"""Module for the creation and use of iterator-based lazy lists.
this module defines a class LazyList which can be used to represent sequences
of values generated lazily. One can also create recursively defined lazy lists
that generate their values based on ones previously generated."""
__author__ = 'Dan Spitz'
__all__ = ('LazyList', 'RecursiveLazyList', 'lazylist')
import itertools
import abc
class LazyList(metaclass = abc.ABCMeta):
"""A Sequence whose values are computed lazily by an iterator.
"""
def __init__(self, iterable):
self._exhausted = False
self._iterator = iter(iterable)
self._data = []
def __len__(self):
"""Get the length of a LazyList's computed data."""
return len(self._data)
def __getitem__(self, i):
"""Get an item from a LazyList.
i should be a positive integer or a slice object."""
if isinstance(i, int):
#index has not yet been yielded by iterator (or iterator exhausted
#before reaching that index)
if i >= len(self):
self.exhaust(i)
elif i < 0:
raise ValueError('cannot index LazyList with negative number')
return self._data[i]
#LazyList slices are iterators over a portion of the list.
elif isinstance(i, slice):
start, stop, step = i.start, i.stop, i.step
if any(x is not None and x < 0 for x in (start, stop, step)):
raise ValueError('cannot index or step through a LazyList with'
'a negative number')
#set start and step to their integer defaults if they are None.
if start is None:
start = 0
if step is None:
step = 1
def LazyListIterator():
count = start
predicate = ((lambda: True) if stop is None
else (lambda: count < stop))
while predicate():
try:
yield self[count]
#slices can go out of actual index range without raising an
#error
except IndexError:
break
count += step
return LazyListIterator()
raise TypeError('i must be an integer or slice')
def __iter__(self):
"""return an iterator over each value in the sequence,
whether it has been computed yet or not."""
return self[:]
def computed(self):
"""Return an iterator over the values in a LazyList that have
already been computed."""
return self[:len(self)]
def exhaust(self, index = None):
"""Exhaust the iterator generating this LazyList's values.
if index is None, this will exhaust the iterator completely.
Otherwise, it will iterate over the iterator until either the list
has a value for index or the iterator is exhausted.
"""
if self._exhausted:
return
if index is None:
ind_range = itertools.count(len(self))
else:
ind_range = range(len(self), index + 1)
for ind in ind_range:
try:
self._data.append(next(self._iterator))
except StopIteration: #iterator is fully exhausted
self._exhausted = True
break
class RecursiveLazyList(LazyList):
@abc.abstractmethod
def _producer(self):
"""generator method that yields the LazyList's contents"""
while False:
yield
def __init__(self, *args, **kwds):
super().__init__(self._producer(*args, **kwds))
def lazylist(producer):
"""Decorator for creating a RecursiveLazyList subclass.
This should decorate a generator function taking the LazyList object as its
first argument which yields the contents of the list in order.
"""
#ABCMeta is the metaclass of LazyList and its subclasses
return abc.ABCMeta(producer.__name__,
(RecursiveLazyList,),
dict(_producer = producer))
#two examples
if __name__ = '__main__':
#fibonnacci sequence in a lazy list.
@lazylist
def fibgen(lst):
yield 0
yield 1
for a, b in zip(lst, lst[1:]):
yield a + b
fibs = fibgen() #now fibs can be indexed or iterated over as if it were
#an infinitely long list containing the fibonnaci sequence
#prime numbers in a lazy list.
@lazylist
def primegen(lst):
yield 2
for candidate in itertools.count(3): #start at next number after 2
#if candidate is not divisible by any smaller prime numbers,
#it is a prime.
if all(candidate % p for p in lst.computed()):
yield candidate
primes = primegen() #same for primes- treat it like an infinitely long list
#containing all prime numbers.
print(fibs[0], fibs[1], fibs[2], primes[0], primes[1], primes[2])
print(list(fibs[:10]), list(primes[:10]))
