7. Advanced

# Generators help you make lazy code.
def double_numbers(iterable):
    for i in iterable:
        yield i + i

# Generators are memory-efficient because they only load the data needed to
# process the next value in the iterable. This allows them to perform
# operations on otherwise prohibitively large value ranges.
# NOTE: `range` replaces `xrange` in Python 3.
for i in double_numbers(range(1, 900000000)):  # `range` is a generator.
    print(i)
    if i >= 30:
        break

# Just as you can create a list comprehension, you can create generator
# comprehensions as well.
values = (-x for x in [1,2,3,4,5])
for x in values:
    print(x)  # prints -1 -2 -3 -4 -5 to console/terminal

# You can also cast a generator comprehension directly to a list.
values = (-x for x in [1,2,3,4,5])
gen_to_list = list(values)
print(gen_to_list)  # => [-1, -2, -3, -4, -5]


# Decorators are a form of syntactic sugar.
# They make code easier to read while accomplishing clunky syntax.

# Wrappers are one type of decorator.
# They're really useful for adding logging to existing functions without needing to modify them.

def log_function(func):
    def wrapper(*args, **kwargs):
        print("Entering function", func.__name__)
        result = func(*args, **kwargs)
        print("Exiting function", func.__name__)
        return result
    return wrapper

@log_function               # equivalent:
def my_function(x,y):       # def my_function(x,y):
    return x+y              #   return x+y
                            # my_function = log_function(my_function)
# The decorator @log_function tells us as we begin reading the function definition
# for my_function that this function will be wrapped with log_function.
# When function definitions are long, it can be hard to parse the non-decorated
# assignment at the end of the definition.

my_function(1,2) # => "Entering function my_function"
                 # => "3"
                 # => "Exiting function my_function"

# But there's a problem.
# What happens if we try to get some information about my_function?

print(my_function.__name__) # => 'wrapper'
print(my_function.__code__.co_argcount) # => 0. The argcount is 0 because both arguments in wrapper()'s signature are optional.

# Because our decorator is equivalent to my_function = log_function(my_function)
# we've replaced information about my_function with information from wrapper

# Fix this using functools

from functools import wraps

def log_function(func):
    @wraps(func) # this ensures docstring, function name, arguments list, etc. are all copied
                 # to the wrapped function - instead of being replaced with wrapper's info
    def wrapper(*args, **kwargs):
        print("Entering function", func.__name__)
        result = func(*args, **kwargs)
        print("Exiting function", func.__name__)
        return result
    return wrapper

@log_function
def my_function(x,y):
    return x+y

my_function(1,2) # => "Entering function my_function"
                 # => "3"
                 # => "Exiting function my_function"

print(my_function.__name__) # => 'my_function'
print(my_function.__code__.co_argcount) # => 2