feature('HotLinks',false);  % temporarily disable bold headers and hyperlinks (matlab.internal.display.isHot=false)
disp(myTable)
myTable        % this calls disp() implicitly
feature('HotLinks',true);   % restore the standard behavior (markup displayed, matlab.internal.display.isHot=true)

Searching for “isHot” or “HotLinks” under the Matlab installation folder, we find that this feature is used in hundreds of places (the exact number depends on your installed toolboxes). The general use appears to be to disable/enable output of hyperlinks to the Matlab console, such as when you display a Matlab class, when its class name is hyperlinked and so is the “Show all properties” message at the bottom. But in certain cases, such as for the table output above, the feature is also used to determine other types of markup (bold headers in this case).

>> feature('HotLinks',0)  % temporarily disable bold headers and hyperlinks
>> groot
ans =
  Graphics Root with properties:
 
          CurrentFigure: [0×0 GraphicsPlaceholder]
    ScreenPixelsPerInch: 96
             ScreenSize: [1 1 1366 768]
       MonitorPositions: [2×4 double]
                  Units: 'pixels'
 
  Use GET to show all properties
>> feature('HotLinks',1)  % restore the standard behavior (markup displayed)
>> groot
ans =
  Graphics Root with properties:
 
          CurrentFigure: [0×0 GraphicsPlaceholder]
    ScreenPixelsPerInch: 96
             ScreenSize: [1 1 1366 768]
       MonitorPositions: [2×4 double]
                  Units: 'pixels'
 
  Show all properties

There’s nothing really earth shuttering in all this, but the HotLinks feature could be useful when outputting console output into a diary file. Of course, if diary would have automatically stripped away markup tags we would not need to resort to such hackery. Then again, this is not the only problem with diary, which is long-overdue an overhaul.

The post The HotLinks feature appeared first on Undocumented Matlab.

dbstop('in', filename, 'at', location, 'if', 'tracer()');

We can use this same technique for other purposes. For example, if we want to do some action (not necessarily log – perhaps do something else) when a certain code point is reached. The benefit here is that we don’t need to modify the code at all – we’re adding ad-hoc code pieces using the conditional breakpoint mechanism without affecting the source code. This is particularly useful when we do not have access to the source code (such as when it’s compiled or write-protected). All you need to do is to ensure that the instrumentation function always returns false so that the breakpoint does not become live and for code execution to continue normally.
The tracer4m utility is quite sophisticated in the sense that it uses mlint and smart regexp to parse the code and know which functions/methods occur on which line numbers and have which type (more details). In this sense, Per used undocumented functionality. I’m certain that Jiro was not aware of the dependency on undocumented features when he posted about the utility, so please don’t take this to mean that Jiro or MathWorks officially support this or any other undocumented functionality. Undocumented aspects are often needed to achieve top functionality, and I’m happy that the POTW blog highlights utilities based on their importance and merit, even if they do happen to use some undocumented aspect.
tracer4m‘s code also contains references to the undocumented profiler option -history, but this is not in fact used by the code itself, only in comments. I use this feature in my profile_history utility, which displays the function call/timing history in an interactive GUI window. This utility complements tracer4m by providing a lot more information, but this can result in a huge amount of information for large and/or long-running programs. In addition, tracer4m has the benefit of only logging those functions/methods that the user finds useful, rather than all the function call, which enables easier debugging when the relevant code area is known. In short, I wish I had known about tracer4m when I created profile_history. Now that I know about it, maybe I’ll incorporate some of its ideas into profile_history in order to make it more useful. Perhaps another moral of this is that we should actively monitor the POTW blog, because true gems are quite often highlighted there.

Function call timeline profiling

The post Runtime code instrumentation appeared first on Undocumented Matlab.

• Survey regarding usage of the javacomponent function: http://www.mathworks.com/javacomponent
• Survey regarding usage of the JavaFrame property: http://www.mathworks.com/javaframe

In MathWorks’ words:

In order to extend your ability to build MATLAB apps, we understand you sometimes need to make use of undocumented Java UI technologies, such as the JavaFrame property. In response to your needs, we are working to develop documented alternatives that address gaps in our app building offerings.
To help inform our work and plans, we would like to understand how you are using the JavaFrame property. Based on your understanding of how it is being used within your app, please take a moment to fill out the following survey. The survey will take approximately 1-2 minutes to finish.

I urge anyone who uses one or both of these features to let MathWorks know how you’re using them, so that they could incorporate that functionality into the core (documented) Matlab. The surveys are really short and to the point. If you wish to send additional information, please email George.Caia at mathworks.com.
The more feedback responses that MathWorks will get, the better it will be able to prioritize its R&D efforts for the benefit of all users, and the more likely are certain features to get a documented solution at some future release. If you don’t take the time now to tell MathWorks how you use these features in your code, don’t complain if and when they break in the future…

My personal uses of these features

• Functionality:
  • Figure: maximize/minimize/restore, enable/disable, always-on-top, toolbar controls, menu customizations (icons, tooltips, font, shortcuts, colors)
  • Table: sorting, filtering, grouping, column auto-sizing, cell-specific behavior (tooltip, context menu, context-sensitive editor, merging cells)
  • Tree control
  • Listbox: cell-specific behavior (tooltip, context menu)
  • Tri-state checkbox
  • uicontrols in general: various event callbacks (e.g. mouse hover/unhover, focus gained/lost)
  • Ability to add Java controls e.g. color/font/date/file selector panel or dropdown, spinner, slider, search box, password field
  • Ability to add 3rd-party components e.g. JFreeCharts, JIDE controls/panels

• Appearance:
  • Figure: undecorated (frameless), other figure frame aspects
  • Table: column/cell-specific rendering (alignment, icons, font, fg/bg color, string formatting)
  • Listbox: auto-hide vertical scrollbar as needed, cell-specific renderer (icon, font, alignment, fg/bg color)
  • Button/checkbox/radio: icons, text alignment, border customization, Look & Feel
  • Right-aligned checkbox (button to the right of label)
  • Panel: border customization (rounded/matte/…)

You can find descriptions/explanations of many of these in posts I made on this website over the years.

The post MathWorks-solicited Java survey appeared first on Undocumented Matlab.

1. Using mexCallMATLABWithTrap (documented)
2. Using utGetWarningStatus (undocumented)

Using mexCallMATLABWithTrap (documented)

The first idea is to use documented mexCallMATLABWithTrap function to execute warning(‘query’,…) command using Matlab’s interpreter and then parse the returned result:

bool mxIsWarningEnabled(const char* warningId)
{
    bool enabled = true;
    if (NULL != warningId)
    {
        mxArray *mxCommandResponse = NULL, *mxException = NULL;
        mxArray *args[2];
        /* warning('query', warningId); */
        args[0] = mxCreateString("query");
        args[1] = mxCreateString(warningId);
        mxException = mexCallMATLABWithTrap(1,&mxCommandResponse,2,args,"warning");
        if (NULL == mxException && NULL != mxCommandResponse)
        {
            if (mxIsStruct(mxCommandResponse))
            {
                const mxArray* state_field = mxGetField(mxCommandResponse, 0, "state");
                if (mxIsChar(state_field))
                {
                    char state_value[8] = {0};
                    enabled = (0 == mxGetString(state_field, state_value, 8)) &&
                              (0 == strcmp(state_value,"on"));
                }
            }
            mxDestroyArray(mxCommandResponse);
        }
        else
        {
            /* 'warning' returned with error */
            mxDestroyArray(mxException);
        }
        mxDestroyArray(args[0]);
        mxDestroyArray(args[1]);
    }
    return enabled;
}

This approach is slow, but works fine in most standard situations. See the bottom of this post for a usage example.
However, this approach has an important drawback – we should be careful with recursive calls to the Matlab interpreter (Matlab -> MEX -> Matlab) and with handling Matlab errors in MEX. It is safe only if we use identical standard libraries and compiler to build both MEX and Matlab.
In other cases, for example when MEX is targeted to work with different versions of Matlab, or was built with a different standard library and compiler, etc. – cross boundary handling of errors (which are just C++ exceptions) might lead to unpredictable results, most likely segfaults.

Using utGetWarningStatus (undocumented)

To avoid all the overhead of calling Matlab interpreter and unsafe error handling, we can use some undocumented internal Matlab functions:

/* Link with libut library to pick-up undocumented functions: */
extern "C" void* utGetWarningManagerContext(void);
extern "C" bool  utIsValidMessageIdentifier(const char *warningId);
extern "C" bool  utGetWarningStatus(void* context, const char *warningId);
/*
   Returns true if warning with warningId enabled
   Matlab versions supported/tested: R2008b - R2016b
*/
bool mxIsWarningEnabled(const char *warningId)
{
    bool enabled = true;
    if (NULL != warningId && utIsValidMessageIdentifier(warningId))
    {
        void* context = utGetWarningManagerContext();
        enabled = (NULL != context) && utGetWarningStatus(context, warningId);
    }
    return enabled;
}

Now the code is clean, fast and safe – we bypass the interpreter and work directly with Matlab kernel. All the undocumented functions involved are present in Matlab for at least 10 years and do simple logical checks under the hood.
The standard function mexWarnMsgIdAndTxt uses similar code to check if it should display the warning or just suppress it, and that code remains unchanged since R2008b. This is a good indication of code stability and makes us believe that it will not be changed in future versions of Matlab.
For both workarounds, usage is simple:

if (mxIsWarningEnabled("Matlab:nearlySingularMatrix"))
{
   /* compute rcond */
}
else
{
   /* do something else */
}

The post Checking status of warning messages in MEX appeared first on Undocumented Matlab.

parpool(numCores)

The first tip is to not [always] use the default number of workers created by parpool (or matlabpool in R2013a or earlier). By default, Matlab creates as many workers as logical CPU cores. On Intel CPUs, the OS reports two logical cores per each physical core due to hyper-threading, for a total of 4 workers on a dual-core machine. However, in many situations, hyperthreading does not improve the performance of a program and may even degrade it (I deliberately wish to avoid the heated debate over this: you can find endless discussions about it online and decide for yourself). Coupled with the non-negligible overhead of starting, coordinating and communicating with twice as many Matlab instances (workers are headless [=GUI-less] Matlab processes after all), we reach a conclusion that it may actually be better in many cases to use only as many workers as physical (not logical) cores.
I know the documentation and configuration panel seem to imply that parpool uses the number of physical cores by default, but in my tests I have seen otherwise (namely, logical cores). Maybe this is system-dependent, and maybe there is a switch somewhere that controls this, I don’t know. I just know that in many cases I found it beneficial to reduce the number of workers to the actual number of physical cores:

p = parpool;     % use as many workers as logical CPUs (4 on my poor laptop...)
p = parpool(2);  % use only 2 parallel workers

Of course, this can vary greatly across programs and platforms, so you should test carefully on your specific setup. I suspect that for the majority of Matlab programs it would turn out that using the number of physical cores is better.
It would of course be better to dynamically retrieve the number of physical cores, rather than hard-coding a constant value (number of workers) into our program. We can get this value in Matlab using the undocumented feature(‘numcores’) function:

numCores = feature('numcores');
p = parpool(numCores);

Running feature(‘numcores’) without assigning its output displays some general debugging information:

>> feature('numcores')
MATLAB detected: 2 physical cores.
MATLAB detected: 4 logical cores.
MATLAB was assigned: 4 logical cores by the OS.
MATLAB is using: 2 logical cores.
MATLAB is not using all logical cores because hyper-threading is enabled.
ans =
     2

Naturally, this specific tip is equally valid for both parfor loops and spmd blocks, since both of them use the pool of workers started by parpool.

Running separate code in parfor loops

The conventional wisdom is that parfor loops (and loops in general) can only run a single code segment over all its iterations. Of course, we can always use conditional constructs (such as if or switch) based on the data. But what if we wanted some workers to run a different code path than the other workers? In spmd blocks we could use a conditional based on the labindex value, but unfortunately labindex is always set to the same value 1 within parfor loops. So how can we let worker A run a different code path than worker B?
An obvious answer is to create a parfor loop having as many elements as there are separate code paths, and use a switch-case mechanism to run the separate paths, as follows:

% Naive implementation example - do NOT use!
parfor idx = 1 : 3
   switch idx
      case 1,  result{1} = fun1(data1, data2);
      case 2,  result{2} = fun2(data3, data4, data5);
      case 3,  result{3} = fun3(data6);
   end
end

There are several problems with this naive implementation. First, it unnecessarily broadcasts all the input data to all workers (more about this issue below). Secondly, it appears clunky and too verbose. A very nice extension of this mechanism, posted by StackOverflow heavyweight Jonas, uses indexed arrays of function handles and input args, thereby solving both problems:

funcList = {@fun1, @fun2, @fun3};
dataList = {data1, data2, data3};  %# or pass file names
parfor idx = 1 : length(funcList)
    result{idx} = funcList{idx}(dataList{idx});
end

Reduce the amount of broadcast data

It is often easy, too-easy, to convert for loops into parfor loops. In many cases, all we need to do is to add the “par” prefix to the for keyword and we’re done (assuming we have no incompatibly-used variables that should be converted into sliced variables etc.). This transformation was intentionally made simple by MathWorks (which is great!). On the other hand, it also hides a lot under the hood. One of the things that is often overlooked in such simple loop transformations is that a large part of the data used within the loop needs to be copied (broadcast) to each of the workers separately. This means that each of the data items needs to be serialized (i.e., copied in memory), packaged, communicated to and accepted by each of the workers. This can mean a lot of memory, networking bandwidth and time-consuming. It can even mean thrashing to hard-disk in case the number of workers times the amount of transferred data exceeds the available RAM. For example, if we have 10GB available RAM and try to communicate 3GB to 4 workers, we will not have enough RAM and the OS will start swapping to hard-disk. This will kill performance and Matlab will appear “hung” and will need to be hard-killed.
You might think that it would be very difficult to reach the RAM limit, but in fact it can be far too easy when you consider the multiplication by the number of workers, and the fact that each worker uses 1+GB of memory just for its MATLAB process, even before the data, and all this in addition to the parent (client) Matlab process. That’s a lot of GBs flying around…
Moreover, it’s enough for one small part of a Matlab struct or array to be used within the parfor loop for the entire Matlab construct to be broadcast to all workers. For example, a very common use-case is to store program data, both raw and processed, within a simple Matlab struct. Let’s say that we have data.raw and data.processed and within the loop we only need data.processed – the entire data variable (which might include many GBs due to the raw data) is broadcast, although the loop’s code only needs data.processed. In such cases, it makes sense to separate the broadcast data into standalone variables, and only use them within the loop:

data.raw = ...
data.processed = ...
% Inefficient variant:
parfor idx = 1 : N
   % do something with data.processed
end
% This is better:
processedData = data.processed;
parfor idx = 1 : N
   % do something with processedData
end

Moreover, if you can convert a broadcast variable into a sliced one, this would be even better: in this case each worker will only be communicated its small share (“slice”) of the entire data, rather than a full copy of the entire data.
All this would of course be much simpler if Matlab’s computational engine was multi-threaded, since then PCT could be implemented using lightweight threads rather than heavyweight processes. The memory and communication overheads would then be drastically reduced and performance would improve significantly. Unfortunately, Matlab’s computational engine is [still] single-threaded, preventing this. Hopefully Matlab’s new engine (which debuted in R2015b) will enable true multithreading at some future release. PCT will still need to retain an option of using headless worker processes to run on multiple machines (i.e., distributed/grid/cloud computing), but single-machine parallelization should employ multithreading instead.
Additional speedup tips can be found in my book “Accelerating MATLAB Performance“.
Do you have some other important parfor tips that you found useful? If so, please post them in a comment below.

The post A few parfor tips appeared first on Undocumented Matlab.

// Most important functions (C):
bool utIsInterruptEnabled(void)
bool utIsInterruptPending(void)
bool utWasInterruptHandled(void)
bool utSetInterruptHandled(bool)
bool utSetInterruptEnabled(bool)
bool utSetInterruptPending(bool)
// Related functions (C, signature unknown):
? utHandlePendingInterrupt(?)
? utRestoreInterruptEnabled(?)
? utLongjmpIfInterruptPending(?)
// utInterruptMode class (C++):
utInterruptMode::utInterruptMode(enum utInterruptMode::Mode)  // constructor
utInterruptMode::~utInterruptMode(void)  // destructor
bool utInterruptMode::isInterruptEnabled(void)
enum utInterruptMode::Mode utInterruptMode::CurrentMode
enum utInterruptMode::Mode utInterruptMode::GetCurrentMode(void)
enum utInterruptMode::Mode utInterruptMode::GetOriginalMode(void)
enum utInterruptMode::Mode utInterruptMode::SetMode(enum utInterruptMode::Mode)
// utInterruptState class (C++):
class utInterruptState::AtomicPendingFlags utInterruptState::flags_pending
void utInterruptState::HandlePeekMsgPending(void)
bool utInterruptState::HandlePendingInterrupt(void)
bool utInterruptState::interrupt_handled
bool utInterruptState::IsInterruptPending(void)
bool utInterruptState::IsPauseMsgPending(void)
class utInterruptState & utInterruptState::operator=(class utInterruptState const &)
void utInterruptState::PeekMessageIfPending(void)
bool utInterruptState::SetInterruptHandled(bool)
bool utInterruptState::SetInterruptPending(bool)
bool utInterruptState::SetIqmInterruptPending(bool)
bool utInterruptState::SetPauseMsgPending(bool)
bool utInterruptState::SetPeekMsgPending(bool)
void utInterruptState::ThrowIfInterruptPending(void)
bool utInterruptState::WasInterruptHandled(void)
unsigned int const utInterruptState::FLAG_PENDING_CTRLC
unsigned int const utInterruptState::FLAG_PENDING_INTERRUPT_MASK
unsigned int const utInterruptState::FLAG_PENDING_IQM_INTERRUPT
unsigned int const utInterruptState::FLAG_PENDING_PAUSE
unsigned int const utInterruptState::FLAG_PENDING_PEEKMSG

Of all these functions, we can make do with just utIsInterruptPending, as shown by Yin (complete with compilation instructions):

/* A demo of Ctrl-C detection in mex-file by Wotao Yin. Jan 29, 2010. */
#include "mex.h"
#if defined (_WIN32)
    #include 
#elif defined (__linux__)
    #include 
#endif
#ifdef __cplusplus
    extern "C" bool utIsInterruptPending();
#else
    extern bool utIsInterruptPending();
#endif
void mexFunction(int nlhs, mxArray *plhs[], int nrhs, const mxArray *prhs[]) {
    int count = 0;
    while(1) {
        #if defined(_WIN32)
            Sleep(1000);        /* Sleep one second */
        #elif defined(__linux__)
            usleep(1000*1000);  /* Sleep one second */
        #endif
        mexPrintf("Count = %d\n", count++);  /* print count and increase it by 1 */
        mexEvalString("drawnow;");           /* flush screen output */
        if (utIsInterruptPending()) {        /* check for a Ctrl-C event */
            mexPrintf("Ctrl-C Detected. END\n\n");
            return;
        }
        if (count == 10) {
            mexPrintf("Count Reached 10. END\n\n");
            return;
        }
    }
}

After returning to Matlab, the Ctrl-C event will stop the execution of the caller function(s). However, sometimes we would like to keep the partial calculation, for example if the calculation can later be resumed from the point of interruption. It’s not possible to save partial result using only the utIsInterruptPending() function. However, it is possible to reset the interrupt state so that Matlab will not stop the execution after returning control from the mex function, and the caller function can get and use the partial calculation. The magic is done using another undocumented libut function named ​utSetInterruptPending(). A short example is included below (provided by Zvi Devir):

// Import libut functions
#pragma comment(lib, "libut.lib")
extern "C" bool utIsInterruptPending();
extern "C" bool utSetInterruptPending(bool);
// Run calculation divided into steps
int n;
for (n = 0; n < count; n++) {
	// some expensive calculation
	a_long_calculation(..., n);
	if (utIsInterruptPending()) {		// check for a Ctrl-C event
		mexPrintf("Ctrl-C detected [%d/%d].\n\n", n, count);
		utSetInterruptPending(false);	// Got it... consume event
		break;
	}
}
// Write back partial or full calculation
...

An elaboration of the idea of Ctrl-C detection was created by Ramon Casero (Oxford) for the Gerardus project. Ramon wrapped Yin's code in C/C++ #define to create an easy-to-use pre-processor function ctrlcCheckPoint(fileName,lineNumber):

...
ctrlcCheckPoint(__FILE__, __LINE__);  // exit if user pressed Ctrl+C
...

Here's the code for the preprocessor header file (GerardusCommon.h) that #defines ctrlcCheckPoint() (naturally, the __FILE__ and __LINE__ parts could also be made part of the #define, for even simpler usage):

 /*
  * Author: Ramon Casero 
  * Copyright © 2011-2013 University of Oxford
  * Version: 0.10.2
  *
  * University of Oxford means the Chancellor, Masters and Scholars of
  * the University of Oxford, having an administrative office at
  * Wellington Square, Oxford OX1 2JD, UK.
  *
  * This file is part of Gerardus.
  *
  * This program is free software: you can redistribute it and/or modify
  * it under the terms of the GNU General Public License as published by
  * the Free Software Foundation, either version 3 of the License, or
  * (at your option) any later version.
  *
  * This program is distributed in the hope that it will be useful,
  * but WITHOUT ANY WARRANTY; without even the implied warranty of
  * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
  * GNU General Public License for more details. The offer of this
  * program under the terms of the License is subject to the License
  * being interpreted in accordance with English Law and subject to any
  * action against the University of Oxford being under the jurisdiction
  * of the English Courts.
  *
  * You should have received a copy of the GNU General Public License
  * along with this program.  If not, see
  * .
  */
#ifndef GERARDUSCOMMON_H
#define GERARDUSCOMMON_H
/* mex headers */
#include 
/* C++ headers */
#include 
#include 
#include 
#include 
#include 
/* ITK headers */
#include "itkOffset.h"
/*
 * utIsInterruptPending(): "undocumented MATLAB API implemented in
 * libut.so, libut.dll, and included in the import library
 * libut.lib. To use utIsInterruptPending in a mex-file, one must
 * manually declare bool utIsInterruptPending() because this function
 * is not included in any header files shipped with MATLAB. Since
 * libut.lib, by default, is not linked by mex, one must explicitly
 * tell mex to use libut.lib." -- Wotao Yin,
 * http://www.caam.rice.edu/~wy1/links/mex_ctrl_c_trick/
 *
 */
#ifdef __cplusplus
    extern "C" bool utIsInterruptPending();
#else
    extern bool utIsInterruptPending();
#endif
/*
 * ctrlcCheckPoint(): function to check whether the user has pressed
 * Ctrl+C, and if so, terminate execution returning an error message
 * with a hyperlink to the offending function's help, and a hyperlink
 * to the line in the source code file this function was called from
 *
 * It is implemented as a C++ macro to check for the CTRL+C flag, and
 * a call to function ctrlcErrMsgTxt() inside, to throw the error. The
 * reason is that if ctrlcCheckPoint() were a function instead of a
 * macro, this would introduce a function call at every iteration of
 * the loop, which is very expensive. But then we don't want to put
 * the whole error message part inside a macro, it's bug-prone and bad
 * programming practice. And once the CTRL+C has been detected,
 * whether the error message is generated a bit faster or not is not
 * important.
 *
 * In practice, to use this function put a call like this e.g. inside
 * loops that may take for a very long time:
 *
 *    // exit if user pressed Ctrl+C
 *    ctrlcCheckPoint(__FILE__, __LINE__);
 *
 * sourceFile: full path and name of the C++ file that calls this
 *             function. This should usually be the preprocessor
 *             directive __FILE__
 *
 * lineNumber: line number where this function is called from. This
 *             should usually be the preprocessor directive __LINE__
 *
 */
inline
void ctrlcErrMsgTxt(std::string sourceFile, int lineNumber) {
  // run from here the following code in the Matlab side:
  //
  // >> path = mfilename('fullpath')
  //
  // this provides the full path and function name of the function
  // that called ctrlcCheckPoint()
  int nlhs = 1; // number of output arguments we expect
  mxArray *plhs[1]; // to store the output argument
  int nrhs = 1; // number of input arguments we are going to pass
  mxArray *prhs[1]; // to store the input argument we are going to pass
  prhs[0] = mxCreateString("fullpath"); // input argument to pass
  if (mexCallMATLAB(nlhs, plhs, nrhs, prhs, "mfilename")) { // run mfilename('fullpath')
    mexErrMsgTxt("ctrlcCheckPoint(): mfilename('fullpath') returned error");
  }
  if (plhs == NULL) {
    mexErrMsgTxt("ctrlcCheckPoint(): mfilename('fullpath') returned NULL array of outputs");
  }
  if (plhs[0] == NULL) {
    mexErrMsgTxt("ctrlcCheckPoint(): mfilename('fullpath') returned NULL output instead of valid path");
  }
  // get full path to current function, including function's name
  // (without the file extension)
  char *pathAndName = mxArrayToString(plhs[0]);
  if (pathAndName == NULL) {
    mexErrMsgTxt("ctrlcCheckPoint(): mfilename('fullpath') output cannot be converted to string");
  }
  // for some reason, using mexErrMsgTxt() to give this output
  // doesn't work. Instead, we have to give the output to the
  // standar error, and then call mexErrMsgTxt() to terminate
  // execution of the program
  std::cerr << "Operation terminated by user during "
	    << ""
	    << mexFunctionName()
	    << " (line " << lineNumber
	    << ")"
	    << std::endl;
  mexErrMsgTxt("");
}
#define ctrlcCheckPoint(sourceFile, lineNumber)		\
  if (utIsInterruptPending()) {				\
    ctrlcErrMsgTxt(sourceFile, lineNumber);		\
  }

This feature has remained as-is since at least 2002 (when Peter first reported it), and apparently works to this day. Why then did I categorize this as "High risk for breaking in a future Matlab versions"? The reason is that internal undocumented MEX functions are prone to break in new Matlab releases (example). Hopefully my report today will prompt MathWorks to make this feature documented, rather than to remove it from a future release 🙂
By the way, if anyone knows any use for the other interrupt-related functions in libut that I listed above, and/or the missing signatures, please leave a note below and I will update here accordingly.
Addendum July 2, 2016: Pavel Holoborodko just posted an asynchronous version of this mechanism, which is an improvement of the synchronous code above. Pavel uses a separate Windows thread to check the Ctrl-C interrupt state. Readers can extend this idea to use threads for other asynchronous (multi-threaded) computations or I/O. In chapter 7 of my book "Accelerating MATLAB Performance" I explain how we can use Posix threads (pthreads) or OpenMP threads for similar multithreading in MEX (unlike Windows threads, pthreads and OpenMP are cross platform). Users can also use other multithreading solutions, such as the open-source Boost library (bundled with Matlab) or Intel's commercial TBB.

The post MEX ctrl-c interrupt appeared first on Undocumented Matlab.

>> hFig=figure; plot(1:5); zoom on
>> set(hFig, 'KeyPressFcn', @myKeyPressCallback)
Warning: Setting the "KeyPressFcn" property is not permitted while this mode is active.
(Type "warning off MATLAB:modes:mode:InvalidPropertySet" to suppress this warning.)
 
> In matlab.uitools.internal.uimodemanager>localModeWarn (line 211)
  In matlab.uitools.internaluimodemanager>@(obj,evd)(localModeWarn(obj,evd,hThis)) (line 86)
 
>> get(hFig, 'KeyPressFcn')  % the KeyPressFcn callback set by the mode manager
ans =
    @localModeKeyPressFcn
    [1x1 matlab.uitools.internal.uimode]
    {1x2 cell                          }

The question of how to override this limitation has appeared multiple times over the years in the CSSM newsgroup (example1, example2) and the Answers forum, most recently yesterday, so I decided to dedicate today’s article to this issue.

In Matlab GUI, a “mode” (aka uimode) is a set of defined behaviors that may be set for any figure window and activated/deactivated via the figure’s menu, toolbar or programmatically. Examples of predefined modes are plot edit, zoom, pan, rotate3d and data-cursor. Only one mode can be active in a figure at any one time – this is managed via a figure’s ModeManager. Activating a figure mode automatically deactivates any other active mode, as can be seen when switching between plot edit, zoom, pan, rotate3d and data-cursor modes.
This mode manager is implemented in the uimodemanager class in %matlabroot%/toolbox/matlab/uitools/+matlab/+uitools/internal/@uimodemanager/uimodemanager.m and its sibling functions in the same folder. Until recently it used to be implemented in Matlab’s old class system, but was recently converted to the new MCOS (classdef) format, without an apparent major overhaul of the underlying logic (which I think is a pity, but refactoring naturally has lower priority than fixing bugs or adding new functionality).
Anyway, until HG2 (R2014b), the solution for bypassing the mode-manager’s callbacks hijacking was as follows (credit: Walter Roberson):

% This will only work in HG1 (R2014a or earlier)
hManager = uigetmodemanager(hFig);
set(hManager.WindowListenerHandles, 'Enable', 'off');
set(hFig, 'WindowKeyPressFcn', []);
set(hFig, 'KeyPressFcn', @myKeyPressCallback);

In HG2 (R2014b), the interface of WindowListenerHandles changed and the above code no longer works:

>> set(hManager.WindowListenerHandles, 'Enable', 'off');
Error using set
Cannot find 'set' method for event.proplistener class.

Luckily, in MathWorks’ refactoring to MCOS, the property name WindowListenerHandles was not changed, nor its status as a public read-only hidden property. So we can still access it directly. The only thing that changed is its meta-properties, and this is due not to any change in the mode-manager’s code, but rather to a much more general change in the implementation of the event.proplistener class:

>> hManager.WindowListenerHandles(1)
ans =
  proplistener with properties:
 
       Object: {[1x1 Figure]}
       Source: {7x1 cell}
    EventName: 'PreSet'
     Callback: @(obj,evd)(localModeWarn(obj,evd,hThis))
      Enabled: 0
    Recursive: 0

In the new proplistener, the Enabled meta-property is now a boolean (logical) value rather than a string, and was renamed Enabled in place of the original Enable. Also, the new proplistener doesn’t inherit hgsetget, so it does not have set and get methods, which is why we got an error above; instead, we should use direct dot-notation.
In summary, the code that should now be used, and is backward compatible with old HG1 releases as well as new HG2 releases, is as follows:

% This should work in both HG1 and HG2:
hManager = uigetmodemanager(hFig);
try
    set(hManager.WindowListenerHandles, 'Enable', 'off');  % HG1
catch
    [hManager.WindowListenerHandles.Enabled] = deal(false);  % HG2
end
set(hFig, 'WindowKeyPressFcn', []);
set(hFig, 'KeyPressFcn', @myKeyPressCallback);

During an active mode, the mode-manager disables user-configured mouse and keyboard callbacks, in order to prevent interference with the mode’s functionality. For example, mouse clicking during zoom mode has a very specific meaning, and user-configured callbacks might interfere with it. Understanding this and taking the proper precautions (for example: chaining the default mode’s callback at the beginning or end of our own callback), we can override this default behavior, as shown above.
Note that the entire mechanism of mode-manager (and the related scribe objects) is undocumented and might change without notice in future Matlab releases. We were fortunate in the current case that the change was small enough that a simple workaround could be found, but this may possibly not be the case next time around. It’s impossible to guess when such features will eventually break: it might happen in the very next release, or 20 years in the future. Since there are no real alternatives, we have little choice other than to use these features, and document the risk (the fact that they use undocumented aspects). In your documentation/comments, add a link back here so that if something ever breaks you’d be able to check if I posted any fix or workaround.
For the sake of completeness, here is a listing of the accessible properties (regular and hidden) of both the mode-manager as well as the zoom uimode, in R2015b:

>> warning('off', 'MATLAB:structOnObject');  % suppress warning about using structs (yes we know it's not good for us...)
>> struct(hManager)
ans =
                    CurrentMode: [1x1 matlab.uitools.internal.uimode]
                  DefaultUIMode: ''
                       Blocking: 0
                   FigureHandle: [1x1 Figure]
          WindowListenerHandles: [1x2 event.proplistener]
    WindowMotionListenerHandles: [1x2 event.proplistener]
                 DeleteListener: [1x1 event.listener]
            PreviousWindowState: []
                RegisteredModes: [1x1 matlab.uitools.internal.uimode]
>> struct(hManager.CurrentMode)
ans =
    WindowButtonMotionFcnInterrupt: 0
                UIControlInterrupt: 0
                   ShowContextMenu: 1
                         IsOneShot: 0
                    UseContextMenu: 'on'
                          Blocking: 0
               WindowJavaListeners: []
                    DeleteListener: [1x1 event.listener]
              FigureDeleteListener: [1x1 event.listener]
                    BusyActivating: 0
                              Name: 'Exploration.Zoom'
                      FigureHandle: [1x1 Figure]
             WindowListenerHandles: [1x2 event.proplistener]
                   RegisteredModes: []
                       CurrentMode: []
               ModeListenerHandles: []
               WindowButtonDownFcn: {@localWindowButtonDownFcn  [1x1 matlab.uitools.internal.uimode]}
                 WindowButtonUpFcn: []
             WindowButtonMotionFcn: {@localMotionFcn  [1x1 matlab.uitools.internal.uimode]}
                 WindowKeyPressFcn: []
               WindowKeyReleaseFcn: []
              WindowScrollWheelFcn: {@localButtonWheelFcn  [1x1 matlab.uitools.internal.uimode]}
                     KeyReleaseFcn: []
                       KeyPressFcn: {@localKeyPressFcn  [1x1 matlab.uitools.internal.uimode]}
                      ModeStartFcn: {@localStartZoom  [1x1 matlab.uitools.internal.uimode]}
                       ModeStopFcn: {@localStopZoom  [1x1 matlab.uitools.internal.uimode]}
                  ButtonDownFilter: []
                     UIContextMenu: []
                     ModeStateData: [1x1 struct]
                WindowFocusLostFcn: []
          UIControlSuspendListener: [1x1 event.listener]
      UIControlSuspendJavaListener: [1x1 handle.listener]
                   UserButtonUpFcn: []
               PreviousWindowState: []
                 ActionPreCallback: []
                ActionPostCallback: []
           WindowMotionFcnListener: [1x1 event.listener]
                       FigureState: [1x1 struct]
                        ParentMode: []
                     DefaultUIMode: ''
             CanvasContainerHandle: []
                            Enable: 'on'

The post Enabling user callbacks during zoom/pan appeared first on Undocumented Matlab.

% Create and display the text label
url = 'UndocumentedMatlab.com';
labelStr = ['More info: ' url ''];
jLabel = javaObjectEDT('javax.swing.JLabel', labelStr);
[hjLabel,hContainer] = javacomponent(jLabel, [10,10,250,20], gcf);
% Modify the mouse cursor when hovering on the label
hjLabel.setCursor(java.awt.Cursor.getPredefinedCursor(java.awt.Cursor.HAND_CURSOR));
% Set the label's tooltip
hjLabel.setToolTipText(['Visit the ' url ' website']);
% Set the mouse-click callback
set(hjLabel, 'MouseClickedCallback', @(h,e)web(['http://' url], '-browser'))

Note the visual illusion here: we do not directly click the hyperlink (note that its href is empty), but rather the label control. The end-result is the same.
Also note that this technique could be used to easily display clickable icons/images, including animated and transparent GIFs, by simply setting the label’s HTML string to display the relevant image. I have already shown how to do this in another post. Uses could range from clickable logo images to clickable help icons.
We could also use a flat (borderless) button. I have already posted related articles about button customizations in Matlab GUIs (here, here and here). In fact, I have already shown how we can use borderless buttons in Matlab axes to display a non-obtrusive control for controlling plot properties. The code would be very similar to the above, except that we would use a JButton rather than a JLabel, and also need to setBorder([]) and similar button-specific modifications. Buttons have a bit more functionality and customizability than simple text labels; the appearance would be the same, but the extra customizability may be handy in very special cases, although not for most use-cases.
One of the benefits of using either JLabels or JButtons is that they enable multiple callbacks (e.g., FocusGained,FocusLost) and properties (e.g., VerticalAlignment,ToolTipText) that the standard Matlab text uicontrol does not provide (not to mention their builtin ability to display HTML, which Matlab’s uicontrol text labels do not posses).

The post Hyperlink text labels appeared first on Undocumented Matlab.

HG2 – addprop function

The addprop function is actually a public method of the dynamicprops class. Both the dynamicprops class as well as its addprop function are fully documented. What is NOT documented, as far as I could tell, is that all of Matlab’s builtin handle graphics objects indirectly inherit dynamicprops, via matlab.graphics.Graphics, which is a high-level superclass for all HG objects. The bottom line is that we can dynamically add run-time properties to any HG object, without affecting any other object. In other words, the new properties will only be added to the handles that we specifically request, and not to any others. This suits me just fine:

hProp = addprop(hPanel, 'hHorizontalScrollBar');
hPanel.hHorizontalScrollBar = hMyScrollbar;
hProp.SetAccess = 'private';  % make this property read-only

The new property hHorizontalScrollBar is now added to the hPanel handle, and can be accessed just like any other read-only property. For example:

>> get(hPanel, 'hHorizontalScrollBar')
ans =
    JavaWrapper
>> hPanel.hHorizontalScrollBar
ans =
    JavaWrapper
>> hPanel.hHorizontalScrollBar = 123
You cannot set the read-only property 'hHorizontalScrollBar' of UIFlowContainer.

Adding new methods is more tricky, since we do not have a corresponding addmethod function. The trick I used was to create a new property having the requested new method’s name, and set its read-only value to a handle of the requested function. For example:

hProp = addprop(hPanel, 'refresh');
hPanel.refresh = @myRefreshFunc;
hProp.SetAccess = 'private';  % make this property read-only

We can then invoke the new refresh “method” using the familiar dot-notation:

hPanel.refresh();

Note: if you ever need to modify the initial value in your code, you should revert the property’s SetAccess meta-property to 'public' before Matlab will enable you to modify the value:

try
    % This will raise an exception if the property already exists
    hProp = addprop(hPanel, propName);
catch
    % Property already exists - find it and set its access to public
    hProp = findprop(hPanel, propName);
    hProp.SetAccess = 'public';
end
hPanel.(propName) = newValue;

HG1 – schema.prop function

In HG1 (R2014a and earlier), we can use the undocumented schema.prop function to add a new property to any HG handle (which is a numeric value in HG1). Donn Shull wrote about schema.prop back in 2011, as part of his series of articles on UDD (Unified Data Dictionary, MCOS’s precursor). In fact, schema.prop is so useful that it has its own blog tag here and appears in no less than 15 separate articles (excluding today). With HG2’s debut 2 years ago, MathWorks tried very hard to rid the Matlab code corpus of all the legacy schema-based, replacing most major functionalities with MCOS-based HG2 code. But so far it has proven impossible to get rid of schema completely, and so schema code is still used extensively in Matlab to this day (R2015b). Search your Matlab path for “schema.prop” and see for yourself.
Anyway, the basic syntax is this:

hProp = schema.prop(hPanel, propName, 'mxArray');

The 'mxArray' specifies that the new property can accept any data type. We can limit the property to only accept certain types of data by specifying a less-generic data type, among those recognized by UDD (details).
Note that the meta-properties of the returned hProp are somewhat different from those of HG2’s hProp. Taking this into account, here is a unified function that adds/updates a new property (with optional initial value) to any HG1/HG2 object:

function addProp(hObject, propName, initialValue, isReadOnly)
    try
        hProp = addprop(hObject, propName);  % HG2
    catch
        try
            hProp = schema.prop(hObject, propName, 'mxArray');  % HG1
        catch
            hProp = findprop(hObject, propName);
        end
    end
    if nargin > 2
        try
            hProp.SetAccess = 'public';  % HG2
        catch
            hProp.AccessFlags.PublicSet = 'on';  % HG1
        end
        hObject.(propName) = initialValue;
    end
    if nargin > 3 && isReadOnly
        try
            % Set the property as read-only
            hProp.SetAccess = 'private';  % HG2
        catch
            hProp.AccessFlags.PublicSet = 'off';  % HG1
        end
    end
end

The post Adding dynamic properties to graphic handles appeared first on Undocumented Matlab.

% Copy function - replacement for matlab.mixin.Copyable.copy() to create object copies
function newObj = copy(obj)
    try
        % R2010b or newer - directly in memory (faster)
        objByteArray = getByteStreamFromArray(obj);
        newObj = getArrayFromByteStream(objByteArray);
    catch
        % R2010a or earlier - serialize via temp file (slower)
        fname = [tempname '.mat'];
        save(fname, 'obj');
        newObj = load(fname);
        newObj = newObj.obj;
        delete(fname);
    end
end

This function can be placed anywhere on the Matlab path and will work on all recent Matlab releases (including R2010b and older), any type of Matlab data (including value or handle objects, UDD objects, structs, arrays etc.), as well as external objects (Java, C#, COM). In short, it works on anything that can be assigned to a Matlab variable:

obj1 = ... % anything really!
obj2 = obj1.copy();  % alternative #1
obj2 = copy(obj1);   % alternative #2

Alternative #1 may look “nicer” to a computer scientist, but alternative #2 is preferable because it also handles the case of non-object data (e.g., [] or ‘abc’ or magic(5) or a struct or cell array), whereas alternative #1 would error in such cases.
In any case, using either alternatives, we no longer need to worry about inheriting our MCOS class from matlab.mixin.Copyable, or backward compatibility with R2010b and older (I may possibly be bashed for this statement, but in my eyes future compatibility is less important than backward compatibility). This is not such a wild edge-case. In fact, I came across the idea for this post last week, when I developed an MCOS project for a consulting client that uses both R2010a and R2012a, and the same code needed to run on both Matlab releases.
Using the serialization functions also solves the case of creating copies for Java/C#/COM objects, which currently have no other solution, except if these objects happen to contain their own copy method.
In summary, using Matlab’s undocumented builtin serialization functions enables easy implementation of a very efficient (in-memory) copy constructor, which is expected to work across all Matlab types and many releases, without requiring any changes to existing code – just placing the above copy function on the Matlab path. This is expected to continue working properly until Matlab decides to remove the serialization functions (which should hopefully never happen, as they are so useful).
Sometimes, the best solutions lie not in sophisticated new features (e.g., matlab.mixin.Copyable), but by using plain ol’ existing building blocks. There’s a good lesson to be learned here I think.
p.s. – I do realize that matlab.mixin.Copyable provides the nice feature of enabling users to control the copy process, including implementing deep or shallow or selective copy. If that’s your need and you have R2011a or newer then good for you, go ahead and inherit Copyable. Today’s post was meant for the regular Joe who doesn’t need this fancy feature, but does need to support R2010b, and/or a simple way to clone Java/C#/COM objects.

The post General-use object copy appeared first on Undocumented Matlab.