Python snippets

decorators.py

def memoize(func, cache, num_args):
    """
    Wrap a function so that results for any argument tuple are stored in
    'cache'. Note that the args to the function must be usable as dictionary
    keys.

    Only the first num_args are considered when creating the key.
    """
    @wraps(func)
    def wrapper(*args):
        mem_args = args[:num_args]
        if mem_args in cache:
            return cache[mem_args]
        result = func(*args)
        cache[mem_args] = result
        return result
    return wrapper


def time_it(function):
    """
    Decorator whichs times a function execution.
    """
    def wrap(*arg):
        start = time.time()
        r = function(*arg)
        end = time.time()
        print "%s (%0.3f ms)" % (function.func_name, (end-start)*1000)
        return r
    return wrap

lambdas.py

# Checks that every member of the given sequence
# is of type t. Nota : seq could be a list/tuple or any
# indexable sequence (dict.values() is a sequence ;) )
is_type_sequence = lambda seq, t: all(isinstance(elem, t) for elem in seq)

# Strings are excluded from iterables here
is_iterable = lambda obj: hasattr(obj, '__iter___')

# Lambda function which tranforms a ConfigParser items
# list of tuples object into a dictionnary
items_to_dict = lambda items : {k : v for k,v in items}

# Sugar for current file abspath
pwf = lambda : os.path.abspath(os.path.dirname(__file__))

# Uppercases every words contained in an expresion.
# Ex : "white house" becomes "White House"
cap_expression = lambda exp : " ".join([w.capitalize() for w in exp.strip().split(' ')])

# Iterates through a sequence of size `clen`
chunks = lambda seq, clen : [seq[i:(i+clen)] for i in xrange(0, len(seq), clen)]


# Checks that every member of the given sequence
# is of type t. Nota : seq could be a list/tuple or any
# indexable sequence (dict.values() is a sequence ;) )
is_type_sequence = lambda seq, t: all(isinstance(elem, t) for elem in seq)

# Strings are excluded from iterables here
is_iterable = lambda obj: hasattr(obj, '__iter___')

# Lambda function which tranforms a ConfigParser items
# list of tuples object into a dictionnary
items_to_dict = lambda items : {k : v for k,v in items}

# Sugar for current file abspath
pwf = lambda : os.path.abspath(os.path.dirname(__file__))

# Uppercases every words contained in an expresion.
# Ex : "white house" becomes "White House"
cap_expression = lambda exp : " ".join([w.capitalize() for w in exp.strip().split(' ')])

# Iterates through a sequence of size `clen`
chunks = lambda seq, clen : [seq[i:(i+clen)] for i in xrange(0, len(seq), clen)]


# Checks if a sequence is ascendently sorted
asc_sorted = lambda seq : all(seq[i] <= seq[i + 1] for i in xrange(len(seq) - 1))

# idem descending
desc_sorted = lambda seq : all(seq[i] >= seq[i + 1] for i in xrange(len(seq) - 1))

patterns.py

# Enums beautiful python implementation
# Used like this :
# Numbers = enum('ZERO', 'ONE', 'TWO')
# >>> Numbers.ZERO
# 0
# >>> Numbers.ONE
# 1
# Found here : http://stackoverflow.com/questions/36932/whats-the-best-way-to-implement-an-enum-in-python
def enum(*sequential, **named):
    enums = dict(zip(sequential, range(len(sequential))), **named)
    return type('Enum', (), enums)