Develop Reference

Java Nashorn compatibility file crashes

When I try to load the Nashorn compatibility file for Rhino (load("nashorn:mozilla_compat.js")) it comes up with the following error:
java.lang.RuntimeException: javax.script.ScriptException: ReferenceError: "net" is not defined in nashorn:mozilla_compat.js at line number 67
I've tried everything to get it to work but nothing has helped :(

This can happen if your script (not mozilla_compat.js itself) contains a declaration with a qualified name like this:
var x = new net.yourdomain.yourpackage.ClassName();
instead of doing
importPackage(Packages.net.yourdomain.yourpackage);
var x = new ClassName();
The former works in Rhino, but not in Nashorn, even with the compatibility script. The latter however will work in both environments.

I ran the following code with the latest JDK 8 update released (8u60) - available for download # http://www.oracle.com/technetwork/java/javase/downloads/jdk8-downloads-2133151.html
import javax.script.*;
public class Main {
public static void main(String[] ar) throws Exception {
ScriptEngineManager m = new ScriptEngineManager();
ScriptEngine e = m.getEngineByName("nashorn");
e.eval("load('nashorn:mozilla_compat.js')");
// this should print 'function' and mozilla_compat.js defines that function
e.eval("print(typeof importClass)");
}
}
And it printed "function" as expected. I checked it on jdk9-dev tip forest build as well. It works with that version as well. Will you please print "java -version" and make sure you're using recent JDK 8 ?

OpenCV for Eclipse

Installation instructions: http://docs.opencv.org/doc/tutorials/introduction/desktop_java/java_dev_intro.html
I have downloaded everything. Everything seems to be working except when I run this sample program:
import org.opencv.core.Core;
import org.opencv.core.CvType;
import org.opencv.core.Mat;
public class Main {
public static void main(String[] args) {
System.out.println("Welcome to OpenCV " + Core.VERSION);
System.loadLibrary(Core.NATIVE_LIBRARY_NAME);
Mat m = Mat.eye(3, 3, CvType.CV_8UC1);
System.out.println("m = " + m.dump());
}
}
I get the output:
m = [1, 0, 0;
0, 1, 0;
0, 0, 1]
(which I hope is right).
But I also get these error messages:
objc[63784]: Class CVWindow is implemented in both /Users/.../cv2.so and /Users/... /libopencv_java246.dylib. One of the two will be used. Which one is undefined.
objc[63784]: Class CVView is implemented in both /Users/.../cv2.so and /Users/.../libopencv_java246.dylib. One of the two will be used. Which one is undefined.
objc[63784]: Class CVSlider is implemented in both /Users/.../cv2.so and /Users/.../libopencv_java246.dylib. One of the two will be used. Which one is undefined.
objc[63784]: Class CaptureDelegate is implemented in both /Users/... /cv2.so and /Users/jsuit/opencv/lib/libopencv_java246.dylib. One of the two will be used. Which one is undefined.
I have tried moving the cv2.so file to another folder, but then the program won't compile.

The problem has to do, as far as I can make out, with a reference to the Python libraries (the .so version) that ends up included within the Java libraries themselves. This would seem to be a build configuration error (following the instructions does produce it).
I was able to eliminate the double-definition error by re-building the Java version without support for the Python libraries in it, using the following in the cmake step (all else the same):
cmake -D BUILD_SHARED_LIBS=OFF -D BUILD_NEW_PYTHON_SUPPORT=NO ..
The newly produced Java libraries and .jar work just as before, but without the error message.
(Note that I can't guarantee this won't cause other problems, especially if you want to do some sort of mixed-language programming, but it does produce libraries useful for Java and Eclipse. You can always build multiple versions of OpenCV, too, some with support for Python, some without, and use whichever one you like if you switch languages at some point.)
Hat tip: http://answers.opencv.org/question/16015/mac-opencv-246-java-exception/

Java and R integration

I am trying to build a java project which contains R codes. Main logic behind that is I want to automate the data structuring and data analysis with in a same project. Partially I am being able to do that. I connected R to Java and my R codes are running well. I did all my set up in the local machine and its giving me all output as I need. As data set is big I am trying to run this on amazon server. But when I am shifting it to server, my project is not working properly. Its not being able to execute library(XLConnect), library(rJava). When ever I am calling this two libraries in my java project it's crashing. Independently in R codes are running and giving me output. What I can I for that, and how to fix thus error. Please help me out from this.
My java codes is
import java.io.InputStreamReader;
import java.io.Reader;
public class TestRMain {
public static void main(String[] arg)throws Exception{
ProcessBuilder broker = new ProcessBuilder("R.exe","--file=E:\\New\\Modified_Best_Config.R");
Process runBroker = broker.start();
Reader reader = new InputStreamReader(runBroker.getInputStream());
int ch;
while((ch = reader.read())!= -1)
System.out.print((char)ch);
reader.close();
runBroker.waitFor();
System.out.println("Execution complete");
}
}
And in the Modified_Best_Config.R I have written these codes
library('ClustOfVar');
library("doBy");
library(XLConnect)
#library(rJava)
#library(xlsx)
path="E:/New/";
############Importing and reading the excel files into R##############
Automated_R <- loadWorkbook("E:/New/Option_Mix_Calculation1.xlsx")
sheet1 <- readWorksheet(Automated_R, sheet = "Current Output")
sheet2 <- readWorksheet(Automated_R, sheet = "Actual Sales monthly")
sheet3 <- readWorksheet(Automated_R, sheet = "Differences")
#####################Importing raw Data###############################
optionData<- read.csv(paste(path,"ModifiedStructureNewBestConfig1.csv",sep=""),head=TRUE,sep=",");
nrow(optionData)
optionDemand=sapply(split(optionData,optionData$Trim),trimSplit);
optionDemand1=t(optionDemand[c(-1,-2),]);
optionDemand1
################Calculating the equipment Demand####################
optionDemand2<-t(optionDemand2[c(-1,0)]);
Rownames <- as.data.frame(row.names(optionDemand2))
writeWorksheet(Automated_R,Rownames, sheet = "Current Output", startRow = 21, startCol = 1)
writeWorksheet(Automated_R,optionDemand2, sheet = "Current Output", startRow = 21, startCol = 2)
saveWorkbook(Automated_R)
But java is stopping its operation after these line.
library("doBy");
Whole set of codes are running on nicely on my local machine. But whenever I am trying to run this on amazon server it's not running. Individually in R this code is running on server. I have couple of more R codes which are running with out any error. What can I do for that, please help me out.

Thanks for updating your question with some example code. I cannot completely replicate your circumstances because I presently don't have immediate access to Amazon EC2, and I don't know the specific type of instance you are using. But here a couple of suggestions for de-bugging your issue, which I have a hunch is being caused by a missing package.
1. Try to install the offending packages via your R script
At the very beginning of your R script, before you try to load any packages, insert the following:
install.packages(c("XLConnect", "rJava"))
If your instance includes a specified CRAN mirror (essentially, the online repository where R will first look to download the package source code from), this should install the packages in the same repo where your other packages are kept on your server. Then, either library or require should load your packages.
(sidenote: rJava is actually a dependency of XLConnect, so it will automatically load anyway if you only specify library(XLConnect))
2. If the above does not work, try installing the packages via the command line
This is essentially what #Ben was suggesting with his comment. Alternatively, see perhaps this link, which deals with a similar problem with a different package. If you can, in terminal on the server, I would try entering the following three commands:
sudo add-apt-repository ppa:marutter/rrutter
sudo apt-get update
sudo apt-get install r-cran-XLConnect
In my experience this has been a good go-to repo when I can't seem to find a package I need to install. But you may or may not have permission to install packages on your server instance.

How to call a java function from python/numpy?

it is clear to me how to extend Python with C++, but what if I want to write a function in Java to be used with numpy?
Here is a simple scenario: I want to compute the average of a numpy array using a Java class. How do I pass the numpy vector to the Java class and gather the result?
Thanks for any help!

I spent some time on my own question and would like to share my answer as I feel there is not much information on this topic on stackoverflow. I also think Java will become more relevant in scientific computing (e.g. see WEKA package for data mining) because of the improvement of performance and other good software development features of Java.
In general, it turns out that using the right tools it is much easier to extend Python with Java than with C/C++!
Overview and assessment of tools to call Java from Python
http://pypi.python.org/pypi/JCC: because of no proper
documentation this tool is useless.
Py4J: requires to start the Java process before using python. As
remarked by others this is a possible point of failure. Moreover, not many examples of use are documented.
JPype: although development seems to be death, it works well and there are
many examples on it on the web (e.g. see http://kogs-www.informatik.uni-hamburg.de/~meine/weka-python/ for using data mining libraries written in Java) . Therefore I decided to focus
on this tool.
Installing JPype on Fedora 16
I am using Fedora 16, since there are some issues when installing JPype on Linux, I describe my approach.
Download JPype, then modify setup.py script by providing the JDK path, in line 48:
self.javaHome = '/usr/java/default'
then run:
sudo python setup.py install
Afters successful installation, check this file:
/usr/lib64/python2.7/site-packages/jpype/_linux.py
and remove or rename the method getDefaultJVMPath() into getDefaultJVMPath_old(), then add the following method:
def getDefaultJVMPath():
return "/usr/java/default/jre/lib/amd64/server/libjvm.so"
Alternative approach: do not make any change in the above file _linux.py, but never use the method getDefaultJVMPath() (or methods which call this method). At the place of using getDefaultJVMPath() provide directly the path to the JVM. Note that there are several paths, for example in my system I also have the following paths, referring to different versions of the JVM (it is not clear to me whether the client or server JVM is better suited):
/usr/lib/jvm/java-1.5.0-gcj-1.5.0.0/jre/lib/x86_64/client/libjvm.so
/usr/lib/jvm/java-1.5.0-gcj-1.5.0.0/jre/lib/x86_64/server/libjvm.so
/usr/lib/jvm/java-1.6.0-openjdk-1.6.0.0.x86_64/jre/lib/amd64/server/libjvm.so
Finally, add the following line to ~/.bashrc (or run it each time before opening a python interpreter):
export JAVA_HOME='/usr/java/default'
(The above directory is in reality just a symbolic link to my last version of JDK, which is located at /usr/java/jdk1.7.0_04).
Note that all the tests in the directory where JPype has been downloaded, i.e. JPype-0.5.4.2/test/testsuite.py will fail (so do not care about them).
To see if it works, test this script in python:
import jpype
jvmPath = jpype.getDefaultJVMPath()
jpype.startJVM(jvmPath)
# print a random text using a Java class
jpype.java.lang.System.out.println ('Berlusconi likes women')
jpype.shutdownJVM()
Calling Java classes from Java also using Numpy
Let's start implementing a Java class containing some functions which I want to apply to numpy arrays. Since there is no concept of state, I use static functions so that I do not need to create any Java object (creating Java objects would not change anything).
/**
* Cookbook to pass numpy arrays to Java via Jpype
* #author Mannaggia
*/
package test.java;
public class Average2 {
public static double compute_average(double[] the_array){
// compute the average
double result=0;
int i;
for (i=0;i<the_array.length;i++){
result=result+the_array[i];
}
return result/the_array.length;
}
// multiplies array by a scalar
public static double[] multiply(double[] the_array, double factor) {
int i;
double[] the_result= new double[the_array.length];
for (i=0;i<the_array.length;i++) {
the_result[i]=the_array[i]*factor;
}
return the_result;
}
/**
* Matrix multiplication.
*/
public static double[][] mult_mat(double[][] mat1, double[][] mat2){
// find sizes
int n1=mat1.length;
int n2=mat2.length;
int m1=mat1[0].length;
int m2=mat2[0].length;
// check that we can multiply
if (n2 !=m1) {
//System.err.println("Error: The number of columns of the first argument must equal the number of rows of the second");
//return null;
throw new IllegalArgumentException("Error: The number of columns of the first argument must equal the number of rows of the second");
}
// if we can, then multiply
double[][] the_results=new double[n1][m2];
int i,j,k;
for (i=0;i<n1;i++){
for (j=0;j<m2;j++){
// initialize
the_results[i][j]=0;
for (k=0;k<m1;k++) {
the_results[i][j]=the_results[i][j]+mat1[i][k]*mat2[k][j];
}
}
}
return the_results;
}
/**
* #param args
*/
public static void main(String[] args) {
// test case
double an_array[]={1.0, 2.0,3.0,4.0};
double res=Average2.compute_average(an_array);
System.out.println("Average is =" + res);
}
}
The name of the class is a bit misleading, as we do not only aim at computing the average of a numpy vector (using the method compute_average), but also multiply a numpy vector by a scalar (method multiply), and finally, the matrix multiplication (method mult_mat).
After compiling the above Java class we can now run the following Python script:
import numpy as np
import jpype
jvmPath = jpype.getDefaultJVMPath()
# we to specify the classpath used by the JVM
classpath='/home/mannaggia/workspace/TestJava/bin'
jpype.startJVM(jvmPath,'-Djava.class.path=%s' % classpath)
# numpy array
the_array=np.array([1.1, 2.3, 4, 6,7])
# build a JArray, not that we need to specify the Java double type using the jpype.JDouble wrapper
the_jarray2=jpype.JArray(jpype.JDouble, the_array.ndim)(the_array.tolist())
Class_average2=testPkg.Average2
res2=Class_average2.compute_average(the_jarray2)
np.abs(np.average(the_array)-res2) # ok perfect match!
# now try to multiply an array
res3=Class_average2.multiply(the_jarray2,jpype.JDouble(3))
# convert to numpy array
res4=np.array(res3) #ok
# matrix multiplication
the_mat1=np.array([[1,2,3], [4,5,6], [7,8,9]],dtype=float)
#the_mat2=np.array([[1,0,0], [0,1,0], [0,0,1]],dtype=float)
the_mat2=np.array([[1], [1], [1]],dtype=float)
the_mat3=np.array([[1, 2, 3]],dtype=float)
the_jmat1=jpype.JArray(jpype.JDouble, the_mat1.ndim)(the_mat1.tolist())
the_jmat2=jpype.JArray(jpype.JDouble, the_mat2.ndim)(the_mat2.tolist())
res5=Class_average2.mult_mat(the_jmat1,the_jmat2)
res6=np.array(res5) #ok
# other test
the_jmat3=jpype.JArray(jpype.JDouble, the_mat3.ndim)(the_mat3.tolist())
res7=Class_average2.mult_mat(the_jmat3,the_jmat2)
res8=np.array(res7)
res9=Class_average2.mult_mat(the_jmat2,the_jmat3)
res10=np.array(res9)
# test error due to invalid matrix multiplication
the_mat4=np.array([[1], [2]],dtype=float)
the_jmat4=jpype.JArray(jpype.JDouble, the_mat4.ndim)(the_mat4.tolist())
res11=Class_average2.mult_mat(the_jmat1,the_jmat4)
jpype.java.lang.System.out.println ('Goodbye!')
jpype.shutdownJVM()

I consider Jython to be one of the best options - which makes it seamless to use java objects in python. I actually integrated weka with my python programs, and it was super easy. Just import the weka classes and call them as you would in java within the python code.
http://www.jython.org/

I'm not sure about numpy support, but the following might be helpful:
http://pypi.python.org/pypi/JCC/

Using ANTLR for static analysis of Java source file

Does anyone have a complete implementation (possibly github or googlecode) for using an ANTLR grammar file and Java source code to analyze Java source. For example, I want to simply be able to count the number of variables, method, etc.
Also using a recent version of ANTLR.

I thought I'd take a crack at this over my lunch break. This may not completely solve your problem, but it might give you a place to start. The example assumes you're doing everything in the same directory.
Download the ANTLR source from GitHub. The pre-compiled "complete" JAR from the ANTLR site contains a known bug. The GitHub repo has the fix.
Extract the ANTLR tarball.
% tar xzf antlr-antlr3-release-3.4-150-g8312471.tar.gz
Build the ANTLR "complete" JAR.
% cd antlr-antlr3-8312471
% mvn -N install
% mvn -Dmaven.test.skip=true
% mvn -Dmaven.test.skip=true package assembly:assembly
% cd -
Download a Java grammar. There are others, but I know this one works.
Compile the grammar to Java source.
% mkdir com/habelitz/jsobjectizer/unmarshaller/antlrbridge/generated
% mv *.g com/habelitz/jsobjectizer/unmarshaller/antlrbridge/generated
% java -classpath antlr-antlr3-8312471/target/antlr-master-3.4.1-SNAPSHOT-completejar.jar org.antlr.Tool -o com/habelitz/jsobjectizer/unmarshaller/antlrbridge/generated Java.g
Compile the Java source.
% javac -classpath antlr-antlr3-8312471/target/antlr-master-3.4.1-SNAPSHOT-completejar.jar com/habelitz/jsobjectizer/unmarshaller/antlrbridge/generated/*.java
Add the following source file, Main.java.
import java.io.IOException;
import java.util.List;
import org.antlr.runtime.*;
import org.antlr.runtime.tree.*;
import com.habelitz.jsobjectizer.unmarshaller.antlrbridge.generated.*;
public class Main {
public static void main(String... args) throws NoSuchFieldException, IllegalAccessException, IOException, RecognitionException {
JavaLexer lexer = new JavaLexer(new ANTLRFileStream(args[1], "UTF-8"));
JavaParser parser = new JavaParser(new CommonTokenStream(lexer));
CommonTree tree = (CommonTree)(parser.javaSource().getTree());
int type = ((Integer)(JavaParser.class.getDeclaredField(args[0]).get(null))).intValue();
System.out.println(count(tree, type));
}
private static int count(CommonTree tree, int type) {
int count = 0;
List children = tree.getChildren();
if (children != null) {
for (Object child : children) {
count += count((CommonTree)(child), type);
}
}
return ((tree.getType() != type) ? count : count + 1);
}
}
Compile.
% javac -classpath .:antlr-antlr3-8312471/target/antlr-master-3.4.1-SNAPSHOT-completejar.jar Main.java
Select a type of Java source that you want to count; for example, VAR_DECLARATOR, FUNCTION_METHOD_DECL, or VOID_METHOD_DECL.
% cat com/habelitz/jsobjectizer/unmarshaller/antlrbridge/generated/Java.tokens
Run on any file, including the recently created Main.java.
% java -classpath .:antlr-antlr3-8312471/target/antlr-master-3.4.1-SNAPSHOT-completejar.jar Main VAR_DECLARATOR Main.java
6
This is imperfect, of course. If you look closely, you may have noticed that the local variable of the enhanced for statement wasn't counted. For that, you'd need to use the type FOR_EACH, rather than VAR_DECLARATOR.
You'll need a good understanding of the elements of Java source, and be able to take reasonable guesses at how those match to the definitions of this particular grammar. You also won't be able to do counts of references. Declarations are easy, but counting uses of a field, for example, requires reference resolution. Does p.C.f refer to a static field f of a class C inside a package p, or does it refer to an instance field f of the object stored by a static field C of a class p? Basic parsers don't resolve references for languages as complex as Java, because the general case can be very difficult. If you want this level of control, you'll need to use a compiler (or something closer to it). The Eclipse compiler is a popular choice.
I should also mention that you have other options besides ANTLR. JavaCC is another parser generator. The static analysis tool PMD, which uses JavaCC as its parser generator, allows you to write custom rules that could be used for the kinds of counts you indicated.