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I am hiring experienced Matlab programmers (Tel Aviv)

I am hiring experienced Matlab programmers for work in Tel Aviv, to join a growing team of Matlab experts working under my supervision. Very interesting work, good salary, and flexible worhours. This job opening is only applicable to candidates who live in central Israel. If you live in the area and are interested, or if you know someone who could be a good fit, please email me: altmany at gmail.

-אני מגייס מתכנת/ת מטלב מנוסה לעבודה בתל אביב בחברת הייעוץ שלי. המשרה בהיקף ובימים/שעות גמישים, העבודה מעניינת והשכר טוב. לפרטים נא לפנות ל
altmany at gmail

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Additional license data

Matlab’s license function returns the primary license number/ID used by Matlab, but no information about the various toolboxes that may be installed. The ver function returns a bit more information, listing the version number and installation date of installed toolboxes (even user toolboxes, such as my IB-Matlab toolbox). However, no additional useful information is provided beyond that:

>> license
ans =
835289
 
>> ver
----------------------------------------------------------------------------------------------------
MATLAB Version: 9.1.0.441655 (R2016b)
MATLAB License Number: 835289
Operating System: Microsoft Windows 7 Professional  Version 6.1 (Build 7601: Service Pack 1)
Java Version: Java 1.7.0_60-b19 with Oracle Corporation Java HotSpot(TM) 64-Bit Server VM mixed mode
----------------------------------------------------------------------------------------------------
MATLAB                                                Version 9.1         (R2016b)           
Curve Fitting Toolbox                                 Version 3.5.4       (R2016b)           
Database Toolbox                                      Version 7.0         (R2016b)           
Datafeed Toolbox                                      Version 5.4         (R2016b)           
Financial Instruments Toolbox                         Version 2.4         (R2016b)           
Financial Toolbox                                     Version 5.8         (R2016b)           
GUI Layout Toolbox                                    Version 2.2.1       (R2015b)           
Global Optimization Toolbox                           Version 3.4.1       (R2016b)           
IB-Matlab - Matlab connector to InteractiveBrokers    Version 1.89        Expires: 1-Apr-2018
Image Processing Toolbox                              Version 9.5         (R2016b)           
MATLAB Coder                                          Version 3.2         (R2016b)           
MATLAB Report Generator                               Version 5.1         (R2016b)           
Optimization Toolbox                                  Version 7.5         (R2016b)           
Parallel Computing Toolbox                            Version 6.9         (R2016b)           
Statistical Graphics Toolbox                          Version 1.2                            
Statistics and Machine Learning Toolbox               Version 11.0        (R2016b)           
 
>> v = ver
v = 
  1×16 struct array with fields:
    Name
    Version
    Release
    Date
 
>> v(1)
ans = 
  struct with fields:
 
       Name: 'Curve Fitting Toolbox'
    Version: '3.5.4'
    Release: '(R2016b)'
       Date: '25-Aug-2016'
 
>> v(8)
ans = 
  struct with fields:
 
       Name: 'IB-Matlab - Matlab connector to InteractiveBrokers'
    Version: '1.89'
    Release: 'Expires: 1-Apr-2018'
       Date: '02-Feb-2017'

It is sometimes useful to know which license number “owns” which product/toolbox, and the expiration date is associated with each of them. Unfortunately, there is no documented way to retrieve this information in Matlab – the only documented way is to go to your account section on the MathWorks website and check there.

Luckily, there is a simpler way that can be used to retrieve additional information, from right inside Matlab, using matlab.internal.licensing.getFeatureInfo:
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Categories: High risk of breaking in future versions, Stock Matlab function, Undocumented feature, Undocumented function
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Parsing XML strings

I have recently consulted in a project where data was provided in XML strings and needed to be parsed in Matlab memory in an efficient manner (in other words, as quickly as possible). Now granted, XML is rather inefficient in storing data (JSON would be much better for this, for example). But I had to work with the given situation, and that required processing the XML.

I basically had two main alternatives:

  • I could either create a dedicated string-parsing function that searches for a particular pattern within the XML string, or
  • I could use a standard XML-parsing library to create the XML model and then parse its nodes

The first alternative is quite error-prone, since it relies on the exact format of the data in the XML. Since the same data can be represented in multiple equivalent XML ways, making the string-parsing function robust as well as efficient would be challenging. I was lazy expedient, so I chose the second alternative.

Unfortunately, Matlab’s xmlread function only accepts input filenames (of *.xml files), it cannot directly parse XML strings. Yummy!

The obvious and simple solution is to simply write the XML string into a temporary *.xml file, read it with xmlread, and then delete the temp file:

% Store the XML data in a temp *.xml file
filename = [tempname '.xml'];
fid = fopen(filename,'Wt');
fwrite(fid,xmlString);
fclose(fid);
 
% Read the file into an XML model object
xmlTreeObject = xmlread(filename);
 
% Delete the temp file
delete(filename);
 
% Parse the XML model object
...

This works well and we could move on with our short lives. But cases such as this, where a built-in function seems to have a silly limitation, really fire up the investigative reporter in me. I decided to drill into xmlread to discover why it couldn’t parse XML strings directly in memory, without requiring costly file I/O. It turns out that xmlread accepts not just file names as input, but also Java object references (specifically, java.io.File, java.io.InputStream or org.xml.sax.InputSource). In fact, there are quite a few other inputs that we could use, to specify a validation parser etc. – I wrote about this briefly back in 2009 (along with other similar semi-documented input altermatives in xmlwrite and xslt).
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Categories: Low risk of breaking in future versions, Semi-documented feature, Stock Matlab function
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Quirks with parfor vs. for

A few months ago, I discussed several tips regarding Matlab’s parfor command, which is used by the Parallel Computing Toolbox (PCT) for parallelizing loops. Today I wish to extend that post with some unexplained oddities when using parfor, compared to a standard for loop.

Data serialization quirks

Dimitri Shvorob may not appear at first glance to be a prolific contributor on Matlab Central, but from the little he has posted over the years I regard him to be a Matlab power-user. So when Dimitri reports something, I take it seriously. Such was the case several months ago, when he contacted me regarding very odd behavior that he saw in his code: the for loop worked well, but the parfor version returned different (incorrect) results. Eventually, Dimitry traced the problem to something originally reported by Dan Austin on his Fluffy Nuke It blog.

The core issue is that if we have a class object that is used within a for loop, Matlab can access the object directly in memory. But with a parfor loop, the object needs to be serialized in order to be sent over to the parallel workers, and deserialized within each worker. If this serialization/deserialization process involves internal class methods, the workers might see a different version of the class object than the one seen in the serial for loop. This could happen, for example, if the serialization/deserialization method croaks on an error, or depends on some dynamic (or random) conditions to create data.

In other words, when we use data objects in a parfor loop, the data object is not necessarily sent “as-is”: additional processing may be involved under the hood that modify the data in a way that may be invisible to the user (or the loop code), resulting in different processing results of the parallel (parfor) vs. serial (for) loops.

For additional aspects of Matlab serialization/deserialization, see my article from 2 years ago (and its interesting feedback comments).

Data precision quirks

The following section was contributed by guest blogger Lior Perlmuter-Shoshany, head algorithmician at a private equity fund.

In my work, I had to work with matrixes in the order of 109 cells. To reduce the memory footprint (and hopefully also improve performance), I decided to work with data of type single instead of Matlab’s default double. Furthermore, in order to speed up the calculation I use parfor rather than for in the main calculation. In the end of the run I am running a mini for-loop to see the best results.

What I discovered to my surprise is that the results from the parfor and for loop variants is not the same!
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Categories: Guest bloggers, Medium risk of breaking in future versions, Memory, Stock Matlab function, Undocumented feature
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Checking status of warning messages in MEX

Once again I would like to welcome guest blogger Pavel Holoborodko, the developer of the Advanpix Multiprecision Computing Toolbox. Pavel has already posted here as a guest blogger about undocumented Matlab MEX functions. Today he will discuss another little-known aspect of advanced MEX programming with Matlab, a repost of an article that was originally posted on his own blog. Happy holidays everybody!

Matlab allows flexible adjustment of visibility of warning messages. Some, or even all, messages can be disabled from showing on the screen by warning command.

The little known fact is that status of some warnings may be used to change the execution path in algorithms. For example, if warning 'Matlab:nearlySingularMatrix' is disabled, then the linear system solver (mldivide operator) might skip estimation of reciprocal condition number which is used exactly for the purpose of detection of nearly singular matrices. If the trick is used, it allows 20%-50% boost in solver performance, since rcond estimation is a time consuming process.

Therefore it is important to be able to retrieve status of warnings in Matlab. Especially in MEX libraries targeted for improved performance. Unfortunately Matlab provides no simple way to check status of warning message from MEX module.

Today’s article outlines two workarounds for the issue:

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

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Categories: Guest bloggers, Medium risk of breaking in future versions, Undocumented function
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Password & spinner controls in Matlab GUI

I often include configuration panels in my programs, to enable the user to configure various program aspects, such as which emails should automatically be sent by the program to alert when certain conditions occur. Last week I presented such a configuration panel, which is mainly composed of standard documented Matlab controls (sub-panels, uitables and uicontrols). As promised, today’s post will discuss two undocumented controls that are often useful in similar configuration panels (not necessarily for emails): password fields and spinners.

Matlab GUI configuration panel including password and spinner controls (click to zoom-in)
Matlab GUI configuration panel including password and spinner controls (click to zoom-in)

Password fields are basically editboxes that hide the typed text with some generic echo character (such as * or a bullet); spinners are editboxes that only enable typing certain preconfigured values (e.g., numbers in a certain range). Both controls are part of the standard Java Swing package, on which the current (non-web-based) Matlab GUIs relies. In both cases, we can use the javacomponent function to place the built-in Swing component in our Matlab GUI.
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Categories: GUI, High risk of breaking in future versions, Java, Semi-documented function
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Sending email/text messages from Matlab

In this day and age, applications are expected to communicate with users by sending email/text messages to alert them about applicative events (“IBM stock purchased @$99.99” or “House is on fire!”). Matlab has included the sendmail function to handle this for many years. Unfortunately, sendmail requires some tweaking to be useful on all but the most basic/insecure mail servers. Today’s post will hopefully fill the missing gaps.

None of the information I’ll present today is really new – it was all there already if you just knew what to search for online. But hopefully today’s post will concentrate all these loose ends in a single place, so it may have some value:

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Categories: GUI, Java, Medium risk of breaking in future versions, Semi-documented feature, Stock Matlab function
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Afterthoughts on implicit expansion

Matlab release R2016b introduced implicit arithmetic expansion, which is a great and long-awaited natural expansion of Matlab’s arithmetic syntax (if you are still unaware of this or what it means, now would be a good time to read about it). This is a well-documented new feature. The reason for today’s post is that this new feature contains an undocumented aspect that should very well have been documented and even highlighted.

The undocumented aspect that I’m referring to is the fact that code that until R2016a produced an error, in R2016b produces a valid result:

% R2016a
>> [1:5] + [1:3]'
Error using  + 
Matrix dimensions must agree.
 
% R2016b
>> [1:5] + [1:3]'
ans =
     2     3     4     5     6
     3     4     5     6     7
     4     5     6     7     8

This incompatibility is indeed documented, but not where it matters most (read on).

I first discovered this feature by chance when trying to track down a very strange phenomenon with client code that produced different numeric results on R2015b and earlier, compared to R2016a Pre-release. After some debugging the problem was traced to a code snippet in the client’s code that looked something like this (simplified):

% Ensure compatible input data
try
    dataA + dataB;  % this will (?) error if dataA, dataB are incompatible
catch
    dataB = dataB';
end

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Categories: Low risk of breaking in future versions, Stock Matlab function, Undocumented feature
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Speeding up Matlab-JDBC SQL queries

Many of my consulting projects involve interfacing a Matlab program to an SQL database. In such cases, using MathWorks’ Database Toolbox is a viable solution. Users who don’t have the toolbox can also easily connect directly to the database using either the standard ODBC bridge (which is horrible for performance and stability), or a direct JDBC connection (which is also what the Database Toolbox uses under the hood). I explained this Matlab-JDBC interface in detail in chapter 2 of my Matlab-Java programming book. A bare-bones implementation of an SQL SELECT query follows (data update queries are a bit different and will not be discussed here):

% Load the appropriate JDBC driver class into Matlab's memory
% (but not directly, to bypass JIT pre-processing - we must do it in run-time!)
driver = eval('com.mysql.jdbc.Driver');  % or com.microsoft.sqlserver.jdbc.SQLServerDriver or whatever
 
% Connect to DB
dbPort = '3306'; % mySQL=3306; SQLServer=1433; Oracle=...
connectionStr = ['jdbc:mysql://' dbURL ':' dbPort '/' schemaName];  % or ['jdbc:sqlserver://' dbURL ':' dbPort ';database=' schemaName ';'] or whatever
dbConnObj = java.sql.DriverManager.getConnection(connectionStr, username, password);
 
% Send an SQL query statement to the DB and get the ResultSet
stmt = dbConnObj.createStatement(java.sql.ResultSet.TYPE_SCROLL_INSENSITIVE, java.sql.ResultSet.CONCUR_READ_ONLY);
try stmt.setFetchSize(1000); catch, end  % the default fetch size is ridiculously small in many DBs
rs = stmt.executeQuery(sqlQueryStr);
 
% Get the column names and data-types from the ResultSet's meta-data
MetaData = rs.getMetaData;
numCols = MetaData.getColumnCount;
data = cell(0,numCols);  % initialize
for colIdx = numCols : -1 : 1
    ColumnNames{colIdx} = char(MetaData.getColumnLabel(colIdx));
    ColumnType{colIdx}  = char(MetaData.getColumnClassName(colIdx));  % http://docs.oracle.com/javase/7/docs/api/java/sql/Types.html
end
ColumnType = regexprep(ColumnType,'.*\.','');
 
% Get the data from the ResultSet into a Matlab cell array
rowIdx = 1;
while rs.next  % loop over all ResultSet rows (records)
    for colIdx = 1 : numCols  % loop over all columns in the row
        switch ColumnType{colIdx}
            case {'Float','Double'}
                data{rowIdx,colIdx} = rs.getDouble(colIdx);
            case {'Long','Integer','Short','BigDecimal'}
                data{rowIdx,colIdx} = double(rs.getDouble(colIdx));
            case 'Boolean'
                data{rowIdx,colIdx} = logical(rs.getBoolean(colIdx));
            otherwise %case {'String','Date','Time','Timestamp'}
                data{rowIdx,colIdx} = char(rs.getString(colIdx));
        end
    end
    rowIdx = rowIdx + 1;
end
 
% Close the connection and clear resources
try rs.close();   catch, end
try stmt.close(); catch, end
try dbConnObj.closeAllStatements(); catch, end
try dbConnObj.close(); catch, end  % comment this to keep the dbConnObj open and reuse it for subsequent queries

Naturally, in a real-world implementation you also need to handle database timeouts and various other errors, handle data-manipulation queries (not just SELECTs), etc.

Anyway, this works well in general, but when you try to fetch a ResultSet that has many thousands of records you start to feel the pain – The SQL statement may execute much faster on the DB server (the time it takes for the stmt.executeQuery call), yet the subsequent double-loop processing to fetch the data from the Java ResultSet object into a Matlab cell array takes much longer.

In one of my recent projects, performance was of paramount importance, and the DB query speed from the code above was simply not good enough. Continue reading

Categories: Java, Low risk of breaking in future versions, Toolbox, Undocumented feature
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Working with non-standard DPI displays

With high-density displays becoming increasingly popular, some users set their display’s DPI to a higher-than-standard (i.e., >100%) value, in order to compensate for the increased pixel density to achieve readable interfaces. This OS setting tells the running applications that there are fewer visible screen pixels, and these are spread over a larger number of physical pixels. This works well for most cases (at least on recent OSes, it was a bit buggy in non-recet ones). Unfortunately, in some cases we might actually want to know the screen size in physical, rather than logical, pixels. Apparently, Matlab root’s ScreenSize property only reports the logical (scaled) pixel size, not the physical (unscaled) one:

>> get(0,'ScreenSize')   % with 100% DPI (unscaled standard)
ans =
        1       1      1366       768
 
>> get(0,'ScreenSize')   % with 125% DPI (scaled)
ans =
        1       1      1092.8     614.4

The same phenomenon also affects other related properties, for example MonitorPositions.

Raimund Schlüßler, a reader on this blog, was kind enough to point me to this problem and its workaround, which I thought worthy to share here: To get the physical screen-size, use the following builtin Java command:

>> jScreenSize = java.awt.Toolkit.getDefaultToolkit.getScreenSize
jScreenSize =
java.awt.Dimension[width=1366,height=768]
 
>> width = jScreenSize.getWidth
width =
        1366
 
>> height = jScreenSize.getHeight
height =
        768

Also see the related recent article on an issue with the DPI-aware feature starting with R2015b.

Upcoming travels – London/Belfast, Zürich & Geneva

I will shortly be traveling to consult some clients in Belfast (via London), Zürich and Geneva. If you are in the area and wish to meet me to discuss how I could bring value to your work, then please email me (altmany at gmail):

  • Belfast: Nov 28 – Dec 1 (flying via London)
  • Zürich: Dec 11-12
  • Geneva: Dec 13-15
Categories: Desktop, Figure window, GUI, Java, Low risk of breaking in future versions, Undocumented feature
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