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Chapter 4:  Defining Functions and Using Built-Ins
==================================================


Functions are the fundamental unit of work in Python. A function in Python
performs a task and returns a result.  In this chapter,
we will start with the basics of functions. Then we look at using the
builtin functions. These are the core functions that are always
available, meaning they don't require an explicit import into your
namespace.  Next we will look at some alternative ways of defining functions, such as
lambdas and classes. We will also look at more advanced types of
functions, namely closures and generator functions.

As you will see, functions are very easy to define and use. Python
encourages an incremental style of development that you can leverage
when writing functions.  So how does this work out in practice? Often when writing a function
it may make sense to start with a sequence of statements and just try
it out in a console. Or maybe just write a short script in an
editor. The idea is to just to prove a path and answer such questions
as, "Does this API work in the way I expect?"  Because top-level code in
a console or script works just like it does in a function, it's easy
to later isolate this code in a function body and then package it as a
function, maybe in a libary, or as a method as part of a class. The
ease of doing this style of development is one aspect that makes
Python such a joy to program in. And of course in the Jython
implementation, it's easy to use this technique within the context of any Java
library.

An important thing to keep in mind is that functions are first-class objects in
Python.  They can be passed around just like any other variable, resulting in
some very powerful solutions.  We'll see some examples of using functions in such
a way later in this chapter.


Function Syntax and Basics
--------------------------

Functions are usually defined by using the *`def`* keyword, the name
of the function, its parameters (if any), and the body of code. We
will start by looking at this example function:
::
    
    def times2(n):
        return n * 2
      

In this example, the function name is *times2* and it accepts a parameter *n*.
The body of the function is only one line, but the work being done is the multiplication
of the parameter by the number 2.  Instead of storing the result in a variable,
this function simply returns it to the calling code.  An example of using this function
would be as follows.

::
    
    >>> times2(8)
    16
    >>> x = times2(5)
    >>> x
    10

Normal usage can treat function definitions as being very simple. But
there's subtle power in every piece of the function definition, due to
the fact that Python is a dynamic language. We look at these pieces
from both a simple (the more typical case) and a more advanced
perspective.  We will also look at some alternative ways of creating functions in a
later section.


The `def` Keyword
~~~~~~~~~~~~~~~~~~~~~~~~~~

Using *`def`* for *define* seems simple enough, and this keyword
certainly can be used to declare a function just like you would in a
static language. You should write most code that way in fact.

However, the more advanced usage is that a function definition can
occur at any level in your code and be introduced at any time. Unlike
the case in a language like C or Java, function definitions are not
declarations. Instead they are *executable statements*. You can nest
functions, and we'll describe that more when we talk about nested
scopes. And you can do things like conditionally define them.

This means it's perfectly valid to write code like the following:

::
    
    if variant:
        def f():
            print "One way"
     else:
        def f():
            print "or another"

Please note, regardless of when and where the definition occurs,
including its variants as above, the function definition will be
compiled into a function object at the same time as the rest of the
module or script that the function is defined in.


Naming the Function
~~~~~~~~~~~~~~~~~~~

We will describe this more in a later section, but the ``dir`` builtin
function will tell us about the names defined in a given namespace,
defaulting to the module, script, or console environment we are
working in. With this new ``times2`` function defined above, we now
see the following (at least) in the console namespace::

  >>> dir()
  ['__doc__', '__name__', 'times2']

We can also just look at what is bound to that name:

::
    
    >>> times2
    <function times2 at 0x1>

(This object is further introspectable. Try ``dir(times2)`` and go
from there.)

We can also redefine a function at any time:

::
    
    >>> def f(): print "Hello, world"
    ... 
    >>> def f(): print "Hi, world"
    ... 
    >>> f()
    Hi, world

This is true not just of running it from the console, but any module
or script. The original version of the function object will persist
until it's no longer referenced, at which point it will be ultimately
be garbage collected. In this case, the only reference was the name
``f``, so it became available for GC immediately upon rebind.

What's important here is that we simply rebound the name.  First it
pointed to one function object, then another. We can see that in
action by simply setting another name (equivalently, a variable) to
``times2``.

::
    
    >>> t2 = times2
    >>> t2(5)
    10

This makes passing a function as a parameter very easy, for a callback
for example. But first, we need to look at function parameters in more
detail.

.. sidebar:: Function Metaprogramming

  A given name can only be associated with one function at a time, so
  can't overload a function with multiple definitions. If you were to
  define two or more functions with the same name, the last one
  defined is used, as we saw.

  However, it is possible to overload a function, or otherwise
  genericize it. You simply need to create a dispatcher function that
  then dispatches to your set of corresponding functions.


Function Parameters and Calling Functions
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

When defining a function, you specify the parameters it
takes. Typically you will see something like the following. The syntax
is familar:

::
    
    def tip_calc(amt, pct)


Calling a function is symmetric. 
You can call a function. The parentheses are mandatory.   Calling functions is also done by with a familiar syntax. For example,
for the function x with parameters ``a,b,c`` that would be
x(a,b,c). Unlike some other dynamic languages like Ruby and Perl, the
use of parentheses is required syntax (due the function name being
just like any other name).

Objects are strongly typed, as we have seen. But function parameters,
like names in general in Python, are not typed.  This means that
any parameter can refer to any type of object.

We see this play out in the ``times2`` function. The ``*`` operator
not only means multiply for numbers, it also means repeat for
sequences (like strings and lists).  So you can use the ``times2``
function as follows::

  >>> times2(4)
  8
  >>> times2('abc')
  'abcabc'
  >>> times2([1,2,3])
  [1, 2, 3, 1, 2, 3]

All parameters in Python are passed by reference. This is identical to
how Java does it with object parameters. However, while Java does
support passing unboxed primitive types by value, there are no such
entities in Python. Everything is an object in Python.

Functions are objects too, and they can be passed as parameters:

::
        
    # Define a function that takes two values and a mathematical function
    >>> def perform_calc(value1, value2, func):
    ...     return func(value1, value2)
    ...
    # Define a mathematical function to pass
    >>> def mult_values(value1, value2):
    ...     return value1 * value2
    ... 
    >>> perform_calc(2, 4, mult_values)
    8
    
    # Define another mathematical function to pass
    >>> def add_values(value1, value2):
    ...     return value1 + value2
    ... 
    >>> perform_calc(2, 4, add_values) 
    6
    >>> 


If you have more than two or so arguments, it often makes more sense
to call a function by parameter, rather than by the defined
order. This tends to create more robust code. So if you have a
function ``draw_point(x,y)``, you might want to call it as
``draw_point(x=10,y=20)``.

Defaults further simplify calling a function. You use the form of
``param=default_value`` when defining the function. For instance, you
might take our ``times2`` function and generalize it.

::
    
    def times_by(n, by=2):
        return n * by

This function is equivalent to ``times2`` when called using that
default value.

There's one point to remember that oftens trips up developers. The
default value is initialized exactly once, when the function is
defined. That's certainly fine for immutable values like numbers,
strings, tuples, frozensets, and similar objects. But you need to
ensure that if the default value is mutable, that it's being used in
this fashion correctly. So a dictionary for a shared cache makes
sense. But this mechanism won't work for but a list where we expect it
is initialized to an empty list upon invocation. If you're doing that,
you need to write that explicitly in your code.

Lastly, a function can take an unspecified number of ordered
arguments, through ``*args``, and keyword args, through
``**kwargs``. These parameter names (``args`` and ``kwargs``) are conventional,
so you can use whatever name makes sense for your function.
The markers ``*`` and ``**`` are used to to determine that this functionality should be used.
The single ``*`` argument allows for passing a sequence of values, and a double ``**`` argument
allows for passing a dictionary of names and values.  If either of these types
of arguments are specified, they must follow any single arguments in the function
declaration.  Furthermore, the double ``**`` must follow the single ``*``.

Definition of a function that takes a sequence of numbers:

::
        
    def sum_args(*nums):
        return sum(nums)

Calling the function using a sequence of numbers:

::
       
    >>> seq = [6,5,4,3]
    >>> sum_args(*seq)
    18

.. note:: 

  This is not exhaustive. You can also use tuple parameters, but in
  practice, they are not typically used, and were removed in Python
  3. We recommend you don't use them. For one thing, they cannot be
  properly introspected from Jython.
  
Recursive Function Calls
^^^^^^^^^^^^^^^^^^^^^^^^

It is also quite common to see cases in which a function calls itself
from inside the function body.  This type of function call is known as a recursive
function call.  Let's take a look at a function that computes the factorial of
a given argument.  This function calls itself passing in the provided argument
decremented by 1 until the argument reaches the value of 0 or 1.

::
    
    def fact(n):
        if n in (0, 1):
            return 1
        else:
            return n * fact(n - 1)

It is important to note that Jython is not unlike CPython in that
it is ultimately stack based.  Stacks are regions of memory where data is added
and removed in a last-in first-out manner.  If a recursive function calls itself too many times
then it is possible to exhaust the stack, which results in an *OutOfMemoryError*.
Therefore, be cautious when developing software using recursion.



Function Body
~~~~~~~~~~~~~

This section will break down the different components that comprise the body of
a function.  The body of a function is the part that performs the work.  Throughout
the next couple of sub-sections, you will see that a function body can be comprised
of many different parts.

Documenting Functions
^^^^^^^^^^^^^^^^^^^^^

First, you should specify a document string for the function. The
docstring, if it exists, is a string that occurs as the first value of
the function body.

::
    
    def times2(n):
        """Given n, returns n * 2"""
        return n * 2

As mentioned in chapter 1, by convention we use triple-quoted strings, even if your docstring is
not multiline. If it is multiline, this is how we recommend you format it.

::
    
    def fact(n):
        """Returns the factorial of n
    
        Computes the factorial of n recursively. Does not check its
        arguments if nonnegative integer or if would stack
        overflow. Use with care! 
        """
    
        if n in (0, 1):
            return 1
        else:
            return n * fact(n - 1)

Any such docstring, but with leading indendetation stripped, becomes
the ``__doc__`` attribute of that function object. Incidentally,
docstrings are also used for modules and classes, and they work
exactly the same way.

You can now use the ``help`` built-in function to get the docstring,
or see them from various IDEs like PyDev for Eclipse and nbPython for
NetBeans as part of the auto-complete.

::
    
    >>> help(fact)
    Help on function fact in module __main__:
    
    fact(n)
        Returns the factorial of n
    
    >>> 


Returning Values
^^^^^^^^^^^^^^^^

All functions return some value.  In ``times2``, we use the ``return`` statement
to exit the function with that value.  Functions can easily return multiple
values at once by returning a tuple or
other structure.  The following is a simple example of a function that returns more
than one value.  In this case, the tip calculator returns the result of a tip based
upon two percentage values.

::
    
    >>> def calc_tips(amount):
    ...     return (amount * .18), (amount * .20)
    ... 
    >>> calc_tips(25.25)
    (4.545, 5.050000000000001)


A function can return at any time, and it can also return any object as its value. So you can have a
functions that look like the following:

::
    
    >>> def check_pos_perform_calc(num1, num2, func):                          
    ...     if num1 > 0 and num2 > 0:                                          
    ...         return func(num1, num2)                                        
    ...     else:                                                              
    ...         return 'Only positive numbers can be used with this function!' 
    ... 
    >>> def mult_values(value1, value2):
    ...     return value1 * value2
    ... 
    >>> check_pos_perform_calc(3, 4, mult_values)
    12
    >>> check_pos_perform_calc(3, -44, mult_values)
    'Only positive numbers can be used with this function!'


If a return statement is not used, the value ``None`` is returned. There is no
equivalent to a ``void`` method in Java, because every function in Python
returns a value. However, the Python console will not show the return
value when it's ``None``, so you need to explicitly print it to see
what is returned.

::
    
    >>> do_nothing()
    >>> print do_nothing()
    None


Introducing Variables
^^^^^^^^^^^^^^^^^^^^^

A function introduces a scope for new names, such as variables. Any
names that are created in the function are only visible within that
scope.  In the following example, the sq variable is defined within the
scope of the function definition itself.  If we try to use it outside
of the function then we'll receive an error.

::
    
    >>> def square_num(num):
    ...    """ Return the square of a number"""
    ...     sq = num * num
    ...     return sq                  
    ... 
    >>> square_num(35)
    1225
    >>> sq
    Traceback (most recent call last):
      File "<stdin>", line 1, in <module>
    NameError: name 'sq' is not defined

.. sidebar:: Global Variables

    global keyword - [Useful for certain circumstances, certainly not
    core/essential, much like nonlocal in Py3K, so let's not put too
    much focus on it.]
    
    The `global` keyword is used to declare that a variable name is from
    the module scope (or script) containing this function. Using
    `global` is rarely necessary in practice, since it is not necessary
    if the name is called as a function or an attribute is accessed
    (through dotted notation).
    
    This is a good example of where Python is providing a complex
    balancing between a complex idea - the lexical scoping of names, and
    the operations on them - and the fact that in practice it is doing
    the right thing.
    
    Here is an example of using a global variable in the same *square_num()* function.
      
    ::
        
        >>> sq = 0
        >>> def square_num(n):
        ...     global sq
        ...     sq = n * n
        ...     return sq
        ... 
        >>> square_num(10)
        100
        >>> sq
        100
 

Other Statements
^^^^^^^^^^^^^^^^

What can go in a function body? Pretty much any statement,
including material that we will cover later in this book. So you can
define functions or classes or use even import, within the scope of
that function.

In particular, performing a potentially expensive operation like
import as last as possible, can reduce the startup time of your
app. It's even possible it will be never needed too.

There are a couple of exceptions to this rule. In both cases, these
statements must go at the beginning of a module, similar to what we
see in a static language like Java:

* Compiler directives. Python supports a limited set of compiler
  directives that have the provocative syntax of ``from __future__
  import X``; see :pep:`236`. These are features that will be
  eventually be made available, generally in the next minor
  revision (such as 2.5 to 2.6). In addition, it's a popular place
  to put Easter eggs, such as ``from __future__ import
  braces``. (Try it in the console, which also relaxes what it
  means to be performed at the beginning.)

* Source encoding declaration. Although technically not a
  statement -- it's in a specially parsed comment -- this must go
  in the first or second line.


Empty Functions
^^^^^^^^^^^^^^^

It is also possible to define an empty function.
Why have a function that does nothing? As in math, it's useful to have
an operation that stands for doing nothing, like "add zero" or
"multiply by one". These identity functions eliminate special
cases. Likewise, as see with ``empty_callback``, we may need to
specify a callback function when calling an API, but nothing actually
needs to be done. By passing in an empty function -- or having this be
the default -- we can simplify the API.  An empty function still needs something
in its body. You can use the ``pass`` statement.

::
    
    def do_nothing():
        pass # here's how to specify an empty body of code

Or you can just have a docstring for the function body as in the following
example.

::
    
    def empty_callback(*args, **kwargs):
        """Use this function where we need to supply a callback,
           but have nothing further to do.
        """

Miscellaneous Information for the Curious Reader
------------------------------------------------

As you already know, Jython is an interpreted language.  That is, the Python
code that we write for a Jython application is ultimately compiled down into
Java bytecode when our program is run.  So oftentimes it is useful for Jython
developers to understand what is going on when this code is interpreted into
Java bytecode.

What do functions look like from Java?  They are instances of an object named
PyObject, supporting the ``__call__`` method.

Additional introspection is available. If a function object is just
a standard function written in Python, it will be of class
PyFunction. A builtin function will be of class
PyBuiltinFunction. But don't assume that in your code, because many
other objects support the function interface (``__call__``), and
these potentially could be proxying, perhaps several layers deep, a
given function. You can only assume it's a PyObject.
  
Much more information is available by going to the Jython wiki.  You can also
send questions to the jython-dev mailing list for more specifics.


Builtin Functions
-----------------

Builtin functions are those functions that are always in the Python
namespace. In other words, these functions -- and builtin exceptions,
boolean values, and some other objects -- are the only truly globally
defined names. If you are familiar with Java, they are somewhat like
the classes from ``java.lang``.

Builtins are rarely sufficient, however; even a simple command line
script generally needs to parse its arguments or read in from its
standard input. So for this case you would need to ``import sys``. And
in the context of Jython, you will need to import the relevant Java
classes you are using, perhaps with ``import java``. But the
builtin functions are really the core function that almost all Python
code uses.

The documentation for covering all of the built-in functions that
are available is extensive.  However, it has been included in this book as
Appendix C.  It should be easy to use Appendix C as a reference when using
a built-in function, or for choosing which built-in function to use.


Alternative Ways to Define Functions
------------------------------------

The *`def`* keyword is not the only way to define a function. Here are
some alternatives:

* Lambda Functions:

*`lambda`* functions. The *`lambda`* keyword creates an unnamed
function. Some people like this because it requires minimal
space, especially when used in a callback.

    
* Classes:

In addition, we can also create objects with classes
whose instance objects look like ordinary functions.  Objects
supporting the __call__ protocol. This will be covered in a
later chapter.  For Java developers, this is familiar. Classes
implement such single-method interfaces as Callable or Runnable.

* Bound Methods:

Instead of calling x.a(), I can pass x.a as a
parameter or bind to another name. Then I can invoke this
name. The first parameter of the method will be passed the bound
object, which in OO terms is the receiver of the method. This is
a simple way of creating callbacks. (In Java you would have just
passed the object of course, then having the callback invoke the
appropriate method such as `call` or `run`.)

* staticmethod, classmethod, descriptors functools, such as for
     partial construction.

* Other function constructors, including yours?


Lambda Functions
~~~~~~~~~~~~~~~~

As stated in the introduction, a lambda function is an anonymous function.  In other
words, a lambda function is not bound to any name.  This
can be useful when you are trying to create compact code or when it does not make
sense to declare a named function because it will only be used once.

A lambda function is usually written inline with other code, and most often the body
of a lamdba function is very short in nature.  A lambda function is comprised of the
following segments:

::
    
    lambda <<argument(s)>> : <<function body>>

A lambda function accepts arguments just like any other function, and it uses
those arguments within it's function body.  Also, just like other functions in
Python a value is always returned.  Let's take a look at a simple lambda function
to get a better understanding of how they work.

*Example of using a lambda function to combine two strings.  In this case, a first
and last name*

::
    
    >>> name_combo = lambda first,last: first + ' ' + last
    >>> name_combo('Jim','Baker')
    'Jim Baker'

In the example above, we assigned the function to a name.  However, a lambda
function can also be defined in-line with other code.  Oftentimes a lambda
function is used within the context of other functions, namely built-ins.  


Generator Functions
-------------------

Generators are functions that construct objects implementing Python's
iterator protocol.

iter() - obj.__iter__
Call obj.next


Generators advance to the next point by calling the special method
``next``. Usually that's done implicitly, typically through a loop or
a consuming function that accepts iterators, including generators.  They return
values by using the *yield* statement.  Each time a *yield* statement is
encountered then the current iteration halts and a value is returned.  Generators
have the ability to remember where they left off.  Each time *next()* is called, the
generator resumes where it had left off.  If a generator
function is not used in the context of a loop, then a *StopIteration* error
will be raised once the generator has been terminated.

Over the next couple of sections, we will take a closer look at generators
and how they work.  Along the way, you will see many examples for creating
and using generators.

Defining Generators
~~~~~~~~~~~~~~~~~~~

A generator function is written so that it consists of one or more
yield points, which are marked through the use of the *yield* statement.
As mentioned previously, each time the ``yield`` statement is encountered,
a value is returned.

::
    
    def g():
        print "before yield point 1"
        # The generator will return a value once it encounters the yield statement
        yield 1
        print "after 1, before 2"
        yield 2
        yield 3

In the example above, the generator function *g()* will halt and return a value
once the first *yield* statement is encountered.  In this case, a 1 will be returned.
The next time *g.next()* is called, the generator will continue until it encounters
the next yield statement.  At that point it will return another value, the 2 in
this case.  Let's see this generator in action.

::
    
    >>> x = g()
    >>> x.next()
    before the yield point 1
    1
    >>> x.next()
    after 1, before 2
    2
    >>> x.next()
    3
    >>> x.next()
    Traceback (most recent call last):
      File "<stdin>", line 1, in <module>
    StopIteration


Let' take a look at another more useful example of a generator.  In the following
example, the *step_to()* function is a generator that increments based
upon a given factor.  The generator starts at zero and increments each time
*next()* is called.  It will stop working once it reaches the value that is
provided by the *stop* argument.

::
    
    >>> def step_to(factor, stop):
    ...     step = factor
    ...     start = 0
    ...     while start <= stop:
    ...         yield start
    ...         start += step
    ...
    >>> for x in step_to(1, 10):
    ...     print x
    ... 
    0
    1
    2
    3
    4
    5
    6
    7
    8
    9
    10
    >>> for x in step_to(2, 10):
    ...     print x
    ... 
    0
    2
    4
    6
    8
    10
    >>> 


If the ``yield`` statement is seen in the scope of a function, then that
function is compiled as if it's a generator function.  Unlike other functions,
you use the ``return`` statement only to say, "I'm done", that is, to exit the
generator.  It is not necessary to explicitly return. You can think of
``return`` as acting like a ``break`` in a for-loop or while-loop.
Let's change the step_to function just a bit to check and ensure
that the factor is less than the stopping point.  We'll add a return
statement to exit the generator if the factor is greater or equal to the stop.

::
        
    >>> def step_return(factor, stop):
    ...     step = factor             
    ...     start = 0                 
    ...     if factor >= stop:
    ...         return
    ...     while start <= stop:
    ...         yield start
    ...         start += step
    ... 
    >>> for x in step_return(1,10):
    ...     print x
    ... 
    0
    1
    2
    3
    4
    5
    6
    7
    8
    9
    10
    >>> for x in step_return(3,10):
    ...     print x
    ... 
    0
    3
    6
    9
    >>> for x in step_return(3,3):
    ...     print x
    ... 

If you attempt to return an argument then a syntax error will be raised.

::
    
    def g():
        yield 1
        yield 2
        return None
    
    for i in g():
        print i
    
    SyntaxError: 'return' with argument inside generator

Many useful generators actually will have an infinite loop around
their yield expression, instead of ever exiting, explicitly or not.  The
generator will essentially work each time *next()* is called throughout the life
of the program.

*Pseudocode for generator using infinite loop*
::
    
    while True:
        yield stuff

This works because a generator object can be garbage collected, just
like any other object implementing the iteration protocol. The fact
that it uses the machinery of function objects to implement itself
doesn't matter.

.. sidebar:: How it actually works

  Generators are actually compiled differently from other
  functions. Each yield point saves the state of unnamed local
  variables (Java temporaries) into the frame object, then returns the
  value to the function that had called ``next`` (or ``send`` in the
  case of a coroutine). The generator is then indefinitely suspended,
  just like any other iterator. Upon calling *next* again, the generator
  is resumed by restoring these local variables, then executing the
  next bytecode instruction following the yield point. This process
  continues until the generator is either garbage collected or it
  exits.

  You can determine if the underlying function is a generator if it's
  code object has the ``CO_GENERATOR`` flag set in ``co_flags``.

  Generators can also be resumed from any thread, although some care
  is necessary to ensure that underlying system state is shared (or
  compatible). We will explore how to use effectively use this
  capability in the chapter on concurrency.


Generator Expressions
~~~~~~~~~~~~~~~~~~~~~

Generator expressions are an alternative way to create the generator object.
Please note that this is not the same as a generator function! It's the
equivalent to what a generator function yields when called.  Generator expressions
basically create an unnamed generator.

::
      
    >>> x = (2 * x for x in [1,2,3,4])
    >>> x
    <generator object at 0x1>
    >>> x()
    Traceback (most recent call last):
      File "<stdin>", line 1, in <module>
    TypeError: 'generator' object is not callable

Let's see this generator expression in action:

::
        
    >>> for v in x:
    ...     print v
    ... 
    2
    4
    6
    8
    >>>

Typically generator expressions tend to be more compact but less versatile than
generator functions.  They are useful for getting things done in a consise
manner.


Namespaces, Nested Scopes and Closures
--------------------------------------

Note that you can introduce other namespaces into your function definition.  It
is possible to include import statements directly within the body of a function.
This allows such imports to be valid only within the context of the function.  For
instance, in the following function definition the imports of *A* and *B* are
only valid within the context of *f()*.

::
    
    def f():
        from NS import A, B

At first glance, including import statements within your function definitions may
seem unneccessary.  However, if you think of a function as an object then it makes
much more sense.  We can pass functions around just like other objects in Python
such as variables.  As mentioned previously, functions can even be passed to
other functions as arguments.  Function namespaces provide the ability to
treat functions as their own separate piece of code.  Oftentimes, functions that
are used in several different places throughout an application are stored in a
separate module.  The module is then imported into the program where needed.

Functions can also be nested within each other to create useful solutions.  Since
functions have their own namespace, any function that is defined within another
function is only valid within the parent function.  Let's take a look at a simple
example of this before we go any further.

::
    
    >>> def parent_function():
    ...     x = [0]
    ...     def child_function():
    ...         x[0] += 1
    ...         return x[0]
    ...     return child_function
    ... 
    >>> p()
    1
    >>> p()
    2
    >>> p()
    3
    >>> p()
    4

While this example is not extremely useful, it allows you to understand a few of
the concepts for nesting functions.  As you can see, the *parent_function* contains
a function named *child_function*.  The *parent_function* calls the *child_function*
passing an argument.  What we have created in this example is a simple *Closure* 
function.  Each time the function is called, it executes the inner function and
increments the variable *x* which is only available within the scope of this
closure.

In the context of Jython, using closures such as the one defined above can be
useful for integrating Java concepts as well.  It is possible to import Java
classes into the scope of your function just as it is possible to work with
other Python modules.  You will learn more about using Java within Jython in
Chapter 7 and Chapter 10.

Function Decorators
-------------------

Decorators are a convenient syntax that describes how to transform a function.
They are essentially a metaprogramming technique that enhances the action
of the function that they decorate.  To program a function decorator,
a function that has already been defined can be used to decorate another function,
which basically allows the decorated function to be passed into the function that
is named in the decorator.  Let's look at a simple example.

*Example 4_1.py*
::
    
    def plus_five(func):
    x = func()
    return x + 5
    
    @plus_five
    def add_nums():
        return 1 + 2

In the given example, the *add_nums()* function is decorated with the *plus_five()*
function.  This has the same effect as passing the *add_nums* function into the
*plus_five* function.  In other words, this decorator is syntactic sugar that
makes this technique easier to use.  The decorator above has the same functionality
as the following code.

::
    
    add_nums = plus_five(add_nums)

Now that we've covered the basics of function decorators it is time to take a look
at a more in-depth example of the concept.  In the following decorator function
example, we are taking a twist on the old tip_calculator function and adding a
sales tax calculation.  As you see, the original *calc_bill* funciton takes
a sequence of amounts, namely the amounts for each item on the bill.  The
*calc_bill* function then simply sums the amounts and returns the value.  In
the given example, we apply the *sales_tax* decorator to the function which
then transforms the function so that it not only calculates and returns the
sum of all amounts on the bill, but it also applies a standard sales tax
to the bill and returns the tax amount and total amounts as well.

*Example 4_2.py*
::
    
    def sales_tax(func):
        ''' Applies a sales tax to a given bill calculator '''
        def calc_tax(*args, **kwargs):
            f = func(*args, **kwargs)
            tax = f * .18
            print "Total before tax: $ %.2f" % (f)
            print "Tax Amount: $ %.2f" % (tax)
            print "Total bill: $ %.2f" % (f + tax)
        return calc_tax
    
    @sales_tax
    def calc_bill(amounts):
        ''' Takes a sequence of amouunts and returns sum '''
        return sum(amounts)

The decorator function contains an inner function that accepts two arguments,
a sequence of arguments and a dictionary of keyword args.  We must pass these
arguments to our original function when calling from the decorator to ensure
that the arguments that we passed to the original function are applied within
the decorator function as well.  In this case, we want to pass a sequence of
amounts to *calc_bill*, so passing the *args, and **kwargs arguments to the
function ensures that our amounts sequnce is passed within the decorator.
The decorator function then performs simple calculations for the tax and total
dollar amounts and prints the results.  Let's see this in action:

::
    
    >>> amounts = [12.95,14.57,9.96]
    >>> calc_bill(amounts)
    Total before tax: $ 37.48
    Tax Amount: $ 6.75
    Total bill: $ 44.23

It is also possible to pass arguments to decorator functions.  In order to do
so, we must nest another function within our decorator function.  The outer
function will accept the arguments to be passed into the decorator function,
the inner function will accept the decorated function, and the inner most
function will perform the work.  We'll take another spin on the tip calculator
example and create a decorator that will apply the tip calculation to the
*calc_bill* function.

::
    
    def tip_amount(tip_pct):
        def calc_tip_wrapper(func):
            def calc_tip_impl(*args, **kwargs):
                f = func(*args, **kwargs)
                print "Total bill before tip: $ %.2f" % (f)
                print "Tip amount: $ %.2f" % (f * tip_pct)
                print "Total with tip: $ %.2f" % (f + (f * tip_pct))
            return calc_tip_impl
        return calc_tip_wrapper

Now let's see this decorator function in action.  As you'll notice, we pass
a percentage amount to the decorator itself and it is applied to the decorator
function.

::
    
    >>> @tip_amount(.18)
    ... def calc_bill(amounts):
    ...     ''' Takes a sequence of amouunts and returns sum '''
    ...     return sum(amounts)    
    ...
    >>> amounts = [20.95, 3.25, 10.75]
    >>> calc_bill(amounts)
    Total bill before tip: $ 34.95
    Tip amount: $ 6.29
    Total with tip: $ 41.24

As you can see, we have a similar result as was produced with the sales tax
calculator.  All of the amounts in the sequence of amounts are summed up and
then the tip is applied. 

Coroutines
----------

Coroutines are often compared to generator functions in that they also make use
of the *yield* statement.  However, a coroutine is exactly the opposite
of a generator in terms of functionality.  A coroutine actually treats a
*yield* statement as an expression, and it accepts data instead of returning
it.  Coroutines are oftentimes overlooked as they may at first seem like
a dounting topic.  However, once it is understood that coroutines and generators
are not the same thing then the concept of how they work is a bit easier to
grasp.

A coroutine is a function that receives data and does something with it.  We will
take a look at a simple coroutine example and then break it down to study
the functionality.

::
    
    def co_example(name):
        print 'Entering coroutine %s' % (name)
        my_text = []
        while True:
            txt = (yield)
            my_text.append(txt)
            print my_text

Here we have a very simplistic coroutine example.  It accepts a value as the
"name" of the coroutine.  It then accepts strings of text, and each time
a string of text is sent to the coroutine, it is appended to a list.  The
*yield* statement is where text is being entered by the user.  It is assigned
to the *txt* variable and then processing continues.  Let's take a look at how
to actually use the coroutine.

::
     
    >>> ex = co_example("example1")
    >>> ex.next()
    Entering coroutine example1

In the code above, we assign the name "example1" to this  coroutine.  We could
actually accept any type of argument for the coroutine and do whatever we want
with it.  We'll see a better example after we understand how this works.  The
next line of code calls *next()* on the function.  The *next()* must be called
once to intialize the coroutine.  Once this has been done, the function is
ready to accept values.

::
    
    >>> ex.send("test1")
    ['test1']
    >>> ex.send("test2")
    ['test1', 'test2']
    >>> ex.send("test3")
    ['test1', 'test2', 'test3']

As you can see, we use the *send()* method to actually send data values into
the coroutine.  In the function itself, the text we *send* is inserted where
the *(yield)* expression is placed.  We can really coninue to use the coroutine
forever, or until our JVM is out of memory.  However, it is a best practice
to *close()* the coroutine once it is no longer needed.  The *close()* call will
cause the coroutine to be garbage collected.

::
    
    >>> ex.close()
    >>> ex.send("test1")
    Traceback (most recent call last):
      File "<stdin>", line 1, in <module>
    StopIteration

If we try to send more data to the function once it has been closed then a
*StopIteration* error is raised.  Coroutines can be very helpful in a number
of situations.  While the example above doesn't do much, there are a number of
great applications to which we can apply the use of coroutines.

Decorators in Coroutines
~~~~~~~~~~~~~~~~~~~~~~~~

While the initialization of a coroutine by calling the *next()* method is not
difficult to do, we can eliminate this step to help make things even easier.  By
applying a decorator function to our coroutine, we can automatically initialize
it so it is ready to receive data.

Let's define a decorator that we can apply to the coroutine in order to make
the call to *next()*.

::
    
    def coroutine_next(f):
        def initialize(*args,**kwargs):
            coroutine = f(*args,**kwargs)
            coroutine.next()
            return coroutine
        return initialize

Now we will apply our decorator to the coroutine function and then make use
of it.

::
    
    >>> @coroutine_next
    ... def co_example(name):
    ...     print 'Entering coroutine %s' % (name)
    ...     my_text = []
    ...     while True:
    ...         txt = (yield)
    ...         my_text.append(txt)
    ...         print my_text
    ... 
    >>> ex2 = co_example("example2")
    Entering coroutine example2
    >>> ex2.send("one")
    ['one']
    >>> ex2.send("two")
    ['one', 'two']
    >>> ex2.close()

As you can see, while it is not necessary to use a decorator for performing
such tasks, it definitely makes things easier to use.

Coroutine Example
~~~~~~~~~~~~~~~~~

Now that we understand how coroutines are used, let's take a look at a more in-depth
example.  Hopefully after reviewing this example you will understand how
useful such functionality can be.

In this example, we will pass the name of a file to the coroutine on initiliziation.
After that, we will send strings of text to the function and it will open the
text file that we sent to it (given that the file resides in the correct location),
and search for the number of matches per a given word.  The numeric result
for the number of matches will be returned to the user.

*Example-4_3.py*
::
    
    def search_file(filename):
        print 'Searching file %s' % (filename)
        my_file = open(filename, 'r')
        file_content = my_file.read()
        my_file.close()
        while True:
            search_text = (yield)
            search_result = 0
            search_result = file_content.count(search_text)
            print 'Number of matches: %d' % (search_result)

The coroutine above opens the given file, reads it's content, and then
searches and returns the number of matches for any given *send* call.

::
    
    >>> search = search_file("example4_3.txt")
    >>> search.next()
    Searching file example4_3.txt
    >>> search.send('python')
    Number of matches: 0
    >>> search.send('Jython')
    Number of matches: 1
    >>> search.send('the')
    Number of matches: 4
    >>> search.send('This')
    Number of matches: 2
    >>> search.close();



Conclusion
----------

In this chapter, we have covered the use of functions in the Python language.  There
are many different use-cases for functions and we have learned techniques that
will allow us to apply the functions to many situations.  Functions are
first-class objects in Python, and they can be treated as any other object.  We
started this chapter by learning the basics of how to define a function.  After
learning about the basics, we began to evolve our knowledge of functions by
learning how to use parameters and make recursive function calls.

There are a wide variety of built-in functions available for use.  If you take
a look at Appendix C of this book you can see a listing of these built-ins.  It
is a good idea to become familiar with what built-ins are available for you.  After
all, it doesn't make much sense to re-write something that has already been done.

This chapter also discussed some alternative ways to define functions including
the lambda notation, as well as some alternative types of functions including
decorators, generators and coroutines.  Wrapping up this chapter, you should now be familiar
with Python functions and how o create and use them.  You should also be
familiar with some of the advanced techniques that can be applied to functions.

In the next chapter, you will learn a bit about input and output with Jython and
the basics of Python I/O.  Later in Part I of this book, we will build upon
object-orientation and learn how to use classes in Python.