## MATLAB Techniques: variadic (variable input and output) arguments

I’ve seen a lot of ugly implementations from people trying to deal with variable number of input and output arguments. The most horrendous one I’ve seen so far came from MIT’s Physionet’s WFDB MATLAB Toolbox. Here’s a snippet showing how wfdbdesc.m handles variable input and output arguments:

function varargout=wfdbdesc(varargin)
% [siginfo,Fs,sigClass]=wfdbdesc(recordName)
...
%Set default pararamter values
inputs={'recordName'};
outputs={'siginfo','Fs','sigClass'};

for n=1:nargin
if(~isempty(varargin{n}))
eval([inputs{n} '=varargin{n};'])
end
end
...
if(nargout>2)
%Get signal class
sigClass=getSignalClass(siginfo,config);
end
for n=1:nargout
eval(['varargout{n}=' outputs{n} ';'])
end



The code itself reeks a very ‘smart’ beginner who didn’t RTFM. The code is so smart (shows some serious thoughts):

• Knows to use nargout to control varargout to avoid the side effects when no output is requested
• [Cargo cult practice]: (unnecessarily) track your variable names
• [Cargo cult practice]: using varargin so it can be symmetric to varargout (also handled similarly). varargout might have a benefit mentioned above, but there is absolutely no benefit to use varargin over direct variable names when you are not forwarding or use inputParser().
• [Cargo cult practice]: tries to be efficient to skip processing empty inputs. Judicially non-symmetric this time (not done to output variables)!

but yet so dumb (hell of unwise, absolutely no good reason for almost every ‘thoughtful’ act put in) at the same time. Definitely MIT: Make it Tough!

This code pattern is so wrong in many levels:

• Unnecessarily obscuring the names by using varargin/varargout
• Managing a list of variable names manually.
• Loop through each item of varargin and varargout cells unnecessarily
• Use eval() just to do simple cell assignments! Makes me cringe!

Actually, eval() is not even needed to achieve all the remaining evils above. Could have used S_in = cell2struct(varargin) and varargout=struct2cell(S_out) instead if one really wants to control the list of variable names manually!

The hurtful sins above came from not knowing a few common cell packing/unpacking idioms when dealing with varargin and varargout, which are cells by definition. Here are the few common use cases:

1. Passing variable arguments to another function (called perfect forwarding in C++): remember C{:} unpacks to comma separated lists!
function caller(varargin)
callee(varargin{:});
2. Limiting the number of outputs to what is actually requested: remember [C{:}] on left hand side (assignment) means the outputs are distributed as components of C that would have been unpacked as comma separated lists, i.e. [C{:}] = f(); means [C{1}, C{2}, C{3}, ...] = f();
function varargout = f()
// This will output no arguments when not requested,
// avoiding echoing in command prompt when the call is not terminated by a semicolon
[varargout{1:nargout}] = eig(rand(3));
3. You can directly modify varargin and varargout by cells without de-referencing them with braces!
function varargout = f(varargin)
// This one is effectively deal()
varargout = varargin(1:nargout);
end

function varargout = f(C)
// This one unpacks each cell's content to each output arguments
varargout = C(1:nargout);
end

One good example combining all of the above is to achieve the no-output argument example in #2 yet neatly return the variables in the workspace directly by name.

function [a, b] = f()
// Original way to code: will return a = 4 when "f()" is called without a semicolon
a = 4;
b = 2;
end

function varargout = f()
// New way: will not return anything even when "f()" is called without a semicolon
a = 4;
b = 2;
varargout = manage_return_arguments(nargout, a, b);
end

function C = manage_return_arguments(nargs, varargin)
C = varargin(1:nargs);
end

I could have skipped nargs in manage_return_arguments() and use evalin(), but this will make the code nastily non-transparent. As a bonus, nargs can be fed with min(nargout, 3) instead of nargout for extra flexibility.

With the technique above, wfdbdesc.m can be simply rewritten as:

function varargout = wfdbdesc(recordName)
% varargout: siginfo, Fs, sigClass
...
varargout = manage_return_arguments(nargout, siginfo, Fs, sigClass);

Unless you are forwarding variable arguments (with technique#1 mentioned above), input arguments can be (and should be) named explicitly. Using varargin would not help you avoid padding the unused input arguments anyway, so there is absolutely no good reason to manage input variables with a flexible list. MATLAB already knows to skip unused arguments at the end as long as the code doesn’t need it. Use exist('someVariable', 'var') instead.

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## MATLAB Practices: Code and variable transparency. eval() is one letter away from evil() Transparency means that all references to variables must be visible in the text of the code

The general wisdom about eval() is: do not use it! At least not until you are really out of reasonable options after consulting more than 3 experts on the newsgroups, forums and support@mathworks.com (if your SMS is current)!

Abusing eval() turns it into evil()!

The elves running inside MATLAB needs to be able to track your variables to reason through your code because:

• it helps your code run much faster (eval() cannot be pre-compiled)
• able to use parallel computing toolbox (it has to know absolutely for sure about any shared writes)
• mlint can warn you about potentially pitfalls through code smell.
• it keeps you sane while debugging!

This is called ‘transparency’: MATLAB has to see what you are doing every step of the way. According to MATLAB’s parallel computing toolbox’s documentation,

Transparency means that all reference to variables must be visible in the text of the code

which I used as a subtitle of this post.

The 3 major built-in functions that breaks transparency are:

1. eval(), evalc(): arbitrary code execution resulting in read and write access to its caller workspace.
2. assignin(): it poofs variables in its caller’s workspace!
3. evalin(): it breaks open the stack and read the variables in its caller’s workspace!

They should have been replaced by skillful use of dynamic field names, advanced uses of left assignment techniques, and freely passing variables as input arguments (remember MATLAB uses copy-on-write: nothing is copied if you just read it).

There are other frequently used native MATLAB functions (under certain usages) that breaks transparency:

1. load(): poof variables from the file to the workspace. The best practice is to load the file as a struct, like S=load('file.mat'); , which is fully transparent. Organizing variables into structs actually reduces mental clutter (namespace pollution)!
2. save(), who(), whos(): basically anything that takes variable names as input and act on the requested variable violates transparency because it’s has the effect of evalin(). I guess the save() function chose to use variable names instead of the actual variables as input because copy-on-write wasn’t available in early days of MATLAB. A example workaround would be:
function save_transparent(filename, varargin)
VN = arrayfun(@inputname, (2:nargin)', 'UniformOutput', false);
if( any( cellfun(@isempty, VN) ) )
error('All variables to save must be named. Cannot be temporaries');
end

S = cell2struct(varargin(:), VN, 1);
save(filename, '-struct', 'S');
end

function save_struct_transparent(filename, S)
save(filename, '-struct', 'S');
end

The good practices to avoid non-transparent load/save also reduces namespace pollution. For example, inputname() peeks into the caller to see what the variable names are, which should not be used lightly. The example above is one of the few uses that I consider justified. I’ve seen novice abusing inputname() because they were not comfortable with cells and structs yet, making it a total mindfuck to reason through.

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## Switch between 32-bit and 64-bit user written software like CVX

CVX is a very convenient convex optimization package that allows the user to specify the optimization objective and constraints directly instead of manually manipulating them (by various transformations) into forms that are accepted by commonly available software like quadprog().

What I want to show today is not CVX, but a technique to handle the many different versions of the same program targeted at each system architecture (32/64-bit, Windows/Mac/Linux). Here’s a snapshot of what’s available with cvx:

 OS 32/64 mexext Download links Linux 32-bit mexglx cvx-glx.zip 64-bit mexa64 cvx-a64.zip Mac 32-bit mexmaci cvx-maci.zip 64-bit mexmaci64 cvx-maci64.zip Windows 32-bit mexw32 cvx-w32.zip 64-bit mexw64 cvx-w64.zip

You can download all packages for different architectures, but make a folder for each of them by their mexext() name. For example, 32-bit Windows’ implementation can go under /mexw32/cvx. Then you can programmatically initialize the right package for say, your startup.m file:

run( fullfile(yourLibrary, mexext(), 'cvx', 'cvx_startup.m') );

I intentionally put the /[mexext()] above /cvx, not the other way round because if you have many different software packages and want to include them in the path, you can do it in one shot without filtering for the platform names:

addpath( genpath( fullfile(yourLibrary, mexext()) ) );

You can consider using computer(‘arch’) in place of mexext(), but the names are different and you have to name your folders accordingly. For CVX, it happens to go by mexext(), so I naturally used mexext() instead.

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## MATLAB Gotchas: Do NOT use getlevels(), getlabels() or categories() for categorical/nominal/ordinal objects Use unique(), unique(cellstr()) instead

I suspect TMW (The MathWorks, maker of MATLAB) hasn’t really thought about dead levels when a categorical object (I mean nominal() and ordinal() as well since they are wrapper child class of categorical()) has elements removed so that some levels doesn’t map to any elements anymore.

For performance reasons, it makes sense to keep the dead levels in because the user can repetitively add and remove the same last level by deleting and adding the same element, causing unnecessary work each time. Naturally, there’s a getlevels()/getlabels()/categories() method in nominal(),ordinal()/categorical() class so you know what raw levels are available. Turns out it’s a horrible idea to expose the raw levels when dead levels are allowed!

Unless you are dealing with the internals of categorical objects, there’s very little reason why one would care or want to know about the dead levels (it’s just a cache for performance). It’s the active levels that are currently mapped to some elements that matters when user make such queries, which is handled correctly by unique().

If there are no dead levels, getlevels() is equivalent to unique(), while categorical() and getlabels() are equivalent to unique(cellstr()), but I’m very likely to run into dead levels because I delete rows of data when I filter by certain criterion.

My first take on it would be to hide getlevels()/getlabels()/categories() from users. But over the years, I’ve grown from a conservative software point of view to accepting more liberal approach, especially after exposure to functional programming ideas. That means I’d rather have a way to know what’s going on inside (keep those functions there), but I’d like to be warned that it’s an evil feature that shouldn’t be used lightly.

Yes, I’m dissing the use of getlevels()/getlabels()/categories() like the infamous eval(). Once in a long while, it’s the neat approach. But for 99% of the time, it’s a strictly worse solution that causes a lot of damages. It’s way more unlikely that getlevels()/getlabels()/categories() will yield what you really mean with dead levels than multiple inheritance in C++ being the right approach on the first try.

If I use unique() all the time, why would I even bother to talk about getlevels()/getlabels()/categories() since I never used them? It’s because TMW didn’t warn users about the dangers in their documentation. These methods looks legit and innocent, but it’s a usage trap like returning stack pointers in C/C++ (you can technically do it, but with almost 100% certainty, you are telling the computer to do something you don’t mean to, in short: wrong).

I have two encounters that other people using the raw categorical levels burns me:

1. One of my coworkers spoke against upgrading our MATLAB licenses (later withdrew his opposition) because the new versions breaks his old code involving nominal()/ordinal() objects. I was perplexed because it didn’t break any of my code despite I used more nominal() and ordinal() objects than anybody in my vicinity.On close inspection, he was using getlevels() and getlabels() all over the place instead of unique(), which works seamlessly in the new MATLAB. Remember I mentioned that the internal design/implementation details of nominal()/ordinal() changed in MATLAB R2013a? The internal treatment of dead levels changed, which is not relevant to end-users, but getlevels()/getlabels() exposed the dead levels and users unwittingly write code that depends on it.
2. The default factory-shipped grpstats() is still ‘broken’ as of R2015b! If you feed grpstats() with a nominal grouping variable, it will give you lines of NaN because it was programmed spits out one row for each level (dead or alive) in the grouping variable. Since the dead levels has nothing to group by the reduction function (@mean if not specified), it spits out NaNs. This is traced to how grp2idx() is used internally: If the grouping variable is a cellstr() or double(), the groups are generated by using unique(), so there are no dead levels whatsoever. But if the grouping variable is a categorical, the developers thought their job is done already and just took it directly from the categorical object’s properties by calling getlabels() and getlevels():
gidx = double(s);
...
gnames = getlabels(s)';
glevels = getlevels(s)';

Apparently they forgot that there’s a reason why the categorical/unique() has the same function name as double/unique() and cellstr/unique(): the point of overloading is to have the same function name for the same intention! The intention of unique() should be uniformly applied across all the data types applicable. Think twice before relying on language support for type info (type traits in C++) to switch code when you can use overloading(MATLAB)/polymorphism(C++). A good architecture should lead you to the correct code logic without the need of overriding good practices.

Rants aside, grpstats() will work as intended if those lines in grp2idx() are changed to:

gidx = double(s);
...
glevels = unique(s(:));
gnames = cellstr(glevels);


A higher level fix would be applying grp2idx() to the grouping variable before it was fed into grpstats():

grpstats(X, grp2idx(g), ...)

The rationale is that the underlying contents doesn’t matter for grouping variables as long as each of them uniquely stand for the group they represent! In other words, categorical() objects are seen as nothing but a bunch of integers, which can be obtained by casting it to double():

gidx = double(s);
grpstats(X, gidx, ...)

This is what grp2idx() calls under the hood anyway when it sees a categorical. The grp2idx() called from grpstats() will see a bunch of integers, which will correctly apply unique() to them, thus removing all dead levels.

Of course, use grp2idx() instead of double() because it works across all data types that applies. Why future-constrain yourself when a more generic implementation is already available?

The sin committed by grpstats() over nominal() is that the variables in glevels and gnames shouldn’t get involved in the first place because they don’t matter and shouldn’t even show up in the outputs. This is what’s fundamentally wrong about it:

[group,...,ngroups] = mgrp2idx(group,rows);
...
// This code assumes there are no gaps in group levels (gnum), which is not always true.
for gnum = 1:ngroups
groups{gnum} = find(group==gnum);
end

We can either blame the for-loop for not skipping dead levels, or blame mgrp2idx (a wrapper of grp2idx) for spitting out the dead levels. It doesn’t really matter which way it is. The most important thing is that dead levels were let loose, and nobody in the developer-user chain understand the implications enough to stop the problem from propagating to the final output.

To summarize, the raw levels in categorical objects is a dirty cache including junk you do not want 99.99% of the time. Use unique() to get the meaningful unique levels instead.

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## MATLAB Compatibility: nominal() and ordinal() objects since R2013a are not compatible with R2012b and before

In the old days (before R2013a), nominal() and ordinal() were separate parallel classes with astoundingly similar structures. That means there’s a lot of copy-paste-mod going on. TMW improved on it by consolidating the ideas into a new categorical() class, which nominal() and ordinal() derives from it.

The documentation mentioned that nominal() and ordinal() might be deprecated in the future, but I contacted their support urging them not to. It’s not for compatibility reasons: nominal() and ordinal() captures the common use cases that these two ideas do not need to be unified, and the names themselves clearly encodes the intention.

If the user want to exploit the commonalities between the two, either it’s already taken care of by the parent’s public methods, or the object can be sliced to make it happen. I looked into the source code for nominal() and ordinal(): it’s pretty much a wrapper over categorical’s methods yet the interface (input arguments) are much simpler and intuitive because we don’t have consider all the more general cases.

Back to the titled topic. Because categorical()’s properties (members) are different from pre R2013a’s nominal() and ordinal() objects, the objects created in R2012b or before cannot be loaded correctly in newer versions. That means the backward compatibility is completely broken for nominal()/ordinal() as far as saved objects are concerned.

There’s no good incentive to solve this problem on the TMWs side because the old nominal()/ordinal() is short-lived and they always want everybody to upgrade. Since I use nominal() most of the time and the ones that really need to be saved are all nominal(), I recommend the converting (‘casting’) them to cellstr by

>> A = nominal({'a','a','b','c'});
>> A = cellstr(A)
A =
'a'    'a'    'b'    'c'

Remember, nominal() is pretty much compressing a ton of cellstr into a few unique items and mapping the indices. No information is lost going back and forth between cellstr() and nominal(). It’s just a little extra computations for the conversion.

As for ordinal(), I rarely need to save it because order/level assignment is almost the very last thing in the processing chain because it changes so frequently (e.g. how would you draw the lines for six levels of fatness?), I might as well just not save it and reprocess the last step (where the code with ordinal() sits) when I need it.

Nonetheless, if you still want to save ordinals() instead of re-crunching it, this time you’ll want to save it as numerical levels by casting the ordinal() into double():

>> A = ordinal([1 2 3; 3 2 1; 2 1 3],{'low' 'medium' 'high'}, [3 1 2])
A =
medium      high        low
low         high        medium
high        medium      low
>> D = double(A)
D =
2     3     1
1     3     2
3     2     1
>> U = unique(A)
U =
low
medium
high
>> L = cellstr(U)
L =
'low'
'medium'
'high'
>> I = double(U)
I =
1
2
3
>> A_reconstructed = ordinal(D, L, I)
A_reconstructed =
medium      high        low
low         high        medium
high        medium      low

You’ll save (D, L, I) from old MATLAB and load it and reconstruct it with the triplets from the new MATLAB (I’d suggest using structs to keep track of the triplets). I know it’s a hairy mess!

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## MATLAB Gotchas: Adding whitespace in strcat()

strcat() is a very handy function in MATLAB that allows you to combine strings using a mixture of cellstr() and char strings and it will auto-expand the char strings to match the cellstr() if necessary.

However, by design intention, strcat() removes trailing white spaces by internally applying deblank() to all char string inputs. It does NOT deblank cellstr() inputs. So if you want to combine date and time with a space, you have to use {‘ ‘} instead of ‘ ‘:

date = '2000-01-01';
time = '00:00:01';
>> strcat(date, ' ', time)  % The ' ' is ignored
ans =
2000-01-0100:00:01
>> strcat(date, {' '}, time)  % The ' ' is ignored
ans =
'2000-01-01 00:00:01'
>>

I find this more confusing than helpful. Including myself and other users, we naturally resort to processing line by line using cellfun() or other tricks just to get around the deblank() problem without taking a second look at the documentation because

• rolling our own implementation is marginally as annoying as the deblank()
• we expect cellstr() to match the dimensions without auto-expanding. I naturally thought it would expand only if it’s a char string.

Well, somebody asked this question on the newsgroup before, so obviously it’s not an intuitive design. It make sense to do it the way MATLAB designed strcat() because we need some way to tell MATLAB whether I want my inputs deblanked or not.

I think it’s more intuitive to have MATLAB’s default strcat() not to deblank() char strings at all and have a strcat_deblanked() that deblanks the inputs before feeding into strcat().

Unfortunately this behavior is there for a long time, so it’s too late to change it without affecting compatibility. Might as well live with it, but this is one of the very few unnatural (or slightly illogical design choice) of MATLAB to keep in mind.

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## MATLAB Features: Persistent Excel ActiveX (DCOM) for xlsread() and xlswrite() R2015b

xlsread() and everything that calls it, such as readtable(), is terribly slow, especially when you have a boatload of Excel files to process. The reason behind it is that xlsread() closes that DCOM handle (which closes the Excel COM session) after it finishes, and restart Excel (DCOM) again when you call xlsread() again to load another spreadsheet file.

That means there’s a lot of opening and closing of the Excel application if you process multiple spreadsheets. The better strategy, which is covered extensively in MATLAB’s File Exchange (FEX), is to have your program open one Excel handle, and process all the spreadsheets within the same DCOM handle before closing it.

This strategy is quite overwhelming for a beginner, and even if you use FEX entries, you still cannot get around the fact that you have to know there’s a handle that manages the Excel session and remember to close it after you are done with it. Nothing beats having xlsread() do it automatically for you.

Starting with R2015b, the Excel DCOM handle called by xlsread() is now persistent: that after you make the first call to xlsread(), Excel.exe will stay in the memory unless you explicitly clear persistent variables or exit MATLAB, so you can reuse them every time xlsread() or xlswrite() is called. Finally!

The code itself is pretty slick. You can find it in ‘matlab.io.internal.getExcelInstance()’. Well, I guess it’s not hard to come up with it, but I guess in TMW, they must have a heated debate about whether it’s a good idea to keep Excel around (taking up resources) when you are done with it. With the computation power required to run R2015b, an extra Excel.exe lying around should be insignificant. Good call!

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## MATLAB Techniques: Who’s your daddy? Ask dbstack(). Unusual uses of dbstack()

Normally having your function know about its caller (other than through the arguments we pass onto the stack) is usually a very bad idea in program organization, because it introduces unnecessary coupling and hinders visibility.

Nonetheless, debugger is a built-in feature of MATLAB and it provides dbstack() so you have access to your call stack as part of your program. Normally, I couldn’t come up with legitimate uses of it outside debugging.

One day, I was trying to write a wrapper function that does the idiom (mentioned in my earlier blog post)

fileparts( mfilename('fullpath') );

because I want the code to be self-documenting. Let’s call the function mfilepath(). Turns out it’s a difficult problem because mfilename(‘fullpath’) reports the path of the current function executing it. In the case of a wrapper, it’s the path of the wrapper function, not its caller that you are hoping to extract its path from.

In other words, if you write a wrapper function, it’s the second layer of the stack that you are interested in. So it can be done with dbstack():

function p = mfullpath()
ST = dbstack('completenames');
try
p = ST(2).file;
catch
p = '';
end

Since exception handling is tightly knit into MATLAB (unlike C++, which you pay for the extra overhead), there aren’t much performance penalty to use a try…catch() block than if I checked if ST actually have a second layer (i.e. has a non-base caller). I can confidently do that because there is only one way for this ST(2).file access operation to fail: mfullpath() is called from the base workspace.

Speaking of catchy titles, I wonder why Loren Shure, a self-proclaimed lover of puns and the blogger of the Art of MATLAB, haven’t exploited the built-in functions ‘who’ and ‘whos’ in her April Fools jokes like

whos your daddy
who let the dogs out


Note that these are legitimate MATLAB syntax that won’t throw you an exception. Unless you have ‘your’, ‘daddy’, ‘let’, ‘the’, ‘dogs’, ‘out’ as variable names, the above will quietly show nothing. It’d be hilarious if they pass that as an easter egg in the official MATLAB. They already have ‘why’,

why not

Error using rng (line 125)
First input must be a nonnegative integer seed less than 2^32, 'shuffle', 'default', or generator settings captured previously using S = RNG.

Error in why (line 10)
dflt = rng(n,'v5uniform');

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## MATLAB Quirks: struct with no fields are not empty

As far as struct() is concerned, I’m more inclined to using Struct of Array (SoA) over Array of Structs (AoS), unless all the use cases screams for SoA. Performance and memory overhead are the obvious reasons, but the true motivation for me to use SoA is that I’m thinking in terms of table-oriented programming (which I’ll discuss in later posts. See table() objects.): each field of a struct is a column in a table (heterogeneous array).

Since a table() is considered empty (by isempty()) if it has EITHER rows INCLUSIVE OR columns (no fields) and the default constructor creates a table, I thought struct() would do the same. NOT TRUE!

First of all, the default constructor of struct() gives ONE struct with NO FIELDS (so it’s supposed to correspond to a table). What’s even harder to remember is that struct2table(struct()) gives a table.

The second thing I missed is that a struct() with NO fields is NOT empty. You can have 3 structs with NO fields! So isempty(struct()) is always false!

I usually run into this problem when I want to seed the execution with an empty struct() and have the loop expand the fields if the file has contents in it, and I’ll check if the seeded struct was untouched to see if I can read data from the file. Next time I will remember to call struct([]) instead of struct(). What a trap!

At the end of the day, while struct is powerful, but I rarely find AoS necessary to do what I wanted once table() is out. AoS has pretty much the same restrictions as in table() that you cannot put different types in the same field across the AoS, but table allows you to index with variables (struct’s field) or rows (struct array index) without changing the data structure (AoS <-> SoA). So unless it’s a performance critical piece of the code, I’ll stick with tables() for most of my struct() needs.

1,666 total views, 5 views today

## MATLAB Techniques: onCleanup() ‘destructor’

If your program opens a file, creates a timer(), or whatever resources that needs to be closed when they are no longer needed, before R2008a, you have to put your close resource calls at two places: one at the end of successful execution, the other at the exception handling in try…catch block:

FID = fopen('a.txt')
try
// ... do something here
fclose(FID);
catch
fclose(FID);
end

Not only it’s messy that you have to duplicate your code, it’s also error prone when you add code in between. If your true intention is to close the resource whenever you exit the current program scope, there’s a better way for you: onCleanup() object. The code above can be simplified as:

FID = fopen('a.txt')
obj = onCleanup(@() fclose(FID));
// ... do something with FID here

The way onCleanup() works is that it creates an object which you define what its destructor (delete()) does on creation (by the constructor of course) by specifying a function handle. This way when ‘obj’ is cleared (either as it goes out of scope or your explicitly cleared it with ‘clear’), the destructor in ‘obj’ will be activated and do the cleanup actions you specified.

Due to copyright reasons, I won’t copy the simple code here. Just open onCleanup.m in MATLAB editor and you’ll see it that the code (excluding comments) has less words than the description above. Pretty neat!

Normally we use onCleanup() inside a function. The best place to put is is right after you opened a resource because anything in between can go wrong (i.e. might throw exceptions): you want ‘obj’ to be swept (i.e. its destructors called) when that happens.

Technically, you can make an onCleanup() object in the base (root) workspace (aka command window). The destructor will be triggered either when you clear the ‘obj’ explicitly using ‘clear’ or when you exit MATLAB. You can see for yourself with this:

obj = onCleanup(@() pause);

It kind of let you do a one-off cleanup on exit instead of a recurring cleanup in finish.m.

So the next time you open a resource that needs to be closed whether the program exits unexpectedly or not, use onCleanup()! It’s one of the elegant, smart uses of OOP.

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