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+Functional Programming HOWTO
+================================
+
+**Version 0.30**
+
+(This is a first draft.  Please send comments/error
+reports/suggestions to amk@amk.ca.  This URL is probably not going to
+be the final location of the document, so be careful about linking to
+it -- you may want to add a disclaimer.)
+
+In this document, we'll take a tour of Python's features suitable for
+implementing programs in a functional style.  After an introduction to
+the concepts of functional programming, we'll look at language
+features such as iterators and generators and relevant library modules
+such as ``itertools`` and ``functools``.
+
+
+.. contents::
+
+Introduction
+----------------------
+
+This section explains the basic concept of functional programming; if
+you're just interested in learning about Python language features,
+skip to the next section.
+
+Programming languages support decomposing problems in several different 
+ways:
+
+* Most programming languages are **procedural**: 
+  programs are lists of instructions that tell the computer what to
+  do with the program's input.
+  C, Pascal, and even Unix shells are procedural languages.
+
+* In **declarative** languages, you write a specification that describes 
+  the problem to be solved, and the language implementation figures out 
+  how to perform the computation efficiently.  SQL is the declarative 
+  language you're most likely to be familiar with; a SQL query describes
+  the data set you want to retrieve, and the SQL engine decides whether to 
+  scan tables or use indexes, which subclauses should be performed first,
+  etc.
+
+* **Object-oriented** programs manipulate  collections of objects.
+  Objects have internal state and support methods that query or modify
+  this internal state in some way. Smalltalk and Java are
+  object-oriented languages.  C++ and Python are languages that
+  support object-oriented programming, but don't force the use 
+  of object-oriented features.
+
+* **Functional** programming decomposes a problem into a set of functions.
+  Ideally, functions only take inputs and produce outputs, and don't have any 
+  internal state that affects the output produced for a given input.
+  Well-known functional languages include the ML family (Standard ML,
+  OCaml, and other variants) and Haskell.
+
+The designers of some computer languages have chosen one approach to 
+programming that's emphasized.  This often makes it difficult to
+write programs that use a different approach.  Other languages are
+multi-paradigm languages that support several different approaches.  Lisp,
+C++, and Python are multi-paradigm; you can write programs or
+libraries that are largely procedural, object-oriented, or functional
+in all of these languages.  In a large program, different sections
+might be written using different approaches; the GUI might be object-oriented
+while the processing logic is procedural or functional, for example.
+
+In a functional program, input flows through a set of functions. Each
+function operates on its input and produces some output.  Functional
+style frowns upon functions with side effects that modify internal
+state or make other changes that aren't visible in the function's
+return value.  Functions that have no side effects at all are 
+called **purely functional**.
+Avoiding side effects means not using data structures
+that get updated as a program runs; every function's output 
+must only depend on its input.
+
+Some languages are very strict about purity and don't even have
+assignment statements such as ``a=3`` or ``c = a + b``, but it's
+difficult to avoid all side effects.  Printing to the screen or
+writing to a disk file are side effects, for example.  For example, in
+Python a ``print`` statement or a ``time.sleep(1)`` both return no
+useful value; they're only called for their side effects of sending
+some text to the screen or pausing execution for a second.
+
+Python programs written in functional style usually won't go to the
+extreme of avoiding all I/O or all assignments; instead, they'll
+provide a functional-appearing interface but will use non-functional
+features internally.  For example, the implementation of a function
+will still use assignments to local variables, but won't modify global
+variables or have other side effects.
+
+Functional programming can be considered the opposite of
+object-oriented programming.  Objects are little capsules containing
+some internal state along with a collection of method calls that let
+you modify this state, and programs consist of making the right set of
+state changes.  Functional programming wants to avoid state changes as
+much as possible and works with data flowing between functions.  In
+Python you might combine the two approaches by writing functions that
+take and return instances representing objects in your application
+(e-mail messages, transactions, etc.).
+
+Functional design may seem like an odd constraint to work under.  Why
+should you avoid objects and side effects?  There are theoretical and
+practical advantages to the functional style:
+
+* Formal provability.
+* Modularity.
+* Composability.
+* Ease of debugging and testing.
+
+Formal provability
+''''''''''''''''''''''
+
+A theoretical benefit is that it's easier to construct a mathematical proof
+that a functional program is correct.
+
+For a long time researchers have been interested in finding ways to
+mathematically prove programs correct.  This is different from testing
+a program on numerous inputs and concluding that its output is usually
+correct, or reading a program's source code and concluding that the
+code looks right; the goal is instead a rigorous proof that a program
+produces the right result for all possible inputs.
+
+The technique used to prove programs correct is to write down 
+**invariants**, properties of the input data and of the program's 
+variables that are always true.  For each line of code, you then show 
+that if invariants X and Y are true **before** the line is executed, 
+the slightly different invariants X' and Y' are true **after**
+the line is executed.  This continues until you reach the end of the
+program, at which point the invariants should match the desired 
+conditions on the program's output.
+
+Functional programming's avoidance of assignments arose because 
+assignments are difficult to handle with this technique; 
+assignments can break invariants that were true before the assignment
+without producing any new invariants that can be propagated onward.
+
+Unfortunately, proving programs correct is largely impractical and not
+relevant to Python software. Even trivial programs require proofs that
+are several pages long; the proof of correctness for a moderately
+complicated program would be enormous, and few or none of the programs
+you use daily (the Python interpreter, your XML parser, your web
+browser) could be proven correct.  Even if you wrote down or generated
+a proof, there would then be the question of verifying the proof;
+maybe there's an error in it, and you wrongly believe you've proved
+the program correct.
+
+Modularity
+''''''''''''''''''''''
+
+A more practical benefit of functional programming is that it forces
+you to break apart your problem into small pieces.  Programs are more
+modular as a result.  It's easier to specify and write a small
+function that does one thing than a large function that performs a
+complicated transformation.  Small functions are also easier to read
+and to check for errors.
+
+
+Ease of debugging and testing 
+''''''''''''''''''''''''''''''''''
+
+Testing and debugging a functional-style program is easier.
+
+Debugging is simplified because functions are generally small and
+clearly specified.  When a program doesn't work, each function is an
+interface point where you can check that the data are correct.  You
+can look at the intermediate inputs and outputs to quickly isolate the
+function that's responsible for a bug.
+
+Testing is easier because each function is a potential subject for a
+unit test.  Functions don't depend on system state that needs to be
+replicated before running a test; instead you only have to synthesize
+the right input and then check that the output matches expectations.
+
+
+
+Composability
+''''''''''''''''''''''
+
+As you work on a functional-style program, you'll write a number of
+functions with varying inputs and outputs.  Some of these functions
+will be unavoidably specialized to a particular application, but
+others will be useful in a wide variety of programs.  For example, a
+function that takes a directory path and returns all the XML files in
+the directory, or a function that takes a filename and returns its
+contents, can be applied to many different situations.
+
+Over time you'll form a personal library of utilities.  Often you'll
+assemble new programs by arranging existing functions in a new
+configuration and writing a few functions specialized for the current
+task.
+
+
+
+Iterators
+-----------------------
+
+I'll start by looking at a Python language feature that's an important
+foundation for writing functional-style programs: iterators.
+
+An iterator is an object representing a stream of data; this object
+returns the data one element at a time.  A Python iterator must
+support a method called ``next()`` that takes no arguments and always
+returns the next element of the stream.  If there are no more elements
+in the stream, ``next()`` must raise the ``StopIteration`` exception.
+Iterators don't have to be finite, though; it's perfectly reasonable
+to write an iterator that produces an infinite stream of data.
+
+The built-in ``iter()`` function takes an arbitrary object and tries
+to return an iterator that will return the object's contents or
+elements, raising ``TypeError`` if the object doesn't support
+iteration.  Several of Python's built-in data types support iteration,
+the most common being lists and dictionaries.  An object is called 
+an **iterable** object if you can get an iterator for it.
+
+You can experiment with the iteration interface manually::
+
+    >>> L = [1,2,3]
+    >>> it = iter(L)
+    >>> print it
+    <iterator object at 0x8116870>
+    >>> it.next()
+    1
+    >>> it.next()
+    2
+    >>> it.next()
+    3
+    >>> it.next()
+    Traceback (most recent call last):
+      File "<stdin>", line 1, in ?
+    StopIteration
+    >>>      
+
+Python expects iterable objects in several different contexts, the 
+most important being the ``for`` statement.  In the statement ``for X in Y``,
+Y must be an iterator or some object for which ``iter()`` can create 
+an iterator.  These two statements are equivalent::
+
+        for i in iter(obj):
+            print i
+
+        for i in obj:
+            print i
+
+Iterators can be materialized as lists or tuples by using the
+``list()`` or ``tuple()`` constructor functions::
+
+    >>> L = [1,2,3]
+    >>> iterator = iter(L)
+    >>> t = tuple(iterator)
+    >>> t
+    (1, 2, 3)
+
+Sequence unpacking also supports iterators: if you know an iterator 
+will return N elements, you can unpack them into an N-tuple::
+
+    >>> L = [1,2,3]
+    >>> iterator = iter(L)
+    >>> a,b,c = iterator
+    >>> a,b,c
+    (1, 2, 3)
+
+Built-in functions such as ``max()`` and ``min()`` can take a single
+iterator argument and will return the largest or smallest element.
+The ``"in"`` and ``"not in"`` operators also support iterators: ``X in
+iterator`` is true if X is found in the stream returned by the
+iterator.  You'll run into obvious problems if the iterator is
+infinite; ``max()``, ``min()``, and ``"not in"`` will never return, and
+if the element X never appears in the stream, the ``"in"`` operator
+won't return either.
+
+Note that you can only go forward in an iterator; there's no way to
+get the previous element, reset the iterator, or make a copy of it.
+Iterator objects can optionally provide these additional capabilities,
+but the iterator protocol only specifies the ``next()`` method.
+Functions may therefore consume all of the iterator's output, and if
+you need to do something different with the same stream, you'll have
+to create a new iterator.
+
+
+
+Data Types That Support Iterators
+'''''''''''''''''''''''''''''''''''
+
+We've already seen how lists and tuples support iterators.  In fact,
+any Python sequence type, such as strings, will automatically support
+creation of an iterator.
+
+Calling ``iter()`` on a dictionary returns an iterator that will loop
+over the dictionary's keys::
+
+    >>> m = {'Jan': 1, 'Feb': 2, 'Mar': 3, 'Apr': 4, 'May': 5, 'Jun': 6,
+    ...      'Jul': 7, 'Aug': 8, 'Sep': 9, 'Oct': 10, 'Nov': 11, 'Dec': 12}
+    >>> for key in m:
+    ...     print key, m[key]
+    Mar 3
+    Feb 2
+    Aug 8
+    Sep 9
+    May 5
+    Jun 6
+    Jul 7
+    Jan 1
+    Apr 4
+    Nov 11
+    Dec 12
+    Oct 10
+
+Note that the order is essentially random, because it's based on the
+hash ordering of the objects in the dictionary.
+
+Applying ``iter()`` to a dictionary always loops over the keys, but
+dictionaries have methods that return other iterators.  If you want to
+iterate over keys, values, or key/value pairs, you can explicitly call
+the ``iterkeys()``, ``itervalues()``, or ``iteritems()`` methods to
+get an appropriate iterator.
+
+The ``dict()`` constructor can accept an iterator that returns a
+finite stream of ``(key, value)`` tuples::
+
+    >>> L = [('Italy', 'Rome'), ('France', 'Paris'), ('US', 'Washington DC')]
+    >>> dict(iter(L))
+    {'Italy': 'Rome', 'US': 'Washington DC', 'France': 'Paris'}
+
+Files also support iteration by calling the ``readline()``
+method until there are no more lines in the file.  This means you can
+read each line of a file like this::
+
+    for line in file:
+        # do something for each line
+        ...
+
+Sets can take their contents from an iterable and let you iterate over
+the set's elements::
+
+    S = set((2, 3, 5, 7, 11, 13))
+    for i in S:
+        print i
+
+
+
+Generator expressions and list comprehensions
+----------------------------------------------------
+
+Two common operations on an iterator's output are 1) performing some
+operation for every element, 2) selecting a subset of elements that
+meet some condition.  For example, given a list of strings, you might
+want to strip off trailing whitespace from each line or extract all
+the strings containing a given substring.
+
+List comprehensions and generator expressions (short form: "listcomps"
+and "genexps") are a concise notation for such operations, borrowed
+from the functional programming language Haskell
+(http://www.haskell.org).  You can strip all the whitespace from a
+stream of strings with the following code::
+
+        line_list = ['  line 1\n', 'line 2  \n', ...]
+
+        # Generator expression -- returns iterator
+        stripped_iter = (line.strip() for line in line_list)
+
+        # List comprehension -- returns list
+        stripped_list = [line.strip() for line in line_list]
+
+You can select only certain elements by adding an ``"if"`` condition::
+
+        stripped_list = [line.strip() for line in line_list
+                         if line != ""]
+
+With a list comprehension, you get back a Python list;
+``stripped_list`` is a list containing the resulting lines, not an
+iterator.  Generator expressions return an iterator that computes the
+values as necessary, not needing to materialize all the values at
+once.  This means that list comprehensions aren't useful if you're
+working with iterators that return an infinite stream or a very large
+amount of data.  Generator expressions are preferable in these
+situations.
+
+Generator expressions are surrounded by parentheses ("()") and list
+comprehensions are surrounded by square brackets ("[]").  Generator
+expressions have the form::
+
+    ( expression for expr in sequence1 
+                 if condition1
+                 for expr2 in sequence2
+                 if condition2
+                 for expr3 in sequence3 ...
+                 if condition3
+                 for exprN in sequenceN
+                 if conditionN )
+
+Again, for a list comprehension only the outside brackets are
+different (square brackets instead of parentheses).
+
+The elements of the generated output will be the successive values of
+``expression``.  The ``if`` clauses are all optional; if present,
+``expression`` is only evaluated and added to the result when
+``condition`` is true.
+
+Generator expressions always have to be written inside parentheses,
+but the parentheses signalling a function call also count.  If you
+want to create an iterator that will be immediately passed to a
+function you can write::
+
+        obj_total = sum(obj.count for obj in list_all_objects())
+
+The ``for...in`` clauses contain the sequences to be iterated over.
+The sequences do not have to be the same length, because they are
+iterated over from left to right, **not** in parallel.  For each
+element in ``sequence1``, ``sequence2`` is looped over from the
+beginning.  ``sequence3``  is then looped over for each 
+resulting pair of elements from ``sequence1`` and ``sequence2``.
+
+To put it another way, a list comprehension or generator expression is
+equivalent to the following Python code::
+
+    for expr1 in sequence1:
+        if not (condition1):
+            continue   # Skip this element
+        for expr2 in sequence2:
+            if not (condition2):
+                continue    # Skip this element
+            ...
+            for exprN in sequenceN:
+                 if not (conditionN):
+                     continue   # Skip this element
+
+                 # Output the value of 
+                 # the expression.
+
+This means that when there are multiple ``for...in`` clauses but no
+``if`` clauses, the length of the resulting output will be equal to
+the product of the lengths of all the sequences.  If you have two
+lists of length 3, the output list is 9 elements long::
+
+    seq1 = 'abc'
+    seq2 = (1,2,3)
+    >>> [ (x,y) for x in seq1 for y in seq2]
+    [('a', 1), ('a', 2), ('a', 3), 
+     ('b', 1), ('b', 2), ('b', 3), 
+     ('c', 1), ('c', 2), ('c', 3)]
+
+To avoid introducing an ambiguity into Python's grammar, if
+``expression`` is creating a tuple, it must be surrounded with
+parentheses.  The first list comprehension below is a syntax error,
+while the second one is correct::
+
+    # Syntax error
+    [ x,y for x in seq1 for y in seq2]
+    # Correct
+    [ (x,y) for x in seq1 for y in seq2]
+
+
+Generators
+-----------------------
+
+Generators are a special class of functions that simplify the task of
+writing iterators.  Regular functions compute a value and return it,
+but generators return an iterator that returns a stream of values.
+
+You're doubtless familiar with how regular function calls work in
+Python or C.  When you call a function, it gets a private namespace
+where its local variables are created.  When the function reaches a
+``return`` statement, the local variables are destroyed and the
+value is returned to the caller.  A later call to the same function
+creates a new private namespace and a fresh set of local
+variables. But, what if the local variables weren't thrown away on
+exiting a function?  What if you could later resume the function where
+it left off?  This is what generators provide; they can be thought of
+as resumable functions.
+
+Here's the simplest example of a generator function::
+
+    def generate_ints(N):
+        for i in range(N):
+            yield i
+
+Any function containing a ``yield`` keyword is a generator function;
+this is detected by Python's bytecode compiler which compiles the
+function specially as a result.
+
+When you call a generator function, it doesn't return a single value;
+instead it returns a generator object that supports the iterator
+protocol.  On executing the ``yield`` expression, the generator
+outputs the value of ``i``, similar to a ``return``
+statement.  The big difference between ``yield`` and a
+``return`` statement is that on reaching a ``yield`` the
+generator's state of execution is suspended and local variables are
+preserved.  On the next call to the generator's ``.next()`` method,
+the function will resume executing.  
+
+Here's a sample usage of the ``generate_ints()`` generator::
+
+    >>> gen = generate_ints(3)
+    >>> gen
+    <generator object at 0x8117f90>
+    >>> gen.next()
+    0
+    >>> gen.next()
+    1
+    >>> gen.next()
+    2
+    >>> gen.next()
+    Traceback (most recent call last):
+      File "stdin", line 1, in ?
+      File "stdin", line 2, in generate_ints
+    StopIteration
+
+You could equally write ``for i in generate_ints(5)``, or
+``a,b,c = generate_ints(3)``.
+
+Inside a generator function, the ``return`` statement can only be used
+without a value, and signals the end of the procession of values;
+after executing a ``return`` the generator cannot return any further
+values.  ``return`` with a value, such as ``return 5``, is a syntax
+error inside a generator function.  The end of the generator's results
+can also be indicated by raising ``StopIteration`` manually, or by
+just letting the flow of execution fall off the bottom of the
+function.
+
+You could achieve the effect of generators manually by writing your
+own class and storing all the local variables of the generator as
+instance variables.  For example, returning a list of integers could
+be done by setting ``self.count`` to 0, and having the
+``next()`` method increment ``self.count`` and return it.
+However, for a moderately complicated generator, writing a
+corresponding class can be much messier.
+
+The test suite included with Python's library, ``test_generators.py``,
+contains a number of more interesting examples.  Here's one generator
+that implements an in-order traversal of a tree using generators
+recursively.
+
+::
+
+    # A recursive generator that generates Tree leaves in in-order.
+    def inorder(t):
+        if t:
+            for x in inorder(t.left):
+                yield x
+
+            yield t.label
+
+            for x in inorder(t.right):
+                yield x
+
+Two other examples in ``test_generators.py`` produce
+solutions for the N-Queens problem (placing N queens on an NxN
+chess board so that no queen threatens another) and the Knight's Tour
+(finding a route that takes a knight to every square of an NxN chessboard
+without visiting any square twice).
+
+
+
+Passing values into a generator
+''''''''''''''''''''''''''''''''''''''''''''''
+
+In Python 2.4 and earlier, generators only produced output.  Once a
+generator's code was invoked to create an iterator, there was no way to
+pass any new information into the function when its execution is
+resumed.  You could hack together this ability by making the
+generator look at a global variable or by passing in some mutable object
+that callers then modify, but these approaches are messy.
+
+In Python 2.5 there's a simple way to pass values into a generator.
+``yield`` became an expression, returning a value that can be assigned
+to a variable or otherwise operated on::
+
+    val = (yield i)
+
+I recommend that you **always** put parentheses around a ``yield``
+expression when you're doing something with the returned value, as in
+the above example.  The parentheses aren't always necessary, but it's
+easier to always add them instead of having to remember when they're
+needed.
+
+(PEP 342 explains the exact rules, which are that a
+``yield``-expression must always be parenthesized except when it
+occurs at the top-level expression on the right-hand side of an
+assignment.  This means you can write ``val = yield i`` but have to
+use parentheses when there's an operation, as in ``val = (yield i)
++ 12``.)
+
+Values are sent into a generator by calling its
+``send(value)`` method.  This method resumes the 
+generator's code and the ``yield`` expression returns the specified
+value.  If the regular ``next()`` method is called, the
+``yield`` returns ``None``.
+
+Here's a simple counter that increments by 1 and allows changing the
+value of the internal counter.
+
+::
+
+    def counter (maximum):
+        i = 0
+        while i < maximum:
+            val = (yield i)
+            # If value provided, change counter
+            if val is not None:
+                i = val
+            else:
+                i += 1
+
+And here's an example of changing the counter:
+
+    >>> it = counter(10)
+    >>> print it.next()
+    0
+    >>> print it.next()
+    1
+    >>> print it.send(8)
+    8
+    >>> print it.next()
+    9
+    >>> print it.next()
+    Traceback (most recent call last):
+      File ``t.py'', line 15, in ?
+        print it.next()
+    StopIteration
+
+Because ``yield`` will often be returning ``None``, you
+should always check for this case.  Don't just use its value in
+expressions unless you're sure that the ``send()`` method
+will be the only method used resume your generator function.
+
+In addition to ``send()``, there are two other new methods on
+generators:
+
+* ``throw(type, value=None, traceback=None)`` is used to raise an exception inside the
+  generator; the exception is raised by the ``yield`` expression
+  where the generator's execution is paused.
+
+* ``close()`` raises a ``GeneratorExit``
+  exception inside the generator to terminate the iteration.  
+  On receiving this
+  exception, the generator's code must either raise
+  ``GeneratorExit`` or ``StopIteration``; catching the 
+  exception and doing anything else is illegal and will trigger
+  a ``RuntimeError``.  ``close()`` will also be called by 
+  Python's garbage collector when the generator is garbage-collected.
+
+  If you need to run cleanup code when a ``GeneratorExit`` occurs,
+  I suggest using a ``try: ... finally:`` suite instead of 
+  catching ``GeneratorExit``.
+
+The cumulative effect of these changes is to turn generators from
+one-way producers of information into both producers and consumers.
+
+Generators also become **coroutines**, a more generalized form of
+subroutines.  Subroutines are entered at one point and exited at
+another point (the top of the function, and a ``return``
+statement), but coroutines can be entered, exited, and resumed at
+many different points (the ``yield`` statements).  
+
+
+Built-in functions
+----------------------------------------------
+
+Let's look in more detail at built-in functions often used with iterators.
+
+Two Python's built-in functions, ``map()`` and ``filter()``, are
+somewhat obsolete; they duplicate the features of list comprehensions
+but return actual lists instead of iterators.  
+
+``map(f, iterA, iterB, ...)`` returns a list containing ``f(iterA[0],
+iterB[0]), f(iterA[1], iterB[1]), f(iterA[2], iterB[2]), ...``.  
+
+::
+
+    def upper(s):
+        return s.upper()
+    map(upper, ['sentence', 'fragment']) =>
+      ['SENTENCE', 'FRAGMENT']
+
+    [upper(s) for s in ['sentence', 'fragment']] =>
+      ['SENTENCE', 'FRAGMENT']
+
+As shown above, you can achieve the same effect with a list
+comprehension.  The ``itertools.imap()`` function does the same thing
+but can handle infinite iterators; it'll be discussed later, in the section on 
+the ``itertools`` module.
+
+``filter(predicate, iter)`` returns a list 
+that contains all the sequence elements that meet a certain condition,
+and is similarly duplicated by list comprehensions.
+A **predicate** is a function that returns the truth value of
+some condition; for use with ``filter()``, the predicate must take a 
+single value.  
+
+::
+
+    def is_even(x):
+        return (x % 2) == 0
+
+    filter(is_even, range(10)) =>
+      [0, 2, 4, 6, 8]
+
+This can also be written as a list comprehension::
+
+    >>> [x for x in range(10) if is_even(x)]
+    [0, 2, 4, 6, 8]
+
+``filter()`` also has a counterpart in the ``itertools`` module,
+``itertools.ifilter()``, that returns an iterator and 
+can therefore handle infinite sequences just as ``itertools.imap()`` can.
+
+``reduce(func, iter, [initial_value])`` doesn't have a counterpart in
+the ``itertools`` module because it cumulatively performs an operation
+on all the iterable's elements and therefore can't be applied to
+infinite iterables.  ``func`` must be a function that takes two elements
+and returns a single value.  ``reduce()`` takes the first two elements
+A and B returned by the iterator and calculates ``func(A, B)``.  It
+then requests the third element, C, calculates ``func(func(A, B),
+C)``, combines this result with the fourth element returned, and
+continues until the iterable is exhausted.  If the iterable returns no
+values at all, a ``TypeError`` exception is raised.  If the initial
+value is supplied, it's used as a starting point and
+``func(initial_value, A)`` is the first calculation.
+
+::
+
+    import operator
+    reduce(operator.concat, ['A', 'BB', 'C']) =>
+      'ABBC'
+    reduce(operator.concat, []) =>
+      TypeError: reduce() of empty sequence with no initial value
+    reduce(operator.mul, [1,2,3], 1) =>
+      6
+    reduce(operator.mul, [], 1) =>
+      1
+
+If you use ``operator.add`` with ``reduce()``, you'll add up all the 
+elements of the iterable.  This case is so common that there's a special
+built-in called ``sum()`` to compute it::
+
+    reduce(operator.add, [1,2,3,4], 0) =>
+      10
+    sum([1,2,3,4]) =>
+      10
+    sum([]) =>
+      0
+
+For many uses of ``reduce()``, though, it can be clearer to just write
+the obvious ``for`` loop::
+
+    # Instead of:
+    product = reduce(operator.mul, [1,2,3], 1)
+
+    # You can write:
+    product = 1
+    for i in [1,2,3]:
+        product *= i
+
+
+``enumerate(iter)`` counts off the elements in the iterable, returning
+2-tuples containing the count and each element.
+
+::
+
+    enumerate(['subject', 'verb', 'object']) =>
+      (0, 'subject'), (1, 'verb'), (2, 'object')
+
+``enumerate()`` is often used when looping through a list 
+and recording the indexes at which certain conditions are met::
+
+    f = open('data.txt', 'r')
+    for i, line in enumerate(f):
+        if line.strip() == '':
+            print 'Blank line at line #%i' % i
+
+``sorted(iterable, [cmp=None], [key=None], [reverse=False)`` 
+collects all the elements of the iterable into a list, sorts 
+the list, and returns the sorted result.  The ``cmp``, ``key``, 
+and ``reverse`` arguments are passed through to the 
+constructed list's ``.sort()`` method.
+
+::
+
+    import random
+    # Generate 8 random numbers between [0, 10000)
+    rand_list = random.sample(range(10000), 8)
+    rand_list =>
+      [769, 7953, 9828, 6431, 8442, 9878, 6213, 2207]
+    sorted(rand_list) =>
+      [769, 2207, 6213, 6431, 7953, 8442, 9828, 9878]
+    sorted(rand_list, reverse=True) =>
+      [9878, 9828, 8442, 7953, 6431, 6213, 2207, 769]
+
+(For a more detailed discussion of sorting, see the Sorting mini-HOWTO
+in the Python wiki at http://wiki.python.org/moin/HowTo/Sorting.)
+
+The ``any(iter)`` and ``all(iter)`` built-ins look at 
+the truth values of an iterable's contents.  ``any()`` returns 
+True if any element in the iterable is a true value, and ``all()`` 
+returns True if all of the elements are true values::
+
+    any([0,1,0]) =>
+      True
+    any([0,0,0]) =>
+      False
+    any([1,1,1]) =>
+      True
+    all([0,1,0]) =>
+      False
+    all([0,0,0]) => 
+      False
+    all([1,1,1]) =>
+      True
+
+
+Small functions and the lambda statement
+----------------------------------------------
+
+When writing functional-style programs, you'll often need little
+functions that act as predicates or that combine elements in some way.
+
+If there's a Python built-in or a module function that's suitable, you
+don't need to define a new function at all::
+
+        stripped_lines = [line.strip() for line in lines]
+        existing_files = filter(os.path.exists, file_list)
+
+If the function you need doesn't exist, you need to write it.  One way
+to write small functions is to use the ``lambda`` statement.  ``lambda``
+takes a number of parameters and an expression combining these parameters,
+and creates a small function that returns the value of the expression::
+
+        lowercase = lambda x: x.lower()
+
+        print_assign = lambda name, value: name + '=' + str(value)
+
+        adder = lambda x, y: x+y
+
+An alternative is to just use the ``def`` statement and define a
+function in the usual way::
+
+        def lowercase(x):
+            return x.lower()
+
+        def print_assign(name, value):
+            return name + '=' + str(value)
+
+        def adder(x,y):
+            return x + y
+
+Which alternative is preferable?  That's a style question; my usual
+course is to avoid using ``lambda``.
+
+One reason for my preference is that ``lambda`` is quite limited in
+the functions it can define.  The result has to be computable as a
+single expression, which means you can't have multiway
+``if... elif... else`` comparisons or ``try... except`` statements.
+If you try to do too much in a ``lambda`` statement, you'll end up
+with an overly complicated expression that's hard to read.  Quick,
+what's the following code doing?
+
+::
+
+    total = reduce(lambda a, b: (0, a[1] + b[1]), items)[1]
+
+You can figure it out, but it takes time to disentangle the expression
+to figure out what's going on.  Using a short nested
+``def`` statements makes things a little bit better::
+
+    def combine (a, b):
+        return 0, a[1] + b[1]
+
+    total = reduce(combine, items)[1]
+
+But it would be best of all if I had simply used a ``for`` loop::
+
+     total = 0
+     for a, b in items:
+         total += b
+
+Or the ``sum()`` built-in and a generator expression::
+
+     total = sum(b for a,b in items)
+
+Many uses of ``reduce()`` are clearer when written as ``for`` loops.
+
+Fredrik Lundh once suggested the following set of rules for refactoring 
+uses of ``lambda``:
+
+1) Write a lambda function.
+2) Write a comment explaining what the heck that lambda does.
+3) Study the comment for a while, and think of a name that captures
+   the essence of the comment.
+4) Convert the lambda to a def statement, using that name.
+5) Remove the comment.
+
+I really like these rules, but you're free to disagree that this 
+lambda-free style is better.
+
+
+The itertools module
+-----------------------
+
+The ``itertools`` module contains a number of commonly-used iterators
+as well as functions for combining several iterators.  This section
+will introduce the module's contents by showing small examples.
+
+The module's functions fall into a few broad classes:
+
+* Functions that create a new iterator based on an existing iterator.
+* Functions for treating an iterator's elements as function arguments.
+* Functions for selecting portions of an iterator's output.
+* A function for grouping an iterator's output.
+
+Creating new iterators
+''''''''''''''''''''''
+
+``itertools.count(n)`` returns an infinite stream of
+integers, increasing by 1 each time.  You can optionally supply the
+starting number, which defaults to 0::
+
+        itertools.count() =>
+          0, 1, 2, 3, 4, 5, 6, 7, 8, 9, ...
+        itertools.count(10) =>
+          10, 11, 12, 13, 14, 15, 16, 17, 18, 19, ...
+
+``itertools.cycle(iter)`` saves a copy of the contents of a provided
+iterable and returns a new iterator that returns its elements from
+first to last.  The new iterator will repeat these elements infinitely.
+
+::
+
+        itertools.cycle([1,2,3,4,5]) =>
+          1, 2, 3, 4, 5, 1, 2, 3, 4, 5, ...
+
+``itertools.repeat(elem, [n])`` returns the provided element ``n``
+times, or returns the element endlessly if ``n`` is not provided.
+
+::
+
+    itertools.repeat('abc') =>
+      abc, abc, abc, abc, abc, abc, abc, abc, abc, abc, ...
+    itertools.repeat('abc', 5) =>
+      abc, abc, abc, abc, abc
+
+``itertools.chain(iterA, iterB, ...)`` takes an arbitrary number of
+iterables as input, and returns all the elements of the first
+iterator, then all the elements of the second, and so on, until all of
+the iterables have been exhausted.
+
+::
+
+    itertools.chain(['a', 'b', 'c'], (1, 2, 3)) =>
+      a, b, c, 1, 2, 3
+
+``itertools.izip(iterA, iterB, ...)`` takes one element from each iterable
+and returns them in a tuple::
+
+    itertools.izip(['a', 'b', 'c'], (1, 2, 3)) =>
+      ('a', 1), ('b', 2), ('c', 3)
+
+It's similiar to the built-in ``zip()`` function, but doesn't
+construct an in-memory list and exhaust all the input iterators before
+returning; instead tuples are constructed and returned only if they're
+requested.  (The technical term for this behaviour is 
+`lazy evaluation <http://en.wikipedia.org/wiki/Lazy_evaluation>`__.)
+
+This iterator is intended to be used with iterables that are all of
+the same length.  If the iterables are of different lengths, the
+resulting stream will be the same length as the shortest iterable.
+
+::
+
+    itertools.izip(['a', 'b'], (1, 2, 3)) =>
+      ('a', 1), ('b', 2)
+
+You should avoid doing this, though, because an element may be taken
+from the longer iterators and discarded.  This means you can't go on
+to use the iterators further because you risk skipping a discarded
+element.
+
+``itertools.islice(iter, [start], stop, [step])`` returns a stream
+that's a slice of the iterator.  With a single ``stop`` argument, 
+it will return the first ``stop``
+elements.  If you supply a starting index, you'll get ``stop-start``
+elements, and if you supply a value for ``step`, elements will be
+skipped accordingly.  Unlike Python's string and list slicing, you
+can't use negative values for ``start``, ``stop``, or ``step``.
+
+::
+
+    itertools.islice(range(10), 8) =>
+      0, 1, 2, 3, 4, 5, 6, 7
+    itertools.islice(range(10), 2, 8) =>
+      2, 3, 4, 5, 6, 7
+    itertools.islice(range(10), 2, 8, 2) =>
+      2, 4, 6
+
+``itertools.tee(iter, [n])`` replicates an iterator; it returns ``n``
+independent iterators that will all return the contents of the source
+iterator.  If you don't supply a value for ``n``, the default is 2.
+Replicating iterators requires saving some of the contents of the source
+iterator, so this can consume significant memory if the iterator is large
+and one of the new iterators is consumed more than the others.
+
+::
+
+        itertools.tee( itertools.count() ) =>
+           iterA, iterB
+
+        where iterA ->
+           0, 1, 2, 3, 4, 5, 6, 7, 8, 9, ...
+
+        and   iterB ->
+           0, 1, 2, 3, 4, 5, 6, 7, 8, 9, ...
+
+
+Calling functions on elements
+'''''''''''''''''''''''''''''
+
+Two functions are used for calling other functions on the contents of an
+iterable.
+
+``itertools.imap(f, iterA, iterB, ...)`` returns 
+a stream containing ``f(iterA[0], iterB[0]), f(iterA[1], iterB[1]),
+f(iterA[2], iterB[2]), ...``::
+
+    itertools.imap(operator.add, [5, 6, 5], [1, 2, 3]) =>
+      6, 8, 8
+
+The ``operator`` module contains a set of functions 
+corresponding to Python's operators.  Some examples are 
+``operator.add(a, b)`` (adds two values), 
+``operator.ne(a, b)`` (same as ``a!=b``),
+and 
+``operator.attrgetter('id')`` (returns a callable that
+fetches the ``"id"`` attribute).
+
+``itertools.starmap(func, iter)`` assumes that the iterable will 
+return a stream of tuples, and calls ``f()`` using these tuples as the 
+arguments::
+
+    itertools.starmap(os.path.join, 
+                      [('/usr', 'bin', 'java'), ('/bin', 'python'),
+                       ('/usr', 'bin', 'perl'),('/usr', 'bin', 'ruby')])
+    =>
+      /usr/bin/java, /bin/python, /usr/bin/perl, /usr/bin/ruby
+
+
+Selecting elements
+''''''''''''''''''
+
+Another group of functions chooses a subset of an iterator's elements
+based on a predicate.
+
+``itertools.ifilter(predicate, iter)`` returns all the elements for
+which the predicate returns true::
+
+    def is_even(x):
+        return (x % 2) == 0
+
+    itertools.ifilter(is_even, itertools.count()) =>
+      0, 2, 4, 6, 8, 10, 12, 14, ...
+
+``itertools.ifilterfalse(predicate, iter)`` is the opposite, 
+returning all elements for which the predicate returns false::
+
+    itertools.ifilterfalse(is_even, itertools.count()) =>
+      1, 3, 5, 7, 9, 11, 13, 15, ...
+
+``itertools.takewhile(predicate, iter)`` returns elements for as long
+as the predicate returns true.  Once the predicate returns false, 
+the iterator will signal the end of its results.
+
+::
+
+    def less_than_10(x):
+        return (x < 10)
+
+    itertools.takewhile(less_than_10, itertools.count()) =>
+      0, 1, 2, 3, 4, 5, 6, 7, 8, 9
+
+    itertools.takewhile(is_even, itertools.count()) =>
+      0
+
+``itertools.dropwhile(predicate, iter)`` discards elements while the
+predicate returns true, and then returns the rest of the iterable's
+results.
+
+::
+
+    itertools.dropwhile(less_than_10, itertools.count()) =>
+      10, 11, 12, 13, 14, 15, 16, 17, 18, 19, ...
+
+    itertools.dropwhile(is_even, itertools.count()) =>
+      1, 2, 3, 4, 5, 6, 7, 8, 9, 10, ...
+
+
+Grouping elements
+'''''''''''''''''
+
+The last function I'll discuss, ``itertools.groupby(iter,
+key_func=None)``, is the most complicated.  ``key_func(elem)`` is a
+function that can compute a key value for each element returned by the
+iterable.  If you don't supply a key function, the key is simply each
+element itself.
+
+``groupby()`` collects all the consecutive elements from the
+underlying iterable that have the same key value, and returns a stream
+of 2-tuples containing a key value and an iterator for the elements
+with that key.  
+
+::
+
+    city_list = [('Decatur', 'AL'), ('Huntsville', 'AL'), ('Selma', 'AL'), 
+                 ('Anchorage', 'AK'), ('Nome', 'AK'),
+                 ('Flagstaff', 'AZ'), ('Phoenix', 'AZ'), ('Tucson', 'AZ'), 
+                 ...
+                ]
+
+    def get_state ((city, state)):
+        return state
+
+    itertools.groupby(city_list, get_state) =>
+      ('AL', iterator-1),
+      ('AK', iterator-2),
+      ('AZ', iterator-3), ...
+
+    where
+    iterator-1 =>
+      ('Decatur', 'AL'), ('Huntsville', 'AL'), ('Selma', 'AL')
+    iterator-2 => 
+      ('Anchorage', 'AK'), ('Nome', 'AK')
+    iterator-3 =>
+      ('Flagstaff', 'AZ'), ('Phoenix', 'AZ'), ('Tucson', 'AZ')
+
+``groupby()`` assumes that the underlying iterable's contents will
+already be sorted based on the key.  Note that the returned iterators
+also use the underlying iterable, so you have to consume the results
+of iterator-1 before requesting iterator-2 and its corresponding key.
+
+
+The functools module
+----------------------------------------------
+
+The ``functools`` module in Python 2.5 contains some higher-order
+functions.  A **higher-order function** takes one or more functions as
+input and returns a new function.  The most useful tool in this module
+is the ``partial()`` function.
+
+For programs written in a functional style, you'll sometimes want to
+construct variants of existing functions that have some of the
+parameters filled in.  Consider a Python function ``f(a, b, c)``; you
+may wish to create a new function ``g(b, c)`` that's equivalent to
+``f(1, b, c)``; you're filling in a value for one of ``f()``'s parameters.  
+This is called "partial function application".
+
+The constructor for ``partial`` takes the arguments ``(function, arg1,
+arg2, ... kwarg1=value1, kwarg2=value2)``.  The resulting object is
+callable, so you can just call it to invoke ``function`` with the
+filled-in arguments.
+
+Here's a small but realistic example::
+
+    import functools
+
+    def log (message, subsystem):
+        "Write the contents of 'message' to the specified subsystem."
+        print '%s: %s' % (subsystem, message)
+        ...
+
+    server_log = functools.partial(log, subsystem='server')
+    server_log('Unable to open socket')
+
+
+The operator module
+-------------------
+
+The ``operator`` module was mentioned earlier.  It contains a set of
+functions corresponding to Python's operators.  These functions 
+are often useful in functional-style code because they save you 
+from writing trivial functions that perform a single operation.
+
+Some of the functions in this module are:
+
+* Math operations: ``add()``, ``sub()``, ``mul()``, ``div()``, ``floordiv()``,
+  ``abs()``, ...
+* Logical operations: ``not_()``, ``truth()``.
+* Bitwise operations: ``and_()``, ``or_()``, ``invert()``.
+* Comparisons: ``eq()``, ``ne()``, ``lt()``, ``le()``, ``gt()``, and ``ge()``.
+* Object identity: ``is_()``, ``is_not()``.
+
+Consult `the operator module's documentation <http://docs.python.org/lib/module-operator.html>`__ for a complete
+list.
+
+
+
+The functional module
+---------------------
+
+Collin Winter's `functional module <http://oakwinter.com/code/functional/>`__ 
+provides a number of more
+advanced tools for functional programming. It also reimplements
+several Python built-ins, trying to make them more intuitive to those
+used to functional programming in other languages.
+
+This section contains an introduction to some of the most important
+functions in ``functional``; full documentation can be found at `the
+project's website <http://oakwinter.com/code/functional/documentation/>`__.
+
+``compose(outer, inner, unpack=False)``
+
+The ``compose()`` function implements function composition.
+In other words, it returns a wrapper around the ``outer`` and ``inner`` callables, such
+that the return value from ``inner`` is fed directly to ``outer``.  That is,
+
+::
+
+        >>> def add(a, b):
+        ...     return a + b
+        ...
+        >>> def double(a):
+        ...     return 2 * a
+        ...
+        >>> compose(double, add)(5, 6)
+        22
+
+is equivalent to
+
+::
+
+        >>> double(add(5, 6))
+        22
+                    
+The ``unpack`` keyword is provided to work around the fact that Python functions are not always
+`fully curried <http://en.wikipedia.org/wiki/Currying>`__.
+By default, it is expected that the ``inner`` function will return a single object and that the ``outer``
+function will take a single argument. Setting the ``unpack`` argument causes ``compose`` to expect a
+tuple from ``inner`` which will be expanded before being passed to ``outer``. Put simply,
+
+::
+
+        compose(f, g)(5, 6)
+                    
+is equivalent to::
+
+        f(g(5, 6))
+                    
+while
+
+::
+
+        compose(f, g, unpack=True)(5, 6)
+                    
+is equivalent to::
+
+        f(*g(5, 6))
+
+Even though ``compose()`` only accepts two functions, it's trivial to
+build up a version that will compose any number of functions. We'll
+use ``reduce()``, ``compose()`` and ``partial()`` (the last of which
+is provided by both ``functional`` and ``functools``).
+
+::
+
+        from functional import compose, partial
+        
+        multi_compose = partial(reduce, compose)
+        
+    
+We can also use ``map()``, ``compose()`` and ``partial()`` to craft a
+version of ``"".join(...)`` that converts its arguments to string::
+
+        from functional import compose, partial
+        
+        join = compose("".join, partial(map, str))
+
+
+``flip(func)``
+                    
+``flip()`` wraps the callable in ``func`` and  
+causes it to receive its non-keyword arguments in reverse order.
+
+::
+
+        >>> def triple(a, b, c):
+        ...     return (a, b, c)
+        ...
+        >>> triple(5, 6, 7)
+        (5, 6, 7)
+        >>>
+        >>> flipped_triple = flip(triple)
+        >>> flipped_triple(5, 6, 7)
+        (7, 6, 5)
+
+``foldl(func, start, iterable)``
+                    
+``foldl()`` takes a binary function, a starting value (usually some kind of 'zero'), and an iterable.
+The function is applied to the starting value and the first element of the list, then the result of
+that and the second element of the list, then the result of that and the third element of the list,
+and so on.
+
+This means that a call such as::
+
+        foldl(f, 0, [1, 2, 3])
+
+is equivalent to::
+
+        f(f(f(0, 1), 2), 3)
+
+    
+``foldl()`` is roughly equivalent to the following recursive function::
+
+        def foldl(func, start, seq):
+            if len(seq) == 0:
+                return start
+
+            return foldl(func, func(start, seq[0]), seq[1:])
+
+Speaking of equivalence, the above ``foldl`` call can be expressed in terms of the built-in ``reduce`` like
+so::
+
+        reduce(f, [1, 2, 3], 0)
+
+
+We can use ``foldl()``, ``operator.concat()`` and ``partial()`` to
+write a cleaner, more aesthetically-pleasing version of Python's
+``"".join(...)`` idiom::
+
+        from functional import foldl, partial
+        from operator import concat
+        
+        join = partial(foldl, concat, "")
+
+
+Revision History and Acknowledgements
+------------------------------------------------
+
+The author would like to thank the following people for offering
+suggestions, corrections and assistance with various drafts of this
+article: Ian Bicking, Nick Coghlan, Nick Efford, Raymond Hettinger,
+Jim Jewett, Mike Krell, Leandro Lameiro, Jussi Salmela, 
+Collin Winter, Blake Winton.
+
+Version 0.1: posted June 30 2006.
+
+Version 0.11: posted July 1 2006.  Typo fixes.
+
+Version 0.2: posted July 10 2006.  Merged genexp and listcomp
+sections into one.  Typo fixes.
+
+Version 0.21: Added more references suggested on the tutor mailing list.
+
+Version 0.30: Adds a section on the ``functional`` module written by
+Collin Winter; adds short section on the operator module; a few other
+edits.
+
+
+References
+--------------------
+
+General
+'''''''''''''''
+
+**Structure and Interpretation of Computer Programs**, by 
+Harold Abelson and Gerald Jay Sussman with Julie Sussman.
+Full text at http://mitpress.mit.edu/sicp/.
+In this classic textbook of computer science,  chapters 2 and 3 discuss the
+use of sequences and streams to organize the data flow inside a
+program.  The book uses Scheme for its examples, but many of the
+design approaches described in these chapters are applicable to
+functional-style Python code.
+
+http://www.defmacro.org/ramblings/fp.html: A general 
+introduction to functional programming that uses Java examples
+and has a lengthy historical introduction.
+
+http://en.wikipedia.org/wiki/Functional_programming:
+General Wikipedia entry describing functional programming.
+
+http://en.wikipedia.org/wiki/Coroutine:
+Entry for coroutines.
+
+http://en.wikipedia.org/wiki/Currying:
+Entry for the concept of currying.
+
+Python-specific
+'''''''''''''''''''''''''''
+
+http://gnosis.cx/TPiP/:
+The first chapter of David Mertz's book :title-reference:`Text Processing in Python` 
+discusses functional programming for text processing, in the section titled
+"Utilizing Higher-Order Functions in Text Processing".
+
+Mertz also wrote a 3-part series of articles on functional programming
+for IBM's DeveloperWorks site; see 
+`part 1 <http://www-128.ibm.com/developerworks/library/l-prog.html>`__,
+`part 2 <http://www-128.ibm.com/developerworks/library/l-prog2.html>`__, and
+`part 3 <http://www-128.ibm.com/developerworks/linux/library/l-prog3.html>`__,
+
+
+Python documentation
+'''''''''''''''''''''''''''
+
+http://docs.python.org/lib/module-itertools.html:
+Documentation ``for the itertools`` module.
+
+http://docs.python.org/lib/module-operator.html:
+Documentation ``for the operator`` module.
+
+http://www.python.org/dev/peps/pep-0289/:
+PEP 289: "Generator Expressions"
+
+http://www.python.org/dev/peps/pep-0342/
+PEP 342: "Coroutines via Enhanced Generators" describes the new generator
+features in Python 2.5.
+
+.. comment
+
+    Topics to place
+    -----------------------------
+
+    XXX os.walk()
+
+    XXX Need a large example.
+
+    But will an example add much?  I'll post a first draft and see
+    what the comments say.
+
+.. comment
+
+    Original outline:
+    Introduction
+            Idea of FP
+                    Programs built out of functions
+                    Functions are strictly input-output, no internal state
+            Opposed to OO programming, where objects have state
+
+            Why FP?
+                    Formal provability
+                            Assignment is difficult to reason about
+                            Not very relevant to Python
+                    Modularity
+                            Small functions that do one thing
+                    Debuggability:
+                            Easy to test due to lack of state
+                            Easy to verify output from intermediate steps
+                    Composability
+                            You assemble a toolbox of functions that can be mixed
+
+    Tackling a problem
+            Need a significant example
+
+    Iterators
+    Generators
+    The itertools module
+    List comprehensions
+    Small functions and the lambda statement
+    Built-in functions
+            map
+            filter
+            reduce
+
+.. comment
+
+    Handy little function for printing part of an iterator -- used
+    while writing this document.
+
+    import itertools
+    def print_iter(it):
+         slice = itertools.islice(it, 10)
+         for elem in slice[:-1]:
+             sys.stdout.write(str(elem))
+             sys.stdout.write(', ')
+        print elem[-1]
+
+
diff --git a/Doc/howto/regex.tex b/Doc/howto/regex.tex
index f9867ae..3c63b3a 100644
--- a/Doc/howto/regex.tex
+++ b/Doc/howto/regex.tex
@@ -367,7 +367,7 @@
 strings, and displays whether the RE matches or fails.
 \file{redemo.py} can be quite useful when trying to debug a
 complicated RE.  Phil Schwartz's
-\ulink{Kodos}{http://kodos.sourceforge.net} is also an interactive
+\ulink{Kodos}{http://www.phil-schwartz.com/kodos.spy} is also an interactive
 tool for developing and testing RE patterns.  This HOWTO will use the
 standard Python interpreter for its examples.