is because tuple
# of length one has an extra comma
', ' if kwargs else '',
', '.join('{}={}'.format(*pair) for pair in kwargs.items()),
))
val = func(*args, **kwargs)

print('it returned', val)
return val```
##### Solution (function)
```def logged(func):
"""Print out the arguments before function call and
after the call print out the returned value
"""

def wrapper(*args, **kwargs):
print('you called {.__name__}({}{}{})'.format(
func,
str(list(args))[1:-1], # cast to list is because tuple
# of length one has an extra comma
', ' if kwargs else '',
', '.join('{}={}'.format(*pair) for pair in kwargs.items()),
))
val = func(*args, **kwargs)
print('it returned', val)
return val
return wrapper```

A version with doctests: logged.py

### Exercise D2 (20 min)

Write a decorator to cache function invocation results. Store pairs `arg:result` in a dictionary in an attribute of the function object. The function being memoized is:

```def fibonacci(n):
assert n >= 0
if n < 2:
return n
else:
return fibonacci(n-1) + fibonacci(n-2)```
##### Solution
```def memoize(func):
func.cache = {}
def wrapper(n):
try:
ans = func.cache[n]
except KeyError:
ans = func.cache[n] = func(n)
return ans
return wrapper```
```@memoize
def fibonacci(n):
"""
>>> print(fibonacci.cache)
{}
>>> fibonacci(1)
1
>>> fibonacci(2)
1
>>> fibonacci(10)
55
>>> fibonacci.cache[10]
55
>>> fibonacci(40)
102334155
"""
assert n >= 0
if n < 2:
return n
else:
return fibonacci(n-1) + fibonacci(n-2)```

### Exercise CM1

Write a context manager similar to @assert_raises@, which checks if the execution took at most the specified amount of time:

```>>> with time_limit(10):
...       short_computation()
...
11339393393939393
>>> with time_limit(10):
...       loooong_computation()
...
⚡ function took 13s to execute — too long```
##### Solution
```import time
import functools
def time_limit(limit):
def decorator(func):
def wraper(*args, **kwargs):
t = time.time()
ans = func(*args, **kwargs)
actual = t - time.time()
if actual > limit:
print('⚡ function took %fs to execute — too long'%actual)
return None
return ans
return functools.update_wrapper(wraper, func)
return decorator```

### Excercise G2 (20 min) [optional]

You are writing a file browser which displays files line by line. The list of files is specified on the commands line (in `sys.argv`). After displaying one line, the program waits for user input. The user can:

1. press Enter to display the next line
2. press n + Enter to forget the rest of the current file and start with the next file
3. or anything else + Enter to display the next line

The first part is already written: it is a function which displays the lines and queries the user for input. Your job is to write the second part — the generator `read_lines` with the following interface: during construction it is passed a list of files to read. If yields line after line from the first file, then from the second file, and so on. When the last file is exhausted, it stops. The user of the generator can also throw an exception into the generator (`SkipThisFile`) which signals the generator to skip the rest of the current file, and just yield a dummy value to be skipped.

``` class SkipThisFile(Exception):
pass

"""this is the generator to be written

"""
for file in files:
yield 'dummy line'

def display_files(*files):
for line in source:
print(line, end='')
inp = input()
if inp == 'n':
print('NEXT')
source.throw(SkipThisFile) # return value is ignored```
##### Solution
``` def read_lines(*files):
for file in files:
for line in open(file):
try:
yield line.rstrip('\n')
except SkipThisFile:
yield 'dummy'
break```

### Exercise D3: plugin registration system (5 min) [optional]

This exercise is to be done at the end if time permits.

This is the plugin registration system from the lecture::

```class WordProcessor(object):
def process(self, text):
for plugin in self.PLUGINS:
text = plugin().cleanup(text)
return text

PLUGINS = []
...

@WordProcessor.plugin
class CleanMdashesExtension(object):
def cleanup(self, text):
return text.replace('&mdash;', u'\N{em dash}')```

…implement the `plugin` decorator!

##### Solution
```class WordProcessor(object):
...

PLUGINS = []

@classmethod
def plugin(cls, plugin):
cls.PLUGINS.append(plugin)```

### Excercise D4 (30 min) [optional]

This exercise is to be done at the end if time permits.

Write a decorator to memoize functions with an arbitrary set of arguments. Memoization is only possible if the arguments are hashable. If the wrapper is called with arguments which are not hashable, then the wrapped function should just be called without caching.

Note: To use `args` and `kwargs` as dictionary keys, they must be hashable, which basically means that they must be immutable. Variable `args` is already a `tuple`, which is fine, but `kwargs` have to be converted. One way is invoke `tuple(sorted(kwargs.items()))`.

##### Example solution
```import functools

def memoize(func):
"""
>>> @memoize
... def f(*args, **kwargs):
...     ans = len(args) + len(kwargs)
...     print(args, kwargs, '->', ans)
...     return ans
>>> f(3)
(3,) {} -> 1
1
>>> f(3)
1
>>> f(*[3])
1
>>> f(a=1, b=2)
() {'a': 1, 'b': 2} -> 2
2
>>> f(b=2, a=1)
2
>>> f([1,2,3])
([1, 2, 3],) {} -> 1
1
>>> f([1,2,3])
([1, 2, 3],) {} -> 1
1
"""
func.cache = {}
def wrapper(*args, **kwargs):
key = (args, tuple(sorted(kwargs.items())))
try:
ans = func.cache[key]
except TypeError:
# key is unhashable
return func(*args, **kwargs)
except KeyError:
# value is not present in cache
ans = func.cache[key] = func(*args, **kwargs)
return ans
return functools.update_wrapper(wrapper, func)```

### Exercise D5 (15 min) [really optional]

Modify `deprecated2` (see the lecture slides) to take an optional argument — a function to call instead of the original function::

```>>> def eot_new(): return 'EOT NEW'
>>> @deprecated3('using eot_new not {func.__name__}', eot_new)
... def eot(): return 'EOT'
...
>>> eot()
using eot_new not eot
'EOT NEW'```

### Exercise X1 [good for a laugh]

Execute the following code and explain the result.

```f = lambda: map((yield), range(10))
for x in f(): print x```

The lambda is equivalent to the following code:

```def f():
return map((yield), range(10))```

The `yield` is executed first, and it returns something (dependent on the way that the generator is used):

```def f():
x = yield
return map(x, range(10))```

Since we are calling `f()` from a `for` loop, we use `.next()`, not `.send()`, so it is equivalent to:

```def f():
yield
return map(None, range(10))```

which is equivalent to (because `map` simply creates a list if `None` is given as the first argument):

```def f():
yield
return range(10)```

which in turn is equivalent to:

```def f():
yield```

because the return value from a generator is ignored.

In case of a normal function, the final return wouldn't be allowed, because it doesn't make sense to return things from a generator function. In case of a lambda function, it's not possible to tell Python to ignore the return value, so the `yield` is allowed, but confusing.