Matlab includes built-in support for automatic conversion of Matlab cell arrays into Java arrays. This is important in cases when we need to pass information to a Java function that expects an array (e.g., `String[]`

).

### Numeric data array

In some cases, namely Java numeric arrays, Matlab also automatically converts the Java array into Matlab arrays. This is actually inconvenient when we would like to access the original Java reference in order to modify some value – since the Java reference is inaccessible from Matlab in this case, the data is immutable.

>> jColor = java.awt.Color.red jColor = java.awt.Color[r=255,g=0,b=0] >> matlabData = jColor.getColorComponents([]) matlabData = 1 0 % < = immutable array of numbers, not a reference to int[] 0

### Non-numeric array

Very often we encounter cases in Java where the information is stored in an array of non-numeric data. In such cases we need to apply a non-automatic conversion from Java into Matlab.

If the objects are of exactly the same type, then we could store them in a simple Matlab array; otherwise (as can be seen in the example below), we could store them in either a simple array of * handle*s, or in a simple cell array:

>> jFrames = java.awt.Frame.getFrames jFrames = java.awt.Frame[]: [javax.swing.SwingUtilities$SharedOwnerFrame ] [com.mathworks.mde.desk.MLMainFrame ] [com.mathworks.mde.desk.MLMultipleClientFrame] [com.mathworks.mwswing.MJFrame ] % Alternative #1 - use a loop >> mFrames = handle([]); for idx = 1 : length(jFrames); mFrames(idx)=handle(jFrames(idx)); end >> mFrames mFrames = handle: 1-by-4 >> mFrames(1) ans = javahandle.javax.swing.SwingUtilities$SharedOwnerFrame >> mFrames(2) ans = javahandle.com.mathworks.mde.desk.MLMainFrame % Alternative #2a - convert into a Matlab cell array >> mFrames = jFrames.cell mFrames = [1x1 javax.swing.SwingUtilities$SharedOwnerFrame ] [1x1 com.mathworks.mde.desk.MLMainFrame ] [1x1 com.mathworks.mde.desk.MLMultipleClientFrame] [1x1 com.mathworks.mwswing.MJFrame ] % Alternative #2b - convert to a cell array (equivalent variant of alternative 2a) >> mFrames = cell(jFrames);

Note that if we only need to access a particular item in the Java vector or array, we could do that directly, without needing to convert the entire data into Matlab first. Simply use `jFrames(1)`

to directly access the first item in the `jFrames`

array, for example.

(note: Java Frames are discussed in chapters 7 and 8 of my Matlab-Java book).

### Vectors and other Collections

Very often we encounter cases in Java where the information is stored in a Java Collection rather than in a simple Java array. The basic mechanism for the conversion in this case is to first convert the Java data into a simple Java array (in cases it was not so in the first place), and then to convert this into a Matlab array using either the automated conversion (if the data is numeric), or using a for loop (ugly and slow!), or into a cell array using the ** cell** function, as explained above.

Different Collections have different manners of converting into a Java array: some Collections return an Iterator/Enumerator that can be processed in a loop (be careful not to reset the iterator reference by re-reading it within the loop):

% Wrong way - causes an infinite loop idx = 1; props = java.lang.System.getProperties; while props.elements.hasMoreElements mPropValue{idx} = props.elements.nextElement; end % Right way idx = 1; propValues = java.lang.System.getProperties.elements; % Enumerator while propValues.hasMoreElements mPropValue{idx} = propValues.nextElement; end

(note: system properties are discussed in section 1.9 of my Matlab-Java book; Collections are discussed in section 2.1)

Other Collections, such as `java.util.Vector`

, have a *toArray()* method that directly converts into a Java array, and we can process from there as described above:

>> jVector = java.util.Vector; >> jVector.add(1); jVector.add(2); jVector.add(3); >> jVector.addAll(jv); jVector.addAll(jv); >> jVector jVector = [1.0, 2.0, 3.0, 1.0, 2.0, 3.0, 1.0, 2.0, 3.0, 1.0, 2.0, 3.0] % Now convert into a Matlab cell array via a Java simple array >> mCellArray = jVector.toArray.cell mCellArray = [1] [2] [3] [1] [2] [3] [1] [2] [3] [1] [2] [3]

### Performance

It so happens, that the undocumented built-in * feature* function (or its near-synonym

*) enables improved performance in this conversion process.*

**system_dependent***(44) accepts a*

**feature**`java.util.Vector`

and converts it directly into a Matlab cell-array, in one third to one-half the time that it would take the equivalent *toArray.cell()*(the third input argument is the number of columns in the result – the reshaping is done automatically):

>> mCellArray = feature(44,jVector,jVector.size) % jVector.size = 12 mCellArray = [1] [2] [3] [1] [2] [3] [1] [2] [3] [1] [2] [3] >> mCellArray = feature(44,jVector,4) mCellArray = [1] [1] [1] [1] [2] [2] [2] [2] [3] [3] [3] [3]

The conversion process is pretty efficient: On my system, the regular *toArray.cell()* takes 0.45 seconds for a 100K vector, compared to 0.21 seconds for the * feature* alternative. However, this small difference could be important in cases where performance is crucial, for example in processing of highly-active Java events in Matlab callbacks, or when retrieving data from a database. And this latter case is indeed where a sample usage of this

*can be found, namely in the*

**feature***function (where it appears as*

**cursor.fetch.m***).*

**system_dependent(44)**Please note that both * feature* and

*are highly prone to change without prior warning in some future Matlab release. On the other hand, the conversion methods that I presented above, excluding*

**system_dependent***, will probably still be valid in all Matlab releases in the near future.*

**feature**
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