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If I wanted to create an array of specified class I would use an approach like this. So creating an array of int looks like this:
Aint = int16.empty(5,0);
Aint(1) = 3;
And it works fine. Now I want to create an array of tf class objects. My approach was similar:
L = tf.empty(5, 0);
s = tf('s');
L(1) = s;
This gives me an error:
Error using InputOutputModel/subsasgn (line 57)
Not enough input arguments.
Error in tf_array (line 6)
L(1) = s;
I also made sure to display class(s) and it correctly says it's tf. What do I do wrong here?
As usual, the MATLAB documentation has an example for how to do this sort of thing:
sys = tf(zeros(1,1,3));
s = tf('s');
for k = 1:3
sys(:,:,k) = k/(s^2+s+k);
end
So, the problem likely is that the indexing L(1) is wrong, it needs to be L(:,:,1).
Do note that tf.empty(5, 0) is instructing to create a 5x0 array (i.e. an empty array). There is no point to this. You might as well just skip this instruction. Because when you later do L(:,:,1), you'll be increasing the array size any way (it starts with 0 elements, you want to assign a new element, it needs to reallocate the array). You should always strive to create the arrays of the right size from the start.
I have a segment of code where a composition of nested loops needs to be run at various times; however, each time the operations within the nested loops are different. Is there a way to make the outer portion (loop composition) somehow a functional piece, so that the internal operations are variable. For example, below, two code blocks are shown which both use the same loop introduction, but have different purposes. According to the principle of DRY, how can I improve this, so as not to need to repeat myself each time a similar loop needs to be used?
% BLOCK 1
for a = 0:max(aVec)
for p = find(aVec'==a)
iDval = iDauVec{p};
switch numel(iDval)
case 2
r = rEqVec(iDval);
qVec(iDval(1)) = qVec(p) * (r(2)^0.5 / (r(1)^0.5 + r(2)^0.5));
qVec(iDval(2)) = qVec(p) - qVec(iDval(1));
case 1
qVec(iDval) = qVec(p);
end
end
end
% BLOCK 2
for gen = 0:max(genVec)-1
for p = find(genVec'==gen)
iDval = iDauVec{p};
QinitVec(iDval) = QinitVec(p)/numel(iDval);
end
end
You can write your loop structure as a function, which takes a function handle as one of its inputs. Within the loop structure, you can call this function to carry out your operation.
It looks as if the code inside the loop needs the values of p and iDval, and needs to assign to different elements of a vector variable in the workspace. In that case a suitable function definition might be something like this:
function vec = applyFunctionInLoop(aVec, vec, iDauVec, funcToApply)
for a = 0:max(aVec)
for p = find(aVec'==a)
iDval = iDauVec{p};
vec = funcToApply(vec, iDval, p);
end
end
end
You would need to put the code for each different operation you want to carry out in this way into a function with suitable input and output arguments:
function qvec = myFunc1(qVec, iDval, p)
switch numel(iDval)
case 2
r = rEqVec(iDval); % see note
qVec(iDval(1)) = qVec(p) * (r(2)^0.5 / (r(1)^0.5 + r(2)^0.5));
qVec(iDval(2)) = qVec(p) - qVec(iDval(1));
case 1
qVec(iDval) = qVec(p);
end
end
function v = myFunc2(v, ix, q)
v(ix) = v(q)/numel(ix);
end
Now you can use your loop structure to apply each function:
qvec = applyFunctionInLoop(aVec, qVec, iDauVec, myFunc1);
QinitVec = applyFunctionInLoop(aVec, QinitVec, iDauVec, myFunc2);
and so on.
In most of the answer I've kept to the same variable names you used in your question, but in the definition of myFunc2 I've changed the names to emphasise that these variables are local to the function definition - the function is not operating on the variables you passed in to it, but on the values of those variables, which is why we have to pass the final value of the vector out again.
Note that if you want to use the values of other variables in your functions, such as rEqVec in myFunc1, you need to think about whether those variables will be available in the function's workspace. I recommend reading these help pages on the Mathworks site:
Share Data Between Workspaces
Dynamic Function Creation with Anonymous and Nested Functions
We have a script that defines values to names similar to #define in c. For example:
script.m:
ERR_NOERROR = 0;
ERR_FATAL = 1;
This script already exists and is used for value replacement when reading data from files.
Now we have a function (or more) that does some analysis and we would like to use the same definition in this function to avoid magic numbers. But when the script is called from the function we get an error.
Attempt to add "ERR_NOERROR" to a static workspace.
See MATLAB Programming, Restrictions on Assigning to Variables for details.
And this does not help much in the understanding of the problem.
The question is how can we make these definitions visible/usable in the functions with having to copying it every time.
Example:
function foo = bar(a)
run(script.m) %also tried running it without the run command
if a == ERR_NOERROR
foo = 5;
else
foo = 6;
end
end
edit:
There was a nested function,below in the function which I was not aware of. This explains the problem.
This kind of scoping error happens when you use nested or anonymous function within a function. The solution is well documented.
To your case, you can avoid nested function, or "Convert the script to a function and pass the variable using arguments", as the documentation suggests.
EDIT: I should have made it clear that the error occurs even if the script is not called within the nested function. Similar scenario is that, in debug mode (by setting up a break point), it will be an error if one tries to create a temporal variable to test something.
This is not a direct answer, rather a recommendation to switch to another method, which will not be mixing scope and workspace.
Instead of defining your constant in a script, you could make a class containing only constant properties. ex: code for error_codes.m:
classdef error_codes
% ---------------------------------------------------------------------
% Constant error code definition
% ---------------------------------------------------------------------
properties (Constant = true)
noerror = 0 ;
fatal = 1 ;
errorlvl2 = 2 ;
errorlvl3 = 3 ;
warning = -1 ;
% etc ...
end
end
I use this style for many different type of constants. For tidiness, I groups them all in a Matlab package directory (The directories which starts with a + character.
The added benefit of using constant class properties is the safety that the values cannot be changed in the middle of the code (your variables defined in a script could easily be overwritten by a careless user).
So assuming my file error_codes.m is placed in a folder:
\...somepath...\+Constants\error_codes.m
and of course the folder +Constants is on the MATLAB path, then to use it as in your example, instead of calling the script, just initialise an instance of the class, then use the constant values when you need them:
function foo = bar(a)
ERR = Constants.error_codes ;
if a == ERR.noerror
foo = 5;
else
foo = 6;
end
or it can works in switch statement too:
switch a
case ERR.noerror
foo = 5 ;
case ERR.warning
foo = 42 ;
case ERR.fatal
foo = [] ;
end
I have a function for cached evaluation. As one of the arguments, it takes a function handle. Under some circumstances, the function handle is unaccessible, and I don't quite understand why. The example below shows what got me stumped:
>> A.a = #plus; feval(#A.a, 1, 1)
ans =
2
>> clear A
>> A.a.a = #plus; feval(#A.a.a, 1, 1)
Error using feval
Undefined function 'A.a.a' for input arguments of type 'double'.
So, if I have a function handle stored as a structure member, I can pass it along fine if it's one level deep, but not if it's two levels deep. In my real use case, I have a structure D that holds many (117) instances of various classes, so I actually have stct.obj.meth, where stct is a structure, obj is a class instance/object, and meth is a method. Passing #stct.obj.meth fails, but if I assign A = stct.obj, then passing #A.meth succeeds.
Under what conditions can I pass a function handle as an argument, so that it's still accessible down the stack?
Edit: Although in the use case above, I could simply remove the # because #plus is already a function handle. However, consider the situation here:
>> type cltest.m
classdef cltest < handle
methods
function C = mymeth(self, a, b)
C = a + b;
end
end
end
>> A.a = cltest();
>> feval(#A.a.mymeth, 1, 1)
Error using feval
Undefined function 'A.a.mymeth' for input arguments of type 'double'.
>> b = A.a;
>> feval(#b.mymeth, 1, 1)
ans =
2
In this case, I need the # before A.a.mymeth...
Introducing classes was a big deal for MATLAB. So big, in fact, that they still do not work properly today. Your example shows that structure access and class method access conflict, because they had to overload the the meaning of dot '.' and didn't get it to work seamlessly. It all more or less works fine when you are calling class methods explicitly by their name on the MATLAB console, e.g. in your example >> A.a.mymeth(1,1). But when you have any type of indirection, it soon breaks.
You tried getting the function handle by >> #A.a.mymeth, which MATLAB cannot make sense of, probably because it gets confused by the mixed structure/class thing. Trying to work around using str2func doesn't work either. It works, again, only for explicit name access, as shown here. It breaks for your example, e.g. >> str2func('b.mymeth'). It does not even work inside the class. Try other indirections and watch them fail.
Additionally, MATLAB does not like giving you a class method's handles. There's no function for it. There's no way to get all function handles in one go, or even dynamically by a name string.
I see three options here. First, try changing your program, if possible. Do these functions need to sit in a classdef?
Second, follow your or nispio's workaround. They both create a temporary variable to hold a reference to the class instance in order to create a non-mixed access to its member methods. The problem is, they both require explicitly naming the function. You have to explicitly put this code for every function involved. No way to abstract that out.
Third, cheat by giving out your class' method handles from the inside. You can give them out in a structure.
classdef cltest < handle
methods
function C = mymeth(self, a, b)
C = a + b;
end
function hs = funhandles(self)
hs = struct('mymeth', #self.mymeth, ...
'mymeth2', #self.mymeth2);
end
end
end
You can then access the handles by name, even dynamically.
>> A.a = cltest;
>> feval(A.a.funhandles.mymeth, 1, 1);
>> feval(A.a.funhandles.('mymeth'), 1, 1)
ans =
2
But be careful, by using this you can access Access=private methods from outside.
Try this:
feval(#(varargin)A.a.mymeth(varargin{:}),1,1);
It is a little kludgy, but it should work.
EDIT:
The way it works is by creating an Anonymous Function that takes a variable number of arguments, and dumps those arguments into the method A.a.mymeth(). So you are not actually passing a pointer to the function A.a.mymeth, you are passing a pointer to a function that calls A.a.mymeth.
An alternative way of achieving the same thing without using varargin would be:
feval(#(x,y)A.a.mymeth(x,y),1,1);
This creates an anonymous function that accepts two arguments, and passes them along to A.a.mymeth.
<speculation> I think that it must be inherent in the way that the unary function handle operator # works. The Matlab parser probably looks at #token and decides whether token is a valid function. In the case of a.mymeth it is smart enough to decide that mymeth is a member of a, and then return the appropriate handle. However, when it sees A.a.mymeth it may discover that A is not a class, nor does A have a member named a.mymeth and therefore no valid function is found. This seems to be supported by the fact that this works:
A.a.a = #plus; feval(A.a.a,1,1)
and this doesn't:
A.a.a = #plus; feval(#A.a.a,1,1)
</speculation>
You can get around it by introducing a separate function that corrects what # operator is not doing:
function h=g(f)
x = functions(f);
if ~strcmp(x.type, 'anonymous')
h = evalin('caller', ['#(varargin)' x.function '(varargin{:})']);
else
h = f;
end
end
Now for your example:
>> feval(g(#A.a.mymeth), 1, 1)
ans =
2
>> feval(g(#b.mymeth), 1, 1)
ans =
2
I think this will have the smallest impact on your code. You can make it a bit more elegant but less robust and/or readable. The uplus method is not defined for function_handle class so you can create uplus.m in folder #function_handle somewhere in your path with this content:
function h=uplus(f)
x = functions(f);
if ~strcmp(x.type, 'anonymous')
h = evalin('caller', ['#(varargin)' x.function '(varargin{:})']);
else
h = f;
end
end
Now you just need to use +# instead of #. For your examples:
>> feval(+#A.a.mymeth, 1, 1)
ans =
2
>> feval(+#b.mymeth, 1, 1)
ans =
2
Aside from parsing the function file, is there a way to get the names of the input and output arguments to a function in matlab?
For example, given the following function file:
divide.m
function [value, remain] = divide(left, right)
value = floor(left / right);
remain = left / right - value;
end
From outside the function, I want to get an array of output arguments, here: ['value', 'remain'], and similarly for the input arguments: ['left', 'right'].
Is there an easy way to do this in matlab? Matlab usually seems to support reflection pretty well.
EDIT Background:
The aim of this is to present the function parameters in a window for the user to enter. I'm writing a kind of signal processing program, and functions to perform operations on these signals are stored in a subfolder. I already have a list and the names of each function from which the user can select, but some functions require additional arguments (e.g. a smooth function might take window size as a parameter).
At the moment, I can add a new function to the subfolder which the program will find, and the user can select it to perform an operation. What I'm missing is for the user to specify the input and output parameters, and here I've hit the hurdle here in that I can't find the names of the functions.
MATLAB offers a way to get information about class metadata (using the meta package), however this is only available for OOP classes not regular functions.
One trick is to write a class definition on the fly, which contain the source of the function you would like to process, and let MATLAB deal with the parsing of the source code (which can be tricky as you'd imagine: function definition line spans multiple lines, comments before the actual definition, etc...)
So the temporary file created in your case would look like:
classdef SomeTempClassName
methods
function [value, remain] = divide(left, right)
%# ...
end
end
end
which can be then passed to meta.class.fromName to parse for metadata...
Here is a quick-and-dirty implementation of this hack:
function [inputNames,outputNames] = getArgNames(functionFile)
%# get some random file name
fname = tempname;
[~,fname] = fileparts(fname);
%# read input function content as string
str = fileread(which(functionFile));
%# build a class containing that function source, and write it to file
fid = fopen([fname '.m'], 'w');
fprintf(fid, 'classdef %s; methods;\n %s\n end; end', fname, str);
fclose(fid);
%# terminating function definition with an end statement is not
%# always required, but now becomes required with classdef
missingEndErrMsg = 'An END might be missing, possibly matching CLASSDEF.';
c = checkcode([fname '.m']); %# run mlint code analyzer on file
if ismember(missingEndErrMsg,{c.message})
% append "end" keyword to class file
str = fileread([fname '.m']);
fid = fopen([fname '.m'], 'w');
fprintf(fid, '%s \n end', str);
fclose(fid);
end
%# refresh path to force MATLAB to detect new class
rehash
%# introspection (deal with cases of nested/sub-function)
m = meta.class.fromName(fname);
idx = find(ismember({m.MethodList.Name},functionFile));
inputNames = m.MethodList(idx).InputNames;
outputNames = m.MethodList(idx).OutputNames;
%# delete temp file when done
delete([fname '.m'])
end
and simply run as:
>> [in,out] = getArgNames('divide')
in =
'left'
'right'
out =
'value'
'remain'
If your problem is limited to the simple case where you want to parse the function declaration line of a primary function in a file (i.e. you won't be dealing with local functions, nested functions, or anonymous functions), then you can extract the input and output argument names as they appear in the file using some standard string operations and regular expressions. The function declaration line has a standard format, but you have to account for a few variations due to:
Varying amounts of white space or blank lines,
The presence of single-line or block comments, and
Having the declaration broken up on more than one line.
(It turns out that accounting for a block comment was the trickiest part...)
I've put together a function get_arg_names that will handle all the above. If you give it a path to the function file, it will return two cell arrays containing your input and output parameter strings (or empty cell arrays if there are none). Note that functions with variable input or output lists will simply list 'varargin' or 'varargout', respectively, for the variable names. Here's the function:
function [inputNames, outputNames] = get_arg_names(filePath)
% Open the file:
fid = fopen(filePath);
% Skip leading comments and empty lines:
defLine = '';
while all(isspace(defLine))
defLine = strip_comments(fgets(fid));
end
% Collect all lines if the definition is on multiple lines:
index = strfind(defLine, '...');
while ~isempty(index)
defLine = [defLine(1:index-1) strip_comments(fgets(fid))];
index = strfind(defLine, '...');
end
% Close the file:
fclose(fid);
% Create the regular expression to match:
matchStr = '\s*function\s+';
if any(defLine == '=')
matchStr = strcat(matchStr, '\[?(?<outArgs>[\w, ]*)\]?\s*=\s*');
end
matchStr = strcat(matchStr, '\w+\s*\(?(?<inArgs>[\w, ]*)\)?');
% Parse the definition line (case insensitive):
argStruct = regexpi(defLine, matchStr, 'names');
% Format the input argument names:
if isfield(argStruct, 'inArgs') && ~isempty(argStruct.inArgs)
inputNames = strtrim(textscan(argStruct.inArgs, '%s', ...
'Delimiter', ','));
else
inputNames = {};
end
% Format the output argument names:
if isfield(argStruct, 'outArgs') && ~isempty(argStruct.outArgs)
outputNames = strtrim(textscan(argStruct.outArgs, '%s', ...
'Delimiter', ','));
else
outputNames = {};
end
% Nested functions:
function str = strip_comments(str)
if strcmp(strtrim(str), '%{')
strip_comment_block;
str = strip_comments(fgets(fid));
else
str = strtok([' ' str], '%');
end
end
function strip_comment_block
str = strtrim(fgets(fid));
while ~strcmp(str, '%}')
if strcmp(str, '%{')
strip_comment_block;
end
str = strtrim(fgets(fid));
end
end
end
This is going to be very hard (read: impossible) to do for general functions (think of things like varargin, etc). Also, in general, relying on variable names as a form of documentation might be... not what you want. I'm going to suggest a different approach.
Since you control the program, what about specifying each module not just with the m-file, but also with a table entry with extra information. You could document the extra parameters, the function itself, notate when options are booleans and present them as checkboxes, etc.
Now, where to put this? I would suggest to have the main m-file function return the structure, as sort of a module loading step, with a function handle that points to the subfunction (or nested function) that does the real work. This preserves the single-file setup that I'm sure you want to keep, and makes for a much more configurable setup for your modules.
function module = divide_load()
module.fn = #my_divide;
module.name = 'Divide';
module.description = 'Divide two signals';
module.param(1).name = 'left';
module.param(1).description = 'left signal';
module.param(1).required_shape = 'columnvector';
% Etc, etc.
function [value, remain] = my_divide(left, right)
value = floor(left / right);
remain = left / right - value;
end
end
When you can't get information from a programming langauge about its contents (e.g., "reflection"), you have to step outside the language.
Another poster suggested "regular expressions", which always fail when applied to parsing real programs because regexps cannot parse context free langauges.
To do this reliably, you need a real M language parser, that will give you access to the parse tree. Then this is fairly easy.
Our DMS Software Reengineering Toolkit has an M language parser available for it, and could do this.
Have you considered using map containers?
You can write your functions along these lines . . .
function [outMAP] = divide(inMAP)
outMAP = containers.Map();
outMAP('value') = floor(inMAP('left') / inMAP('right'));
outMAP('remain') = inMAP('left') / inMAP('right') - outMAP('value');
end
...and call them like this ...
inMAP = containers.Map({'left', 'right'}, {4, 5});
outMAP = divide(inMAP);
...and then simply examine tha variable names using the following syntax...
>> keys(inMAP)
ans =
'left' 'right'
inputname(argnum) http://www.mathworks.com/help/techdoc/ref/inputname.html .