package Examples
class StaticLib {
   static factorial(n){
      def fact=1.0
      for (def k=1; k< =n; k++)
         fact*=k
      return fact
   }
}

Java programmers will note there are no public keywords: in Groovy public is the default. Next the method declaration has no return type. Neither does the input n. In Groovy, typing is optional. This is exploited on the next line, where fact is declared using the def keyword. Groovy will determine its type at runtime much as Matlab does for variables. The same is true for the loop control variable k.
To run the code at the Matlab command prompt, use commands as you would for Java e.g:

>> import Examples.StaticLib
>> StaticLib.factorial(40)
>> StaticLib.factorial(int64(40))
>> StaticLib.factorial(java.math.BigInteger(40))

all produce:

ans =
815915283247897734345611269596115894272000000000.0

This is an exact, and correct, result because Groovy instantiated fact as a java.math.BigDecimal when it was initialized with a floating-point value. Note also, that Groovy’s dynamic typing meant only one method was needed– not one for every possible class of input. With Groovy, you need to write much less code. (Caution: running this code with very large n will work, but slowly, and potentially take up all available memory to store the result).
The small typing change below makes a substantial difference to the code’s operation:

package Examples
   class NewLib {
      def factorial = {n ->
      def fact = 1.0
      for (def k = 1; k < = n; k++)
         fact *= k
      return fact
   }
}

Here, factorial is a property, not a method, of the class and its contents are the code – in Groovy this is a closure (in Matlab it would be called an anonymous function).
Create an instance of the NewLib class in Matlab, call the get method on the factorial property and run the code in Matlab as follows:

>> myObj=Examples.NewLib();
>> func=myObj.getFactorial();
>> func.call(40)
ans =
815915283247897734345611269596115894272000000000.0

Using Groovy saved a lot of work: there was no need to write a no argument constructor or a getFactorial() method. Groovy did that automatically.
Here is another code snippet where Groovy’s dynamic typing makes it look much more like Matlab than Java:

for (obj in props) {
   switch (obj.key) {
      …
      case "Alpha":
      case "EdgeColor":
      case "LineColor":
      case "XData":
      case "YData":
      case "ZData":
      plot.("set" + obj.key)(props.(obj.key))
      break
      …
   }
}

props is a LinkedHashMap. We start by running through each of the entries in this map in a loop using:

for (obj in props)

Each entry is assigned to obj in turn. Again, we let Groovy determine the class of obj dynamically – it will be of class java.util.LinkedHashMap$Entry, but we do not need to worry about that detail.
For each entry we retrieve the key and use that as the variable for the switch block. In this case, the keys are all strings and, in Groovy, strings can be used in case statements (Java 7 would be needed for that if the code were written in Java; note that Java 7 is still not integrated in Matlab as of this date).
Within the switch block, the code invokes a setter on an object called plot. Rather than write separate lines for each possible property in the key list, one line is enough:

plot.("set" + obj.key)(props.(obj.key))

The key string is pre-pended with “set” and the corresponding method is invoked passing the value from the LinkedHashMap entry as input – so a particular iteration of the loop, this might equate for example to plot.setAlpha(0.5)
For a Matlab programmer who is put off from delving into Java because of the tedious boiler-plate code it requires, Groovy may be an attractive alternative. Groovy also has many powerful features for handling regular expressions, a set of builder classes (for example a thread-safe SwingBuilder class), writing domain specific languages and for meta-object programming including run-time injection of new methods. There is also a growing set of Groovy plugin modules.
Not all Java programmers like Groovy. The dynamic features inevitably create run-time overheads and mean that type-mismatches that would be detected at compile-time in Java may produce run-time exceptions with Groovy, but Groovy 2.0 addresses some of these issues by introducing new compiler annotations. My own use of it has been limited, partly because of these issues: I have used it to create a static library to link Matlab, R, SciLab etc to a graphics package written in Java. The dynamic features of Groovy have been useful there to reduce the work involved.

* Note: Groovy 2.0 was released 28.06.2012. Initial experiments suggest it does not work within Matlab, perhaps because of version clashes with jars on the Matlab static class path.
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