I have a code which consists of a single file containing multiple functions, some of which use persistent variables. In order for it to work correctly, persistent variables must be empty.
There are documented ways to clear persistent variables in a multi-function file, such as:
clear functionName % least destructive
clear functions % more destructive
clear all % most destructive
Unfortunately, I cannot guarantee that the user remembers to clear the persistent variables before calling the function, so I'm exploring ways to perform the clearing operation at the beginning of the code. To illustrate the problem, consider the following example:
function clearPersistent(methodId)
if ~nargin, methodId = 0; end
switch methodId
case 0
% do nothing
case 1
clear(mfilename);
case 2
eval(sprintf('clear %s', mfilename));
case 3
clear functions;
case 4
clear all;
end
subfunction();
subfunction();
end
function [] = subfunction()
persistent val
if isempty(val)
disp("val is empty");
val = 123;
else
disp("val is not empty");
end
end
When first running this, we get:
>> clearPersistent
val is empty
val is not empty
I would expect that running the function again at this point, with any of the non-0 inputs would result in the val variable being cleared, but alas - this is not the case. After val is set, unless we use one of the alternatives shown in the top snippet externally, or modify the .m file, it remains set.
My question: Is it possible to clear persistent variable in subfunctions from within the body of the main function, and if yes - how?
In other words, I'm looking for some code that I can put in clearPersistent before calling the subfunctions, such that the output is consistently:
val is empty
val is not empty
P.S.
Here's a related past question (which doesn't deal with this specific use case): List/view/clear persistent variables in Matlab.
I'm aware of the possibility of rewriting the code to not use persistent variables at all (e.g. by passing data around, using appdata, adding a 'clear' flag to all subfunctions, etc.).
Please note that editing the source code of the function and saving implicitly clears it (along with all persistent variables).
I'm aware that the documentation states that "The clear function does not clear persistent variables in local or nested functions.
Additional background on the problem:
The structure of the actual code is as follows:
Main function (called once)
└ Global optimization solver (called once)
└ Objective function (called an unknown N≫1 times)
└ 1st function that uses persistents
└ 2nd function that uses persistents
As mentioned in the comments, there are several reasons why some variables were made persistent:
Loose coupling / SoC: The objective function does not need to be aware of how the subfunctions work.
Encapsulation: It is an implementation detail. The persistent variables do not need to exist outside the scope of the function that uses them (i.e. nobody else ever needs them).
Performance: The persistent variables contain matrices that are fairly expensive to compute, but this operation needs to happen only once per invocation of the main function.
One (side?) effect of using persistent variables is making the entire code stateful (with two states: before and after the expensive computations). The original issue stems from the fact that the state was not being correctly reset between invocations of the main function, causing runs to rely on a state created with previous (and thus invalid) configurations.
It is possible to avoid being stateful by computing the one-time values in the main function (which currently only parses user-supplied configurations, calls the solver, and finally stores/displays outputs), then passing them alongside the user configurations into the objective function, which would then pass them on to the subfunctions. This approach solves the state-clearing problem, but hurts encapsulation and increases coupling, which in turn might hurt maintainability.
Unfortunately, the objective function has no flag that says 'init' etc., so we don't know if it's called for the 1st or the nth time, without keeping track of this ourselves (AKA state).
The ideal solution would have several properties:
Compute expensive quantities once.
Be stateless.
Not pass irrelevant data around (i.e. "need to know basis"; individual function workspaces only contain the data they need).
clear fname and clear functions removes the M-file from memory. The next time you run the function, it is read from disk again, parsed and compiled into bytecode. Thus, you slow down the next execution of the function.
Clearing a function or sub-function from within a function thus does not work. You're running the function, you cannot clear its file from memory.
My solution would be to add an option to subfunction to clear its persistent variable, like so:
function clearPersistent()
subfunction('clear');
subfunction();
subfunction();
end
function [] = subfunction(option)
persistent val
if nargin>0 && ischar(option) && strcmp(option,'clear')
val = [];
return
end
if isempty(val)
disp("val is empty");
val = 123;
else
disp("val is not empty");
end
end
Of course you could initialize your value when called as subfunction('init') instead.
A different solution that might work for your usecase is to separate the computation of val and its use. I would find this easier to read than any of the other solutions, and would be more performant too.
function main()
val = computeval();
subfunction(val);
subfunction(val);
end
Given your edit, you could put the objective function in a separate file (in the private subdirectory). You will be able to clear it.
An alternative to persistent variables would be to create a user class with a constructor that computed the expensive state, and another method to compute the objective function. This could also be a classdef file in the private subdirectory. I think this is nicer because you won’t need to remember to call clear.
In both these cases you don’t have a single file containing all the code any more. I think you need to give up on one of those two ideals: either break data encapsulation or split the code across two files (code encapsulation?).
Why not using global variables?
You can create a global struct that contains your variables and it can be managed using a variable_manager:
function main
variable_manager('init')
subfunction1()
subfunction2()
end
function variable_manager(action)
global globals
switch action
case 'init'
globals = struct('val',[],'foo',[]);
case 'clear'
globals = structfun(#(x)[],globals,'UniformOutput', false);
% case ....
% ...
end
end
function subfunction1
global globals
if isempty(globals.val)
disp("val is empty");
globals.val = 123;
else
disp("val is not empty");
end
end
function subfunction2
global globals
if isempty(globals.foo)
disp("foo is empty");
globals.foo = 321;
else
disp("foo is not empty");
end
end
As mentioned in the question, one of the possibilities is using appdata, which is not too different from global (at least when associating them with "object 0" - which is the MATLAB instance itself). To avoid "collisions" with other scripts/functions/etc. we introduce a random string (if we generate a string in every function that uses this storage technique, it would almost certainly guarantee no collisions). The main downside of this approach is that the string has to be hard-coded in multiple places, or the structure of the code should be changed such that the functions that use this appdata are nested within the function that defines it.
The two ways to write this are:
function clearPersistent()
% Initialization - clear the first time:
clearAppData();
% "Business logic"
subfunction();
subfunction();
% Clear again, just to be sure:
clearAppData();
end % clearPersistent
function [] = subfunction()
APPDATA_NAME = "pZGKmHt6HzkkakvdfLV8"; % Some random string, to avoid "global collisions"
val = getappdata(0, APPDATA_NAME);
if isempty(val)
disp("val is empty");
val = 123;
setappdata(0, APPDATA_NAME, val);
else
disp("val is not empty");
end
end % subfunction
function [] = clearAppData()
APPDATA_NAME = "pZGKmHt6HzkkakvdfLV8";
if isappdata(0, APPDATA_NAME)
rmappdata(0, APPDATA_NAME);
end
end % clearAppData
and:
function clearPersistent()
APPDATA_NAME = "pZGKmHt6HzkkakvdfLV8";
% Initialization - clear the first time:
clearAppData();
% "Business logic"
subfunction();
subfunction();
% Clear again, just to be sure:
clearAppData();
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function [] = subfunction()
val = getappdata(0, APPDATA_NAME);
if isempty(val)
disp("val is empty");
val = 123;
setappdata(0, APPDATA_NAME, val);
else
disp("val is not empty");
end
end % subfunction
function [] = clearAppData()
if isappdata(0, APPDATA_NAME)
rmappdata(0, APPDATA_NAME);
end
end % clearAppData
end % clearPersistent
Related
I am writing a class-based test suite in MATLAB for a timeseries handling package. The first test in my suite needs to check whether a connection exists to a Haver database on a network drive. If the connection does not exist, then the first test should abort the rest of the suite using one of the fatalAssert methods.
One complicating factor, which I have excluded from the exposition below, but I will mention now is that I need to use an anonymous function to check the connection to Haver (unless someone has a better idea). My package handles data from multiple sources, Haver only being one of them.
I have a parent-class test suite that performs general tests for all of the sources. I then inherit this parent-class into specific child-class test suites and set specific parameters in their respective TestMethodSetup method. One of these parameters is an anonymous function, connfun, and a location, connloc, which I use in the parent-class to test the connection. The reason I do this is because the parent tests are executed first, so I would have to wait for all of those to end if I wanted to test the connection in the child class.
This also complicates the order of execution. If I want to assign the connfun in the child class, then I have to use either the TestMethodSetup or TestClassSetup of the child class (open to recommendations on which is best here) and put this connection test in the Test method of the parent class. I noticed the if I put checkConn in the TestMethodSetup and TestClassSetup of the parent class was running before that of the child class, I was unable to pass the anonymous function and the test would be incomplete.
Putting the previous point aside for a moment, this was my first attempt at writing the test in the parent-class (note that I used a fatalAssertEqual instead of a fatalAssertTrue because isconnection() does not return a logical):
methods (Test)
function checkConn(testCase)
connloc = 'pathToHaverDatabase';
connfun = #(x) isconnection(haver(x));
testCase.fatalAssertEqual(connfun(connloc), 1);
end
end
The above works when there is a connection, but the problem that I bumped into with this is that when I cannot access connloc, an error ocurrs during the call to haver(). So instead of returning a 1 or 0 from the isconnection() call that I can fatalAssertEqual on, all of checkConn errors out due to haver(). This then leads to the rest of the tests running (and failing, which is exactly what I want to avoid).
My next idea works for both cases, but it feels like bad code, and does not have the anonymous function specification described above.
methods (Test)
function checkConn(testCase)
connloc = 'pathToHaverDatabase';
connfun = #(x) isconnection(haver(x));
try
isconn = connfun(connloc);
catch
isconn = 0;
end
testCase.fatalAssertEqual(isconn, 1)
end
end
When I wrote this, I did not necessarily want to distinguish between not having access to the network drive, not being able to call the haver() function, and getting an isconnection equal to 0 because the last case covers all three. But I realized that if I did differentiate them, then it would be a bit more robust, but it's still missing the anonymous function taht I could pass from child to parent.
properties
connloc = 'pathToHaverDatabase';
end
methods (Test)
function checkDrive(testCase)
isfound = fillattrib(testCase.connloc);
testCase.fatalAssertTrue(isfound);
end
function checkHaver(testCase)
try
hav = haver(testCase.connloc);
ishaver = ~isempty(hav);
catch
ishaver = false;
end
testCase.fatalAssertTrue(ishaver);
end
function checkConn(testCase)
connfun = #(x) isconnection(haver(x));
testCase.fatalAssertEqual(connfun(testCase.connloc), 1);
end
end
Ideally, what I would want is a fatalAssert method (or something similar) that ends the test suite when its input is an error. Something that would perhaps be called fatalAssertNotError, but I don't think that exists. If it did, the last line of my first function would simply be testCase.fatalAssertNotError(connfun(connloc)) and I would not have to worry about all the cases.
I'm very open to dynamic rewrite of this whole test setup, so any specific comments or general advice are welcome!
First of all, I think the fatalAssert case is a strong use case to provide something like fatalAssertNotError. One reason why it is not part of the package is because many/most times people don't want to check whether something doesn't error, they just want to call the code, and if it errors, it fails for the test author automatically and it is much simpler. However, other qualification types like fatal assertions and assumptions perhaps point to the need to provide this so you can choose the outcome of the test in the presence of an error, in cases where you don't want it to fail (like with assumptions) or you want it to fail "more strongly" like with fatal assertions.
That being said, I am still not convinced that you can't achieve what you are ultimately trying to do without it. The question I have centers around why you can't use TestClassSetup. It is not clear to me exactly why you weren't able to customize in the derived test class to get the behavior you want. For example, does something like this work?
classdef BaseTest < matlab.unittest.TestCase
properties(Abstract)
connloc
connfun
end
methods(TestClassSetup)
function validateConnection(testCase)
% If this errors it behaves like an assertion (not fatal assertion)
% and fails all tests in the test class. If it doesn't error but
% doesn't return 1 then the assertion failure will occur.
testCase.assertEqual(connfun(connloc), 1,
'Could not establish a connection to the database');
end
end
end
classdef DerivedTest < BaseTest
properties
connloc = 'pathToHaverDatabase';
connfun = #(x) isconnection(haver(x));
end
methods(Test)
function testSomething(testCase)
% Have at least one test method to test it out
end
end
end
Hope that helps!
If you really want to use a function you can define a nested one like this:
methods (Test)
function checkConn(testCase)
connloc = 'pathToHaverDatabase';
function res = connfun(x)
try
res = isconnection(haver(x));
catch
res = false
end
end
testCase.fatalAssertEqual(connfun(connloc), 1);
end
end
Nested functions can be a bit confusing to me because of the way they share data with the parent function. There really is no difference between an anonymous function and a nested function.
The alternative is to put the function at the end of the file, outside the classdef block:
classdef ...
%...
methods (Test)
function checkConn(testCase)
connloc = 'pathToHaverDatabase';
function res = connfun(x)
try
res = isconnection(haver(x));
catch
res = false
end
end
testCase.fatalAssertEqual(connfun(connloc), 1);
end
end
%...
end
function res = connfun(x)
try
res = isconnection(haver(x));
catch
res = false
end
end
But I honestly don't understand why you need to have a function call within fatalAssertEqual. The code you have seems perfectly fine to me.
Background -- I was reading up on accessing shadowed functions, and started playing with builtin . I wrote a little function:
function klear(x)
% go to parent environment...
evalin('base', builtin('clear','x')) ;
end
This throws the error:
Error using clear
Too many output arguments.
I think this happens because evalin demands an output from whatever it's being fed, but clear is one of the functions which has no return value.
So two questions: am I interpreting this correctly, and if so, is there an alternative function that allows me to execute a function in the parent environment (that doesn't require an output)?
Note: I'm fully aware of the arguments against trying to access shadowed funcs (or rather, to avoid naming functions in a way that overload base funcs, etc). This is primarily a question to help me learn what can and can't be done in MATLAB.
Note 2
My original goal was to write an overload function that would require an input argument, to avoid the malware-ish behavior of clear, which defaults to deleting everything. In Q&D pseudocode,
function clear(x)
if ~exist('x','var') return
execute_in_base_env(builtin(clear(x)))
end
There's a couple issues with your clear override:
It will always clear in the base workspace regardless of where it's called from.
It doesn't support multiple inputs, which is a common use case for clear.
Instead I'd have it check for whether it was called from the base workspace, and special-case that for your check for whether it's clearing everything. If some function is calling plain clear to clear all its variables, that's bad practice, but it's still how that function's logic works, and you don't want to break that. Otherwise it could error, or worse, return incorrect results.
So, something like this:
function clear(varargin)
stk = dbstack;
if numel(stk) == 1 && (nargin == 0 || ismember('all', varargin))
fprintf('clear: balking at clearing all vars in base workspace. Nothing cleared.\n');
return;
end
% Check for quoting problems
for i = 1:numel(varargin)
if any(varargin{i} == '''')
error('You have a quote in one of your args. That''s not valid.');
end
end
% Construct a clear() call that works with evalin()
arg_strs = strcat('''', varargin, '''');
arg_strs = [{'''clear'''} arg_strs];
expr = ['builtin(' strjoin(arg_strs, ', '), ')'];
% Do it
evalin('caller', expr);
end
I hope it goes without saying that this is an atrocious hack that I wouldn't recommend in practice. :)
What happens in your code:
evalin('base', builtin('clear','x'));
is that builtin is evaluated in the current context, and because it is used as an argument to evalin, it is expected to produce an output. It is exactly the same as:
ans = builtin('clear','x');
evalin('base',ans);
The error message you see occurs in the first of those two lines of code, not in the second. It is not because of evalin, which does support calling statements that don't produce an output argument.
evalin requires a string to evaluate. You need to build this string:
str = 'builtin(''clear'',''x'')';
evalin('base',ans);
(In MATLAB, the quote character is escaped by doubling it.)
You function thus would look like this:
function clear(var)
try
evalin('base',['builtin(''clear'',''',var,''')'])
catch
% ignore error
end
end
(Inserting a string into another string this way is rather awkward, one of the many reasons I don't like eval and friends).
It might be better to use evalin('caller',...) in this case, so that when you call the new clear from within a function, it deletes something in the function's workspace, not the base one. I think 'base' should only be used from within a GUI that is expected to control variables in the user's workspace, not from a function that could be called anywhere and is expected (by its name in this case) to do something local.
There are reasons why this might be genuinely useful, but in general you should try to avoid the use of clear just as much as the use of eval and friends. clear slows down program execution. It is much easier (both on the user and on the MATLAB JIT) to assign an empty array to a variable to remove its contents from memory (as suggested by rahnema1 in a comment. Your base workspace would not be cluttered with variables if you used function more: write functions, not scripts!
I use the symbolic toolbox in matlab to generate some very long symbolic expressions. Then I use matlabFunction to generate a function file.
Say there are three parameters: p1, p2 and p3.
I have a cell with strings {'p1', 'p2', 'p3'}.
In the derivation of the model I generate symbolic variables p1, p2 and p3 out of them using eval in a loop and stack them in a vector par.
Then when in matlabFunction, I specify par as input.
Moreover, I save the cell string in a .mat file.
Then when I want to simulate this model, I can construct this parameter array using that cell of strings from the .mat file out of 30 available parameters and their values.
Advantages: No need to keep track of the different parameters if I add one to . I can change the order, mess around, but older models still work.
Disadvantage:
Turning things into a function file leads to this error (psi is one of the parameters):
Error: File: f_derive_model.m Line: 96 Column: 5
"psi" previously appeared to be used as a function or
command, conflicting with its use here as the name of a
variable.
A possible cause of this error is that you forgot to
initialize the variable, or you have initialized it
implicitly using load or eval.
Apparently some unnescescary checking is going on because the variable will be intialized in an eval statement.
Question: How can I avoid eval but keep the list of parameters indepent from the model stuff.
Code deriving the long equations:
% Model parameters
mdl.parameters = {'mp','mb','lp','lb','g','d','mP','mM','k','kt'};
par = [];
for i=1:length(mdl.parameters)
eval(strcat(mdl.parameters{i}, '=sym(''', mdl.parameters{i}, "');"));
eval(sprintf(['par = [par;' mdl.parameters{i} '];']));
end
%% Calculate stuff
matlabFunction(MM,'file',[modelName '_mass'],'vars',{par},'outputs',{'M'});
Code using the generated file:
getparams
load('m3d_1')
par = [];
for i=1:length(mdl.parameters)
eval(sprintf(['par = [par;params.' mdl.parameters{i} '];']));
end
See how, as long as I specify the correct value to for example params.mp, it always gets assigned to the input corresponding to the symbolic variable mp in the par vector. I do not want to lose that and have to keep track of the order and so on, nor do I want to call my functions with all the parameters one by one.
Actually, I see nothing wrong in your approach even if the "public opinion" affirms that it's better to avoid using the eval function. An alternative would be using the assignin function as follows:
% use 'caller' instead of 'base' if this code runs within a function
for i = 1:numel(mdl.parameters)
var_name = mdl.parameters{i};
assignin('base',var_name,sym(var_name));
end
In the second case (the one concerning the par variable) I would instead use the getfield function:
par_len = numel(mdl.parameters);
par = cell(par_len,1);
for i = 1:par_len
par{i} = getfield(params,mdl.parameters{i});
end
or, alternatively, this approach:
par_len = numel(mdl.parameters);
par = cell(par_len,1);
for i = 1:par_len
par{i} = params.(mdl.parameters{i});
end
I have written a function to perform permutations of n obejects.
I have the variables a=[1],[2],[3];k=1 and n=4;a contains the objects which are 1,2 and 3 respectively.The following is the function code that i have written:
function [res]=perm(a,k,n,jj)
if k==n
res{jj}=a;
jj=jj+1;
else
for i=k:n
t=a{k};
a{k}=a{i};
a{i}=t;
perm(a,k+1,n,jj)
t=a{k};
a{k}=a{i};
a{i}=t;
end
end
end
However, when i call the function as:
jj=1;
[res]=perm(a,k,n,jj)
I am getting the following error displayed:
Error in ==> perm at 3
if k==n
??? Output argument "res" (and maybe others) not assigned during call to "J:\main
project\perm.m>perm".
Error in ==> mainp at 254
[res]=perm(a,k,n,jj)
The following is the code in the main program with regard to the permutation:
mr=4
for i=1:mr
a{i}=i;
end
n=mr;
%This assignment is for the ease to work with.
%just stored the indices till mr for the purpose of permutation
k=1;
%this is the k that the function perm has
jj=1;
[res]=perm(a,k,n,jj)
Can somebody please help me resolve this?Thanks in advance.
Your else block leaves res undefined.
I think you're assuming that res is a global variable and all invocations of perm will write into different parts of a single cell array. That isn't true. It is an output variable, local to the current call. There's no sharing during recursion, they all have independent cell arrays named res. The parameter jj is also not shared, so adding one is useless as well.
If you want to use this technique of building up the output, you'll need to make sure it is defined at a wider scope than the recursion. For example, use a local helper function:
function [res]=perm(a,k,n,jj)
res = {};
perm_impl(a,k);
function [] = perm_impl(a,k) // doesn't get its own local res, n, or jj
if k==n
res{jj}=a;
jj=jj+1;
else
for i=k:n
t=a{k};
a{k}=a{i};
a{i}=t;
perm_impl(a,k+1)
t=a{k};
a{k}=a{i};
a{i}=t;
end
end
end
end
Now all runs of perm_impl work on the same cell array res, because Matlab documentation says:
Variables within nested functions are accessible to more than just their immediate function. A variable, x, to which you assign a value or use within a nested function resides in the workspace of the outermost function that both contains the nested function and accesses x.
If you intentionally use a variable in this manner, it is not a problem. For examples, see the MATLAB Programming Demo on Nested Functions.
However, if you unintentionally use a variable in this manner, it can result in unexpected behavior. If the highlighting indicates that the scope of a variable spans multiple functions, and that was not your intent, consider:
Renaming the nested function variable so it does not match the outer function variable name.
Passing the variable into the function as an input argument instead of using the variable directly within the nested function
I can't tell whether a was supposed to be shared or not...
I have a file funcs.m that stores anonymous functions. They must be usable by the files in the directory where it is. Currently, I use the anonymous functions so that I execute the file funcs.m in different files but I think this is a a wrong way of doing things. The other functions such as main.m and its nested function nest.m need to use the anonymous functions from funcs.m. I think paths won't solve this problem because the files are in the same folder. Basically I could solve this problem by copy-pasting the anonymous functions to every file but code-smell so:
Is there some way of reusing the funcs.m having the anon functions in Matlab?
Example
main.m
function main
funcs; % loads the anonymous functions
nest(par1,...,parN)
end
nest.m
function nest(par1,...,parN)
funcs; %ERRR: This fires err, why? Look: this was sourced already in main.m!
function neededOnlyHere(par100)
bq(q,A) %This needs the functions of the funcs
end
neededOnlyHere(somePar) %ERR to use the anon funcs from funcs
end
Functions main.m and nest.m use this function funcs.m having the anonymous funcs
bq=#(q,A) q*A; %Bolded q
I=#(ii,jj,A) find(A(ii,:)==1 & A(jj,:)==0);
AiNotj=zeros(1,Ncut);
...
ERROR
Attempt to add "bq" to a static workspace.
See MATLAB Programming, Restrictions on
Assigning to Variables for details.
Error in funcs (line 10)
bq=#(q,A) q*A;
%Bolded q
Why it's breaking
You get the error when calling it in nest.m because having a nested function makes its enclosing function's workspace a "static workspace"; that is, variable names cannot be added via eval(), assignin(), or other "dynamic" techniques; only variables that are explicitly assigned in that function's text are allowed. Evaluating a script to define local variables - which is what you're doing when calling funcs.m - is "dynamic", so prohibited in functions with nested functions. It works in main.m because main has no nested functions and is thus a "dynamic" workspace.
There are a couple ways you could change it to work with static workspaces and nested functions. The first thing to ask is whether you really need to make them anonymous functions?
Using package functions instead
If you don't need them to be anonymous functions per se, just break them out and put each one as a regular function in its own .m file; e.g. bg.m, I.m, AiNotj.m, and so on. Then they're all available to all other functions in that directory.
If that turns in to a mess of files, or if you want to scope them and maybe make them available only to the selected functions that really need them (that is, the functions currently calling funcs()), then you can stick them in a package. Create a subdirectory called +myfuncs and move all the little function files in there; e.g. +myfuncs/bq.m, +myfuncs/I.m, +myfuncs/AiNotj.m. (The + prefix tells Matlab the directory is a package.) Then you can pull all of them in to your function scope by doing import myfuncs.* as a direct replacement for where you're currently calling funcs().
function nest(par1,...,parN)
import myfuncs.*;
function neededOnlyHere(par100)
bq(q,A) % This will work, resolving to myfuncs.bq
end
You can do the import myfuncs.* from the command line to make them available interactively, too.
This is probably how Matlab itself wants you to organize clusters of related functions like this, and would be my first approach. It's the least "smelly" IMHO. If you really wanted to be able to edit them all in a single file like funcs.m for convenience, you could write a little code munger in Perl or whatever that parsed funcs.m and output them all as equivalent individual functions as a preprocessing step. (I think it's a bit of a bummer that you can't define multiple top-level functions in an M-file like this, but oh well.)
If you really need to work with anonymous functions, there are some workarounds.
Passing functions in a struct
You can change your funcs() function to actually return a struct of all those anonymous functions, using field names instead of local variable names.
function out = funcs
out.bq=#(q,A) q*A; %Bolded q
out.I=#(ii,jj,A) find(A(ii,:)==1 & A(jj,:)==0);
out.AiNotj=zeros(1,Ncut);
For this, you'd have to prefix all the function references with the struct name you're holding them in. Don't know how big a deal this is for you.
function nest(par1,...,parN)
fs = funcs;
function neededOnlyHere(par100)
fs.bq(q,A) %This needs the functions of the funcs
end
Preallocating variables
To get funcs() to work as-is, you can statically pre-allocate variables with all the function names you're going to use, so the Matlab parser recognizes them as statically assigned variables. Then when you call funcs(), it will re-assign the values of the existing variables, which is permissible in dynamic workspaces.
function nest(par1,...,parN)
[bq, I, AiNotj] = deal(); % Preallocate all names from funcs
funcs;
function neededOnlyHere(par100)
bq(q,A) %This needs the functions of the funcs
end
This would be a bit of a pain, because you'd have to re-edit every file that uses funcs whenever a new function name is added. You could at least write a little perl script to auto-generate that line of code by parsing funcs.m and outputting a "[bg, I, AiNotj,...] = deal();" with all the functions it finds, and you can just copy that in to your code.
Another way to do this would be to have funcs actually return all the functions in its output list. This would have the benefit of continuing to work even as you add new functions to funcs.m, as long as you don't remove or change the order of your existing anonymous functions.
function [bg,I,AiNotj] = funcs()
bg = ...
I = ...
% And then in the calling functions:
[bg,I,AiNotj] = funcs(); % which you can copy and paste from funcs.m's header
There are many ways of passing anonymous functions:
1) Pass the function itself:
function main
f = #(t) t^2 - 3;
param = randn(12,1);
g = test22(param,f);
disp (g)
end
function g = test22(param,f)
g = f(param(2));
disp(param(2))
end
2) Use globals (which usually should be avoided in complex code)
function main
global f
f = #(t) t^2 - 3;
param = randn(12,1);
g = test22(param);
disp (g)
end
function g = test22(param)
global f
g = f(param(2));
disp(param(2))
end