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Python 3 Patterns & Idioms / html / _sources / Jython.txt

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********************************************************************************
Jython
********************************************************************************

.. note::  This chapter is being brought up to date with Jython 2.5,
   	   and will need changes when Jython 3 comes out.

.. note::  Some of the descriptions in this chapter are introductory, so
   	   that the material can be used to introduce Java programmers to
	   Jython.

Sometimes it's easier and faster to temporarily step into another
language to solve a particular aspect of your problem.

This chapter looks at the value of crossing language boundaries. It is
often advantageous to solve a problem using more than one programming
language; as you'll see, a problem that is very difficult or tedious
to solve in one language can often be solved quickly and easily in
another. By combining languages, you can create your product much more
quickly and cheaply.

One use of this idea is the *Interpreter* design pattern, which adds
an interpreted language to your program to allow the end user to
easily customize a solution. If the application user needs greater run
time flexibility, for example to create scripts describing the desired
behavior of the system, you can use *Interpreter* by creating and
embedding a language interpreter into your program.

In Java, the easiest and most powerful way to do this is with *Jython*
[#]_, an implementation of Python in pure Java byte codes. As you will
see, this brings together the benefits of both worlds.

Jython is generated entirely in Java byte codes, so incorporating it
into your application is quite simple, and it's as portable as Java
is. It has an extremely clean interface with Java: Java can call
Python classes, and Python can call Java classes.

Because Jython is just Java classes, it can often be "stealthed" into
companies that have rigid processes for using new languges and
tools. If Java has been accepted, such companies often accept anything
that runs on the JVM without question.

The Python/Jython language can be freely embedded into your for-profit
application without signing any license agreement, paying royalties,
or dealing with strings of any kind. There are basically no
restrictions when you're using Python/Jython.

Python is designed with classes from the ground up and provides pure
support for object-oriented programming (both C++ and Java violate
purity in various ways). Python scales up so that you can create large
programs without losing control of the code. Java projects have been
quickly created using Jython, then later optimized by rewriting into
Java sections of the Jython code that have profiled as bottlenecks.

Installation
=======================================================================

To install Jython, go to `http://jython.sourceforge.net
<http://jython.sourceforge.net>`_.  

.. note:: Select "test the beta".

The download is a **.class** file, which will run an installer when
you execute it using ``java -jar``.

You also need the Java Development Kit (JDK), and to add
**jython-complete.jar** to your Java CLASSPATH.  As an example, here
is the appropriate section in my ``.bashrc`` for **\*nix**; for Windows you
need to do the equivalent::

   export set JYTHON_HOME="/Users/bruceeckel/jython2.5b0"
   export set CLASSPATH=.:..:$JYTHON_HOME/jython-complete.jar

When you run Jython, you might get the warning: ``can't create package
cache dir, '/cachedir/packages'``. Jython will still work, but startup
will be slower because caching isn't happening. Jython caching
requires ``/cachedir/packages/`` in the ``python.home`` directory. It
is often the case on **\*nix** that users lack sufficient priveleges to
create or write to this directory. Because the problem is merely
permissions, something like ``mkdir cachedir; chmod a+rw cachedir``
within the Jython directory should eliminate this warning message.

Getting the Trunk
-----------------------------------------------------------------------

.. note:: This section has not been successfuly tested yet.

The Jython development trunk is very stable so it's safe to get as the most recent
version of the implementation. The subversion command is::

	svn co https://jython.svn.sourceforge.net/svnroot/jython/trunk/jython

Then just invoke ``ant`` against the ``build.xml`` file.
``dist/bin/jython`` is a shell script that starts up jython in console mode.
Lastly, modify the registry (in dist/registry) so that::

	python.console=org.python.util.ReadlineConsole
	python.console.readlinelib=GnuReadline

(``readline`` is GPL, so it makes it a bit harder to automate this part of the distro).
See: http://wiki.python.org/jython/ReadlineSetup


Scripting
=======================================================================

One compelling benefit of using a dynamic language on the JVM is
scripting.  You can rapidly create and test code, and solve problems
more quickly.

Here's an example that shows a little of what you can do in a Jython
script, and also gives you a sense of performance::

	# Jython/Simple.py
	import platform, glob, time
	from subprocess import Popen, PIPE

	print platform.uname() # What are we running on?
	print glob.glob("*.py") # Find files with .py extensions
	# Send a command to the OS and capture the results:
	print Popen(["ping", "-c", "1", "www.mindview.net"], 
	               stdout=PIPE).communicate()[0]
        # Time an operation:
	start = time.time()
	for n in xrange(1000000):
	    for i in xrange(10): 
	            oct(i)
        print time.time() - start

..  note:: The ``timeit`` module in the alpha distribution could not
   	   be used as it tries to turn off the Java garbage collector.

If you run this program under both cpython and Jython, you'll see that
the timed loop produces very similar results; Jython 2.5 is in beta so
this is quite impressive and should get faster -- there's even talk
that Jython could run faster than cpython, because of the optimization
benefits of the JVM. The total runtime of the cpython version is
faster because of its rapid startup time; the JVM always has a delay
for startup.

Note that things that are very quick to write in Jython require much
more code (and often research) in Java. Here's an example that uses a
Python *list comprehension* with the **os.walk()** function to visit
all the directories in a directory tree, and find all the files with
names that end in **.java** and contain the word **PythonInterpreter**::

       # Jython/Walk_comprehension.py
       import os

       restFiles = [os.path.join(d[0], f) for d in os.walk(".")
       		    for f in d[2] if f.endswith(".java") and
                    "PythonInterpreter" in open(os.path.join(d[0], f)).read()]

       for r in restFiles:
       	   print(r)

You can certainly achieve this in Java. It will just take a lot longer.

Often more sophisticated programs begin as scripts, and then evolve.
The fact that you can quickly try things out allows you to test
concepts, and then create more refined code as needed.

Interpreter Motivation
=======================================================================

Remember that each design pattern allows one or more factors to
change, so it's important to first be aware of which factor is
changing. Sometimes the end users of your application (rather than the
programmers of that application) need complete flexibility in the way
that they configure some aspect of the program.  That is, they need to
do some kind of simple programming. The *Interpreter* pattern provides
this flexibility by adding a language interpreter.

The problem is that creating your own language and building an
interpreter is a time-consuming distraction from the process of
developing your application.  You must ask whether you want to finish
writing your application or make a new language.  The best solution is
to reuse code: embed an interpreter that's already been built and
debugged for you.

Creating a Language
-------------------------------------------------------------------------

It turns out to be remarkably simple to use Jython to create an
interpreted language inside your application. Consider the greenhouse
controller example from *Thinking in Java*. This is a situation where
you want the end user -- the person managing the greenhouse -- to have
configuration control over the system, and so a simple scripting
language is an ideal solution.  This is often called a
*domain-specific language* (DSL) because it solves a particular domain
problem.

To create the language, we'll simply write a set of Python classes,
and the constructor of each will add itself to a (static) master
list. The common data and behavior will be factored into the base
class **Event**. Each **Event** object will contain an **action**
string (for simplicity -- in reality, you'd have some sort of
functionality) and a time when the event is supposed to run.  The
constructor initializes these fields, and then adds the new **Event**
object to a static list called **events** (defining it in the class,
but outside of any methods, is what makes it static)::

    # Jython/GreenHouseLanguage.py

    class Event:
        events = [] # static

        def __init__(self, action, time):
            self.action = action
            self.time = time
            Event.events.append(self)

        def __cmp__ (self, other):
            "So sort() will compare only on time."
            return cmp(self.time, other.time)

        def run(self):
            print("%.2f: %s" % (self.time, self.action))

        @staticmethod
        def run_events():
            Event.events.sort();
            for e in Event.events:
                e.run()

    class LightOn(Event):
        def __init__(self, time):
            Event.__init__(self, "Light on", time)

    class LightOff(Event):
        def __init__(self, time):
            Event.__init__(self, "Light off", time)

    class WaterOn(Event):
        def __init__(self, time):
            Event.__init__(self, "Water on", time)

    class WaterOff(Event):
        def __init__(self, time):
            Event.__init__(self, "Water off", time)

    class ThermostatNight(Event):
        def __init__(self, time):
            Event.__init__(self,"Thermostat night", time)

    class ThermostatDay(Event):
        def __init__(self, time):
            Event.__init__(self, "Thermostat day", time)

    class Bell(Event):
        def __init__(self, time):
            Event.__init__(self, "Ring bell", time)

    if __name__ == "__main__":
        ThermostatNight(5.00)
        LightOff(2.00)
        WaterOn(3.30)
        WaterOff(4.45)
        LightOn(1.00)
        ThermostatDay(6.00)
        Bell(7.00)
        Event.run_events()

.. note:: To run this program say ``python GreenHouseLanguage.py`` or
   	  ``jython GreenHouseLanguage.py``.

The constructor of each derived class calls the base-class
constructor, which adds the new object to the list. The **run()**
function sorts the list, which automatically uses the **__cmp__()**
method defined in **Event** to base comparisons on time only. In this
example, it only prints out the list, but in the real system it would
wait for the time of each event to come up and then run the event.

The **__main__** section performs a simple test on the classes.

The above file -- which is an ordinary Python program -- is now a
module that can be included in another Python program.  But instead of
using it in an ordinary Python program, let's use Jython, inside of
Java. This turns out to be remarkably simple: you import some Jython
classes, create a **PythonInterpreter** object, and cause the Python
files to be loaded:

..  code-block:: java

    // Jython/GreenHouseController.java
    import org.python.core.*;
    import org.python.util.PythonInterpreter;

    public class GreenHouseController {
      public static void main(String[] args) throws PyException  {
        PythonInterpreter interp = new PythonInterpreter();
        System.out.println("Loading GreenHouse Language");
        interp.execfile("GreenHouseLanguage.py");
        System.out.println("Loading GreenHouse Script");
        interp.execfile("Schedule.ghs");
        System.out.println("Executing GreenHouse Script");
        interp.exec("run()");
      }
    }

The **PythonInterpreter** object is a complete Python interpreter that
accepts commands from the Java program. One of these commands is
**execfile()**, which tells it to execute all the statements it finds
in a particular file. By executing **GreenHouseLanguage.py**, all the
classes from that file are loaded into our **PythonInterpreter**
object, and so it now "holds" the greenhouse controller language. The
**Schedule.ghs** file is the one created by the end user to control
the greenhouse. Here's an example::

    # Jython/Schedule.ghs
    Bell(7.00)
    ThermostatDay(6.00)
    WaterOn(3.30)
    LightOn(1.00)
    ThermostatNight(5.00)
    LightOff(2.00)
    WaterOff(4.45)

This is the goal of the interpreter design pattern: to make the
configuration of your program as simple as possible for the end
user. With Jython you can achieve this with almost no effort at all.

One of the other methods available to the **PythonInterpreter** is
**exec()**, which allows you to send a command to the interpreter. In
the above program, the **run()** function is called using **exec()**.

Using Java libraries
=======================================================================

Jython wraps Java libraries so that any of them can be used directly
or via inheritance. In addition, Python shorthand simplifies coding.

As an example, consider the **HTMLButton.java** example from *Thinking
in Java*. Here is its conversion to Jython::

    # Jython/PythonSwing.py
    # The HTMLButton.java example from "Thinking in Java"
    # converted into Jython.
    from javax.swing import JFrame, JButton, JLabel
    from java.awt import FlowLayout

    frame = JFrame("HTMLButton", visible=1,
      defaultCloseOperation=JFrame.EXIT_ON_CLOSE)

    def kapow(e):
        frame.contentPane.add(JLabel("<html>"+
          "<i><font size=+4>Kapow!"))
        # Force a re-layout to
        # include the new label:
        frame.validate()

    button = JButton("<html><b><font size=+2>" +
      "<center>Hello!<br><i>Press me now!",
      actionPerformed=kapow)
    frame.contentPane.layout = FlowLayout()
    frame.contentPane.add(button)
    frame.pack()
    frame.size=200, 500

If you compare the Java version of the program to the above Jython
implementation, you'll see that Jython is shorter and generally easier
to understand. For example, to set up the frame in the Java version
you had to make several calls: the constructor for **JFrame()**, the
**setVisible()** method and the **setDefaultCloseOperation()** method,
whereas in the above code all three of these operations are performed
with a single constructor call.

Also notice that the **JButton** is configured with an
**actionListener()** method inside the constructor, with the
assignment to **kapow**. In addition, Jython's JavaBean awareness
means that a call to any method with a name that begins with "**set**"
can be replaced with an assignment, as you see above.

The only method that did not come over from Java is the **pack()**
method, which seems to be essential in order to force the layout to
happen properly.  It's also important that the call to **pack()**
appear *before* the **size** setting.

Inheriting from Java library Classes
-------------------------------------------------------------------------------

You can easily inherit from standard Java library classes in
Jython. Here's the **Dialogs.java** example from *Thinking in Java*,
converted into Jython::

    # Jython/PythonDialogs.py
    # Dialogs.java from "Thinking in Java" converted into Jython.
    from java.awt import FlowLayout
    from javax.swing import JFrame, JDialog, JLabel
    from javax.swing import JButton

    class MyDialog(JDialog):
        def __init__(self, parent=None):
            JDialog.__init__(self, title="My dialog", modal=1)
            self.contentPane.layout = FlowLayout()
            self.contentPane.add(JLabel("A dialog!"))
            self.contentPane.add(JButton("OK",
              actionPerformed =
                lambda e, t=self: t.dispose()))
            self.pack()

    frame = JFrame("Dialogs", visible=1,
      defaultCloseOperation=JFrame.EXIT_ON_CLOSE)
    dlg = MyDialog()
    frame.contentPane.add(
      JButton("Press here to get a Dialog Box",
        actionPerformed = lambda e: dlg.show()))
    frame.pack()


**MyDialog** is inherited from **JDialog**, and you can see named
arguments being used in the call to the base-class constructor.

In the creation of the "OK" **JButton**, note that the
**actionPerformed** method is set right inside the constructor, and
that the function is created using the Python **lambda** keyword. This
creates a nameless function with the arguments appearing before the
colon and the expression that generates the returned value after the
colon. As you should know, the Java prototype for the
**actionPerformed()** method only contains a single argument, but the
lambda expression indicates two. However, the second argument is
provided with a default value, so the function *can* be called with
only one argument. The reason for the second argument is seen in the
default value, because this is a way to pass **self** into the lambda
expression, so that it can be used to dispose of the dialog.

Compare this code with the version that's published in *Thinking in
Java*.  You'll find that Python language features allow a much more
succinct and direct implementation.


Controlling Java from Jython
=======================================================================

There's a tremendous amount that you can accomplish by controlling
Python from Java.  But one of the amazing things about Jython is that
it makes Java classes almost transparently available from within
Jython. Basically, a Java class looks like a Python class. This is
true for standard Java library classes as well as classes that you
create yourself, as you can see here::

    # Jython/JavaClassInPython.py
    # Using Java classes within Jython
    # run with: jython.bat JavaClassInPython.py
    from java.util import Date, HashSet, HashMap
    from Jython.javaclass import JavaClass
    from math import sin

    d = Date() # Creating a Java Date object
    print(d) # Calls toString()

    # A "generator" to easily create data:
    class ValGen:
        def __init__(self, maxVal):
            self.val = range(maxVal)
        # Called during 'for' iteration:
        def __getitem__(self, i):
            # Returns a tuple of two elements:
            return self.val[i], sin(self.val[i])

    # Java standard containers:
    jmap = HashMap()
    jset = HashSet()

    for x, y in ValGen(10):
        jmap.put(x, y)
        jset.add(y)
        jset.add(y)

    print(jmap)
    print(jset)

    # Iterating through a set:
    for z in jset:
        print(z, z.__class__)

    print(jmap[3]) # Uses Python dictionary indexing
    for x in jmap.keySet(): # keySet() is a Map method
        print(x, jmap[x])

    # Using a Java class that you create yourself is
    # just as easy:
    jc = JavaClass()
    jc2 = JavaClass("Created within Jython")
    print(jc2.getVal())
    jc.setVal("Using a Java class is trivial")
    print(jc.getVal())
    print(jc.getChars())
    jc.val = "Using bean properties"
    print(jc.val)

..  todo:: rewrite to distinguish python generator from above description, or
    	   choose different name.

Note that the **import** statements map to the Java package structure
exactly as you would expect. In the first example, a **Date()** object
is created as if it were a native Python class, and printing this
object just calls **toString()**.

**ValGen** implements the concept of a "generator" which is used a
great deal in the C++ STL (*Standard Template Library*, part of the
Standard C++ Library). A generator is an object that produces a new
object every time its "generation method" is called, and it is quite
convenient for filling containers. Here, I wanted to use it in a
**for** iteration, and so I needed the generation method to be the one
that is called by the iteration process. This is a special method
called **__getitem__()**, which is actually the overloaded operator
for indexing, '**[ ]**'. A **for** loop calls this method every time
it wants to move the iteration forward, and when the elements run out,
**__getitem__()** throws an out-of-bounds exception and that signals
the end of the **for** loop (in other languages, you would never use
an exception for ordinary control flow, but in Python it seems to work
quite well). This exception happens automatically when **self.val[i]**
runs out of elements, so the **__getitem__()** code turns out to be
simple. The only complexity is that **__getitem__()** appears to
return *two* objects instead of just one. What Python does is
automatically package multiple return values into a tuple, so you
still only end up returning a single object (in C++ or Java you would
have to create your own data structure to accomplish this). In
addition, in the **for** loop where **ValGen** is used, Python
automatically "unpacks" the tuple so that you can have multiple
iterators in the **for**. These are the kinds of syntax
simplifications that make Python so endearing.

The **jmap** and **jset** objects are instances of Java's **HashMap**
and **HashSet**, again created as if those classes were just native
Python components. In the **for** loop, the **put()** and **add()**
methods work just like they do in Java. Also, indexing into a Java
**Map** uses the same notation as for dictionaries, but note that to
iterate through the keys in a **Map** you must use the **Map** method
**keySet()** rather than the Python dictionary method **keys()**.

The final part of the example shows the use of a Java class that I
created from scratch, to demonstrate how trivial it is. Notice also
that Jython intuitively understands JavaBeans properties, since you
can either use the **getVal()** and **setVal()** methods, or assign to
and read from the equivalent **val** property. Also, **getChars()**
returns a **Character[]** in Java, and this automatically becomes an
array in Python.

The easiest way to use Java classes that you create for use inside a
Python program is to put them inside a package. Although Jython can
also import unpackaged java classes (**import JavaClass**), all such
unpackaged java classes will be treated as if they were defined in
different packages so they can only see each other's public methods.

Java packages translate into Jython modules, and Jython must import a
module in order to be able to use the Java class. Here is the Java
code for **JavaClass**:

..  code-block:: java

    // Jython/javaclass/JavaClass.java
    package Jython.javaclass;
    import java.util.*;

    public class JavaClass {
      private String s = "";
      public JavaClass() {
        System.out.println("JavaClass()");
      }
      public JavaClass(String a) {
        s = a;
        System.out.println("JavaClass(String)");
      }
      public String getVal() {
        System.out.println("getVal()");
        return s;
      }
      public void setVal(String a) {
        System.out.println("setVal()");
        s = a;
      }
      public Character[] getChars() {
        System.out.println("getChars()");
        Character[] r = new Character[s.length()];
        for(int i = 0; i < s.length(); i++)
          r[i] = new Character(s.charAt(i));
        return r;
      }
      public static void main(String[] args) {
        JavaClass
          x1 = new JavaClass(),
          x2 = new JavaClass("UnitTest");
        System.out.println(x2.getVal());
        x1.setVal("SpamEggsSausageAndSpam");
        System.out.println(Arrays.toString(x1.getChars()));
      }
    }

You can see that this is just an ordinary Java class, without any
awareness that it will be used in a Jython program. For this reason,
one of the important uses of Jython is in testing Java code
[#]_. Because Python is such a powerful, flexible, dynamic language it
is an ideal tool for automated test frameworks, without making any
changes to the Java code that's being tested.

Inner Classes
------------------------------------------------------------------------------

Inner classes becomes attributes on the class object. Instances of
**static** inner classes can be created with the usual call::

    com.foo.JavaClass.StaticInnerClass()

Non-**static** inner classes must have an outer class instance
supplied explicitly as the first argument::

    com.foo.JavaClass.InnerClass(com.foo.JavaClass())

Controlling the Interpreter
=======================================================================

In the rest of this chapter, we shall look at more sophisticated ways
to interact with Jython. The simplest way to exercise more control
over the **PythonInterpreter** object from within Java is to send data
to the interpreter, and pull data back out.

Putting Data In
--------------------------------------------------------------------------------

To inject data into your Python program, the **PythonInterpreter**
class has a deceptively simple method: **set()**. However, **set()**
takes many different data types and performs conversions upon them.
The following example is a reasonably thorough exercise of the various
**set()** possibilities, along with comments that should give a fairly
complete explanation:

..  code-block:: java

    // Jython/PythonInterpreterSetting.java
    // Passing data from Java to python when using
    // the PythonInterpreter object.
    import org.python.util.PythonInterpreter;
    import org.python.core.*;
    import java.util.*;

    public class PythonInterpreterSetting {
      public static void main(String[] args) throws PyException  {
        PythonInterpreter interp = new PythonInterpreter();
        // It automatically converts Strings
        // into native Python strings:
        interp.set("a", "This is a test");
        interp.exec("print(a)");
        interp.exec("print(a[5:])"); // A slice
        // It also knows what to do with arrays:
        String[] s = { "How", "Do", "You", "Do?" };
        interp.set("b", s);
        interp.exec("for x in b: print(x[0], x)");
        // set() only takes Objects, so it can't
        // figure out primitives. Instead,
        // you have to use wrappers:
        interp.set("c", new PyInteger(1));
        interp.set("d", new PyFloat(2.2));
        interp.exec("print(c + d)");
        // You can also use Java's object wrappers:
        interp.set("c", new Integer(9));
        interp.set("d", new Float(3.14));
        interp.exec("print(c + d)");
        // Define a Python function to print arrays:
        interp.exec(
          "def prt(x): \n" +
          "  print(x)\n" +
          "  for i in x: \n" +
          "    print(i,)\n" +
          "  print(x.__class__)\n");
        // Arrays are Objects, so it has no trouble
        // figuring out the types contained in arrays:
        Object[] types = {
          new boolean[]{ true, false, false, true },
          new char[]{ 'a', 'b', 'c', 'd' },
          new byte[]{ 1, 2, 3, 4 },
          new int[]{ 10, 20, 30, 40 },
          new long[]{ 100, 200, 300, 400 },
          new float[]{ 1.1f, 2.2f, 3.3f, 4.4f },
          new double[]{ 1.1, 2.2, 3.3, 4.4 },
        };
        for(int i = 0; i < types.length; i++) {
          interp.set("e", types[i]);
          interp.exec("prt(e)");
        }
        // It uses toString() to print Java objects:
        interp.set("f", new Date());
        interp.exec("print(f)");
        // You can pass it a List
        // and index into it...
        List x = new ArrayList();
        for(int i = 0; i < 10; i++)
            x.add(new Integer(i * 10));
        interp.set("g", x);
        interp.exec("print(g)");
        interp.exec("print(g[1])");
        // ... But it's not quite smart enough
        // to treat it as a Python array:
        interp.exec("print(g.__class__)");
        // interp.exec("print(g[5:])"); // Fails
        // must extract the Java array:
        System.out.println("ArrayList to array:");
        interp.set("h", x.toArray());
        interp.exec("print(h.__class__)");
        interp.exec("print(h[5:])");
        // Passing in a Map:
        Map m = new HashMap();
        m.put(new Integer(1), new Character('a'));
        m.put(new Integer(3), new Character('b'));
        m.put(new Integer(5), new Character('c'));
        m.put(new Integer(7), new Character('d'));
        m.put(new Integer(11), new Character('e'));
        System.out.println("m: " + m);
        interp.set("m", m);
        interp.exec("print(m, m.__class__," +
          "m[1], m[1].__class__)");
        // Not a Python dictionary, so this fails:
        //! interp.exec("for x in m.keys():" +
        //!   "print(x, m[x])");
        // To convert a Map to a Python dictionary, use PyUtil:
        interp.set("m", PyUtil.toPyDictionary(m));
        interp.exec("print(m, m.__class__, " +
          "m[1], m[1].__class__)");
        interp.exec("for x in m.keys():print(x,m[x])");
      }
    }

As usual with Java, the distinction between real objects and primitive
types causes trouble. In general, if you pass a regular object to
**set()**, it knows what to do with it, but if you want to pass in a
primitive you must perform a conversion. One way to do this is to
create a "Py" type, such as **PyInteger** or **PyFloat**. but it turns
out you can also use Java's own object wrappers like **Integer** and
**Float**, which is probably going to be a lot easier to remember.

Early in the program you'll see an **exec()** containing the Python
statement::

    print(a[5:])

The colon inside the indexing statement indicates a Python *slice*,
which produces a range of elements from the original array. In this
case, it produces an array containing the elements from number 5 until
the end of the array. You could also say '**a[3:5]**' to produce
elements 3 through 5, or '**a[:5]**' to produce the elements zero
through 5. The reason a slice is used in this statement is to make
sure that the Java **String** has really been converted to a Python
string, which can also be treated as an array of characters.

You can see that it's possible, using **exec()**, to create a Python
function (although it's a bit awkward). The **prt()** function prints
the whole array, and then (to make sure it's a real Python array),
iterates through each element of the array and prints it. Finally, it
prints the class of the array, so we can see what conversion has taken
place (Python not only has run-time type information, it also has the
equivalent of Java reflection). The **prt()** function is used to
print arrays that come from each of the Java primitive types.

Although a Java **ArrayList** does pass into the interpreter using
**set()**, and you can index into it as if it were an array, trying to
create a slice fails. To completely convert it into an array, one
approach is to simply extract a Java array using **toArray()**, and
pass that in. The **set()** method converts it to a **PyArray** -- one
of the classes provided with Jython -- which can be treated as a
Python array (you can also explicitly create a **PyArray**, but this
seems unnecessary).

Finally, a **Map** is created and passed directly into the
interpreter. While it is possible to do simple things like index into
the resulting object, it's not a real Python dictionary so you can't
(for example) call the **keys()** method.  There is no straightforward
way to convert a Java **Map** into a Python dictionary, and so I wrote
a utility called **toPyDictionary()** and made it a **static** method
of **net.mindview.python.PyUtil**. This also includes utilities to
extract a Python array into a Java **List**, and a Python dictionary
into a Java **Map**:

..  code-block:: java

    // Jython/PyUtil.java
    // PythonInterpreter utilities
    import org.python.util.PythonInterpreter;
    import org.python.core.*;
    import java.util.*;

    public class PyUtil {
      /** Extract a Python tuple or array into a Java
      List (which can be converted into other kinds
      of lists and sets inside Java).
      @param interp The Python interpreter object
      @param pyName The id of the python list object
      */
      public static List
      toList(PythonInterpreter interp, String pyName){
        return new ArrayList(Arrays.asList(
          (Object[])interp.get(
            pyName, Object[].class)));
      }
      /** Extract a Python dictionary into a Java Map
      @param interp The Python interpreter object
      @param pyName The id of the python dictionary
      */
      public static Map
      toMap(PythonInterpreter interp, String pyName){
        PyList pa = ((PyDictionary)interp.get(
          pyName)).items();
        Map map = new HashMap();
        while(pa.__len__() != 0) {
          PyTuple po = (PyTuple)pa.pop();
          Object first = po.__finditem__(0)
            .__tojava__(Object.class);
          Object second = po.__finditem__(1)
            .__tojava__(Object.class);
          map.put(first, second);
        }
        return map;
      }
      /** Turn a Java Map into a PyDictionary,
      suitable for placing into a PythonInterpreter
      @param map The Java Map object
      */
      public static PyDictionary toPyDictionary(Map map) {
        Map m = new HashMap();
        Iterator it = map.entrySet().iterator();
        while(it.hasNext()) {
          Map.Entry e = (Map.Entry)it.next();
          m.put(Py.java2py(e.getKey()),
            Py.java2py(e.getValue()));
        }
        return new PyDictionary(m);
      }
    }

Here is the unit testing code:

..  code-block:: java

    // Jython/TestPyUtil.java
    import org.python.util.PythonInterpreter;
    import java.util.*;

    public class TestPyUtil {
      PythonInterpreter pi = new PythonInterpreter();
      public void test1() {
        pi.exec("tup=('fee','fi','fo','fum','fi')");
        List lst = PyUtil.toList(pi, "tup");
        System.out.println(lst);
        System.out.println(new HashSet(lst));
      }
      public void test2() {
        pi.exec("ints=[1,3,5,7,9,11,13,17,19]");
        List lst = PyUtil.toList(pi, "ints");
        System.out.println(lst);
      }
      public void test3() {
        pi.exec("dict = { 1 : 'a', 3 : 'b', " +
          "5 : 'c', 9 : 'd', 11 : 'e'}");
        Map mp = PyUtil.toMap(pi, "dict");
        System.out.println(mp);
      }
      public void test4() {
        Map m = new HashMap();
        m.put("twas", new Integer(11));
        m.put("brillig", new Integer(27));
        m.put("and", new Integer(47));
        m.put("the", new Integer(42));
        m.put("slithy", new Integer(33));
        m.put("toves", new Integer(55));
        System.out.println(m);
        pi.set("m", PyUtil.toPyDictionary(m));
        pi.exec("print(m)");
        pi.exec("print(m['slithy'])");
      }
      public static void main(String args[]) {
        TestPyUtil test = new TestPyUtil();
        test.test1();
        test.test2();
        test.test3();
        test.test4();
      }
    }


We'll see the use of the extraction tools in the next section.

Getting Data Out
--------------------------------------------------------------------------------

There are a number of different ways to extract data from the
**PythonInterpreter**. If you simply call the **get()** method,
passing it the object identifier as a string, it returns a
**PyObject** (part of the **org.python.core** support classes). It's
possible to "cast" it using the **__tojava__()** method, but there are
better alternatives:


1.  The convenience methods in the **Py** class, such as **py2int()**,
    take a **PyObject** and convert it to a number of different types.

2.  An overloaded version of **get()** takes the desired Java
    **Class** object as a second argument, and produces an object that
    has that run-time type (so you still need to perform a cast on the
    result in your Java code).

Using the second approach, getting an array from the
**PythonInterpreter** is quite easy. This is especially useful because
Python is exceptionally good at manipulating strings and files, and so
you will commonly want to extract the results as an array of
strings. For example, you can do a wildcard expansion of file names
using Python's **glob()**, as shown further down in the following
code:

..  code-block:: java

    // Jython/PythonInterpreterGetting.java
    // Getting data from the PythonInterpreter object.
    import org.python.util.PythonInterpreter;
    import org.python.core.*;
    import java.util.*;

    public class PythonInterpreterGetting {
      public static void
      main(String[] args) throws PyException  {
        PythonInterpreter interp = new PythonInterpreter();
        interp.exec("a = 100");
        // If you just use the ordinary get(),
        // it returns a PyObject:
        PyObject a = interp.get("a");
        // There's not much you can do with a generic
        // PyObject, but you can print it out:
        System.out.println("a = " + a);
        // If you know the type it's supposed to be,
        // you can "cast" it using __tojava__() to
        // that Java type and manipulate it in Java.
        // To use 'a' as an int, you must use
        // the Integer wrapper class:
        int ai= ((Integer)a.__tojava__(Integer.class))
          .intValue();
        // There are also convenience functions:
        ai = Py.py2int(a);
        System.out.println("ai + 47 = " + (ai + 47));
        // You can convert it to different types:
        float af = Py.py2float(a);
        System.out.println("af + 47 = " + (af + 47));
        // If you try to cast it to an inappropriate
        // type you'll get a runtime exception:
        //! String as = (String)a.__tojava__(
        //!   String.class);

        // If you know the type, a more useful method
        // is the overloaded get() that takes the
        // desired class as the 2nd argument:
        interp.exec("x = 1 + 2");
        int x = ((Integer)interp
          .get("x", Integer.class)).intValue();
        System.out.println("x = " + x);

        // Since Python is so good at manipulating
        // strings and files, you will often need to
        // extract an array of Strings. Here, a file
        // is read as a Python array:
        interp.exec("lines = " +
          "open('PythonInterpreterGetting.java')" +
          ".readlines()");
        // Pull it in as a Java array of String:
        String[] lines = (String[])
          interp.get("lines", String[].class);
        for(int i = 0; i < 10; i++)
          System.out.print(lines[i]);

        // As an example of useful string tools,
        // global expansion of ambiguous file names
        // using glob is very useful, but it's not
        // part of the standard Jython package, so
        // you'll have to make sure that your
        // Python path is set to include these, or
        // that you deliver the necessary Python
        // files with your application.
        interp.exec("from glob import glob");
        interp.exec("files = glob('*.java')");
        String[] files = (String[])
          interp.get("files", String[].class);
        for(int i = 0; i < files.length; i++)
          System.out.println(files[i]);

        // You can extract tuples and arrays into
        // Java Lists with net.mindview.PyUtil:
        interp.exec("tup = ('fee', 'fi', 'fo', 'fum', 'fi')");
        List tup = PyUtil.toList(interp, "tup");
        System.out.println(tup);
        // It really is a list of String objects:
        System.out.println(tup.get(0).getClass());
        // You can easily convert it to a Set:
        Set tups = new HashSet(tup);
        System.out.println(tups);
        interp.exec("ints=[1,3,5,7,9,11,13,17,19]");
        List ints = PyUtil.toList(interp, "ints");
        System.out.println(ints);
        // It really is a List of Integer objects:
        System.out.println((ints.get(1)).getClass());

        // If you have a Python dictionary, it can
        // be extracted into a Java Map, again with
        // net.mindview.PyUtil:
        interp.exec("dict = { 1 : 'a', 3 : 'b'," +
          "5 : 'c', 9 : 'd', 11 : 'e' }");
        Map map = PyUtil.toMap(interp, "dict");
        System.out.println("map: " + map);
        // It really is Java objects, not PyObjects:
        Iterator it = map.entrySet().iterator();
        Map.Entry e = (Map.Entry)it.next();
        System.out.println(e.getKey().getClass());
        System.out.println(e.getValue().getClass());
      }
    }

The last two examples show the extraction of Python tuples and lists
into Java **List**\s, and Python dictionaries into Java
**Map**\s. Both of these cases require more processing than is
provided in the standard Jython library, so I have again created
utilities in **net.mindview.pyton.PyUtil**: **toList()** to produce a
**List** from a Python sequence, and **toMap()** to produce a **Map**
from a Python dictionary. The **PyUtil** methods make it easier to
take important data structures back and forth between Java and Python.

Multiple Interpreters
--------------------------------------------------------------------------------

It's also worth noting that you can have multiple
**PythonInterpreter** objects in a program, and each one has its own
name space:

..  code-block:: java

    // Jython/MultipleJythons.java
    // You can run multiple interpreters, each
    // with its own name space.
    import org.python.util.PythonInterpreter;
    import org.python.core.*;

    public class MultipleJythons {
      public static void
      main(String[] args) throws PyException  {
        PythonInterpreter
          interp1 =  new PythonInterpreter(),
          interp2 =  new PythonInterpreter();
        interp1.set("a", new PyInteger(42));
        interp2.set("a", new PyInteger(47));
        interp1.exec("print(a)");
        interp2.exec("print(a)");
        PyObject x1 = interp1.get("a");
        PyObject x2 = interp2.get("a");
        System.out.println("a from interp1: " + x1);
        System.out.println("a from interp2: " + x2);
      }
    }


When you run the program you'll see that the value of **a** is
distinct within each **PythonInterpreter**.

Creating Java classes with Jython
=======================================================================

.. note:: Jython 2.5.0 does not support **jythonc**. Support is
   	  planned for 2.5.1. **jythonc** basically converted python
   	  source to java source, the replacement will generate
   	  bytecodes directly, and enable jython code to be imported
   	  directly into java (via generated proxies).

Jython can also create Java classes directly from your Jython
code. This can produce very useful results, as you are then able to
treat the results as if they are native Java classes, albeit with
Python power under the hood.

To produce Java classes from Python code, Jython comes with a compiler
called **jythonc**.

The process of creating Python classes that will produce Java classes
is a bit more complex than when calling Java classes from Python,
because the methods in Java classes are statically typed, while Python
functions and methods are dynamically typed. Thus, you must somehow
tell **jythonc** that a Python method is intended to have a particular
set of argument types and that its return value is a particular
type. You accomplish this with the **@sig** string, which is placed
right after the beginning of the Python method definition (this is the
standard location for the Python documentation string). For example::

    def returnArray(self):
        "@sig public java.lang.String[] returnArray()"

The Python definition doesn't specify any return type, but the @sig
string gives the full type information about what is being passed and
returned. The **jythonc** compiler uses this information to generate
the correct Java code.

There's one other set of rules you must follow in order to get a
successful compilation: you must inherit from a Java class or
interface in your Python class (you do not need to specify the
**@sig** signature for methods defined in the
superclass/interface). If you do not do this, you won't get your
desired methods -- unfortunately, **jythonc** gives you no warnings or
errors in this case, but you won't get what you want. If you don't see
what's missing, it can be very frustrating.

In addition, you must import the appropriate java class and give the
correct package specification.  In the example below, **java** is
imported so you must inherit from **java.lang.Object**, but you could
also say **from java.lang import Object** and then you'd just inherit
from **Object** without the package specification. Unfortunately, you
don't get any warnings or errors if you get this wrong, so you must be
patient and keep trying.

Here is an example of a Python class created to produce a Java
class. In this case, the Python file is used to build a Java
**.class** file, so the class file is the desired target:

..  code-block:: python

    # Jython/PythonToJavaClass.py
    # A Python class converted into a Java class
    # Compile with:
    # jythonc --package python.java.test PythonToJavaClass.py
    from jarray import array
    import java

    class PythonToJavaClass(java.lang.Object):
        # The '@sig' signature string is used to create the
        # proper signature in the resulting Java code:
        def __init__(self):
            "@sig public PythonToJavaClass()"
            print("Constructor for PythonToJavaClass")

        def simple(self):
            "@sig public void simple()"
            print("simple()")

        # Returning values to Java:
        def returnString(self):
            "@sig public java.lang.String returnString()"
            return "howdy"

        # You must construct arrays to return along
        # with the type of the array:
        def returnArray(self):
            "@sig public java.lang.String[] returnArray()"
            test = [ "fee", "fi", "fo", "fum" ]
            return array(test, java.lang.String)

        def ints(self):
            "@sig public java.lang.Integer[] ints()"
            test = [ 1, 3, 5, 7, 11, 13, 17, 19, 23 ]
            return array(test, java.lang.Integer)

        def doubles(self):
            "@sig public java.lang.Double[] doubles()"
            test = [ 1, 3, 5, 7, 11, 13, 17, 19, 23 ]
            return array(test, java.lang.Double)

        # Passing arguments in from Java:
        def argIn1(self, a):
            "@sig public void argIn1(java.lang.String a)"
            print("a: %s" % a)
            print("a.__class__", a.__class__)

        def argIn2(self, a):
            "@sig public void argIn1(java.lang.Integer a)"
            print("a + 100: %d" % (a + 100))
            print("a.__class__", a.__class__)

        def argIn3(self, a):
            "@sig public void argIn3(java.util.List a)"
            print("received List:", a, a.__class__)
            print("element type:", a[0].__class__)
            print("a[3] + a[5]:", a[5] + a[7])
            #! print("a[2:5]:", a[2:5]) # Doesn't work

        def argIn4(self, a):
            "@sig public void \
               argIn4(org.python.core.PyArray a)"
            print("received type:", a.__class__)
            print("a: ", a)
            print("element type:", a[0].__class__)
            print("a[3] + a[5]:", a[5] + a[7])
            print("a[2:5]:", a[2:5] # A real Python array)

        # A map must be passed in as a PyDictionary:
        def argIn5(self, m):
            "@sig public void \
               argIn5(org.python.core.PyDictionary m)"
            print("received Map: ", m, m.__class__)
            print("m['3']:", m['3'])
            for x in m.keys():
                print(x, m[x])

First note that **PythonToJavaClass** is inherited from
**java.lang.Object**; if you don't do this you will quietly get a Java
class without the right signatures. You are not required to inherit
from **Object**; any other Java class will do.

This class is designed to demonstrate different arguments and return
values, to provide you with enough examples that you'll be able to
easily create your own signature strings. The first three of these are
fairly self-explanatory, but note the full qualification of the Java
name in the signature string.

In **returnArray()**, a Python array must be returned as a Java
array. To do this, the Jython **array()** function (from the
**jarray** module) must be used, along with the type of the class for
the resulting array. Any time you need to return an array to Java, you
must use **array()**, as seen in the methods **ints()** and
**doubles()**.

The last methods show how to pass arguments in from Java. Basic types
happen automatically as long as you specify them in the **@sig**
string, but you must use objects and you cannot pass in primitives
(that is, primitives must be ensconced in wrapper objects, such as
**Integer**).

In **argIn3()**, you can see that a Java **List** is transparently
converted to something that behaves just like a Python array, but is
not a true array because you cannot take a slice from it. If you want
a true Python array, then you must create and pass a **PyArray** as in
**argIn4()**, where the slice is successful. Similarly, a Java **Map**
must come in as a **PyDictionary** in order to be treated as a Python
dictionary.

Here is the Java program to exercise the Java classes produced by the
above Python code. You can't compile **TestPythonToJavaClass.java**
until **PythonToJavaClass.class** is available:

..  code-block:: java

    // Jython/TestPythonToJavaClass.java
    import java.lang.reflect.*;
    import java.util.*;
    import org.python.core.*;
    import java.util.*;
    import net.mindview.python.*;
    // The package with the Python-generated classes:
    import python.java.test.*;

    public class TestPythonToJavaClass {
      PythonToJavaClass p2j = new PythonToJavaClass();
      public void testDumpClassInfo() {
        System.out.println(
          Arrays.toString(
            p2j.getClass().getConstructors()));
        Method[] methods = p2j.getClass().getMethods();
        for(int i = 0; i < methods.length; i++) {
          String nm = methods[i].toString();
          if(nm.indexOf("PythonToJavaClass") != -1)
            System.out.println(nm);
        }
      }
      public static void main(String[] args) {
        p2j.simple();
        System.out.println(p2j.returnString());
        System.out.println(
          Arrays.toString(p2j.returnArray()));
        System.out.println(
          Arrays.toString(p2j.ints());
        System.out.println(
          Arrays.toString(p2j.doubles()));
        p2j.argIn1("Testing argIn1()");
        p2j.argIn2(new Integer(47));
        ArrayList a = new ArrayList();
        for(int i = 0; i < 10; i++)
          a.add(new Integer(i));
        p2j.argIn3(a);
        p2j.argIn4(
          new PyArray(Integer.class, a.toArray()));
        Map m = new HashMap();
        for(int i = 0; i < 10; i++)
          m.put("" + i, new Float(i));
        p2j.argIn5(PyUtil.toPyDictionary(m));
      }
    }

For Python support, you'll usually only need to import the classes in
**org.python.core**. Everything else in the above example is fairly
straightforward, as **PythonToJavaClass** appears, from the Java side,
to be just another Java class. **dumpClassInfo()** uses reflection to
verify that the method signatures specified in
**PythonToJavaClass.py** have come through properly.

Building Java Classes from Python
--------------------------------------------------------------------------------

Part of the trick of creating Java classes from Python code is the
@sig information in the method documentation strings. But there's a
second problem which stems from the fact that Python has no "package"
keyword -- the Python equivalent of packages (modules) are implicitly
created based on the file name.  However, to bring the resulting class
files into the Java program, **jythonc** must be given information
about how to create the Java package for the Python code. This is done
on the **jythonc** command line using the **--package** flag, followed
by the package name you wish to produce (including the separation
dots, just as you would give the package name using the **package**
keyword in a Java program). This will put the resulting **.class**
files in the appropriate subdirectory off of the current
directory. Then you only need to import the package in your Java
program, as shown above (you'll need '**.**' in your CLASSPATH in
order to run it from the code directory).

Here are the **make** dependency rules that I used to build the above
example (the backslashes at the ends of the lines are understood by
**make** to be line continuations)::

    TestPythonToJavaClass.class: \\
            TestPythonToJavaClass.java \\
            python\java\test\PythonToJavaClass.class
        javac TestPythonToJavaClass.java

    python\java\test\PythonToJavaClass.class: \\
            PythonToJavaClass.py
        jythonc.bat --package python.java.test \\
        PythonToJavaClass.py

The first target, **TestPythonToJavaClass.class**, depends on both
**TestPythonToJavaClass.java** and the **PythonToJavaClass.class**,
which is the Python code that's converted to a class file. This
latter, in turn, depends on the Python source code. Note that it's
important that the directory where the target lives be specified, so
that the makefile will create the Java program with the minimum
necessary amount of rebuilding.

Summary
=======================================================================

This chapter has arguably gone much deeper into Jython than required
to use the interpreter design pattern. Indeed, once you decide that
you need to use interpreter and that you're not going to get lost
inventing your own language, the solution of installing Jython is
quite simple, and you can at least get started by following the
**GreenHouseController** example.

Of course, that example is often too simple and you may need something
more sophisticated, often requiring more interesting data to be passed
back and forth. When I encountered the limited documentation, I felt
it necessary to come up with a more thorough examination of Jython.

In the process, note that there could be another equally powerful
design pattern lurking in here, which could perhaps be called
*multiple languages* or *language hybridizing*. This is based on the
experience of having each language solve a certain class of problems
better than the other; by combining languages you can solve problems
much faster than with either language by itself. CORBA is another way
to bridge across languages, and at the same time bridging between
computers and operating systems.

To me, Python and Java present a very potent combination for program
development because of Java's architecture and tool set, and Python's
extremely rapid development (generally considered to be 5-10 times
faster than C++ or Java).  Python is usually slower, however, but even
if you end up re-coding parts of your program for speed, the initial
fast development will allow you to more quickly flesh out the system
and uncover and solve the critical sections. And often, the execution
speed of Python is not a problem -- in those cases it's an even bigger
win. A number of commercial products already use Java and Jython, and
because of the terrific productivity leverage I expect to see this
happen more in the future.

Exercises
=======================================================================

#.  Modify **GreenHouseLanguage.py** so that it checks the times for
    the events and runs those events at the appropriate times.

#.  Modify **GreenHouseLanguage.py** so that it calls a function for
    **action** instead of just printing a string.

#.  Create a Swing application with a **JTextField** (where the user
    will enter commands) and a **JTextArea** (where the command
    results will be displayed).  Connect to a **PythonInterpreter**
    object so that the output will be sent to the **JTextArea** (which
    should scroll). You'll need to locate the **PythonInterpreter**
    command that redirects the output to a Java stream.

#.  Modify **GreenHouseLanguage.py** to add a master controller class
    (instead of the static array inside **Event**) and provide a
    **run()** method for each of the subclasses. Each **run()** should
    create and use an object from the standard Java library during its
    execution. Modify **GreenHouseController.java** to use this new
    class.

#.  Modify the resulting **GreenHouseLanguage.py** from exercise two
    to produce Java classes (add the @sig documentation strings to
    produce the correct Java signatures, and create a makefile to
    build the Java **.class** files). Write a Java program that uses
    these classes.

#.  Modify **GreenHouseLanguage.py** so that the subclasses of
    **Event** are not discrete classes, but are instead *generated* by
    a single function which creates the class and the associated
    string dynamically.

.. rubric:: Footnotes

.. [#] 	The original version of this was called *JPython*\, but the
        project changed and the name was changed to emphasize the
        distinctness of the new version.

.. [#]  Changing the registry setting
        **python.security.respectJavaAccessibility = true** to
        **false** makes testing even more powerful because it allows
        the test script to use *all* methods, even protected and
        package- private.