Callbacks – Undocumented Matlab Charting Matlab's unsupported hidden underbelly Fri, 20 Oct 2017 09:57:44 +0000 en-US hourly 1 Matlab GUI training seminars – Zurich, 29-30 August 2017 Fri, 04 Aug 2017 09:37:52 +0000
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  2. GUI integrated browser control A fully-capable browser component is included in Matlab and can easily be incorporated in regular Matlab GUI applications. This article shows how....
  3. FindJObj GUI – display container hierarchy The FindJObj utility can be used to present a GUI that displays a Matlab container's internal Java components, properties and callbacks....
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Advanced Matlab training, Zurich 29-30 August 2017
Advanced Matlab training courses/seminars will be presented by me (Yair) in Zürich, Switzerland on 29-30 August, 2017:

  • August 29 (full day) – Interactive Matlab GUI
  • August 30 (full day) – Advanced Matlab GUI

The seminars are targeted at Matlab users who wish to improve their program’s usability and professional appearance. Basic familiarity with the Matlab environment and coding/programming is assumed. The courses will present a mix of both documented and undocumented aspects, which is not available anywhere else. The curriculum is listed below.

This is a unique opportunity to enhance your Matlab coding skills and improve your program’s usability in a couple of days.

If you are interested in either or both of these seminars, please Email me (altmany at gmail dot com).

I can also schedule a dedicated visit to your location, for onsite Matlab training customized to your organization’s specific needs. Additional information can be found on my Training page.

Around the time of the training, I will be traveling to various locations around Switzerland. If you wish to meet me in person to discuss how I could bring value to your project, then please email me (altmany at gmail):

  • Geneva: Aug 22 – 27
  • Bern: Aug 27 – 28
  • Zürich: Aug 28 – 30
  • Stuttgart: Aug 30 – 31
  • Basel: Sep 1 – 3

 Email me

Interactive Matlab GUI – 29 August, 2017

  1. Introduction to Matlab Graphical User Interfaces (GUI)
    • Design principles and best practices
    • Matlab GUI alternatives
    • Typical evolution of Matlab GUI developers
  2. GUIDE – MATLAB’s GUI Design Editor
    • Using GUIDE to design a custom GUI
    • Available built-in MATLAB uicontrols
    • Customizing uicontrols
    • Important figure and uicontrol properties
    • GUIDE utility windows
    • The GUIDE-generated file-duo
  3. Customizing GUI appearance and behavior
    • Programmatic GUI creation and control
    • GUIDE vs. m-programming
    • Attaching callback functionality to GUI components
    • Sharing data between GUI components
    • The handles data struct
    • Using handle visibility
    • Position, size and units
    • Formatting GUI using HTML
  4. Uitable
    • Displaying data in a MATLAB GUI uitable
    • Controlling column data type
    • Customizing uitable appearance
    • Reading uitable data
    • Uitable callbacks
    • Additional customizations using Java
  5. Matlab’s new App Designer and web-based GUI
    • App Designer environment, widgets and code
    • The web-based future of Matlab GUI and assumed roadmap
    • App Designer vs. GUIDE – pros and cons comparison
  6. Performance and interactivity considerations
    • Speeding up the initial GUI generation
    • Improving GUI responsiveness
    • Actual vs. perceived performance
    • Continuous interface feedback
    • Avoiding common performance pitfalls
    • Tradeoff considerations

At the end of this seminar, you will have learned how to:

  • apply GUI design principles in Matlab
  • create simple Matlab GUIs
  • manipulate and customize graphs, images and GUI components
  • display Matlab data in a variety of GUI manners, including data tables
  • decide between using GUIDE, App Designer and/or programmatic GUI
  • understand tradeoffs in design and run-time performance
  • comprehend performance implications, to improve GUI speed and responsiveness

Advanced Matlab GUI – 30 August, 2017

  1. Advanced topics in Matlab GUI
    • GUI callback interrupts and re-entrancy
    • GUI units and resizing
    • Advanced HTML formatting
    • Using hidden (undocumented) properties
    • Listening to action and property-change events
    • Uitab, uitree, uiundo and other uitools
  2. Customizing the figure window
    • Creating and customizing the figure’s main menu
    • Creating and using context menus
    • Creating and customizing figure toolbars
  3. Using Java with Matlab GUI
    • Matlab and Java Swing
    • Integrating Java controls in Matlab GUI
    • Handling Java events as Matlab callbacks
    • Integrating built-in Matlab controls/widgets
    • Integrating JIDE’s advanced features and professional controls
    • Integrating 3rd-party Java components: charts/graphs/widgets/reports
  4. Advanced Matlab-Java GUI
    • Customizing standard Matlab uicontrols
    • Figure-level customization (maximize/minimize, disable etc.)
    • Containers and position – Matlab vs. Java
    • Compatibility aspects and trade-offs
    • Safe programming with Java in Matlab
    • Java’s EDT and timing considerations
    • Deployment (compiler) aspects

At the end of this seminar, you will have learned how to:

  • customize the figure toolbar and main menu
  • use HTML to format GUI appearance
  • integrate Java controls in Matlab GUI
  • customize your Matlab GUI to a degree that you never knew was possible
  • create a modern-looking professional GUI in Matlab
]]> 0
Listbox selection hacks Wed, 13 Jul 2016 15:36:19 +0000
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  3. Matlab and the Event Dispatch Thread (EDT) The Java Swing Event Dispatch Thread (EDT) is very important for Matlab GUI timings. This article explains the potential pitfalls and their avoidance using undocumented Matlab functionality....
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Last week a reader on the CSSM newsgroup asked whether it is possible to programmatically deselect all listbox items. By default, Matlab listboxes enable a single item selection: trying to deselect it interactively has no effect, while trying to set the listbox’s Value property to empty ([]) results in the listbox disappearing and a warning issued to the Matlab console:

Single-selection Matlab listbox

>> hListbox = uicontrol('Style','list', 'String',{'item #1','item #2','item #3','item #4','item #5','item #6'});
>> set(hListbox,'Value',[]);
Warning: Single-selection 'listbox' control requires a scalar Value.
Control will not be rendered until all of its parameter values are valid
(Type "warning off MATLAB:hg:uicontrol:ValueMustBeScalar" to suppress this warning.)

The reader’s question was whether there is a way to bypass this limitation so that no listbox item will be selected. The answer to this question was provided by MathWorker Steve(n) Lord. Steve is a very long-time benefactor of the Matlab community with endless, tireless, and patient advise to queries small and large (way beyond the point that would have frustrated mere mortals). Steve pointed out that by default, Matlab listboxes only enable a single selection – not more and not less. However, when the listbox’s Max value is set to be >1, the listbox enables multiple-items selection, meaning that Value accepts and reports an array of item indices, and there is nothing that prevents this array from being empty (meaning no items selected):

>> hListbox = uicontrol('Style','list', 'Max',2, 'String',{'item #1','item #2','item #3','item #4','item #5','item #6'});
>> set(hListbox,'Value',[]);  % this is ok - listbox appears with no items selected

Note: actually, the listbox checks the value of MaxMin, but by default Min=0 and there is really no reason to modify this default value, just Max.

While this makes sense if you think about it, the existing documentation makes no mention of this fact:

The Max property value helps determine whether the user can select multiple items in the list box simultaneously. If Max – Min > 1, then the user can select multiple items simultaneously. Otherwise, the user cannot select multiple items simultaneously. If you set the Max and Min properties to allow multiple selections, then the Value property value can be a vector of indices.

Some readers might think that this feature is not really undocumented, since it does not directly conflict with the documentation text, but then so are many other undocumented aspects and features on this blog, which are not mentioned anywhere in the official documentation. I contend that if this feature is officially supported, then it deserves an explicit sentence in the official documentation.

However, the original CSSM reader wanted to preserve Matlab’s single-selection model while enabling deselection of an item. Basically, the reader wanted a selection model that enables 0 or 1 selections, but not 2 or more. This requires some tweaking using the listbox’s selection callback:

function test(hListbox, eventData)
   value = get(hListbox, 'Value');
   if numel(value) > 1
       set(hListbox, 'Value', value(1));

…or a callback-function version that is a bit better because it takes the previous selection into account and tries to set the new selection to the latest-selected item (this works in most cases, but not with shift-clicks as explained below):

function myCallbackFunc(hListbox, eventData)
   lastValue = getappdata(hListbox, 'lastValue');
   value = get(hListbox, 'Value');
   if ~isequal(value, lastValue)
      value2 = setdiff(value, lastValue);
      if isempty(value2)
         setappdata(hListbox, 'lastValue', value);
         value = value2(1);  % see quirk below
         setappdata(hListbox, 'lastValue', value);
         set(hListbox, 'Value', value);

This does the job of enabling only a single selection at the same time as allowing the user to interactively deselect that item (by ctrl-clicking it).

There’s just a few quirks: If the user selects a block of items (using shift-click), then only the second-from-top item in the block is selected, rather than the expected last-selected item. This is due to line #9 in the callback code which selects the first value. Matlab does not provide us with information about which item was clicked, so this cannot be helped using pure Matlab. Another quirk that cannot easily be solved using pure Matlab is the flicker that occurs when the selection changes and is then corrected by the callback.

We can solve both of these problems using the listbox’s underlying Java component, which we can retrieve using my findjobj utility:

% No need for the standard Matlab callback now
% Get the underlying Java component peer
jScrollPane = findjobj(h);
jListbox = jScrollPane.getViewport.getView;
jListbox = handle(jListbox,'CallbackProperties');  % enable callbacks
% Attach our callback to the listbox's Java peer
jListbox.ValueChangedCallback = {@myCallbackFunc, hListbox};
function myCallbackFunc(jListbox, eventData, hListbox)
   if numel(jListbox.getSelectedIndices) > 1
      set(hListbox, 'Value', jListbox.getLeadSelectionIndex+1);  % +1 because Java indices start at 0

We can use a similar mechanism to control other aspects of selection, for example to enable only up to 3 selections but no more etc.

We can use this underlying Java component peer for a few other useful selection-related hacks: First, we can use the peer’s RightSelectionEnabled property or setRightSelectionEnabled() method to enable the user to select by right-clicking listbox items (this is disabled by default):

jListbox.setRightSelectionEnabled(true);  % false by default
set(jListbox,'RightSelectionEnabled',true);  % equivalent alternative

A similarly useful property is DragSelectionEnabled (or the corresponding setDragSelectionEnabled() method), which is true by default, and controls whether the selection is extended to other items when the mouse drags an item up or down the listbox.

Finally, we can control whether in multi-selection mode we enable the user to only select a single contiguous block of items, or not (which is Matlab’s default behavior). This is set via the SelectionMode property (or associated setSelectionMode() method), as follows:

jListbox.setSelectionMode(1);  % equivalent alternative (less maintainable/readable, but simpler)

SINGLE_SELECTION (default for Max=1)SINGLE_INTERVAL_SELECTION (only possible with Java)MULTIPLE_INTERVAL_SELECTION (default for Max>1)
(Matlab default for Max=1)(only possible with Java)(Matlab default for Max>1)

Additional listbox customizations can be found in related posts on this blog (see links below), or in section 6.6 of my Matlab-Java Programming Secrets book (which is still selling nicely almost five years after its publication, to the pleasant surprise of my publisher…).

]]> 4
Figure keypress modifiers Wed, 04 Nov 2015 21:19:59 +0000
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  3. Minimize/maximize figure window Matlab figure windows can easily be maximized, minimized and restored using a bit of undocumented magic powder...
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Matlab figures have a documented property called SelectionType that returns information about keypress modifiers such as or that were pressed during mouse clicks. Using this property has several drawbacks IMHO:

  • The reported SelectionType value is 'extend' for shift-clicks and 'alt' for ctrl-clicks, not very intuitive.
  • There is no support for alt-clicks, which are reported as regular ('normal') mouse clicks. In fact, 'alt' is reported for ctrl-clicks rather than for alt-clicks, which is very confusing.
  • There is no support for modifier combinations such as ctrl+shift-click. These again are reported as regular ('normal') mouse clicks.
  • SelectionType is only updated for mouse clicks, not for keyboard clicks. This again is very confusing. To extract the keypress modifier for key-click events we need to get the Modifier property of the key-press event, and this returns a cell-array of strings (e.g., {'shift','control','alt'}). Note that in this case, unlike SelectionType, the modifier names are as expected, alt-clicks is recognised and so are modifier combinations.
% Documented: we need to get the modifier data in two different manners
set(gcf, 'WindowButtonDownFcn', @(h,e) disp(get(gcf,'SelectionType')));  % mouse clicks: displays a single string: 'normal','alt','extend' or 'open'
set(gcf, 'WindowKeyPressFcn',   @(h,e) disp(e.Modifier));  % keyboard clicks: displays a cell-array of strings, e.g. {'shift','control','alt'}

The inconsistency between the functionality for mouse and keyboard clicks, and the limitations of the SelectionType property, are striking and most annoying. Some might say that it’s been like this for the past 2 decades so Matlab users have probably gotten used to it by now. But I must admit that after over 2 decades with Matlab I still need to refer to the documentation to figure out the correct behavior. Maybe that’s because I’m getting old, or maybe it means that the behavior is indeed not as intuitive as one could hope for.

For this reason, I was very happy to discover several years ago that there was a much simpler, easier and consistent solution, although (alas) undocumented. The trick is to simply query the undocumented hidden figure property CurrentModifier: CurrentModifier is updated during both mouse and keyboard clicks, in the same consistent manner, in both cases returning the same cell-array of strings as the Modifier property of the standard key-press event:

% Undocumented: the following displays a cell-array of strings having a consistent format, e.g. {'shift','control','alt'}
set(gcf, 'WindowButtonDownFcn', @(h,e) disp(get(gcf,'CurrentModifier')));  % mouse clicks
set(gcf, 'WindowKeyPressFcn',   @(h,e) disp(get(gcf,'CurrentModifier')));  % keyboard clicks

Checking whether any modifier was pressed becomes trivial:

modifiers = get(gcf,'CurrentModifier');
wasShiftPressed = ismember('shift',   modifiers);  % true/false
wasCtrlPressed  = ismember('control', modifiers);  % true/false
wasAltPressed   = ismember('alt',     modifiers);  % true/false

Hurrah for simplicity and consistency!

Note that despite the fact that CurrentModifier is hidden and undocumented, it has existed as-is for many years, and even survived (unchanged) the major transition from HG1 to HG2 in R2014b. This has to mean that MathWorks recognized the importance of CurrentModifier and took the deliberate decision (and associate dev effort) to preserve it. So why wasn’t this useful property made documented ages ago, or at the very least at the HG2 transition point? I don’t have a good answer to this riddle.

]]> 5
Callback functions performance Wed, 09 Sep 2015 23:36:25 +0000
Related posts:
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  2. Panel-level uicontrols Matlab's uipanel contains a hidden handle to the title label, which can be modified into a checkbox or radio-button control...
  3. HG2 update HG2 appears to be nearing release. It is now a stable mature system. ...
  4. uicontextmenu performance Matlab uicontextmenus are not automatically deleted with their associated objects, leading to leaks and slow-downs. ...
Matlab enables a variety of ways to define callbacks for asynchronous events (such as interactive GUI actions or timer invocations). We can provide a function handle, a cell-array (of function handle and extra parameters), and in some cases also a string that will be eval‘ed in run-time. For example:

hButton = uicontrol(..., 'Callback', @myCallbackFunc);  % function handle
hButton = uicontrol(..., 'Callback', {@myCallbackFunc,data1,data2});  % cell-array
hButton = uicontrol(..., 'Callback', 'disp clicked!');  % string to eval

The first format, function handle, is by far the most common in Matlab code. This format has two variant: we can specify the direct handle to the function (as in @myCallbackFunc), or we could use an anonymous function, like this:

hButton = uicontrol(..., 'Callback', @(h,e) myCallbackFunc(h,e));  % anonymous function handle

All Matlab callbacks accept two input args by default: the control’s handle (hButton in this example), and a struct or object that contain the event’s data in internal fields. In our anonymous function variant, we therefore defined a function that accepts two input args (h,e) and calls myCallbackFunc(h,e).

These two variants are functionally equivalent:

hButton = uicontrol(..., 'Callback', @myCallbackFunc);             % direct function handle
hButton = uicontrol(..., 'Callback', @(h,e) myCallbackFunc(h,e));  % anonymous function handle

In my experience, the anonymous function variant is widely used – I see it extensively in many of my consulting clients’ code. Unfortunately, there could be a huge performance penalty when using this variant compared to a direct function handle, which many people are simply not aware of. I believe that even many MathWorkers are not aware of this, based on a recent conversation I’ve had with someone in the know, as well as from the numerous usage examples in internal Matlab code: see the screenshot below for some examples; there are numerous others scattered throughout the Matlab code corpus.

Part of the reason for this penalty not being well known may be that Matlab’s Profiler does not directly attribute the overheads. Here is a typical screenshot:

Profiling anonymous callback function performance

Profiling anonymous callback function performance

In this example, a heavily-laden GUI figure window was closed, triggering multiple cleanup callbacks, most of them belonging to internal Matlab code. Closing the figure took a whopping 8 secs. As can be seen from the screenshot, the callbacks themselves only take ~0.66 secs, and an additional 7.4 secs (92% of the total) is unattributed to any specific line. Think about it for a moment: we can only really see what’s happening in 8% of the time – the Profiler provides no clue about the root cause of the remaining 92%.

The solution in this case was to notice that the callback was defined using an anonymous function, @(h,e)obj.tableDeletedCallbackFcn(e). Changing all such instances to @obj.tableDeletedCallbackFcn (the function interface naturally needed to change to accept h as the first input arg) drastically cut the processing time, since direct function handles do not carry the same performance overheads as anonymous functions. In this specific example, closing the figure window now became almost instantaneous (<1 sec).


There are several morals that I think can be gained from this:

  1. When we see unattributed time in the Profiler summary report, odds are high that this is due to function-call overheads. MathWorks have significantly reduced such overheads in the new R2015b (released last week), but anonymous [and to some degree also class methods] functions still carry a non-negligible invocation overheads that should be avoided if possible, by using direct [possibly non-MCOS] functions.
  2. Use direct function handles rather than anonymous function handles, wherever possible
  3. In the future, MathWorks will hopefully improve Matlab’s new engine (“LXE”) to automatically identify cases of @(h,e)func(h,e) and replace them with faster calls to @func, but in any case it would be wise to manually make this change in our code today. It would immediately improve readability, maintainability and performance, while still being entirely future-compatible.
  4. In the future, MathWorks may also possibly improve the overheads of anonymous function invocations. This is more tricky than the straight-forward lexical substitution above, because anonymous functions need to carry the run-time workspace with them. This is a little known and certainly very little-used fact, which means that in practice most usage patterns of anonymous functions can be statically analyzed and converted into much faster direct function handles that carry no run-time workspace info. This is indeed tricky, but it could directly improve performance of many Matlab programs that naively use anonymous functions.
  5. Matlab’s Profiler should really be improved to provide more information about unattributed time spent in internal Matlab code, to provide users clues that would help them reduce it. Some information could be gained by using the Profiler’s -detail builtin input args (which was documented until several releases ago, but then apparently became unsupported). I think that the Profiler should still be made to provide better insights in such cases.

Oh, and did I mention already the nice work MathWorks did with 15b’s LXE? Matlab’s JIT replacement was many years in the making, possibly since the mid 2000’s. We now see just the tip of the iceberg of this new engine: I hope that additional benefits will become apparent in future releases.

For a definitive benchmark of Matlab’s function-call overheads in various variants, readers are referred to Andrew Janke’s excellent utility (with some pre-15b usage results and analysis). Running this benchmark on my machine shows significant overhead reduction in function-call overheads in 15b in many (but not all) invocation types.

For those people wondering, 15b’s LXE does improve HG2’s performance, but just by a small bit – still not enough to offset the large performance hit of HG2 vs. HG1 in several key aspects. MathWorks is actively working to improve HG2’s performance, but unfortunately there is still no breakthrough as of 15b.

Additional details on various performance issues related to Matlab function calls (and graphics and anything else in Matlab) can be found in my recent book, Accelerating MATLAB Performance.

]]> 11
Undocumented HG2 graphics events Wed, 27 May 2015 17:20:10 +0000
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R2014b brought a refreshing new graphics engine and appearance, internally called HG2 (the official marketing name is long and impossible to remember, and certainly not as catchy). I’ve already posted a series of articles about HG2. Today I wish to discuss an undocumented aspect of HG2 that I’ve encountered several times over the past months, and most recently today. The problem is that while in the previous HG1 system (R2014a and earlier) we could add property-change listener callbacks to practically any graphics object, this is no longer true for HG2. Many graphics properties, that are calculated on-the-fly based on other property values, cannot be listened-to, and so we cannot attach callbacks that trigger when their values change.

Property-change listeners in HG1

Take for example my post about setting axes tick labels format from 3 years ago: the idea there was to attach a Matlab callback function to the PropertyPostSet event of the XTick, YTick and/or ZTick properties, so that when they change their values (upon zoom/pan/resize), the corresponding tick-labels would be reformatted based on the user-specified format:

Formatted labels, automatically updated Formatted labels, automatically updated

Formatted labels, automatically updated

A simple HG1 usage might look as follows:

addlistener(handle(hAxes), 'YTick', 'PostSet', @reformatTickLabels);
function reformatTickLabels(hProperty, eventData)
        hAxes = eventData.AffectedObject;
        hAxes = ancestor(eventData.Source,'Axes');
    tickValues = get(hAxes, 'YTick');
    tickLabels = arrayfun(@(x)(sprintf('%.1fV',x)), tickValues, 'UniformOutput',false);
    set(hAxes, 'YTickLabel', tickLabels)

I prepared a utility called ticklabelformat that automates much of the set-up above. Feel free to download this utility from the Matlab File Exchange. Its usage syntax is as follows:

ticklabelformat(gca,'y','%.6g V')  % sets y axis on current axes to display 6 significant digits
ticklabelformat(gca,'xy','%.2f')   % sets x & y axes on current axes to display 2 decimal digits
ticklabelformat(gca,'z',@myCbFcn)  % sets a function to update the Z tick labels on current axes
ticklabelformat(gca,'z',{@myCbFcn,extraData})  % sets an update function as above, with extra data

Property-change listeners in HG2

Unfortunately, this fails in HG2 when trying to listen to automatically-recalculated (non-Observable) properties such as the Position or axes Tick properties. We can only listen to non-calculated (Observable) properties such as Tag or YLim. Readers might think that this answers the need, since the ticks change when the axes limits change. This is true, but does not cover all cases. For example, when we resize/maximize the figure, Matlab may decide to modify the displayed ticks, although the axes limits remain unchanged.

So we need to have a way to monitor changes even in auto-calculated properties. Luckily this can be done by listening to a set of new undocumented HG2 events. It turns out that HG2’s axes ( objects) have no less than 17 declared events, and 14 of them are hidden in R2015a:

>> events(gca)   % list the non-hidden axes events
Events for class
>> mc = metaclass(gca)
mc = 
  GraphicsMetaClass with properties:
                     Name: ''
              Description: 'TODO: Fill in Description'
      DetailedDescription: ''
                   Hidden: 0
                   Sealed: 1
                 Abstract: 0
              Enumeration: 0
          ConstructOnLoad: 1
         HandleCompatible: 1
          InferiorClasses: {0x1 cell}
        ContainingPackage: [1x1 meta.package]
             PropertyList: [414x1]
               MethodList: [79x1 meta.method]
                EventList: [17x1 meta.event]    EnumerationMemberList: [0x1 meta.EnumeratedValue]
           SuperclassList: [7x1 meta.class]
>> mc.EventList(10)
ans = 
  event with properties:
                   Name: 'MarkedClean'
            Description: 'description'
    DetailedDescription: 'detailed description'
                 Hidden: 1
           NotifyAccess: 'public'
           ListenAccess: 'public'
          DefiningClass: [1x1]
>> [{mc.EventList.Name}; ...
    {mc.EventList.ListenAccess}; ...
    arrayfun(@mat2str, [mc.EventList.Hidden], 'Uniform',false)]'
ans = 
    'LocationChanged'             'public'       'true' 
    'SizeChanged'                 'public'       'true' 
    'ClaReset'                    'public'       'true' 
    'ClaPreReset'                 'public'       'true' 
    'Cla'                         'public'       'true' 
    'ObjectBeingDestroyed'        'public'       'false'  % not hidden
    'Hit'                         'public'       'true' 
    'LegendableObjectsUpdated'    'public'       'true' 
    'MarkedDirty'                 'public'       'true' 
    'MarkedClean'                 'public'       'true' 
    'PreUpdate'                   'protected'    'true' 
    'PostUpdate'                  'protected'    'true' 
    'Error'                       'public'       'true' 
    'Reparent'                    'public'       'true' 
    'Reset'                       'public'       'true' 
    'PropertyAdded'               'public'       'false'  % not hidden
    'PropertyRemoved'             'public'       'false'  % not hidden

Similar hidden events exist for all HG2 graphics objects. The MarkedDirty and MarkedClean events are available for practically all graphic objects. We can listen to them (luckily, their ListenAccess meta-property is defined as ‘public’) to get a notification whenever the corresponding object (axes, or any other graphics component such as a plot-line or axes ruler etc.) is being redrawn. We can then refresh our own properties. It makes sense to attach such callbacks to MarkedClean rather than MarkedDirty, because the property values are naturally stabled and reliable only after MarkedClean. In some specific cases, we might wish to listen to one of the other events, which luckily have meaningful names.

For example, in my ticklabelformat utility I’ve implemented the following code (simplified here for readability – download the utility to see the actual code), which listens to the MarkedClean event on the axes’ YRuler property:

    % HG1 (R2014a or older)
    hAx = handle(hAxes);
    hProp = findprop(hAx, 'YTick');
    hListener = handle.listener(hAx, hProp, 'PropertyPostSet', @reformatTickLabels);
    setappdata(hAxes, 'YTickListener', hListener);  % must be persisted in order to remain in effect
    % HG2 (R2014b or newer)
    addlistener(hAx, 'YTick', 'PostSet', @reformatTickLabels);
    % *Tick properties don't trigger PostSet events when updated automatically in R2014b
    %addlistener(hAx, 'YLim', 'PostSet', @reformatTickLabels);  % this solution does not cover all use-cases
    addlistener(hAx.YRuler, 'MarkedClean', @reformatTickLabels);
% Adjust tick labels now

In some cases, the triggered event might pass some useful information in the eventData object that is passed to the callback function as the second input parameter. This data may be different for different events, and is also highly susceptible to changes across Matlab releases, so use with care. I believe that the event names themselves (MarkedClean etc.) are less susceptible to change across Matlab releases, but they might.

Performance aspects

The MarkedClean event is triggered numerous times, from obvious triggers such as calling drawnow to less-obvious triggers such as resizing the figure or modifying a plot-line’s properties. We therefore need to be very careful that our callback function is (1) non-reentrant, (2) is not active too often (e.g., more than 5 times per sec), (3) does not modify properties unnecessarily, and in general (4) executes as fast as possible. For example:

function reformatTickLabels(hProperty, eventData)
    persistent inCallback
    if ~isempty(inCallback),  return;  end
    inCallback = 1;  % prevent callback re-entry (not 100% fool-proof)
    % Update labels only every 0.2 secs or more
    persistent lastTime
        tnow = datenummx(clock);  % fast
        tnow = now;  % slower
    ONE_SEC = 1/24/60/60;
    if ~isempty(lastTime) && tnow - lastTime < 0.2*ONE_SEC
        inCallback = [];  % re-enable callback
    lastTime = tnow;
    % This is the main callback logic
        hAxes = eventData.AffectedObject;
        hAxes = ancestor(eventData.Source,'Axes');
    prevTickValues = getappdata(hAxes, 'YTick');
    tickValues = get(hAxes, 'YTick');
    if ~isequal(prevTickValues, tickValues)
        tickLabels = arrayfun(@(x)(sprintf('%.1fV',x)), tickValues, 'UniformOutput',false);
        set(hAxes, 'YTickLabel', tickLabels)
    inCallback = [];  % re-enable callback

Unfortunately, it seems that retrieving some property values (such as the axes’s YTick values) may by itself trigger the MarkedClean event for some reason that eludes my understanding (why should merely getting the existing values modify the graphics in any way?). Adding callback re-entrancy checks as above might alleviate the pain of such recursive callback invocations.

A related performance aspect is that it could be better to listen to a sub-component’s MarkedClean than to the parent axes’ MarkedClean, which might be triggered more often, for changes that are entirely unrelated to the sub-component that we wish to monitor. For example, if we only monitor YRuler, then it makes no sense to listen to the parent axes’ MarkedClean event that might trigger due to a change in the XRuler.

In some cases, it may be better to listen to specific events rather than the all-encompassing MarkedClean. For example, if we are only concerned about changes to the Position property, we should listen to the LocationChanged and/or SizeChanged events (more details).

Additional graphics-related performance tips can be found in my Accelerating MATLAB Performance book.

Have you used MarkedClean or some other undocumented HG2 event in your code for some nice effect? If so, please share your experience in a comment below.

]]> 14
copyobj behavior change in HG2 Wed, 13 May 2015 16:00:49 +0000
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As a followup to last-week’s post on class-object and generic data copies, I would like to welcome back guest blogger Robert Cumming, who developed a commercial Matlab GUI framework. Today, Robert will highlight a behavior change of Matlab’s copyobj function in HG2.

One of the latest features that was introduced to the GUI Toolbox was the ability to undock or copy panels, that would be displayed in a standalone figure window, but remain connected to the underlying class object:

Panel copy in the GUI framework toolbox

Panel copy in the GUI framework toolbox

These panel copies had to remain fully functional, including all children and callbacks, and they needed to retain all connections back to the source data. In the example above I have altered the plot to show that it’s an actual copy of the data, but has separate behavior from the original panel.

To simply undock a uipanel to a new figure, we can simply re parent it by updating its Parent property to the new figure handle. To make a copy we need to utilize the copyobj function, rather than re-parenting. copyobj can be used to make a copy of all graphic objects that are “grouped” under a common parent, placing their copy in a new parent. In HG2 (R2014b onwards) the default operation of copyobj has changed.

When I started developing this feature everything looked okay and all the objects appeared copied. However, none of the callbacks were functional and all the information stored in the object’s ApplicationData was missing.

I had used copyobj in the past, so I knew that it originally worked ok, so I investigated what was happening. Matlab’s documentation for HG2 code transition suggests re-running the original code to create the second object to populate the callbacks. Unfortunately, this may not be suitable in all cases. Certainly in this case it would be much harder to do, than if the original callbacks had been copied directly. Another suggestion is to use the new ‘lagacy’ option’:

copyobj(___,’legacy’) copies object callback properties and object application data. This behavior is consistent with versions of copyobj before MATLAB® release R2014b.

So, instead of re-running the original code to create the second object to populate the callbacks, we can simply use the new ‘legacy’ option to copy all the callbacks and ApplicationData:

copyobj(hPanel, hNewParent, 'legacy')

Note: for some reason, this new ‘legacy’ option is mentioned in both the doc page and the above-mentioned HG2 code-transition page, but not in the often used help section (help copyobj). There is also no link to the relevant HG2 code-transition page in either the help section or the doc page. I find it unfortunate that for such a backward-incompatible behavior change, MathWorks has not seen fit to document the information more prominently.

Other things to note (this is probably not an exhaustive list…) when you are using copyobj:

  • Any event listeners won’t be copied
  • Any uicontextmenus will not be copied – it will in fact behave strangely due to the fact that it will have the uicontextmenu – but the parent is the original figure – and when you right-click on the object it will change the figure focus. For example:
    hFig= figure;
    ax = axes;
    uic = uicontextmenu ('parent', hFig);
    uim = uimenu('label','My Label', 'parent',uic);
    ax.UIContextMenu = uic;
    copyChildren = copyobj (ax, hFig, 'legacy');
    hFig2 = figure;
    copyChildren.Parent = hFig2;

Another note on undocked copies – you will need to manage your callbacks appropriately so that the callbacks manage whether they are being run by the original figure or in a new undocked figure.


  1. copyobj has changed in HG2 – but the “legacy” switch allows you to use it as before.
  2. It is unfortunate that backward compatibility was not fully preserved (nor documented enough) in HG2, but at least we have an escape hatch in this case.
  3. Take care with the legacy option as you may need to alter uicontextmenus and re-attach listeners as required.
]]> 0
Matlab callbacks for Java events in R2014a Thu, 08 May 2014 10:27:03 +0000
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Exactly five years ago, in one of this blog’s first articles, I wrote an article explaining how to trap the standard Java Swing events in Matlab, in order to achieve much more extensive behavior customizability than Matlab’s standard uicontrols. Three years ago I wrote a related article showing how to trap any Java events as simple Matlab callbacks, even those from custom Java classes. This worked very well over the years, until now.

Over the past few weeks, several people have commented and emailed me about a problem that occurs in R2014a but not in earlier releases: Whereas Java objects in R2013b and earlier automatically exposed their public events as Matlab callbacks, they no longer do so in R2014a.

As a simple example to illustrate the point, let’s take a simple Java JButton and modify its label when the mouse moves over it, using the mouseEntered, mouseExited events. In R2013b and earlier this was pretty easy:

% Create and display a simple JButton
jButton = javax.swing.JButton('click me');
javacomponent(jButton, [50,50,80,20], gcf);
% Set the Matlab callbacks to the JButton's events
set(jButton, 'MouseEnteredCallback', @(h,e)set(jButton,'Text','NOW !!!'))
set(jButton, 'MouseExitedCallback',  @(h,e)jButton.setText('click me'))

Dynamic Matlab callback behavior based on Java events

(Note how I used two distinct variants to set the button’s text, using either the Matlabized Text property with the set function, or the standard JButton’s setText() method. We can use either of these variants: they are equivalent and largely a matter of personal taste, although the Java method is generally preferable.)

Unfortunately, in R2014a we get an error, since the jButton object no longer exposes its events as Matlab callbacks:

>> set(jButton, 'MouseEnteredCallback', @(h,e)set(jButton,'Text','NOW !!!'))
The name 'MouseEnteredCallback' is not an accessible property for an instance of class 'javax.swing.JButton'.

The solution – wrap the Java reference in a Matlab handle

Do not set Matlab callbacks on the so-called “naked” Java reference. Instead, wrap the reference with a Matlab handle:

>> jButton = handle(jButton, 'CallbackProperties');
hjButton =
% we can now set the callbacks on jButton as before

The simple act of using the handle wrapper rather than the naked Java reference also prevents some memory leaks, as I explained here. While you’re at it, also wrap (or more precisely, auto-delegate) the Java reference with a javaObjectEDT, if it is a Swing GUI component, in order to avoid EDT timing issues. Using these wrappers form two of my six rules for safe Java programming in Matlab, that I outlined in section 1.5 of my Matlab-Java programming book. Anyone who followed that advice would not need to modify their code for R2014a. Even if you didn’t, the solution is quite painless: simply add the wrapper line for any Java reference whose events you wish to trap.

Note that the explanation above relates to all Java objects that expose events, not just for GUI ones. I used JButton merely to illustrate the point.

]]> 14
uicontextmenu performance Wed, 02 Apr 2014 18:00:29 +0000
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I would like to introduce guest blogger Robert Cumming, an independent contractor based in the UK who has recently worked on certification of the newest advanced civil aircraft. Today Robert will discuss the performance of uicontextmenus in interactive GUIs, which were used extensively in flight test analysis.

Have you ever noticed that a GUI slows down over time? I was responsible for designing a highly complex interactive GUI, which plotted flight test data for engineers and designers to analyze data for comparison with pre-flight predictions. This involved extensive plotting of data (pressure, forces/moments, anemometry, actuator settings etc….), where individual data points were required to have specific/customizable uicontextmenus.

Matlab’s documentation on uicontextmenus discusses adding them to plots, but makes no mention of the cleaning up afterwards.

Let’s start with some GUI basics. First we create a figure with a simple axes and a line:

x = [-10:0.2:10];
y = x.^2;
h = figure;
ax = axes ( 'parent',h );
hplot = plot ( ax, x, y );

Adding a uicontextmenu to the plot creates extra objects:

uic = uicontextmenu;
uimenu ( uic, 'Label','Menu A.1' );
set ( hplot, 'uicontextmenu',uic );
fprintf ( 'Figure (h) has %i objects\n', length ( findobj ( h ) ) );

In this instance there are 5 objects, the individual menu and uicontextmenu have created an additional 2 objects. All of this is quite basic as you would expect.

Basic plot with a custom context menu

Basic plot with a custom context menu

We now clear the plot using cla and draw a new line with its own new uicontextmenu. Note that we don’t save the plot handle this time:

cla ( ax );
uic = uicontextmenu;
uimenu ( uic, 'Label','Menu B.1' );
plot ( ax, x, -y , 'uicontextmenu',uic );
fprintf ( 'Figure (h) has %i objects\n', length ( findobj ( h ) ) );

Another basic plot with a new context menu

Another basic plot with a new context menu

This time the fprintf line tells us that the figure has 7 objects. This may not have been expected, as the first plot was cleared and the original context menu is no longer accessible (cla removed the plot and the line object hplot).

Let’s check these object handles:

>> ishandle ( findobj( h ) )'
ans =
     1     1     1     1     1     1     1     1     1     1     1     1     1

We see that all the objects are valid handles. At first this may perhaps appear confusing: after all, the plot with “Menu A.1” was deleted. Let’s check this:

>> ishandle ( hplot )
ans =
>> get(hplot)
Error using handle.handle/get
Invalid or deleted object.

So it appears that although the plot line was indeed deleted, its associated context menu was not. The reason for this is that the context menu is created as a child of the figure window, not the plot. We are simply using the plot line’s UIContextMenu property to allow the user to obtain access to it and its associated menus.

Once we understand this we can do two things:

  1. Use the same uicontextmenu for each plot
  2. Built individual uicontextmenus for each plot, but remember to clean up afterwards

Is this really such a big issue?

You may be wondering how big a problem is this creation of extra objects. The answer is that for simple cases like this it is really not a big issue. But let’s consider a more realistic case where we also assign callbacks to the menus. First we will create a figure with an axes, for plotting on and a uicontrol edit for displaying a message:

function uicontextExample
   h.main = figure; = axes ( 'parent',h.main, 'NextPlot','add', 'position',[0.1 0.2 0.8 0.7] );
   h.msg = uicontrol ( 'style','edit', 'units','normalized', 'position',[0.08 0.01 0.65 0.1], 'backgroundcolor','white' );
   uicontrol ( 'style','pushbutton', 'units','normalized', 'position',[0.75 0.01 0.2 0.1], 'Callback',{@RedrawX20 h}, 'string','re-draw x20' );
   redraw ( [], [], h )  % see below

The callback redraw (shown below) draws a simple curve and assigns individual uicontextmenus to each individual item in the plot. Each uicontextmenu has 12 menu items:

function redraw ( obj, event, h, varargin )
   start = tic;
   x = -50:50;
   y = x.^2;
   for ii = 1 : length(x)
      uim = uicontextmenu ( 'parent',h.main );
      for jj = 1 : 10
         menuLabel = sprintf ( 'Menu %i.%i', ii, jj );
         uimenu ( 'parent',uim, 'Label',menuLabel, 'Callback',{@redraw h} )
      xStr = sprintf ( 'X = %f', x(ii) );
      yStr = sprintf ( 'Y = %f', y(ii) );
      uimenu ( 'parent',uim, 'Label',xStr, 'Callback',{@redraw h} )
      uimenu ( 'parent',uim, 'Label',yStr, 'Callback',{@redraw h} )
      plot (, x(ii), y(ii), 'rs', 'uicontextmenu',uim );
   objs = findobj ( h.main );
   s = sprintf ( 'figure contains %i objects - drawn in %3.2f seconds', length(objs), toc(start) );
   set ( h.msg, 'string',s );

To help demonstrate the slow-down in speed, the pushbutton uicontrol will redraw the plot 20 times, and show the results from the profiler:

function RedrawX20 ( obj, event, h )
   profile on
   set ( obj, 'enable','off' )
   for ii = 1 : 20
      redraw ( [], [], h );
   set ( obj, 'enable','on' )
   profile viewer

The first time we run this, on a reasonable laptop it takes 0.24 seconds to draw the figure with all the menus:

Multiple context menus assigned to individual data points

Multiple context menus assigned to individual data points

When we press the <re-draw x20> button we get:

figure contains 2731 objects - drawn in 0.28 seconds
figure contains 4044 objects - drawn in 0.28 seconds
figure contains 5357 objects - drawn in 0.28 seconds
figure contains 6670 objects - drawn in 0.30 seconds
figure contains 7983 objects - drawn in 0.32 seconds
figure contains 9296 objects - drawn in 0.30 seconds
figure contains 10609 objects - drawn in 0.31 seconds
figure contains 11922 objects - drawn in 0.30 seconds
figure contains 13235 objects - drawn in 0.32 seconds
figure contains 14548 objects - drawn in 0.30 seconds
figure contains 15861 objects - drawn in 0.31 seconds
figure contains 17174 objects - drawn in 0.31 seconds
figure contains 18487 objects - drawn in 0.32 seconds
figure contains 19800 objects - drawn in 0.33 seconds
figure contains 21113 objects - drawn in 0.32 seconds
figure contains 22426 objects - drawn in 0.33 seconds
figure contains 23739 objects - drawn in 0.35 seconds
figure contains 25052 objects - drawn in 0.34 seconds
figure contains 26365 objects - drawn in 0.35 seconds
figure contains 27678 objects - drawn in 0.35 seconds

The run time shows significant degradation, and we have many left-over objects. The profiler output confirms where the time is being spent:

Profiling results - context menu creation is an evident hotspot

Profiling results - context menu creation is an evident hotspot

As expected the menus creation takes most of the time. Note that the timing that you will get on your own computer for this example may vary and the slowdown may be less noticeable.

Let’s extend the example to include extra input arguments in the callback (in this example they are not used – but in the industrial example extra input arguments are very possible, and were required by the customer):

for ii=1:length(x)
   uim = uicontextmenu ( 'parent',h.main );
   for jj = 1 : 10
      menuLabel = sprintf ( 'Menu %i.%i', ii, jj );
      uimenu ( 'parent',uim, 'Label',menuLabel, 'Callback',{@redraw h 1 0 1} )
   xStr = sprintf ( 'X = %f', x(ii) );
   yStr = sprintf ( 'Y = %f', y(ii) );
   uimenu ( 'parent',uim, 'Label',xStr, 'Callback',{@redraw h 1 0 1} )
   uimenu ( 'parent',uim, 'Label',yStr, 'Callback',{@redraw h 1 0 1} )
   plot (, x(ii), y(ii), 'rs', 'uicontextmenu',uim );

Re-running the code and pressing <re-draw x20> we get:

figure contains 1418 objects - drawn in 0.29 seconds
figure contains 2731 objects - drawn in 0.37 seconds
figure contains 4044 objects - drawn in 0.48 seconds
figure contains 5357 objects - drawn in 0.65 seconds
figure contains 23739 objects - drawn in 4.99 seconds
figure contains 25052 objects - drawn in 5.34 seconds
figure contains 26365 objects - drawn in 5.88 seconds
figure contains 27678 objects - drawn in 6.22 seconds

Note that the 6.22 seconds is just the time that it took the last individual redraw, not the total time to draw 20 times (which was just over a minute). The profiler is again used to confirm that the vast majority of the time (57 seconds) was spent in the uimenu calls, mostly on line #23. In comparison, all other lines together took just 4 seconds to run.

The simple act of adding extra inputs to the callback has completely transformed the speed of our code.

How real is this example?
In code written for a customer, the context menu had a variety of sub menus (dependent on the parameter being plotted – from 5 to ~20), and they each had multiple parameters passed into the callbacks. Over a relatively short period of time the user would cycle through a lot of data and the number of uicontextmenus being created was surprisingly large. For example, users would easily look at 100 individual sensors recorded at 10Hz for 2 minutes (100*10*60*2). If all sensors and points are plotted individually that would be 120,000 uicontextmenus!

So how do we resolve this?

The problem is addressed by simply deleting the context menu handles once they are no longer needed. This can be done by adding a delete command after cla at the top of the redraw function, in order to remove the redundant uicontextmenus:

function redraw ( obj, event, h, varargin )
   delete ( findobj ( h.main, 'type','uicontextmenu' ) )   set ( h.msg, 'string','redrawing' );
   start = tic;

If we now click <redraw x20> we see that the number of objects and the time to create them remain its essentially the same as the first call: 1418 objects and 0.28 seconds:

figure contains 1418 objects - drawn in 0.28 seconds
figure contains 1418 objects - drawn in 0.30 seconds
figure contains 1418 objects - drawn in 0.33 seconds
figure contains 1418 objects - drawn in 0.29 seconds
figure contains 1418 objects - drawn in 0.29 seconds

Advanced users could use handle listeners to attached a callback such that when a plot element is deleted, so too are its associated context menus.


Things to consider with uicontextmenus:

  1. Always delete uicontextmenus that you have finished with.
  2. If you have multiple uicontextmenus you will want to only delete the ones associated with the axes being cleared – otherwise you will delete more than you want to.
  3. Try to reduce the number of input arguments to your callbacks, group into a structure or cell array.
  4. Re-use uicontextmenus where possible.

Have you had similar experiences? Or other issues where GUI slow down over time? If so, please leave a comment below.

]]> 5
Editbox data input validation Wed, 02 Oct 2013 14:00:51 +0000
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Last week I explained how Matlab’s editbox control can be customized using its underlying Java component. Today I extend that article by explaining how we can use this information to provide a very user-friendly input-validation function for Matlab editboxes.

Zip-code entry validation example

Zip-code entry validation example

As before, we first need to get the Matlab control’s underlying Java control. This is done using the findjobj utility:

% Prepare the log editbox
hEditbox = uicontrol('Style','edit', 'String','Matlab', ...);
% Get the underlying Java editbox
jEditbox = findjobj(hLogPanel);
    % Multi-line editboxes are contained within a scroll-panel
    jEditbox = handle(jEditbox.getViewport.getView, 'CallbackProperties');
    % probably a single-line editbox


Once we have the jEditbox reference handle, we can use some ~30 different event callbacks that it exposes. Some of the useful callbacks include:

  • ActionPerformedCallback – fired when <enter> is clicked in the editbox
  • CaretUpdateCallback – fired when the caret position has changed
  • KeyTypedCallback – fired whenever a keyboard button is typed when the editbox has focus

For example:

set(jEditbox, 'KeyPressedCallback',@myValidationFunction);
% Callback function definition
function myValidationFunction(jEditbox, eventData)
    % The following comments refer to the case of Alt-Shift-b
    keyChar = get(eventData,'KeyChar');  % or: eventData.getKeyChar  ==> 'B'
    isAltDown = get(eventData,'AltDown');  % ==> 'on'
    isAltDown = eventData.isAltDown;  % ==> true
    modifiers = get(eventData,'Modifiers');  % or: eventData.getModifiers ==> 9 = 1 (Shift) + 8 (Alt)
    modifiersDescription = char(eventData.getKeyModifiersText(modifiers));  % ==> 'Alt+Shift'
    % (now decide what to do with this key-press...)

Using such a validation function enables us to immediately convert typed characters into ‘*’ (in password fields) or check that the input conforms to a standard format such as a 5-digit zip code, a valid-looking email address or a valid-looking credit-card number (Luhn’s algorithm, recently featured in a video tutorial by Doug Hull). Of course, there are dedicated JIDE controls that do much of this already, but let’s not digress.

In today’s example, we shall implement a simple input-validation function for a 5-digit zip code. When the input data is invalid, the editbox will be colored red and an appropriate message will appear next to the editbox. In addition, invalid (non-digit) characters shall be rejected with a beep sound.

Zip-code validation example

To illustrate the above, let’s use an example of a 5-digit zip-code entry validation. We start by creating an empty editbox with an associated message label next to it:

figure('Color','w', 'Menubar','none');
hEditbox = uicontrol('Style','edit', 'Pos',[10,10,60,20], 'String','Matlab');  % start with an invalid string
hMessageLabel = uicontrol('Style','text', 'Pos',[80,10,200,20], 'horizontal','left', 'background','w', 'Foreground','red');

Next we get the underlying jEditbox reference and set its entry-validation callback function:

jEditbox = findjobj(hEditbox);  % single-line editbox so there's need to drill-down the scroll-pane
set(jEditbox, 'KeyPressedCallback',{@editboxValidation,hMessageLabel});

Note how we pass the message-label’s handle as an extra input parameter to the callback function.

Now we define the callback function editboxValidation and place it on the Matlab path (for example, by placing the following within editboxValidation.m in a folder that is already on your path):

% editboxValidation - ensure that an editbox input is a valid 5-digit zip code
function editboxValidation(jEditbox, eventData, hMessageLabel)
    keyChar = eventData.getKeyChar;  % see note below about how we can trick Matlab here
    if ~alldigits(keyChar) && isprintable(keyChar)
        fixedText = strrep(char(jEditbox.getText), keyChar, '');
    updateAppearance(jEditbox, hMessageLabel);
% Check whether a string only contains digits
function flag = alldigits(str)
    flag = ~isnan(str2double(str));
% Check whether a character is printable
function flag = isprintable(keyChar)
    keyVal = double(keyChar);
    flag = ~isempty(keyVal) && keyVal > 31 && keyVal < 128;
% Update the GUI appearance based on the editbox text
function updateAppearance(jEditbox, hMessageLabel)
    currentText = char(jEditbox.getText);
    if isempty(currentText)
        set(hMessageLabel, 'String','Please enter a 5-digit zip-code')
        jEditbox.setBorder(javax.swing.border.LineBorder(, 3, false));
    elseif ~alldigits(currentText)
        set(hMessageLabel, 'String','Invalid zip: should only contain digits')
        jEditbox.setBorder(javax.swing.border.LineBorder(, 3, false));
    elseif length(currentText) ~= 5
        set(hMessageLabel, 'String','Invalid zip: should have exactly 5 digits')
        jEditbox.setBorder(javax.swing.border.LineBorder(, 3, false));
        set(hMessageLabel, 'String','');
        jEditbox.setBorder(javax.swing.border.LineBorder(java.awt.Color.gray, 1, false));

Finally, we call the validation function directly after creating our GUI, to let the user know that the zip-code needs to be entered:

% Note how we trick Matlab by using eventData as a struct rather than a Java object
% (i.e., getKeyChar is set here to be a simple struct field, rather than a Java method)
eventData.getKeyChar = '';

Zip-code entry validation example

Zip-code entry validation example

Naturally, this is a very simple example, and you can easily expand it for your needs. But it does show the basic components of any input validation: checking the new data, updating the control as appropriate, and modifying the appearance of the underlying control and of other associated controls.

Available report – “Advanced Customizations of Matlab Uicontrols”

Interested readers can find more information about these and other possible customizations in my report on “Advanced Customizations of Matlab Uicontrols“. This 90-page PDF report can be purchased here ($39, please allow 24 hours for delivery by email). The report explains how to customize Matlab’s uicontrols in ways that are simply not possible using documented Matlab properties. This includes treatment of push buttons, toggle buttons, radio buttons, checkboxes, editboxes, listboxes, popup menus (aka combo-boxes/drop-downs), sliders, labels, and tooltips. Much of the information in the report is also available in hard-copy format in chapter 6 of my Matlab-Java programming book.

]]> 4
Customizing editboxes Wed, 25 Sep 2013 14:00:26 +0000
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  1. Uicontrol callbacks This post details undocumented callbacks exposed by the underlying Java object of Matlab uicontrols, that can be used to modify the control's behavior in a multitude of different events...
  2. Setting listbox mouse actions Matlab listbox uicontrol can be modified to detect mouse events for right-click context menus, dynamic tooltips etc....
  3. Customizing Matlab labels Matlab's text uicontrol is not very customizable, and does not support HTML or Tex formatting. This article shows how to display HTML labels in Matlab and some undocumented customizations...
  4. GUI integrated HTML panel Simple HTML can be presented in a Java component integrated in Matlab GUI, without requiring the heavy browser control....
As a natural follow-up to last week’s article about rich-content log panels (multi-line editbox), today I discuss some additional customizations that can be done to Matlab’s editbox control.

Matlab’s dual editbox controls

There are two distinct uicontrols called ‘editbox’ in Matlab: a single-line editbox and a multi-line editbox. Matlab automatically uses the single-line control if the Max property is set to 1 (the default value, backward compatible with early Matlab versions). If Max > 1, the multi-line editbox is used. Today’s article will focus on features shared by both the single-line and multi-line editbox controls.

Beware of a possible pitfall using Matlab’s editbox controls: when switching styles, including switching between the single-line and multi-line editbox versions, Matlab replaces the underlying Java component with a new component that has default properties. Therefore, if we need any customizations to the uicontrol, then we should ensure that they are done after setting the final uicontrol style, otherwise they will be discarded.

Underlying Java object

As discussed in many prior articles, the first step in customization is to get access to the Matlab control’s underlying Java control. This is done using the findjobj utility:

% Prepare the log editbox
hEditbox = uicontrol('Style','edit', 'String','Matlab', ...);
% Get the underlying Java editbox
jEditbox = findjobj(hLogPanel);
    % Multi-line editboxes are contained within a scroll-panel
    jEditbox = handle(jEditbox.getViewport.getView, 'CallbackProperties');
    % probably a single-line editbox


As I have explained long ago, all uicontrol borders can be customized using the underlying jEditbox handle’s Border property:

% Create a new border
lineColor = java.awt.Color(1,0,0);  % =red
thickness = 3;  % pixels
roundedCorners = true;
newBorder = javax.swing.border.LineBorder(lineColor,thickness,roundedCorners);
% Override the default control's border
jEditbox.Border = newBorder;  % or: set(jEditbox,'Border',newBorder) or: jEditbox.setBorder(newBorder)
% Remove the border altogether
jEditbox.Border = [];
% Redraw the modified control after we have changed its appearance

editbox with regular border

editbox with regular border

editbox with custom border

editbox with custom border

editbox with no border

editbox with no border

Much more complex and interesting borders can be created in much the same way. Interested readers are referred to the official documentation of Java Borders or any decent Swing textbook.

Text selection

Several jEditbox properties control the text-selection functionality:

  • SelectionStart, SelectionEnd control the characters within the displayed text that are selected (typically with some shaded background color). A value of 0 means the first character, 1 means the second character etc. Setting SelectionStart=0 and SelectionEnd=intmax selects the entire text. We can also use,selectionEnd). For example:
    set(jEditbox, 'SelectionStart',1, 'SelectionEnd',5);,5)  % equivalent alternative

    Setting the selected text

    Setting the selected text

  • SelectionColor, SelectedTextColor change the foreground and background colors of the selected text. These properties are overridden whenever the editbox gains focus, so we need to be override them in the editbox’s FocusGainedCallback:
    cbStr = ['set(gcbo,''SelectionColor'',,' ...
    set(jEditbox, 'FocusGainedCallback', cbStr);

    Non-standard selection colors and FocusGainedCallback

    Non-standard selection colors, FocusGainedCallback

  • SelectedText is a read-only property holding the text currently selected, between the SelectionStart and SelectionEnd positions. Associated property Text holds the entire text within the editbox. Note that both these properties hold a java.lang.String object, which should be cast to a Matlab string via Matlab’s built-in char function, unless we use Matlab’s get function (which does this conversion for us automatically):
    str = char(jEditbox.getSelectedText);
    str = get(jEditbox,'SelectedText');  % equivalent alternative

Customizing the input caret

The Caret property, which is common to all Java Swing data-entry components, references a javax.swing.text.DefaultCaret object that controls the text caret appearance (this is naturally relevant only for editable editboxes).

The caret object has its own properties that can be customized. For example: BlinkRateRate, Visible and UpdatePolicy. The caret’s StateChangedCallback is invoked whenever the caret position is updated.

Some additional caret-related properties can be set using jEditbox properties: CaretColor and CaretPosition (which uses 0-based, like SelectionStart and SelectionEnd above). Here is an example that modifies the default caret color to something a bit more lively:

% Set the caret color to red
jEditbox.setCaretColor(;	% an alternative

Red CaretColor

Red CaretColor


Several properties of the jEditbox handle control the editbox behavior beyond what is available by the Matlab uicontrol:

  • Editable – (default=true) a boolean flag indicating whether or not the editbox text can be modified. Note that the Matlab HG handle (hEditbox) only allows setting the Enable property (its jEditbox Java counterpart property is called Enabled), but not to set an enabled yet uneditable control – this can only be done using the Java Editable property.
  • DisabledTextColor controls the text color (default=gray) when the editbox is disabled.
  • DragEnabled – (default=false) a boolean flag indicating whether the editbox contents can be mouse-dragged externally as a DND source (for example, onto an editor, command line or any DND target). The DropMode, DropLocation, DropTarget and TransferHandler properties enable the editbox act as a DND target, accepting externally-dragged data as input sources.
  • FocusAccelerator – (default=char(0)) sets the keyboard accelerator sequence that will cause the receiving text component to get the focus. The accelerator will be the key combination of the <Alt> key and the specified character, converted to upper-case. Any previous key accelerator setting, including menu-bar accelerators, will be superseded. A char(0) key has the effect of turning off the focus accelerator. By default, there is no focus accelerator key (i.e., an accelerator of \0=char(0)). For example, let us set the accelerator to <Alt>-E, overriding the menu-bar’s default accelerator for the Edit menu:
    >> jEditbox.setFocusAccelerator('e');
    >> jEditbox.getFocusAccelerator		% let us check...
    ans =
    E	% 'e' converted to 'E', meaning an Alt-E accelerator

Additional editbox behavior can be customized using the dozens of callback functions that jEditbox exposes. These callbacks enable modifying the appearance and behavior of the editbox based on events such as mouse movements (into/over/out-of), focus gain/loss, key-clicks (that enable input data validation) etc. I plan to describe a data input-validation function based on these callbacks, using some of the customizations shown above, in next week’s article.

Available report – “Advanced Customizations of Matlab Uicontrols”

Interested readers can find more information about these and other possible customizations in my report on “Advanced Customizations of Matlab Uicontrols“. This 90-page PDF report can be purchased here ($39, please allow 24 hours for delivery by email). The report explains how to customize Matlab’s uicontrols in ways that are simply not possible using documented Matlab properties. This includes treatment of push buttons, toggle buttons, radio buttons, checkboxes, editboxes, listboxes, popup menus (aka combo-boxes/drop-downs), sliders, labels, and tooltips. Much of the information in the report is also available in hard-copy format in chapter 6 of my Matlab-Java programming book.

]]> 4
Disabling menu entries in deployed docked figures Wed, 14 Nov 2012 18:00:46 +0000
Related posts:
  1. setPrompt – Setting the Matlab Desktop prompt The Matlab Desktop's Command-Window prompt can easily be modified using some undocumented features...
  2. Docking figures in compiled applications Figures in compiled applications cannot officially be docked since R2008a, but this can be done using a simple undocumented trick....
  3. FindJObj – find a Matlab component’s underlying Java object The FindJObj utility can be used to access and display the internal components of Matlab controls and containers. This article explains its uses and inner mechanism....
  4. Matlab callbacks for Java events Events raised in Java code can be caught and handled in Matlab callback functions - this article explains how...
Last week I presented an article explaining how to solve an issue with deployed (compiled) Matlab applications. Today I’d like to welcome guest blogger Alexander Mering, who will explain how to disable standard Matlab menu items in deployed docked Matlab figures. Alexander has been an avid follower of this blog, and from his CSSM posts we can tell that he’s been heavily using advanced GUI features presented in this blog. His article today nicely shows how we can use different building-blocks presented in different articles in this blog, to achieve something new and useful.

As Yair pointed out in many occasions, the power of Matlab could be greatly enhanced using the underlying Java mechanism. To me, while developing a larger standalone tool for technical calculations, these hints are worth a mint (as I guess for many of you).

One of these very useful hints is the ability to dock figure windows in standalone applications. This perfectly fits to my understanding of a “clean desktop”, i.e., having as less as possible separate windows. Since in many calculations dozens of figures are generated, the desktop gets up crowded very fast – if these are not grouped. So docking is essential (at least for me). Unfortunately there seems to be a serious problem with the resulting menu entries (at least in R2011b on Win XP), leading to a crash of the standalone application. Based on the posts by Yair, I will sketch a possible route to avoid this issue.

The symptom

In the compiled application, docking could be accomplished by accessing the figure frame’s underlying Java level:

% get java frame for sophisticated modifications
jframe = get(handle(figure_handle), 'JavaFrame');
% allow docking

Using this modification, the user is now allowed to dock and undock the figures manually. For initial docking of the figure,


could be used during figure creation. Unfortunately, there are menu entries in the Figures container which are either unwanted (since not usable) or even directly crash the standalone applications:

Useless Debug menu items in deployed applications

Useless Debug menu items in deployed applications

Menu items crashing deployed applications

Menu items crashing deployed applications

Since crashing menu entries will be found and used by end-users (though these are somehow hidden), these prohibit the usage of the docking feature as long as these could be invoked. So how can we disable / remove these menu items?

The unsuccessful solution

Unfortunately, the straight forward solution of getting the handle to the Figures containers’ menu bar and remove the unwanted items does not work. The reason for this is the (to me unexpected behavior) that the menu bar seems to be rebuilt whenever a figure is docked/undocked.

This is actually the same behavior that automatically rebuilds the Editor’s menu bar whenever an editor file is added/removed. The Editor container is basically the same docking container as the Figures container, as shown by Yair’s setFigDockGroup utility:

Docking a figure in the Editor container (group)

Docking a figure in the Editor container (group)

Therefore, removing unwanted menu items only helps until the next figure docking/undocking. To make it even worse: also pressing any of the buttons within the document bar (if having more than one figure) somehow rebuilds the entire menu structure, reverting our changes. So the solution becomes a bit more complex.

The working solution

For the working solution, many pieces presented by Yair should be put together. The first piece results from the question how to detect a dock/undock event. Since no such callback is defined, we need to use a property listener as Yair showed in his post about the continuous slider callback:

% listen to the WindowStyle property to detect docking / undocking events
hProp = findprop(handle(figure_handle),'WindowStyle');  % a schema.prop object
% if the event occurs, invoke the callback
hlistener = handle.listener(handle(figure_handle), hProp, 'PropertyPostSet',{@(source, event) Callback_DockingFcn});
% attach listener to the GUI since it needs to be known (as long as the figure exists)
setappdata(figure_handle, 'Handle_Listener', hlistener);

Now, whenever the figure’s WindowStyle property (which controls the docking state) is changed, our docking callback is invoked.

The next piece of the puzzle takes care of the menu rebuild whenever any document bar button is pressed. To overcome this behavior, the idea is to define the MousePressed callback of theses buttons to (again) invoke the docking callback. This is necessary for two reasons: First, pressing the button (i.e., changing the current figure) rebuilds the menu, overwriting our changed menu entries. Secondly, all other buttons are also somehow rebuilt and the callbacks are removed if a new figure is docked.

The handles to the document bar buttons could be found using Yair’s findjobj utility. We have already seen that the Editor container is analogous to the Figures docking container. So let’s use the method described by Yair for accessing the Editor container, to access the Figures container:

figures_container = javaObjectEDT(matlab_instance.getGroupContainer('Figures'));
figures_frame = javaObjectEDT(figures_container.getTopLevelAncestor);

Once we get the Java Frame for the Figures container, the buttons could be found by digging through its children. This finally allows to set the callback using

DTDocumentBar = javaObjectEDT(figures_frame.getRootPane.getLayeredPane.getComponent(1).getComponent(1).getComponent(0).getComponent(0).getComponent(1).getComponent(0));
ContentPanel = javaObjectEDT(DTDocumentBar.getComponent(0).getComponent(0).getViewport.getView);
if ~isempty(ContentPanel.getComponents) % less than two documents are open and no DTDocumentbar exists
    drawnow; pause(0.05)
    GroupPanel = javaObjectEDT(ContentPanel.getComponent(0));
    GroupPanel_Elements = javaObjectEDT(GroupPanel.getComponents);
    % change the MousePressed Callback for each of the buttons to invoke the function which disables the menu
    for n = 1 : GroupPanel.getComponentCount
        thisElement = GroupPanel_Elements(n);
        if isequal(char(thisElement.getClass.toString), 'class com.mathworks.widgets.desk.DTDocumentBar$DocumentButton')
            set(handle(thisElement, 'CallbackProperties'), 'MousePressedCallback', {@(source, event) Cbrake_Callback_Diagrams_DockingFcn})
    drawnow; pause(0.05)

where the loop runs through the current buttons in the document bar.

As the last step of our procedure, we finally remove (or disable) the menu entries which are unwanted. This is achieved by extracting the handle to the Figures menu by:

figures_menu = javaObjectEDT(figures_frame.getJMenuBar);

Running through the menu items, searching for the unwanted entries (as long as they have pre-defined menu-item names) at the end sets us into the position to take care of the menu items:

% run through top-level menu items
for n = 1 : figures_menu.getMenuCount
    % completely deactivate Debugging options
    if isequal(char(figures_menu.getMenu(n-1).getName), 'DesktopDebugMenu')
        DesktopDebugMenuPos = n - 1;
    % Remove some items from the Desktop menu
    if isequal(char(figures_menu.getMenu(n-1).getName), 'DesktopMenu')
        desktop_menu = javaObjectEDT(figures_menu.getMenu(n-1));
        DeletePos = [];
        for m = 1: desktop_menu.getMenuComponentCount
            if ismember({char(desktop_menu.getMenuComponent(m-1).getName)}, ...
                        {'ToggleFigure PaletteCheckBoxMenuItem', 'TogglePlot BrowserCheckBoxMenuItem', 'ToggleProperty EditorCheckBoxMenuItem'})
                DeletePos(end+1) = m - 1;
        for m = length(DeletePos) : -1 : 1
% finally remove the "Debug" menu
if ~isempty(DesktopDebugMenuPos)

Since this callback is invoked whenever a figure is docked/undocked, or the currently shown figure is changed (by pressing the document bar button), all unwanted menu items within the Figures menu could be removed.

As a result, the new Figures container menu looks like:

Deployed menu without unwanted items

Deployed menu without unwanted items


I must admit that above solution is still imperfect. For instance, sometimes there is a larger delay between the docking (or button press event) and the removing of the menu item. Nevertheless, this solution allows me to distribute my standalone with docked figures without having menu items directly leading to a fatal error.

Obviously, the solution has some positive side effects:

  • As could be seen from the screen shot, the Matlab desktop becomes available also within your compiled applications. This might be wanted. If not, it could be removed the same way as the other menu items. One drawback of making the desktop available should be mentioned: In my tests, the standalone Matlab desktop shows the whole list of recent files I have in the Matlab editor at compile time. This is somehow ugly but not that problematic.
  • Additional menu items could be added, giving more possibilities for modifications.

I have uploaded a first version of the docking and creation functions, together with a small test project, to the Matlab file Exchange. Readers are welcome to download the code and send me improvement suggestions. Or you could simply leave a comment below.

]]> 13
Waiting for asynchronous events Wed, 18 Jul 2012 18:50:57 +0000
Related posts:
  1. setPrompt – Setting the Matlab Desktop prompt The Matlab Desktop's Command-Window prompt can easily be modified using some undocumented features...
  2. Matlab callbacks for Java events Events raised in Java code can be caught and handled in Matlab callback functions - this article explains how...
  3. Uitable sorting Matlab's uitables can be sortable using simple undocumented features...
  4. Disabling menu entries in deployed docked figures Matlab's standard menu items can and should be removed from deployed docked figures. This article explains how. ...
It often happens that we need our program to wait for some condition to occur. This condition may depend on some external process that updates the condition asynchronously, meaning in a non-predictable (non-specific) time. A typical example is user mouse or keyboard actions in a GUI dialog window, or a specific signal that is received from hardware.

waitfor and other built-in functions

Matlab has a couple of built-in functions for blocking Matlab’s main processing thread until certain asynchronous GUI events occurs. waitfor is documented to block code execution until either the specified GUI handle object is deleted, or is updated (possibly to a specified value), or Ctrl-C is pressed in matlab’s Command Window. uiwait similarly blocks execution, until a specified figure handle is deleted/closed, or a separate processing thread (typically, a callback function) calls the corresponding uiresume (I discussed uiwait/uiresume, together with their uisuspend/uirestore siblings, last year). uiwait, unlike waitfor, has an optional timeout parameter; on the other hand, uiwait cannot wait for a non-deletion event on a regular uicontrol property, as waitfor can.

Other related built-in Matlab functions are waitforbuttonpress, pause (which awaits user mouse/keyboard clicks), and ginput, gtext, rbbox, dragrect (which awaits mouse clicks in a plot/image axes). Some toolboxes have other similar blocking functions, for example roipoly in the Image Processing toolbox.

Waiting for events on non-HG objects

But what if we need to wait for an event to happen on a non-Matlab (HG) object? Say on an ActiveX control property or even on a non-graphical Java object?

It turns out that waitfor can also be used in these cases. Although not documented, waitfor actually accepts handles not only of HG objects (e.g., figure handles) but also of other types of handles, such as regular Java reference handles. The usage is the same as for regular HG handles:

waitfor(objectHandleOrReference, 'propertyName', propertyValue);

For example, to wait for data to be available in a java.util.Hashtable object, which has a public boolean isEmpty() method and therefore returns a value of ‘on’ or ‘off’ for get(object,’Empty’):

waitfor(javaHashTableReference, 'Empty', 'off');

(Note that Matlab automatically converts a Java boolean into ‘on’/’off’ in such cases, so we need to use ‘on’/’off’ rather than true/false; this is not always the case – a counter-case is presented immediately below)

Setting a timeout on waitfor

To set a timeout on our blocked wait, a feature of uiwait that is missing in the built-in waitfor, we can use a dedicated one-time timer object. Timer callbacks use a separate thread from the main Matlab processing thread, and are therefore processed even when the Main thread is blocked. The implementation is quite easy, as shown below:

% Wait for data updates to complete (isDone = false if timeout, true if data ok)
function isDone = waitForDone(object,timeout)
    % Initialize: timeout flag = false
    % Create and start the separate timeout timer thread
    hTimer = timer('TimerFcn',@(h,e)object.setDone(true), 'StartDelay',timeout);
    % Wait for the object property to change or for timeout, whichever comes first
    % waitfor is over - either because of timeout or because the data changed
    % To determine which, check whether the timer callback was activated
    isDone = (hTimer.TasksExecuted == 0);
    % Delete the time object
    try stop(hTimer);   catch, end
    try delete(hTimer); catch, end
    % Return the flag indicating whether or not timeout was reached
end  % waitForDone


In some cases (for example, waiting on a specific field value within a struct, which waitfor does not support), we need to revert to using plain-ol’ polling, rather than the more efficient blocked wait. In such cases, I strongly advice to place a certain pause within the polling loop:

delay = 0.01;  % 10 milliseconds
while ~object.isDone  % set by the callback
    pause(delay);  % a slight pause to let all the data gather

Or, a variant with timeout:

delay = 0.01;
for idx = 1 : (timeout/delay)  % wait up to N secs before giving up
    if object.isDone  % set by the callback
    pause(delay);  % a slight pause to let all the data gather

The reason we need this deliberate pause is to enable the CPU to process other processing threads, namely that thread which is responsible for updating the field value in due time. Without this pause, it would take the CPU much longer (if at all) to get to that thread, and our overall application performance will actually degrade, since the event will take longer to get processed.

Adding deliberate pause delays as a mechanism to improve overall performance may sound counter-intuitive, but this is in fact the case here. Performance tuning can indeed be counter-intuitive sometimes, until you learn the underlying reasons when it becomes clear (I’ve shown several examples of this in the past, here, here, here and here).

We should take care not to set too high a pause delay, since that will unnecessarily delay processing of the incoming event; on the other hand, setting it too low will return the thread-starvation issue explained above. A pause value of 5-50 millisecs (0.005-0.05) should be about right for the vast majority of applications. Note that different platforms run your application at different speeds, so be careful not to over-tune this delay to a particular computer.

Interested in learning more Matlab performance or advanced programming tricks? Then consider joining my Matlab Performance Tuning, Advanced Matlab GUI and/or Matlab-Java programming seminars in Geneva on August 21-23, 2012 – email me (altmany at gmail dot com) for details.

]]> 31
Customizing menu items part 2 Wed, 02 May 2012 11:57:38 +0000
Related posts:
  1. Customizing menu items part 3 Matlab menu items can easily display custom icons, using just a tiny bit of Java magic powder. ...
  2. Matlab and the Event Dispatch Thread (EDT) The Java Swing Event Dispatch Thread (EDT) is very important for Matlab GUI timings. This article explains the potential pitfalls and their avoidance using undocumented Matlab functionality....
  3. Blurred Matlab figure window Matlab figure windows can be blurred using a semi-transparent overlaid window - this article explains how...
  4. Minimize/maximize figure window Matlab figure windows can easily be maximized, minimized and restored using a bit of undocumented magic powder...
Last week I explained how to customize Matlab’s menu items using some undocumented tricks that do not need Java. Today I will show how using just a tiny bit of Java magic powder we can add much more complex customizations to menu items. Menu customizations are explored in depth in section 4.6 of my book.

Accessing the underlying Java object

Matlab menus (uimenu) are basically simple wrappers for the much more powerful and flexible Java Swing JMenu and JMenuItem on which they are based. Many important functionalities that are available in Java menus are missing from the Matlab uimenus.

Getting the Java reference for the figure window’s main menu is very easy:

jFrame = get(handle(hFig),'JavaFrame');
    % R2008a and later
    jMenuBar = jFrame.fHG1Client.getMenuBar;
    % R2007b and earlier
    jMenuBar = jFrame.fFigureClient.getMenuBar;

Note that we have used the figure handle’s hidden JavaFrame property, accessed through the reference’s handle() wrapper, to prevent an annoying warning message.

There are many customizations that can only be done using the Java handle: setting icons, several dozen callback types, tooltips, background color, font, text alignment, and so on. etc. Interested readers may wish to get/set/inspect/methodsview/uiinspect the jSave reference handle and/or to read the documentation for JMenuItem. Some useful examples are provided below.

Dynamic menu behavior

As a first example of Java-based customization, let us add DHTML-like behavior to the menu, such that the menu items will automatically be displayed when the mouse hovers over the item, without waiting for a user mouse click. First, get the jMenuBar reference as described above. Now, set the MouseEnteredCallback to automatically simulate a user mouse click on each menu item using its doClick() method. Setting the callback should be done separately to each of the top-level menu components:

for menuIdx = 1 : jMenuBar.getComponentCount
    jMenu = jMenuBar.getComponent(menuIdx-1);
    hjMenu = handle(jMenu,'CallbackProperties');

Note that using this mechanism may be awkward if the top-level menu does not have a sub-menu but is rather a stand-alone menu item. For example, if the top-level menu-item “Help” is a stand-alone menu button (i.e., there are no help menu-items, just the single Help item), then moving the mouse over this item will trigger the help event, although the user did not actually click on the item. To prevent this behavior, we should modify the code snippet above to only work on those menu items that have sub-items.

Custom accelerator shortcut keys

As another example, Matlab automatically assigns a non-modifiable keyboard accelerator key modifier of , while JMenus allow any combination of Alt/Ctrl/Shift/Meta (depending on the platform). Let us modify the default File/Save accelerator key from ‘Ctrl-S’ to ‘Alt-Shift-S’ as an example. We need a reference for the “Save” menu item. Note that unlike regular Java components, menu items are retrieved using the getMenuComponent() method and not getComponent():

% File main menu is the first main menu item => index=0
jFileMenu = jMenuBar.getComponent(0);
% Save menu item is the 5th menu item (separators included)
jSave = jFileMenu.getMenuComponent(4); %Java indexes start with 0!
inspect(jSave) 	% just to be sure: label='Save' => good!
% Finally, set a new accelerator key for this menu item:
jAccelerator = javax.swing.KeyStroke.getKeyStroke('alt shift S');

That is all there is to it – the label is modified automatically to reflect the new keyboard accelerator key. More info on setting different combinations of accelerator keys and modifiers can be found in the official Java documentation for KeyStroke.

Modification of menu item accelerator and tooltip

Modification of menu item accelerator and tooltip

Note that the Save menu-item reference can only be retrieved after opening the File menu at least once earlier; otherwise, an exception will be thrown when trying to access the menu item. The File menu does NOT need to remain open – it only needs to have been opened sometime earlier, for its menu items to be rendered. This can be done either interactively (by selecting the File menu) or programmatically:

% Simulate mouse clicks to force the File main-menu to open & close
jFileMenu.doClick; % open the File menu
jFileMenu.doClick; % close the menu
% Now the Save menu is accessible:
jSave = jFileMenu.getMenuComponent(4);

Tooltip and highlight

For some unknown reason, MathWorks did not include a tooltip property in its Matlab menu handle, contrary to all the other Matlab GUI components. So we must use the Java handle, specifically the ToolTipText property:

jSave.setToolTipText('modified menu item with tooltip');

Java menu items also contain a property called Armed, which is a logical value (default=false). When turned on, the menu item is highlighted just as when it is selected (see the Save As… menu item in the screenshot above). On a Windows system, this means a blue background:


When the item is actually selected and then de-selected, Armed reverts to a false (off) value. Alternating the Armed property value can achieve an effect of flashing the menu item.


In addition to the standard Swing control callbacks discussed in an earlier article, menu items possess several additional callbacks that are specific to menu items, including:

  • ActionPerformedCallback – fired when the menu item is invoked
  • StateChangedCallback – fired when the menu item is selected or deselected
  • MenuDragMouseXXXCallback (XXX=Dragged/Entered/Exited/Released) – fired when the menu item is dragged, for the corresponding event
  • MenuKeyXXXCallback (XXX=Pressed/Released/Typed) – fired when a keyboard click event occurs (the menu item’s accelerator was typed)

Using these callbacks, together with the 30-odd standard Swing callbacks, we can control our menu’s behavior to a much higher degree than possible using the standard Matlab handle.

Next week, I will conclude this mini-series with an article explaining how to customize menu item icons.

]]> 14
Customizing menu items part 1 Wed, 25 Apr 2012 18:14:08 +0000
Related posts:
  1. Tab panels – uitab and relatives This article describes several undocumented Matlab functions that support tab-panels...
  2. Context-Sensitive Help Matlab has a hidden/unsupported built-in mechanism for easy implementation of context-sensitive help...
  3. Undocumented mouse pointer functions Matlab contains several well-documented functions and properties for the mouse pointer. However, some very-useful functions have remained undocumented and unsupported. This post details their usage....
  4. Matlab layout managers: uicontainer and relatives Matlab contains a few undocumented GUI layout managers, which greatly facilitate handling GUI components in dynamically-changing figures....
Over the past years, I have not posted articles dealing with menu items. I have shown how to directly access menu items’ hidden handles, but not much more than that. A year ago I promised a mini-series on menu customizations, and it’s time to keep my promise. In today’s article, the first in the mini-series, I will present several undocumented menu customization topics that rely on pure-Matlab (i.e, no Java today). The next article in this series will focus on Java-based customizations.

Invoking menu item callbacks

As noted above, a figure window’s menu items can be directly accessed. Once we access a menu item’s handle, we can extract its Callback property and directly invoke it (for example, using the semi-documented hgfeval function). Note that menu callbacks are kept in Callback, while toolbar callbacks are kept in ClickedCallback.

Menu callbacks generally use internal semi-documented functions (i.e., having a readable help section but no doc, online help, or official support), which are part of Matlab’s uitools folder. These functions are specific to each top-level menu tree: filemenufcn, editmenufcn, viewmenufcn, insertmenufcn, toolsmenufcn, desktopmenufcn, winmenu, and helpmenufcn implement the figure’s eight respective top-level menu trees’ callbacks. These functions accept an optional figure handle (otherwise, gcbf is assumed), followed by a string specifying the specific menu item whose action needs to be run. webmenufcn implements the Help menu’s Web Resources sub-menu callbacks in a similar manner.

Use of these functions makes it easy to invoke a menu action directly from our Matlab code: instead of accessing the relevant menu item and invoking its Callback, we simply find out the menu item string in advance and use it directly. For example,

filemenufcn FileClose;

uimenufcn is a related fully-undocumented (built-in) function, available since Matlab R11 (late 1990s). It accepts a figure handle (or the zero [0] handle to indicate the desktop) and action name. For example, the fully-documented commandwindow function uses the following code to bring the Command Window into focus:

uimenufcn(0, 'WindowCommandWindow');

Customizing menus via uitools

makemenu is another semi-documented uitool function that enables easy creation of hierarchical menu trees with separators and accelerators. It is a simple and effective wrapper for uimenu. makemenu is a useful function that has been made obsolete (grandfathered) without any known replacement.

makemenu accepts four parameters: a figure handle, a char matrix of labels (‘>’ indicating sub item, ‘>>’ indicating sub-sub items etc.; ‘&’ indicating keyboard shortcut; ‘^x’ indicating an accelerator key; ‘-‘ indicating a separator line), a char matrix of callbacks, and an optional char matrix of tags (empty by default). makemenu makes use of another semi-documented grandfathered function, menulabel, to parse the specified label components. makemenu returns an array of handles of the created uimenu items:

labels = str2mat('&File', ...    % File top menu
           '>&New^n', ...           % File=>New
           '>&Open', ...            % File=>Open
           '>>Open &document^d', ...    % File=>Open=>doc
           '>>Open &graph^g', ...       % File=>Open=>graph
           '>-------', ...          % File=>separator line
           '>&Save^s', ...          % File=>Save
           '&Edit', ...		% Edit top menu
           '&View', ...		% View top menu
           '>&Axis^a', ...          % View=>Axis
           '>&Selection region^r'); % View=>Selection
calls = str2mat('', ...		% no action: File top menu
           'disp(''New'')', ...
           '', ...			% no action: Open sub-menu
           'disp(''Open doc'')', ...
           'disp(''Open graph'')', ...
           '', ...			% no action: Separator
           'disp(''Save'')', ...
           '', ...			% no action: Edit top menu
           '', ...			% no action: View top menu
           'disp(''View axis'')', ...
           'disp(''View selection region'')');
handles = makemenu(hFig, labels, calls);

A simple figure menu

A simple figure menu

Customizing menus via HTML

Since menu items share the same HTML/CSS support feature as all Java Swing labels, we can specify font size/face/color, bold, italic, underline, superscript/subscript, and practically any HTML formatting.

Note that some features, such as the font or foreground/background colors, have specific properties that we can set using the Java handle, instead of using HTML. The benefit of using HTML is that it enables setting all the formatting in a single property. HTML does not require using Java – just pure Matlab (see the following example).

Multi-line menu items can easily be done with HTML: simply include a <br> element in the label – the menu item will split into two lines and automatically resize vertically when displayed:

txt1 = '<html><b><u><i>Save</i></u>';
txt2 = '<font color="red"><sup>this file</sup></font></b></html>';
txt3 = '<br />this file as...';
set(findall(hFig,'tag','figMenuFileSave'),   'Label',[txt1,txt2]);
set(findall(hFig,'tag','figMenuFileSaveAs'), 'Label',[txt1,txt3]);

A multi-line HTML-rendered menu item

A multi-line HTML-rendered menu item

set(hMenuItem, 'Label',['<html>&2: C:\My Documents\doc.txt<br />'
   '<font size="-1" face="Courier New" color="red">&nbsp;&nbsp; '
   'Date: 15-Jun-2011 13:23:45<br />&nbsp;&nbsp; Size: 123 KB</font></html>']);

HTML-rendered menu items

HTML-rendered menu items

Much more complex customizations can be achieved using Java. So stay tuned to part 2 of this mini-series…

Note: Menu customization is explored in depth in section 4.6 of my book.

]]> 6
Matlab-Java memory leaks, performance Fri, 20 Jan 2012 00:56:10 +0000
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  2. Undocumented XML functionality Matlab's built-in XML-processing functions have several undocumented features that can be used by Java-savvy users...
  3. Preallocation performance Preallocation is a standard Matlab speedup technique. Still, it has several undocumented aspects. ...
  4. Array resizing performance Several alternatives are explored for dynamic array growth performance in Matlab loops. ...
There are several ways of retrieving information from a Java object into Matlab. On the face of it, all these methods look similar. But it turns out that there are important differences between them in terms of memory leakage and performance.

The problem: “Matlab crashes” – now go figure…

A client of one of my Matlab programs recently complained that Matlab crashes after several hours of extensive use of the program. The problem looked like something that is memory related (messages such as Matlab’s out-of-memory error or Java’s heap-space error). Apparently this happens even on 64-bit systems having lots of memory, where memory should never be a problem.

Well, we know that this is only in theory, but in practice Matlab’s internal memory management has problems that occasionally lead to such crashes. This is one of the reasons, by the way, that recent Matlab releases have added the preference option of increasing the default Java heap space (the previous way to do this was a bit complex). Still, even with a high Java heap space setting and lots of RAM, Matlab crashed after using my program for several hours.

Not pleasant at all, even a bit of an embarrassment for me. I’m used to crashing Matlab, but only as a result of my playing around with the internals – I would hate it to happen to my clients.

Finding the leak

While we can do little with Matlab’s internal memory manager, I started searching for the exact location of the memory leak and then to find a way to overcome it. I’ll save readers the description about the grueling task of finding out exactly where the memory leak occurred in a program that has thousands of lines of code and where events get fired asynchronously on a constant basis. Matlab Profiler’s undocumented memory profiling option helped me quite a bit, as well as lots of intuition and trial-and-error. Detecting memory leaks is never easy, and I consider myself somewhat lucky this time to have both detected the leak source and a workaround.

It turned out that the leakage happens in a callback that gets invoked multiple times per second by a Java object (see related articles here and here). Each time the Matlab callback function is invoked, it reads the event information from the supplied Java event-data (the callback’s second input parameter). Apparently, about 1KB of memory gets leaked whenever this event-data is being read. This may appear a very small leak, but multiply this by some 50-100K callback invocations per hour and you get a leakage of 50-100MB/hour. Not a small leak at all; more of a flood you could say…

Using get()

The leakage culprit turned out to be the following code snippet:

% 160 uSecs per call, with memory leak
eventData  = get(hEventData,{'EventName','ParamNames','EventData'});
eventName  = eventData{1};
paramNames = eventData{2};
paramData  = eventData{3}.cell;

In this innocent-looking code, hEventData is a Java object that contains the EventName, ParamNames, EventData properties: EventName is a Java String, that is automatically converted by Matlab’s get() function into a Matlab string (char array); ParamNames is a Java array of Strings, that gets automatically converted into a Matlab cell-array of string; and EventData is a Java array of Objects that needs to be converted into a Matlab cell array using the built-in cell function, as described in one of my recent articles.

The code is indeed innocent, works really well and is actually extremely fast: each invocation of this code segment takes less than 0.2 millisecs. Unfortunately, because of the memory leak I needed to find a better alternative.

Using handle()

The first idea was to use the built-in handle() function, under the assumption that it would solve the memory leak, as reported here. In fact, MathWorks specifically advises to use handle() rather than to work with “naked” Java objects, when setting Java object callbacks. The official documentation of the set function says:

Do not use the set function on Java objects as it will cause a memory leak.

It stands to reason then that a similar memory leak happens with get and that a similar use of handle would solve this problem:

% 300 uSecs per call, with memory leak
s = get(handle(hEventData));
eventName  = s.EventName;
paramNames = s.ParamNames;
paramData  = cell(s.EventData);

Unfortunately, this variant, although working correctly, still leaks memory, and also performs almost twice as worse than the original version, taking some 0.3 milliseconds to execute per invocation. Looks like this is a dead end.

Using Java accessor methods

The next attempt was to use the Java object’s internal accessor methods for the requested properties. These are public methods of the form getXXX(), isXXX(), setXXX() that enable Matlab to treat XXX as a property by its get and set functions. In our case, we need to use the getter methods, as follows:

% 380 uSecs per call, no memory leak
eventName  = char(hEventData.getEventName);
paramNames = cell(hEventData.getParamNames);
paramData  = cell(hEventData.getEventData);

Here, the method getEventName() returns a Java String, that we convert into a Matlab string using the char function. In our previous two variants, the get function did this conversion for us automatically, but when we use the Java method directly we need to convert the results ourselves. Similarly, when we call getParamNames(), we need to use the cell function to convert the Java String[] array into a Matlab cell array.

This version at last doesn’t leak any memory. Unfortunately, it has an even worse performance: each invocation takes almost 0.4 milliseconds. The difference may seem insignificant. However, recall that this callback gets called dozens of times each second, so the total adds up quickly. It would be nice if there were a faster alternative that does not leak any memory.

Using struct()

Luckily, I found just such an alternative. At 0.24 millisecs per invocation, it is almost as fast as the leaky best-performance original get version. Best of all, it leaks no memory, at least none that I could detect.

The mechanism relies on the little-known fact that public fields of Java objects can be retrieved in Matlab using the built-in struct function. For example:

>> fields = struct(java.awt.Rectangle)
fields = 
             x: 0
             y: 0
         width: 0
        height: 0
      OUT_LEFT: 1
       OUT_TOP: 2
     OUT_RIGHT: 4
>> fields = struct(java.awt.Dimension)
fields = 
     width: 0
    height: 0

Note that this useful mechanism is not mentioned in the main documentation page for accessing Java object fields, although it is indeed mentioned in another doc-page – I guess this is a documentation oversight.

In any case, I converted my Java object to use public (rather than private) fields, so that I could use this struct mechanism (Matlab can only access public fields). Yes I know that using private fields is a better programming practice and all that (I’ve programmed OOP for some 15 years…), but sometimes we need to do ugly things in the interest of performance. The latest version now looks like this:

% 240 uSecs per call, no memory leak
s = struct(hEventData);
eventName  = char(s.eventName);
paramNames = cell(s.paramNames);
paramData  = cell(s.eventData);

This solved the memory leakage issue for my client. I felt fortunate that I was not only able to detect Matlab’s memory leak but also find a working workaround without sacrificing performance or functionality.

In this particular case, I was lucky to have full control over my Java object, to be able to convert its fields to become public. Unfortunately, we do not always have similar control over the object that we use, because they were coded by a third party.

By the way, Matlab itself uses this struct mechanism in its code-base. For example, Matlab timers are implemented using Java objects (com.mathworks.timer.TimerTask). The timer callback in Matlab code converts the Java timer event data into a Matlab struct using the struct function, in %matlabroot%/toolbox/matlab/iofun/@timer/timercb.m. The users of the timer callbacks then get passed a simple Matlab EventData struct without ever knowing that the original data came from a Java object.

As an interesting corollary, this same struct mechanism can be used to detect internal properties of Matlab class objects. For example, in the timers again, we can get the underlying timer’s Java object as follows (note the highlighted warning, which I find a bit ironic given the context):

>> timerObj = timerfind
   Timer Object: timer-1
   Timer Settings
      ExecutionMode: singleShot
             Period: 1
           BusyMode: drop
            Running: off
           TimerFcn: @myTimerFcn
           ErrorFcn: ''
           StartFcn: ''
            StopFcn: ''
>> timerFields = struct(timerObj)
Warning: Calling STRUCT on an object prevents the object from hiding its implementation details and should thus be avoided.Use DISP or DISPLAY to see the visible public details of an object. See 'help struct' for more information.(Type "warning off MATLAB:structOnObject" to suppress this warning.)timerFields = 
         ud: {}
    jobject: [1x1]
]]> 34
Common javacomponent problems Wed, 07 Dec 2011 18:00:38 +0000
Related posts:
  1. Matlab and the Event Dispatch Thread (EDT) The Java Swing Event Dispatch Thread (EDT) is very important for Matlab GUI timings. This article explains the potential pitfalls and their avoidance using undocumented Matlab functionality....
  2. Figure toolbar components Matlab's toolbars can be customized using a combination of undocumented Matlab and Java hacks. This article describes how to access existing toolbar icons and how to add non-button toolbar components....
  3. The javacomponent function Matlab's built-in javacomponent function can be used to display Java components in Matlab application - this article details its usages and limitations...
  4. Customizing uitree nodes – part 2 This article shows how Matlab GUI tree nodes can be customized with checkboxes and similar controls...
The javacomponent function, which I described here last year, is a very important built-in Matlab function that enables placing Java components in Matlab figure GUI. Using javacomponent is pretty straight-forward. However, there are a few quirks that users should be aware of. In today’s article I’ll try to highlight some of them and discuss workarounds.

Figure visibility

Java components can only be placed onscreen when their containing Matlab figure has been made visible. This means that calls to javacomponent cannot be placed at the GUIDE-created *_OpeningFcn() function, because that function is invoked before the figure window is made visible.

Of course, we can always force the figure to become visible by setting the figure’s Visible property to 'on' in this function, before calling our javacomponents. But IMHO, a better option would be to simply place the javacomponents in the corresponding GUIDE-created *_OutputFcn() function, which is invoked after the figure window is made visible.

Auto-hiding with parent container

Java components are not automatically hidden with their ancestor container panel. This is also the root cause of the failure of Java components to disappear when switching tabs in a uitab.

One simple workaround for this that I often use is to link the Visible properties of the javacomponent container and the parent container:

jButton = javax.swing.JButton('click me!');
[jhButton, hContainer] = javacomponent(jButton, [100,100,60,30], hParent);
setappdata(hParent, 'linked_props__', linkprop([hParent,hContainer],'Visible'));

This has indeed been fixed in R2010b. If you ask me, this should have been standard behavior of javacomponent since the very beginning…

Although there is no need for the workaround in R2010b onward, I usually keep the workaround because it doesn’t hurt and enables backward compatibility for users who may have an older Matlab release.

Possible parent container types

javacomponent accepts parent handles that are figures, toolbars, uipanels, or uicontainers (some of these are not documented as possible parents in some Matlab releases, but they are). Since R2008a, parents of type uisplittool and uitogglesplittool can also be used. Unfortunately, frames are not uicontainers and, therefore, cannot be used as javacomponent parents.

Note: Due to a bug in R2007a, javacomponents cannot be added to uicontainers, since javacomponent.m checks if isa(hParent,'uicontainer') (and similarly for 'uiflowcontainer', 'uigridcontainer'), instead of isa(hParent,'hg.uicontainer') (and similarly for the others). If we modify javacomponent.m accordingly (add “hg.” in lines 98-100), this bug will be fixed. Since R2007b, isa(…,'hg.uicontainer') is equivalent to isa(…,'uicontainer'), so this fix is unnecessary.

Input parameters

Unlike many other Matlab functions, javacomponent does not accept optional parameter-value (P-V) pairs. If we want to customize the appearance of the Java component, it is better to create it , customize it, and only then to present it onscreen using javacomponent. If we first present the component and then customize it, there might be all sorts of undesirable flicker effects while the component is changing its properties.

One of the parameters that unfortunately cannot be customized before calling javacomponent is the component’s position onscreen. javacomponent only accepts a position vector in pixel units. To modify the component to use normalized units, we need to modify the container’s properties:

jButton = javax.swing.JButton('click me!');
[jhButton, hContainer] = javacomponent(jButton, [100,100,60,30], gcf);
set(hContainer, 'Units','norm');

Similarly, we can set the container’s UserData and ApplicationData only after the call to javacomponent.

Background color

The default background color of javacomponents is a slightly different shade of gray than the default uicontrol background color. Please refer to my recent article for a detailed discussion of this issue.

Vertical alignment

Java components are slightly mis-aligned vertically with combo-box (Style=’popup’) uicontrols, even when positioned using the same Y position. This is actually due to an apparent bug in Matlab’s implementation of the combo-box uicontrol, and not in the Java component’s: Apparently, the Matlab control does not obey its specified height and uses some other default height.

If we place javacomponents side-by-side with a regular Matlab popup uicontrols and give them all the same Y position, we can see this mis-alignment. It is only a few pixels, but the effect is visible and disturbing. To fix it, we need to add a small offset to the javacomponent‘s container’s Position property to make both the initial Y position slightly lower, and the height value slightly higher than the values for the corresponding Matlab combo-box control.


Unlike Matlab uicontrol callbacks, Java callbacks are activated even when the affected value does not change. Therefore, setting a value in the component’s callback could well cause an infinite loop of invoked callbacks.

Matlab programmers often use the practice of modifying component value within the callback function, as a means of fixing user-entered values to be within a certain data range, or in order to format the displayed value. This is relatively harmless in Matlab (if done correctly) since updating a Matlab uicontrol‘s value to the current value does not retrigger the callback. But since this is not the case with Java callbacks, users should beware not to use the same practice there. In cases where this cannot be avoided, users should at least implement some sort of callback re-entrancy prevention logic.


Java components typically need to use the independent Java Event processing Thread (EDT). EDT is very important for Matlab GUI, as explained in this article. Failure to use EDT properly in Matlab can lead to unexpected GUI behavior and even Matlab hangs or crashes.

If the javacomponent function is called in a very specific syntax format where the first input arg is a string (the name of the Java class to be created), then the newly-created component is placed on the EDT. However, this is not the normal manner in which javacomponent is used: A much more typical use-case is where javacomponent is passed a reference handle to a previously-created Java component. In such cases, it is the user’s responsibility to place the component on the EDT. Until R2008a this should be done using the awtcreate function; since R2008b, we can use the much simpler javaObjectEDT function (javaObjectEDT actually existed since R2008a, but was buggy on that release so I suggest using it only starting in R2008b; it became documented starting in R2009a). In fact, modern javacomponent saves us the trouble, by automatically using javaObjectEDT to auto-delegate the component onto the EDT, even if we happen to have created it on the MT.

The paragraph above may lead us to believe that we only need to worry about EDT in R2008a and earlier. Unfortunately, this is not the case. Modern GUI requires using many sub-components, that are attached to their main component (e.g., CellRenderers and CellEditors). javacomponent only bothers to place the main component on the EDT – not any of the sub-components. We need to handle these separately. Liberally auto-delegating components to the EDT seems like a safe and painless habit to have.

]]> 28
Controlling callback re-entrancy Wed, 10 Aug 2011 18:00:57 +0000
Related posts:
  1. Matlab-Java interface using a static control The switchyard function design pattern can be very useful when setting Matlab callbacks to Java GUI controls. This article explains why and how....
  2. copyobj behavior change in HG2 the behavior of Matlab's copyobj function changed in R2014b (HG2), and callbacks are no longer copied. ...
  3. Matlab and the Event Dispatch Thread (EDT) The Java Swing Event Dispatch Thread (EDT) is very important for Matlab GUI timings. This article explains the potential pitfalls and their avoidance using undocumented Matlab functionality....
  4. Advanced JIDE Property Grids JIDE property grids can use complex cell renderer and editor components and can signal property change events asynchronously to Matlab callbacks...
I’d like to welcome guest blogger Malcolm Lidierth of King’s College London. Malcolm is well known in the Matlab-Java community for his jcontrol utility. Some months ago, I mentioned his work on another File Exchange contribution, MUtilities when I discussed transparent Matlab figure windows. Today, Malcolm discusses one of his lesser-known but extremely important isMultipleCall utility.

Every now and again, a very simple bit of code turns out to be more useful than the author initially imagined. Something I have repeatedly used is the isMultipleCall function which I posted to MATLAB Central’s File Exchange a year or so ago.

The isMultipleCall function uses fully-documented pure-MATLAB to extend the control that can be achieved over callbacks.

Here was the problem: I had a modular system built in MATLAB which allowed third-party developers to add their own plugins. I wanted a mechanism to force the dismissal (“bail-out”) of a callback even when the Interruptible property of the parent object was set to ‘on’. Such callback re-entrancy issues are common for rapidly-firing events, and debugging and fixing them is usually not easy.

The callback’s dismissal code would need to be fast because it might be called many dozens of times, e.g. in a WindowButtonMotion callback. An obvious approach was to check the function call stack using MATLAB’s dbstack function. Although, at first, this seemed likely to be too slow, profiling showed it was not – taking < 40µsec per call – and within a WindowButtonMotion callback in a real GUI, I could not perceive any slowing of the code.

Here is the function:

function flag=isMultipleCall()
  flag = false; 
  % Get the stack
  s = dbstack();
  if numel(s)< =2
    % Stack too short for a multiple call
  % How many calls to the calling function are in the stack?
  names = {s(:).name};
  TF = strcmp(s(2).name,names);
  count = sum(TF);
  if count>1
    % More than 1
    flag = true; 

With isMultipleCall invoked from another function (see note below), dbstack will return a structure with a minimum of 2 elements – the first relating to isMultipleCall itself and the second to the calling function. So with numel(s) <= 2, there can be no multiple calls and we can return false immediately thus saving time in doing any further testing. For numel(s) > 2 we simply check to see whether the calling functions referenced in s(2) appears anywhere else on the stack. If it does, then we return true; otherwise false.

Then, in our callback code we simply use:

if isMultipleCall();  return;  end

If this line is placed first in the callback function code, it essentially mimics the behavior that you might expect after setting the Interruptible property of the event firing object to ‘off’. Adding a drawnow() at the end of the callback will ensure that any waiting callbacks in the queue are dismissed:

function MyCallback(hObj, EventData)
  % Quick bail-out if callback code is called before another has ended
  if isMultipleCall();  return;  end
  ...  % do some actual callback work here

There are several ways in which isMultipleCall can extend the standard MALAB functionality. First, by moving isMultipleCall reference from the first line of the callback we can create both an interruptible and an uninteruptible code block, e.g.

function MyCallback(hObj, EventData)
  %Code Block 1
  if isMultipleCall();  return;  end
  %Code Block 2

Second, as isMultipleCall controls the callbacks – not the objects that trigger them – we can individually control the callbacks of objects which fire multiple events. That is particularly useful with Java components, which gives a third extension – isMultipleCall can be used in any function: not just the callbacks of standard MATLAB components, but also of Java or COM components.

Finally, as the callback, not the object is being controlled, we can control a callback that may be shared between multiple objects e.g. a menu component and a toolbar button.

Not bad for 13 lines of code.

Note: isMultipleCall must be called from a function, not from a string in the callback property.

Do you have any other favorite mechanism for controlling callback re-entrancy? If so, please post a comment.

]]> 16
uisplittool & uitogglesplittool callbacks Wed, 15 Dec 2010 18:00:35 +0000
Related posts:
  1. uiundo – Matlab’s undocumented undo/redo manager The built-in uiundo function provides easy yet undocumented access to Matlab's powerful undo/redo functionality. This article explains its usage....
  2. Figure toolbar components Matlab's toolbars can be customized using a combination of undocumented Matlab and Java hacks. This article describes how to access existing toolbar icons and how to add non-button toolbar components....
  3. Inactive Control Tooltips & Event Chaining Inactive Matlab uicontrols cannot normally display their tooltips. This article shows how to do this with a combination of undocumented Matlab and Java hacks....
  4. uisplittool & uitogglesplittool Matlab's undocumented uisplittool and uitogglesplittool are powerful controls that can easily be added to Matlab toolbars - this article explains how...
Last week, I presented the undocumented uisplittool and uitogglesplittool functions and showed how they can be added to a Matlab figure toolbar. Today I wish to conclude this topic by explaining how these controls can be customized with user-defined callbacks and pop-up menus.

Callback functionality

Both uisplittool and uitogglesplittool have a Callback property, in addition to the standard ClickedCallback property that is available in uipushtools and uitoggletools.

The standard ClickedCallback is invoked when the main button is clicked, while Callback is invoked when the narrow arrow button is clicked. uitogglesplittool, like uitoggletool, also has settable OnCallback and OffCallback callback properties.

The accepted convention is that ClickedCallback should invoke the default control action (in our case, an Undo/Redo of the topmost uiundo action stack), while Callback should display a drop-down of selectable actions.

While this can be done programmatically using the Callback property, this functionality is already pre-built into uisplittool and uitogglesplittool for our benefit. To access it, we need to get the control’s underlying Java component.

Accessing the underlying Java component is normally done using the findjobj utility, but in this case we have a shortcut: the control handle’s hidden JavaContainer property that holds the underlying com.mathworks.hg.peer.SplitButtonPeer (or .ToggleSplitButtonPeer) Java reference handle. This Java object’s MenuComponent property returns a reference to the control’s drop-down sub-component (which is a com.mathworks.mwswing.MJPopupMenu object):

>> jUndo = get(hUndo,'JavaContainer')
jUndo =
>> jMenu = get(jUndo,'MenuComponent')  % or: =jUndo.getMenuComponent
jMenu =
com.mathworks.mwswing.MJPopupMenu[Dropdown Picker ButtonMenu,...]

Let’s add a few simple textual options:

jOption1 = jMenu.add('Option #1');
jOption1 = jMenu.add('Option #2');
set(jOption1, 'ActionPerformedCallback', 'disp(''option #1'')');
set(jOption2, 'ActionPerformedCallback', {@myCallbackFcn, extraData});

setting uisplittool & uitogglesplittool popup-menus

setting uisplittool & uitogglesplittool popup-menus

Popup-menus are described in more detail elsewhere (and in future articles). In the past I have already explained how icons and HTML markup can be added to menu items. Sub-menus can also be added.

A complete example

Let’s now use this information, together with last year’s set of articles about Matlab’s undocumented uiundo functionality, to generate a complete and more realistic example, of undo/redo toolbar buttons.

Undo and redo are actions that are particularly suited for uisplittool, since its main button enables us to easily undo/redo the latest action (like a simple toolbar button, by clicking the main uisplittool button) as well as select items from the actions drop-down (like a combo-box, by clicking the attached arrow button) – all this using a single component.

% Display our GUI
hEditbox = uicontrol('style','edit', 'position',[20,60,40,40]); 
set(hEditbox, 'Enable','off', 'string','0');
hSlider = uicontrol('style','slider','userdata',hEditbox);
% Display the figure toolbar that was hidden by the uicontrol function
% Add the Undo/Redo buttons
hToolbar = findall(gcf,'tag','FigureToolBar');
hUndo = uisplittool('parent',hToolbar);
hRedo = uitogglesplittool('parent',hToolbar);
% Load the Redo icon
icon = fullfile(matlabroot,'/toolbox/matlab/icons/greenarrowicon.gif');
[cdata,map] = imread(icon);
% Convert white pixels into a transparent background
map(find(map(:,1)+map(:,2)+map(:,3)==3)) = NaN;
% Convert into 3D RGB-space
cdataRedo = ind2rgb(cdata,map);
cdataUndo = cdataRedo(:,[16:-1:1],:);
% Add the icon (and its mirror image = undo) to latest toolbar
set(hUndo, 'cdata',cdataUndo, 'tooltip','undo','Separator','on', ...
set(hRedo, 'cdata',cdataRedo, 'tooltip','redo', ...
% Re-arrange the Undo/Redo buttons  
jToolbar = get(get(hToolbar,'JavaContainer'),'ComponentPeer');
jButtons = jToolbar.getComponents;
for buttonIdx = length(jButtons)-3 : -1 : 7  % end-to-front
   jToolbar.setComponentZOrder(jButtons(buttonIdx), buttonIdx+1);
jToolbar.setComponentZOrder(jButtons(end-2), 5);    % Separator
jToolbar.setComponentZOrder(jButtons(end-1), 6);    % Undo
jToolbar.setComponentZOrder(jButtons(end), 7);      % Redo
% Retrieve redo/undo object
undoObj = getappdata(gcf,'uitools_FigureToolManager');
if isempty(undoObj)
   undoObj = uitools.FigureToolManager(gcf);
% Populate Undo actions drop-down list
jUndo = get(hUndo,'JavaContainer');
jMenu = get(jUndo,'MenuComponent');
undoActions = get(undoObj.CommandManager.UndoStack,'Name');
for actionIdx = length(undoActions) : -1 : 1    % end-to-front
    jActionItem = jMenu.add(undoActions(actionIdx));
    set(jActionItem, 'ActionPerformedCallback', @myUndoCallbackFcn);
% Drop-down callback function
function myUndoCallbackFcn(jActionItem,hEvent)
    % user processing needs to be placed here
end  % myUndoCallbackFcn

undo/redo buttons implemented using uisplittool

undo/redo buttons implemented using uisplittool

In a real-world application, the code-segment above that populated the drop-down list would be placed within the slider’s test_uiundo() callback function, and we would set a similar drop-down for the hRedu button. In addition, we would dynamically modify the button tooltips. As a final customization, we could modify the figure’s main menu. Menu customization will be discussed in a future separate set of articles.

Have you used uisplittool or uitogglesplittool in your GUI? If so, please tell us what use you have made of them, in a comment below.

]]> 3
Matlab callbacks for Java events Tue, 30 Nov 2010 22:09:35 +0000
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A few days ago, a user posted a question on StackOverflow asking whether it is possible to trap a Java-based event in a Matlab callback.

It so happens that only a few weeks ago I completed a consulting project which required exactly this. The project was to integrate a Matlab computational engine with a Java interface to Interactive Brokers (IB) – a well-known online brokerage firm. The idea was to use the Java interface to fetch real-time data about securities (stocks, bonds, options etc.), use a Matlab processing utility, then use the Java interface again to send Buy or Sell orders back to IB.

If you are interested in the final result (i.e., a complete and field-tested Matlab-IB interface), look here.

The challenge

A big challenge in this project (aside from handling quite a few IB interface quirks), was to propagate events from the Java interface to the Matlab application. This had to be done asynchronously, since events such as order execution can occur at any time following the order placement. Moreover, even simple requests such as retrieving security information (bid/ask prices for example) is handled by IB via Java events, not as simple function return values.

Handling Java-based events in Matlab is not a trivial task. Not only merely undocumented, but it is also not intuitive. I have spent quite a few hours trying to crack this issue. In fact, I believe it was one of my more challenging tasks in figuring out the undocumented aspects of the Matlab-Java interface. Few other challenges were as difficult, yet with a happy ending (Drag & Drop is a similar issue – I will describe it in another article sometime).

The solution

Fast-forward all the fruitless attempted variations, here is the bottom line. Refer to the following simple Java class example:

public class EventTest
    private java.util.Vector data = new java.util.Vector();
    public synchronized void addMyTestListener(MyTestListener lis) {
    public synchronized void removeMyTestListener(MyTestListener lis) {
    public interface MyTestListener extends java.util.EventListener {
        void testEvent(MyTestEvent event);
    public class MyTestEvent extends java.util.EventObject {
        private static final long serialVersionUID = 1L;
        public float oldValue,newValue;        
        MyTestEvent(Object obj, float oldValue, float newValue) {
            this.oldValue = oldValue;
            this.newValue = newValue;
    public void notifyMyTest() {
        java.util.Vector dataCopy;
        synchronized(this) {
            dataCopy = (java.util.Vector)data.clone();
        for (int i=0; i < dataCopy.size(); i++) {
            MyTestEvent event = new MyTestEvent(this, 0, 1);

When compiling, three class files are created: EventTest.class, EventTest$MyTestEvent.class and EventTest$MyTestListener.class. Place them on Matlab’s Java static classpath, using edit(‘classpath.txt’) (using the dynamic classpath causes many problems that using the static classpath solves). They can now be accessed as follows:

>> which EventTest
EventTest is a Java method  % EventTest constructor
>> evt = EventTest
evt =
>> evt.get
	Class = [ (1 by 1) java.lang.Class array]
	TestEventCallback = 
	TestEventCallbackData = []
	BeingDeleted = off
	ButtonDownFcn = 
	Children = []
	Clipping = on
	CreateFcn = 
	DeleteFcn = 
	BusyAction = queue
	HandleVisibility = on
	HitTest = on
	Interruptible = on
	Parent = []
	Selected = off
	SelectionHighlight = on
	Tag = 
	Type = EventTest
	UIContextMenu = []
	UserData = []
	Visible = on
>> set(evt)
	TestEventCallback: string -or- function handle -or- cell array
>> set(evt,'TestEventCallback',@(h,e)disp(h))
>> get(evt)
	Class = [ (1 by 1) java.lang.Class array]
	TestEventCallback = [ (1 by 1) function_handle array]    % < = ok
	TestEventCallbackData = []
>> evt.notifyMyTest   % invoke Java event
              0.0009765625   % < = Matlab callback

Note how Matlab automatically converted the Java event testEvent, declared in interface MyTestListener, into a Matlab callback TestEventCallback (the first character is always capitalized). All Java events are automatically converted in this fashion, by appending a ‘Callback’ suffix. Here is a code snippet from R2008a’s \toolbox\matlab\uitools\@opaque\addlistener.m that shows this (slightly edited):

hSrc = handle(jobj,'callbackproperties');
allfields = sortrows(fields(set(hSrc)));
for i = 1:length(allfields)
   fn = allfields{i};
   if ~isempty(findstr('Callback',fn))
callback = @(o,e) cbBridge(o,e,response);
hdl = handle.listener(handle(jobj), eventName, callback);
function cbBridge(o,e,response)
   hgfeval(response, java(o), e.JavaEvent)

Note that hgfeval, which is used within the cbBridge callback function, is a semi-documented pure-Matlab built-in function, which I described a few weeks ago.

If several events have the same case-insensitive name, then the additional callbacks will have an appended underscore character (e.g., ‘TestEventCallback_’):

// In the Java class:
public interface MyTestListener extends java.util.EventListener
    void testEvent(MyTestEvent e);
    void testevent(TestEvent2 e);
% …and back in Matlab:
>> evt=EventTest; evt.get
	Class = [ (1 by 1) java.lang.Class array]
	TestEventCallback = 
	TestEventCallbackData = []
	TestEventCallback_ = 
	TestEventCallback_Data = [] 

To complete this discussion, it should be noted that Matlab also automatically defines corresponding Events in the Java object’s classhandle. Unfortunately, classhandle events are not differentiated in a similar manner – in this case only a single event is created, named Testevent. classhandle events, and their relationship to the preceding discussion, will be described in Donn Scull’s upcoming series on UDD.

An alternative to using callbacks on Java events, as shown above, is to use undocumented handle.listeners:

hListener = handle.listener(handle(evt),'TestEvent',callback);

There are several other odds and ends, but this article should be sufficient for implementing a fully-functional Java event handling mechanism in Matlab. Good luck!

p.s. – if you’re still stuck, consider hiring me for a short consulting project. I’ll be happy to help.

]]> 85
The hgfeval function Wed, 27 Oct 2010 18:00:59 +0000
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Last week’s article about using a static GUI control, made use of the hgfeval function for its callback switch-yard dispatching. Today’s article will expose this important but often overlooked function.

hgfeval is a built-in semi-documented function. This means that the function is explained in a comment within the hgfeval.m file (which can be seen via the edit(‘hgfeval’) command), that is nonetheless not part of the official help or doc sections. It is an unsupported function originally intended only for internal Matlab use, which of course doesn’t mean we can’t use it. hgfeval is currently used extensively within the Matlab code-base.

Callback chaining

hgfeval‘s main purpose is to programmatically (synchronously) invoke a callback, rather than it being dynamically (a-synchronously) invoked when some event occurs. This need often occurs when chaining events. In a nutshell, we need to programmatically invoke Callback A (an executable code [=string], function handle, or cell array of function handle with extra parameters) within our code, which is typically located in another callback (B). For example:

function CallbackB(hObject, hEventData, varargin)
    callbackA = get(gcf, 'ButtonDownFcn');

hgfeval is important when we want to force the objects passed as the callback’s first two input parameters (hObject and hEventData): hgfeval pre-pends all its extra parameters (arguments #2+) to the callback’ed function. So, calling hgfeval({@myFunc,arg1,arg2}, argA, argB) will actually invoke myFunc(argA,argB,arg1,arg2). Using this syntax we guaranty that whatever the invoked callback may be, its initial arguments will always be set to the necessary argA,argB.

This is often used in what MathWorks calls a callback-bridge. For example, in the following example we set the cbBridge function as a callback bridge for myCallback, that forces java(o) as the reference object (the invoked callback’s first argument) and e.JavaEvent as the second arg; all user args will then follow as optional additional args:

hdl = handle.listener(handle(jobj), eventName, ...
function cbBridge(o,e,myCallback)
    hgfeval(myCallback, java(o), e.JavaEvent)

In this example, the myCallback variable can be any valid Matlab callback specification: string, function handle, or cell array.

If we had used myCallback directly, then myCallback would have been invoked with o and e rather than java(o) and e.JavaEvent. The point here is that using the callback bridge and hgfeval enabled us to tailor the parameters passed to the callback function.

Forward & backward compatibility

hgfeval has been built-in within all standard Matlab 7 releases, so while it may be discontinued in some future Matlab release, it did have a very long life span.

Even if for some reason this function is removed in some future Matlab release, it can easily be reproduced – after all, it only contains 35 very simple lines of pure Matlab code. In fact, for some project that had to rely on Matlab 6 (in which hgfeval was not yet available), it was an almost trivial task to retrofit an hgfeval look-alike (I had to remove function handles and similar minor tweaks):

%% hgfeval replacement for Matlab 6 compatibility
function hgfeval(fcn,varargin)
    if isempty(fcn),  return;  end
    if iscell(fcn)
    elseif ischar(fcn)
        evalin('base', fcn);
%end  %no function end in Matlab 6...

Have you ever used hgfeval in your code? If so, please share your experience in a comment below.

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