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itertools.py
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itertools.py
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# Note that PyPy contains also a built-in module 'itertools' which will
# hide this one if compiled in.
"""Functional tools for creating and using iterators.
Infinite iterators:
count([n]) --> n, n+1, n+2, ...
cycle(p) --> p0, p1, ... plast, p0, p1, ...
repeat(elem [,n]) --> elem, elem, elem, ... endlessly or up to n times
Iterators terminating on the shortest input sequence:
izip(p, q, ...) --> (p[0], q[0]), (p[1], q[1]), ...
ifilter(pred, seq) --> elements of seq where pred(elem) is True
ifilterfalse(pred, seq) --> elements of seq where pred(elem) is False
islice(seq, [start,] stop [, step]) --> elements from
seq[start:stop:step]
imap(fun, p, q, ...) --> fun(p0, q0), fun(p1, q1), ...
starmap(fun, seq) --> fun(*seq[0]), fun(*seq[1]), ...
tee(it, n=2) --> (it1, it2 , ... itn) splits one iterator into n
chain(p, q, ...) --> p0, p1, ... plast, q0, q1, ...
takewhile(pred, seq) --> seq[0], seq[1], until pred fails
dropwhile(pred, seq) --> seq[n], seq[n+1], starting when pred fails
groupby(iterable[, keyfunc]) --> sub-iterators grouped by value of keyfunc(v)
"""
__all__ = ['chain', 'count', 'cycle', 'dropwhile', 'groupby', 'ifilter',
'ifilterfalse', 'imap', 'islice', 'izip', 'repeat', 'starmap',
'takewhile', 'tee', 'compress', 'product']
try: from __pypy__ import builtinify
except ImportError: builtinify = lambda f: f
class chain(object):
"""Make an iterator that returns elements from the first iterable
until it is exhausted, then proceeds to the next iterable, until
all of the iterables are exhausted. Used for treating consecutive
sequences as a single sequence.
Equivalent to :
def chain(*iterables):
for it in iterables:
for element in it:
yield element
"""
def __init__(self, *iterables):
self._iterables_iter = iter(map(iter, iterables))
# little trick for the first chain.next() call
self._cur_iterable_iter = iter([])
def __iter__(self):
return self
def next(self):
while True:
try:
return self._cur_iterable_iter.next()
except StopIteration:
self._cur_iterable_iter = self._iterables_iter.next()
except AttributeError:
# CPython raises a TypeError when next() is not defined
raise TypeError('%s has no next() method' % \
(self._cur_iterable_iter))
class compress(object):
def __init__(self, data, selectors):
self.data = iter(data)
self.selectors = iter(selectors)
def __iter__(self):
return self
def next(self):
while True:
next_item = self.data.next()
next_selector = self.selectors.next()
if bool(next_selector):
return next_item
class count(object):
"""Make an iterator that returns consecutive integers starting
with n. If not specified n defaults to zero. Does not currently
support python long integers. Often used as an argument to imap()
to generate consecutive data points. Also, used with izip() to
add sequence numbers.
Equivalent to :
def count(n=0):
if not isinstance(n, int):
raise TypeError("%s is not a regular integer" % n)
while True:
yield n
n += 1
"""
def __init__(self, n=0):
if not isinstance(n, int):
raise TypeError('%s is not a regular integer' % n)
self.times = n-1
def __iter__(self):
return self
def next(self):
self.times += 1
return self.times
def __repr__(self):
return 'count(%d)' % (self.times + 1)
class cycle(object):
"""Make an iterator returning elements from the iterable and
saving a copy of each. When the iterable is exhausted, return
elements from the saved copy. Repeats indefinitely.
Equivalent to :
def cycle(iterable):
saved = []
for element in iterable:
yield element
saved.append(element)
while saved:
for element in saved:
yield element
"""
def __init__(self, iterable):
self._cur_iter = iter(iterable)
self._saved = []
self._must_save = True
def __iter__(self):
return self
def next(self):
# XXX Could probably be improved
try:
next_elt = self._cur_iter.next()
if self._must_save:
self._saved.append(next_elt)
except StopIteration:
self._cur_iter = iter(self._saved)
next_elt = self._cur_iter.next()
self._must_save = False
except AttributeError:
# CPython raises a TypeError when next() is not defined
raise TypeError('%s has no next() method' % \
(self._cur_iter))
return next_elt
class dropwhile(object):
"""Make an iterator that drops elements from the iterable as long
as the predicate is true; afterwards, returns every
element. Note, the iterator does not produce any output until the
predicate is true, so it may have a lengthy start-up time.
Equivalent to :
def dropwhile(predicate, iterable):
iterable = iter(iterable)
for x in iterable:
if not predicate(x):
yield x
break
for x in iterable:
yield x
"""
def __init__(self, predicate, iterable):
self._predicate = predicate
self._iter = iter(iterable)
self._dropped = False
def __iter__(self):
return self
def next(self):
try:
value = self._iter.next()
except AttributeError:
# CPython raises a TypeError when next() is not defined
raise TypeError('%s has no next() method' % \
(self._iter))
if self._dropped:
return value
while self._predicate(value):
value = self._iter.next()
self._dropped = True
return value
class groupby(object):
"""Make an iterator that returns consecutive keys and groups from the
iterable. The key is a function computing a key value for each
element. If not specified or is None, key defaults to an identity
function and returns the element unchanged. Generally, the
iterable needs to already be sorted on the same key function.
The returned group is itself an iterator that shares the
underlying iterable with groupby(). Because the source is shared,
when the groupby object is advanced, the previous group is no
longer visible. So, if that data is needed later, it should be
stored as a list:
groups = []
uniquekeys = []
for k, g in groupby(data, keyfunc):
groups.append(list(g)) # Store group iterator as a list
uniquekeys.append(k)
"""
def __init__(self, iterable, key=None):
if key is None:
key = lambda x: x
self.keyfunc = key
self.it = iter(iterable)
self.tgtkey = self.currkey = self.currvalue = xrange(0)
def __iter__(self):
return self
def next(self):
while self.currkey == self.tgtkey:
try:
self.currvalue = self.it.next() # Exit on StopIteration
except AttributeError:
# CPython raises a TypeError when next() is not defined
raise TypeError('%s has no next() method' % \
(self.it))
self.currkey = self.keyfunc(self.currvalue)
self.tgtkey = self.currkey
return (self.currkey, self._grouper(self.tgtkey))
def _grouper(self, tgtkey):
while self.currkey == tgtkey:
yield self.currvalue
self.currvalue = self.it.next() # Exit on StopIteration
self.currkey = self.keyfunc(self.currvalue)
class _ifilter_base(object):
"""base class for ifilter and ifilterflase"""
def __init__(self, predicate, iterable):
# Make sure iterable *IS* iterable
self._iter = iter(iterable)
if predicate is None:
self._predicate = bool
else:
self._predicate = predicate
def __iter__(self):
return self
class ifilter(_ifilter_base):
"""Make an iterator that filters elements from iterable returning
only those for which the predicate is True. If predicate is
None, return the items that are true.
Equivalent to :
def ifilter:
if predicate is None:
predicate = bool
for x in iterable:
if predicate(x):
yield x
"""
def next(self):
try:
next_elt = self._iter.next()
except AttributeError:
# CPython raises a TypeError when next() is not defined
raise TypeError('%s has no next() method' % \
(self._iter))
while True:
if self._predicate(next_elt):
return next_elt
next_elt = self._iter.next()
class ifilterfalse(_ifilter_base):
"""Make an iterator that filters elements from iterable returning
only those for which the predicate is False. If predicate is
None, return the items that are false.
Equivalent to :
def ifilterfalse(predicate, iterable):
if predicate is None:
predicate = bool
for x in iterable:
if not predicate(x):
yield x
"""
def next(self):
try:
next_elt = self._iter.next()
except AttributeError:
# CPython raises a TypeError when next() is not defined
raise TypeError('%s has no next() method' % \
(self._iter))
while True:
if not self._predicate(next_elt):
return next_elt
next_elt = self._iter.next()
class imap(object):
"""Make an iterator that computes the function using arguments
from each of the iterables. If function is set to None, then
imap() returns the arguments as a tuple. Like map() but stops
when the shortest iterable is exhausted instead of filling in
None for shorter iterables. The reason for the difference is that
infinite iterator arguments are typically an error for map()
(because the output is fully evaluated) but represent a common
and useful way of supplying arguments to imap().
Equivalent to :
def imap(function, *iterables):
iterables = map(iter, iterables)
while True:
args = [i.next() for i in iterables]
if function is None:
yield tuple(args)
else:
yield function(*args)
"""
def __init__(self, function, iterable, *other_iterables):
self._func = function
self._iters = map(iter, (iterable, ) + other_iterables)
def __iter__(self):
return self
def next(self):
try:
args = [it.next() for it in self._iters]
except AttributeError:
# CPython raises a TypeError when next() is not defined
raise TypeError('%s has no next() method' % \
(it))
if self._func is None:
return tuple(args)
else:
return self._func(*args)
class islice(object):
"""Make an iterator that returns selected elements from the
iterable. If start is non-zero, then elements from the iterable
are skipped until start is reached. Afterward, elements are
returned consecutively unless step is set higher than one which
results in items being skipped. If stop is None, then iteration
continues until the iterator is exhausted, if at all; otherwise,
it stops at the specified position. Unlike regular slicing,
islice() does not support negative values for start, stop, or
step. Can be used to extract related fields from data where the
internal structure has been flattened (for example, a multi-line
report may list a name field on every third line).
"""
def __init__(self, iterable, *args):
s = slice(*args)
self.start, self.stop, self.step = s.start or 0, s.stop, s.step
if not isinstance(self.start, (int, long)):
raise ValueError("Start argument must be an integer")
if self.stop is not None and not isinstance(self.stop, (int,long)):
raise ValueError("Stop argument must be an integer or None")
if self.step is None:
self.step = 1
if self.start<0 or (self.stop is not None and self.stop<0
) or self.step<=0:
raise ValueError, "indices for islice() must be positive"
self.it = iter(iterable)
self.donext = None
self.cnt = 0
def __iter__(self):
return self
def next(self):
if self.donext is None:
try:
self.donext = self.it.next
except AttributeError:
raise TypeError
nextindex = self.start
if self.stop is not None and nextindex >= self.stop:
raise StopIteration
while self.cnt <= nextindex:
nextitem = self.donext()
self.cnt += 1
self.start += self.step
return nextitem
class izip(object):
"""Make an iterator that aggregates elements from each of the
iterables. Like zip() except that it returns an iterator instead
of a list. Used for lock-step iteration over several iterables at
a time.
Equivalent to :
def izip(*iterables):
iterables = map(iter, iterables)
while iterables:
result = [i.next() for i in iterables]
yield tuple(result)
"""
def __init__(self, *iterables):
self._iterators = map(iter, iterables)
self._result = [None] * len(self._iterators)
def __iter__(self):
return self
def next(self):
if not self._iterators:
raise StopIteration()
try:
return tuple([i.next() for i in self._iterators])
except AttributeError:
# CPython raises a TypeError when next() is not defined
raise TypeError('%s has no next() method' % (i))
class product(object):
def __init__(self, *args, **kw):
if len(kw) > 1:
raise TypeError("product() takes at most 1 argument (%d given)" %
len(kw))
self.repeat = kw.get('repeat', 1)
self.gears = [x for x in args] * self.repeat
self.num_gears = len(self.gears)
# initialization of indicies to loop over
self.indicies = [(0, len(self.gears[x]))
for x in range(0, self.num_gears)]
self.cont = True
def roll_gears(self):
# Starting from the end of the gear indicies work to the front
# incrementing the gear until the limit is reached. When the limit
# is reached carry operation to the next gear
should_carry = True
for n in range(0, self.num_gears):
nth_gear = self.num_gears - n - 1
if should_carry:
count, lim = self.indicies[nth_gear]
count += 1
if count == lim and nth_gear == 0:
self.cont = False
if count == lim:
should_carry = True
count = 0
else:
should_carry = False
self.indicies[nth_gear] = (count, lim)
else:
break
def __iter__(self):
return self
def next(self):
if not self.cont:
raise StopIteration
l = []
for x in range(0, self.num_gears):
index, limit = self.indicies[x]
l.append(self.gears[x][index])
self.roll_gears()
return tuple(l)
class repeat(object):
"""Make an iterator that returns object over and over again.
Runs indefinitely unless the times argument is specified. Used
as argument to imap() for invariant parameters to the called
function. Also used with izip() to create an invariant part of a
tuple record.
Equivalent to :
def repeat(object, times=None):
if times is None:
while True:
yield object
else:
for i in xrange(times):
yield object
"""
def __init__(self, obj, times=None):
self._obj = obj
if times is not None:
xrange(times) # Raise a TypeError
if times < 0:
times = 0
self._times = times
def __iter__(self):
return self
def next(self):
# next() *need* to decrement self._times when consumed
if self._times is not None:
if self._times <= 0:
raise StopIteration()
self._times -= 1
return self._obj
def __repr__(self):
if self._times is not None:
return 'repeat(%r, %r)' % (self._obj, self._times)
else:
return 'repeat(%r)' % (self._obj,)
def __len__(self):
if self._times == -1 or self._times is None:
raise TypeError("len() of uniszed object")
return self._times
class starmap(object):
"""Make an iterator that computes the function using arguments
tuples obtained from the iterable. Used instead of imap() when
argument parameters are already grouped in tuples from a single
iterable (the data has been ``pre-zipped''). The difference
between imap() and starmap() parallels the distinction between
function(a,b) and function(*c).
Equivalent to :
def starmap(function, iterable):
iterable = iter(iterable)
while True:
yield function(*iterable.next())
"""
def __init__(self, function, iterable):
self._func = function
self._iter = iter(iterable)
def __iter__(self):
return self
def next(self):
# CPython raises a TypeError when the iterator doesn't return a tuple
try:
t = self._iter.next()
except AttributeError:
# CPython raises a TypeError when next() is not defined
raise TypeError('%s has no next() method' % self._iter)
if not isinstance(t, tuple):
raise TypeError("iterator must return a tuple")
return self._func(*t)
class takewhile(object):
"""Make an iterator that returns elements from the iterable as
long as the predicate is true.
Equivalent to :
def takewhile(predicate, iterable):
for x in iterable:
if predicate(x):
yield x
else:
break
"""
def __init__(self, predicate, iterable):
self._predicate = predicate
self._iter = iter(iterable)
def __iter__(self):
return self
def next(self):
try:
value = self._iter.next()
except AttributeError:
# CPython raises a TypeError when next() is not defined
raise TypeError('%s has no next() method' % \
(self._iter))
if not self._predicate(value):
raise StopIteration()
return value
class TeeData(object):
"""Holds cached values for TeeObjects"""
def __init__(self, iterator):
self.data = []
self._iter = iterator
def __getitem__(self, i):
# iterates until 'i' if not done yet
while i>= len(self.data):
try:
self.data.append( self._iter.next() )
except AttributeError:
# CPython raises a TypeError when next() is not defined
raise TypeError('%s has no next() method' % self._iter)
return self.data[i]
class TeeObject(object):
"""Iterables / Iterators as returned by the tee() function"""
def __init__(self, iterable=None, tee_data=None):
if tee_data:
self.tee_data = tee_data
self.pos = 0
# <=> Copy constructor
elif isinstance(iterable, TeeObject):
self.tee_data = iterable.tee_data
self.pos = iterable.pos
else:
self.tee_data = TeeData(iter(iterable))
self.pos = 0
def next(self):
data = self.tee_data[self.pos]
self.pos += 1
return data
def __iter__(self):
return self
@builtinify
def tee(iterable, n=2):
"""Return n independent iterators from a single iterable.
Note : once tee() has made a split, the original iterable
should not be used anywhere else; otherwise, the iterable could get
advanced without the tee objects being informed.
Note : this member of the toolkit may require significant auxiliary
storage (depending on how much temporary data needs to be stored).
In general, if one iterator is going to use most or all of the
data before the other iterator, it is faster to use list() instead
of tee()
Equivalent to :
def tee(iterable, n=2):
def gen(next, data={}, cnt=[0]):
for i in count():
if i == cnt[0]:
item = data[i] = next()
cnt[0] += 1
else:
item = data.pop(i)
yield item
it = iter(iterable)
return tuple([gen(it.next) for i in range(n)])
"""
if isinstance(iterable, TeeObject):
# a,b = tee(range(10)) ; c,d = tee(a) ; self.assert_(a is c)
return tuple([iterable] +
[TeeObject(tee_data=iterable.tee_data) for i in xrange(n-1)])
tee_data = TeeData(iter(iterable))
return tuple([TeeObject(tee_data=tee_data) for i in xrange(n)])