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reSTify PEP 323 (#351)

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@ -5,362 +5,375 @@ Last-Modified: $Date$
Author: Alex Martelli <aleaxit@gmail.com>
Status: Deferred
Type: Standards Track
Content-Type: text/plain
Content-Type: text/x-rst
Created: 25-Oct-2003
Python-Version: 2.5
Post-History: 29-Oct-2003
Deferral
========
This PEP has been deferred. Copyable iterators are a nice idea, but after
four years, no implementation or widespread interest has emerged.
This PEP has been deferred. Copyable iterators are a nice idea, but after
four years, no implementation or widespread interest has emerged.
Abstract
========
This PEP suggests that some iterator types should support shallow
copies of their instances by exposing a __copy__ method which meets
some specific requirements, and indicates how code using an iterator
might exploit such a __copy__ method when present.
This PEP suggests that some iterator types should support shallow
copies of their instances by exposing a ``__copy__`` method which meets
some specific requirements, and indicates how code using an iterator
might exploit such a ``__copy__`` method when present.
Update and Comments
===================
Support for __copy__ was included in Py2.4's itertools.tee().
Support for ``__copy__`` was included in Py2.4's ``itertools.tee()``.
Adding __copy__ methods to existing iterators will change the
behavior under tee(). Currently, the copied iterators remain
tied to the original iterator. If the original advances, then
so do all of the copies. Good practice is to overwrite the
original so that anamolies don't result: a,b=tee(a).
Code that doesn't follow that practice may observe a semantic
change if a __copy__ method is added to an iterator.
Adding ``__copy__`` methods to existing iterators will change the
behavior under ``tee()``. Currently, the copied iterators remain
tied to the original iterator. If the original advances, then
so do all of the copies. Good practice is to overwrite the
original so that anamolies don't result: ``a,b=tee(a)``.
Code that doesn't follow that practice may observe a semantic
change if a ``__copy__`` method is added to an iterator.
Motivation
==========
In Python up to 2.3, most built-in iterator types don't let the user
copy their instances. User-coded iterators that do let their clients
call copy.copy on their instances may, or may not, happen to return,
as a result of the copy, a separate iterator object that may be
iterated upon independently from the original.
In Python up to 2.3, most built-in iterator types don't let the user
copy their instances. User-coded iterators that do let their clients
call copy.copy on their instances may, or may not, happen to return,
as a result of the copy, a separate iterator object that may be
iterated upon independently from the original.
Currently, "support" for copy.copy in a user-coded iterator type is
almost invariably "accidental" -- i.e., the standard machinery of the
copy method in Python's standard library's copy module does build and
return a copy. However, the copy will be independently iterable with
respect to the original only if calling .next() on an instance of that
class happens to change instance state solely by rebinding some
attributes to new values, and not by mutating some attributes'
existing values.
Currently, "support" for copy.copy in a user-coded iterator type is
almost invariably "accidental" -- i.e., the standard machinery of the
copy method in Python's standard library's copy module does build and
return a copy. However, the copy will be independently iterable with
respect to the original only if calling ``.next()`` on an instance of that
class happens to change instance state solely by rebinding some
attributes to new values, and not by mutating some attributes'
existing values.
For example, an iterator whose "index" state is held as an integer
attribute will probably give usable copies, since (integers being
immutable) .next() presumably just rebinds that attribute. On the
other hand, another iterator whose "index" state is held as a list
attribute will probably mutate the same list object when .next()
executes, and therefore copies of such an iterator will not be
iterable separately and independently from the original.
For example, an iterator whose "index" state is held as an integer
attribute will probably give usable copies, since (integers being
immutable) ``.next()`` presumably just rebinds that attribute. On the
other hand, another iterator whose "index" state is held as a list
attribute will probably mutate the same list object when ``.next()``
executes, and therefore copies of such an iterator will not be
iterable separately and independently from the original.
Given this existing situation, copy.copy(it) on some iterator object
isn't very useful, nor, therefore, is it at all widely used. However,
there are many cases in which being able to get a "snapshot" of an
iterator, as a "bookmark", so as to be able to keep iterating along
the sequence but later iterate again on the same sequence from the
bookmark onwards, is useful. To support such "bookmarking", module
itertools, in 2.4, has grown a 'tee' function, to be used as:
Given this existing situation, ``copy.copy(it)`` on some iterator object
isn't very useful, nor, therefore, is it at all widely used. However,
there are many cases in which being able to get a "snapshot" of an
iterator, as a "bookmark", so as to be able to keep iterating along
the sequence but later iterate again on the same sequence from the
bookmark onwards, is useful. To support such "bookmarking", module
itertools, in 2.4, has grown a 'tee' function, to be used as::
it, bookmark = itertools.tee(it)
it, bookmark = itertools.tee(it)
The previous value of 'it' must not be used again, which is why this
typical usage idiom rebinds the name. After this call, 'it' and
'bookmark' are independently-iterable iterators on the same underlying
sequence as the original value of 'it': this satisfies application
needs for "iterator copying".
The previous value of 'it' must not be used again, which is why this
typical usage idiom rebinds the name. After this call, 'it' and
'bookmark' are independently-iterable iterators on the same underlying
sequence as the original value of 'it': this satisfies application
needs for "iterator copying".
However, when itertools.tee can make no hypotheses about the nature of
the iterator it is passed as an argument, it must save in memory all
items through which one of the two 'teed' iterators, but not yet both,
have stepped. This can be quite costly in terms of memory, if the two
iterators get very far from each other in their stepping; indeed, in
some cases it may be preferable to make a list from the iterator so as
to be able to step repeatedly through the subsequence, or, if that is
too costy in terms of memory, save items to disk, again in order to be
able to iterate through them repeatedly.
However, when itertools.tee can make no hypotheses about the nature of
the iterator it is passed as an argument, it must save in memory all
items through which one of the two 'teed' iterators, but not yet both,
have stepped. This can be quite costly in terms of memory, if the two
iterators get very far from each other in their stepping; indeed, in
some cases it may be preferable to make a list from the iterator so as
to be able to step repeatedly through the subsequence, or, if that is
too costy in terms of memory, save items to disk, again in order to be
able to iterate through them repeatedly.
This PEP proposes another idea that will, in some important cases,
allow itertools.tee to do its job with minimal cost in terms of
memory; user code may also occasionally be able to exploit the idea in
order to decide whether to copy an iterator, make a list from it, or
use an auxiliary disk file.
This PEP proposes another idea that will, in some important cases,
allow ``itertools.tee`` to do its job with minimal cost in terms of
memory; user code may also occasionally be able to exploit the idea in
order to decide whether to copy an iterator, make a list from it, or
use an auxiliary disk file.
The key consideration is that some important iterators, such as those
which built-in function iter builds over sequences, would be
intrinsically easy to copy: just get another reference to the same
sequence, and a copy of the integer index. However, in Python 2.3,
those iterators don't expose the state, and don't support copy.copy.
The key consideration is that some important iterators, such as those
which built-in function iter builds over sequences, would be
intrinsically easy to copy: just get another reference to the same
sequence, and a copy of the integer index. However, in Python 2.3,
those iterators don't expose the state, and don't support ``copy.copy``.
The purpose of this PEP, therefore, is to have those iterator types
expose a suitable __copy__ method. Similarly, user-coded iterator
types that can provide copies of their instances, suitable for
separate and independent iteration, with limited costs in time and
space, should also expose a suitable __copy__ method. While
copy.copy also supports other ways to let a type control the way
its instances are copied, it is suggested, for simplicity, that
iterator types that support copying always do so by exposing a
__copy__ method, and not in the other ways copy.copy supports.
The purpose of this PEP, therefore, is to have those iterator types
expose a suitable ``__copy__`` method. Similarly, user-coded iterator
types that can provide copies of their instances, suitable for
separate and independent iteration, with limited costs in time and
space, should also expose a suitable ``__copy__`` method. While
copy.copy also supports other ways to let a type control the way
its instances are copied, it is suggested, for simplicity, that
iterator types that support copying always do so by exposing a
``__copy__`` method, and not in the other ways ``copy.copy`` supports.
Having iterators expose a suitable __copy__ when feasible will afford
easy optimization of itertools.tee and similar user code, as in:
Having iterators expose a suitable ``__copy__`` when feasible will afford
easy optimization of itertools.tee and similar user code, as in::
def tee(it):
it = iter(it)
try: copier = it.__copy__
except AttributeError:
# non-copyable iterator, do all the needed hard work
# [snipped!]
else:
return it, copier()
def tee(it):
it = iter(it)
try: copier = it.__copy__
except AttributeError:
# non-copyable iterator, do all the needed hard work
# [snipped!]
else:
return it, copier()
Note that this function does NOT call "copy.copy(it)", which (even
after this PEP is implemented) might well still "just happen to
succeed". for some iterator type that is implemented as a user-coded
class. without really supplying an adequate "independently iterable"
copy object as its result.
Note that this function does NOT call "copy.copy(it)", which (even
after this PEP is implemented) might well still "just happen to
succeed". for some iterator type that is implemented as a user-coded
class. without really supplying an adequate "independently iterable"
copy object as its result.
Specification
=============
Any iterator type X may expose a method __copy__ that is callable
without arguments on any instance x of X. The method should be
exposed if and only if the iterator type can provide copyability with
reasonably little computational and memory effort. Furthermore, the
new object y returned by method __copy__ should be a new instance
of X that is iterable independently and separately from x, stepping
along the same "underlying sequence" of items.
Any iterator type X may expose a method ``__copy__`` that is callable
without arguments on any instance x of X. The method should be
exposed if and only if the iterator type can provide copyability with
reasonably little computational and memory effort. Furthermore, the
new object y returned by method ``__copy__`` should be a new instance
of X that is iterable independently and separately from x, stepping
along the same "underlying sequence" of items.
For example, suppose a class Iter essentially duplicated the
functionality of the iter builtin for iterating on a sequence:
For example, suppose a class Iter essentially duplicated the
functionality of the iter builtin for iterating on a sequence::
class Iter(object):
class Iter(object):
def __init__(self, sequence):
self.sequence = sequence
self.index = 0
def __init__(self, sequence):
self.sequence = sequence
self.index = 0
def __iter__(self):
return self
def __iter__(self):
return self
def next(self):
try: result = self.sequence[self.index]
except IndexError: raise StopIteration
self.index += 1
return result
def next(self):
try: result = self.sequence[self.index]
except IndexError: raise StopIteration
self.index += 1
return result
To make this Iter class compliant with this PEP, the following
addition to the body of class Iter would suffice:
To make this Iter class compliant with this PEP, the following
addition to the body of class Iter would suffice::
def __copy__(self):
result = self.__class__(self.sequence)
result.index = self.index
return result
def __copy__(self):
result = self.__class__(self.sequence)
result.index = self.index
return result
Note that __copy__, in this case, does not even try to copy the
sequence; if the sequence is altered while either or both of the
original and copied iterators are still stepping on it, the iteration
behavior is quite likely to go awry anyway -- it is not __copy__'s
responsibility to change this normal Python behavior for iterators
which iterate on mutable sequences (that might, perhaps, be the
specification for a __deepcopy__ method of iterators, which, however,
this PEP does not deal with).
Note that ``__copy__``, in this case, does not even try to copy the
sequence; if the sequence is altered while either or both of the
original and copied iterators are still stepping on it, the iteration
behavior is quite likely to go awry anyway -- it is not ``__copy__``'s
responsibility to change this normal Python behavior for iterators
which iterate on mutable sequences (that might, perhaps, be the
specification for a ``__deepcopy__`` method of iterators, which, however,
this PEP does not deal with).
Consider also a "random iterator", which provides a nonterminating
sequence of results from some method of a random instance, called
with given arguments:
Consider also a "random iterator", which provides a nonterminating
sequence of results from some method of a random instance, called
with given arguments::
class RandomIterator(object):
class RandomIterator(object):
def __init__(self, bound_method, *args):
self.call = bound_method
self.args = args
def __init__(self, bound_method, *args):
self.call = bound_method
self.args = args
def __iter__(self):
return self
def __iter__(self):
return self
def next(self):
return self.call(*self.args)
def next(self):
return self.call(*self.args)
def __copy__(self):
import copy, new
im_self = copy.copy(self.call.im_self)
method = new.instancemethod(self.call.im_func, im_self)
return self.__class__(method, *self.args)
def __copy__(self):
import copy, new
im_self = copy.copy(self.call.im_self)
method = new.instancemethod(self.call.im_func, im_self)
return self.__class__(method, *self.args)
This iterator type is slightly more general than its name implies, as
it supports calls to any bound method (or other callable, but if the
callable is not a bound method, then method __copy__ will fail). But
the use case is for the purpose of generating random streams, as in:
This iterator type is slightly more general than its name implies, as
it supports calls to any bound method (or other callable, but if the
callable is not a bound method, then method ``__copy__`` will fail). But
the use case is for the purpose of generating random streams, as in::
import random
import random
def show5(it):
for i, result in enumerate(it):
print '%6.3f'%result,
if i==4: break
print
def show5(it):
for i, result in enumerate(it):
print '%6.3f'%result,
if i==4: break
print
normit = RandomIterator(random.Random().gauss, 0, 1)
show5(normit)
copit = normit.__copy__()
show5(normit)
show5(copit)
normit = RandomIterator(random.Random().gauss, 0, 1)
show5(normit)
copit = normit.__copy__()
show5(normit)
show5(copit)
which will display some output such as:
which will display some output such as::
-0.536 1.936 -1.182 -1.690 -1.184
0.666 -0.701 1.214 0.348 1.373
0.666 -0.701 1.214 0.348 1.373
-0.536 1.936 -1.182 -1.690 -1.184
0.666 -0.701 1.214 0.348 1.373
0.666 -0.701 1.214 0.348 1.373
the key point being that the second and third lines are equal, because
the normit and copit iterators will step along the same "underlying
sequence". (As an aside, note that to get a copy of self.call.im_self
we must use copy.copy, NOT try getting at a __copy__ method directly,
because for example instances of random.Random support copying via
__getstate__ and __setstate__, NOT via __copy__; indeed, using
copy.copy is the normal way to get a shallow copy of any object --
copyable iterators are different because of the already-mentioned
uncertainty about the result of copy.copy supporting these "copyable
iterator" specs).
the key point being that the second and third lines are equal, because
the normit and copit iterators will step along the same "underlying
sequence". (As an aside, note that to get a copy of ``self.call.im_self``
we must use ``copy.copy``, NOT try getting at a ``__copy__`` method directly,
because for example instances of ``random.Random`` support copying via
``__getstate__`` and ``__setstate__``, NOT via ``__copy__``; indeed, using
copy.copy is the normal way to get a shallow copy of any object --
copyable iterators are different because of the already-mentioned
uncertainty about the result of ``copy.copy`` supporting these "copyable
iterator" specs).
Details
=======
Besides adding to the Python docs a recommendation that user-coded
iterator types support a __copy__ method (if and only if it can be
implemented with small costs in memory and runtime, and produce an
independently-iterable copy of an iterator object), this PEP's
implementation will specifically include the addition of copyability
to the iterators over sequences that built-in iter returns, and also
to the iterators over a dictionary returned by the methods __iter__,
iterkeys, itervalues, and iteritems of built-in type dict.
Besides adding to the Python docs a recommendation that user-coded
iterator types support a ``__copy__`` method (if and only if it can be
implemented with small costs in memory and runtime, and produce an
independently-iterable copy of an iterator object), this PEP's
implementation will specifically include the addition of copyability
to the iterators over sequences that built-in iter returns, and also
to the iterators over a dictionary returned by the methods ``__iter__``,
iterkeys, itervalues, and iteritems of built-in type dict.
Iterators produced by generator functions will not be copyable.
However, iterators produced by the new "generator expressions" of
Python 2.4 (PEP 289 [3]) should be copyable if their underlying
iterator[s] are; the strict limitations on what is possible in a
generator expression, compared to the much vaster generality of a
generator, should make that feasible. Similarly, the iterators
produced by the built-in function enumerate, and certain functions
suppiled by module itertools, should be copyable if the underlying
iterators are.
Iterators produced by generator functions will not be copyable.
However, iterators produced by the new "generator expressions" of
Python 2.4 (PEP 289 [3]_) should be copyable if their underlying
``iterator[s]`` are; the strict limitations on what is possible in a
generator expression, compared to the much vaster generality of a
generator, should make that feasible. Similarly, the iterators
produced by the built-in function ``enumerate``, and certain functions
suppiled by module itertools, should be copyable if the underlying
iterators are.
The implementation of this PEP will also include the optimization of
the new itertools.tee function mentioned in the Motivation section.
The implementation of this PEP will also include the optimization of
the new itertools.tee function mentioned in the Motivation section.
Rationale
=========
The main use case for (shallow) copying of an iterator is the same as
for the function itertools.tee (new in 2.4). User code will not
directly attempt to copy an iterator, because it would have to deal
separately with uncopyable cases; calling itertools.tee will
internally perform the copy when appropriate, and implicitly fallback
to a maximally efficient non-copying strategy for iterators that are
not copyable. (Occasionally, user code may want more direct control,
specifically in order to deal with non-copyable iterators by other
strategies, such as making a list or saving the sequence to disk).
The main use case for (shallow) copying of an iterator is the same as
for the function ``itertools.tee`` (new in 2.4). User code will not
directly attempt to copy an iterator, because it would have to deal
separately with uncopyable cases; calling ``itertools.tee`` will
internally perform the copy when appropriate, and implicitly fallback
to a maximally efficient non-copying strategy for iterators that are
not copyable. (Occasionally, user code may want more direct control,
specifically in order to deal with non-copyable iterators by other
strategies, such as making a list or saving the sequence to disk).
A tee'd iterator may serve as a "reference point", allowing processing
of a sequence to continue or resume from a known point, while the
other independent iterator can be freely advanced to "explore" a
further part of the sequence as needed. A simple example: a generator
function which, given an iterator of numbers (assumed to be positive),
returns a corresponding iterator, each of whose items is the fraction
of the total corresponding to each corresponding item of the input
iterator. The caller may pass the total as a value, if known in
advance; otherwise, the iterator returned by calling this generator
function will first compute the total.
A tee'd iterator may serve as a "reference point", allowing processing
of a sequence to continue or resume from a known point, while the
other independent iterator can be freely advanced to "explore" a
further part of the sequence as needed. A simple example: a generator
function which, given an iterator of numbers (assumed to be positive),
returns a corresponding iterator, each of whose items is the fraction
of the total corresponding to each corresponding item of the input
iterator. The caller may pass the total as a value, if known in
advance; otherwise, the iterator returned by calling this generator
function will first compute the total.
def fractions(numbers, total=None):
if total is None:
numbers, aux = itertools.tee(numbers)
total = sum(aux)
total = float(total)
for item in numbers:
yield item / total
::
The ability to tee the numbers iterator allows this generator to
precompute the total, if needed, without necessarily requiring
O(N) auxiliary memory if the numbers iterator is copyable.
def fractions(numbers, total=None):
if total is None:
numbers, aux = itertools.tee(numbers)
total = sum(aux)
total = float(total)
for item in numbers:
yield item / total
As another example of "iterator bookmarking", consider a stream of
numbers with an occasional string as a "postfix operator" now and
then. By far most frequent such operator is a '+', whereupon we must
sum all previous numbers (since the last previous operator if any, or
else since the start) and yield the result. Sometimes we find a '*'
instead, which is the same except that the previous numbers must
instead be multiplied, not summed.
The ability to tee the numbers iterator allows this generator to
precompute the total, if needed, without necessarily requiring
O(N) auxiliary memory if the numbers iterator is copyable.
def filter_weird_stream(stream):
it = iter(stream)
while True:
it, bookmark = itertools.tee(it)
total = 0
for item in it:
if item=='+':
yield total
break
elif item=='*':
product = 1
for item in bookmark:
if item=='*':
yield product
break
else:
product *= item
else:
total += item
As another example of "iterator bookmarking", consider a stream of
numbers with an occasional string as a "postfix operator" now and
then. By far most frequent such operator is a '+', whereupon we must
sum all previous numbers (since the last previous operator if any, or
else since the start) and yield the result. Sometimes we find a '*'
instead, which is the same except that the previous numbers must
instead be multiplied, not summed.
Similar use cases of itertools.tee can support such tasks as
"undo" on a stream of commands represented by an iterator,
"backtracking" on the parse of a stream of tokens, and so on.
(Of course, in each case, one should also consider simpler
possibilities such as saving relevant portions of the sequence
into lists while stepping on the sequence with just one iterator,
depending on the details of one's task).
::
def filter_weird_stream(stream):
it = iter(stream)
while True:
it, bookmark = itertools.tee(it)
total = 0
for item in it:
if item=='+':
yield total
break
elif item=='*':
product = 1
for item in bookmark:
if item=='*':
yield product
break
else:
product *= item
else:
total += item
Similar use cases of itertools.tee can support such tasks as
"undo" on a stream of commands represented by an iterator,
"backtracking" on the parse of a stream of tokens, and so on.
(Of course, in each case, one should also consider simpler
possibilities such as saving relevant portions of the sequence
into lists while stepping on the sequence with just one iterator,
depending on the details of one's task).
Here is an example, in pure Python, of how the 'enumerate'
built-in could be extended to support __copy__ if its underlying
iterator also supported __copy__:
Here is an example, in pure Python, of how the 'enumerate'
built-in could be extended to support ``__copy__`` if its underlying
iterator also supported ``__copy__``::
class enumerate(object):
class enumerate(object):
def __init__(self, it):
self.it = iter(it)
self.i = -1
def __init__(self, it):
self.it = iter(it)
self.i = -1
def __iter__(self):
return self
def __iter__(self):
return self
def next(self):
self.i += 1
return self.i, self.it.next()
def next(self):
self.i += 1
return self.i, self.it.next()
def __copy__(self):
result = self.__class__.__new__()
result.it = self.it.__copy__()
result.i = self.i
return result
def __copy__(self):
result = self.__class__.__new__()
result.it = self.it.__copy__()
result.i = self.i
return result
Here is an example of the kind of "fragility" produced by "accidental
copyability" of an iterator -- the reason why one must NOT use
copy.copy expecting, if it succeeds, to receive as a result an
iterator which is iterable-on independently from the original. Here
is an iterator class that iterates (in preorder) on "trees" which, for
simplicity, are just nested lists -- any item that's a list is treated
as a subtree, any other item as a leaf.
Here is an example of the kind of "fragility" produced by "accidental
copyability" of an iterator -- the reason why one must NOT use
copy.copy expecting, if it succeeds, to receive as a result an
iterator which is iterable-on independently from the original. Here
is an iterator class that iterates (in preorder) on "trees" which, for
simplicity, are just nested lists -- any item that's a list is treated
as a subtree, any other item as a leaf.
::
class ListreeIter(object):
@ -387,96 +400,100 @@ Rationale
self.indx.append(-1)
return self.next()
Now, for example, the following code:
Now, for example, the following code::
import copy
x = [ [1,2,3], [4, 5, [6, 7, 8], 9], 10, 11, [12] ]
import copy
x = [ [1,2,3], [4, 5, [6, 7, 8], 9], 10, 11, [12] ]
print 'showing all items:',
it = ListreeIter(x)
for i in it:
print i,
if i==6: cop = copy.copy(it)
print
print 'showing all items:',
it = ListreeIter(x)
for i in it:
print i,
if i==6: cop = copy.copy(it)
print
print 'showing items >6 again:'
for i in cop: print i,
print
print 'showing items >6 again:'
for i in cop: print i,
print
does NOT work as intended -- the "cop" iterator gets consumed, and
exhausted, step by step as the original "it" iterator is, because
the accidental (rather than deliberate) copying performed by
copy.copy shares, rather than duplicating the "index" list, which
is the mutable attribute it.indx (a list of numerical indices).
Thus, this "client code" of the iterator, which attempts to iterate
twice over a portion of the sequence via a copy.copy on the
iterator, is NOT correct.
does NOT work as intended -- the "cop" iterator gets consumed, and
exhausted, step by step as the original "it" iterator is, because
the accidental (rather than deliberate) copying performed by
``copy.copy`` shares, rather than duplicating the "index" list, which
is the mutable attribute ``it.indx`` (a list of numerical indices).
Thus, this "client code" of the iterator, which attempts to iterate
twice over a portion of the sequence via a ``copy.copy`` on the
iterator, is NOT correct.
Some correct solutions include using itertools.tee, i.e., changing
the first for loop into:
Some correct solutions include using ``itertools.tee``, i.e., changing
the first for loop into::
for i in it:
print i,
if i==6:
it, cop = itertools.tee(it)
break
for i in it: print i,
for i in it:
print i,
if i==6:
it, cop = itertools.tee(it)
break
for i in it: print i,
(note that we MUST break the loop in two, otherwise we'd still
be looping on the ORIGINAL value of it, which must NOT be used
further after the call to tee!!!); or making a list, i.e.:
(note that we MUST break the loop in two, otherwise we'd still
be looping on the ORIGINAL value of it, which must NOT be used
further after the call to tee!!!); or making a list, i.e.
for i in it:
print i,
if i==6:
cop = lit = list(it)
break
for i in lit: print i,
::
(again, the loop must be broken in two, since iterator 'it'
gets exhausted by the call list(it)).
for i in it:
print i,
if i==6:
cop = lit = list(it)
break
for i in lit: print i,
Finally, all of these solutions would work if Listiter supplied
a suitable __copy__ method, as this PEP recommends:
(again, the loop must be broken in two, since iterator 'it'
gets exhausted by the call ``list(it)``).
def __copy__(self):
result = self.__class__.new()
result.tree = copy.copy(self.tree)
result.indx = copy.copy(self.indx)
return result
Finally, all of these solutions would work if Listiter supplied
a suitable ``__copy__`` method, as this PEP recommends::
There is no need to get any "deeper" in the copy, but the two
mutable "index state" attributes must indeed be copied in order
to achieve a "proper" (independently iterable) iterator-copy.
def __copy__(self):
result = self.__class__.new()
result.tree = copy.copy(self.tree)
result.indx = copy.copy(self.indx)
return result
The recommended solution is to have class Listiter supply this
__copy__ method AND have client code use itertools.tee (with
the split-in-two-parts loop as shown above). This will make
client code maximally tolerant of different iterator types it
might be using AND achieve good performance for tee'ing of this
specific iterator type at the same time.
There is no need to get any "deeper" in the copy, but the two
mutable "index state" attributes must indeed be copied in order
to achieve a "proper" (independently iterable) iterator-copy.
The recommended solution is to have class Listiter supply this
``__copy__`` method AND have client code use ``itertools.tee`` (with
the split-in-two-parts loop as shown above). This will make
client code maximally tolerant of different iterator types it
might be using AND achieve good performance for tee'ing of this
specific iterator type at the same time.
References
==========
[1] Discussion on python-dev starting at post:
https://mail.python.org/pipermail/python-dev/2003-October/038969.html
.. [1] Discussion on python-dev starting at post:
https://mail.python.org/pipermail/python-dev/2003-October/038969.html
[2] Online documentation for the copy module of the standard library:
http://docs.python.org/library/copy.html
.. [2] Online documentation for the copy module of the standard library:
http://docs.python.org/library/copy.html
[3] PEP 289, Generator Expressions, Hettinger
http://www.python.org/dev/peps/pep-0289/
.. [3] PEP 289, Generator Expressions, Hettinger
http://www.python.org/dev/peps/pep-0289/
Copyright
=========
This document has been placed in the public domain.
This document has been placed in the public domain.
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