If I call a matlab function with:
func(1,2,3,4,5)
it works perfectly.
But if I do:
a=[1,2,3,4,5] %(a[1;2;3;4;5] gives same result)
then:
func(a)
gives me:
??? Error ==> func at 11
Not enough input arguments.
Line 11 in func.m is:
error(nargchk(5, 6, nargin));
I notice that this works perfectly:
func(a(1),a(2),a(3),a(4),a(5))
How can I use the vector 'a' as a parameter to a function? I have another function otherfunc(b) which returns a, and would like to use its output as a paramater like this func(otherfunc(b)).
Comma-seperated lists
(CSL) can be passed to functions as parameter list,
so what you need is a CSL as 1,2,3,4,5 constructed from an array.
It can be generated using cell array like this:
a=[1,2,3,4,5];
c = num2cell(a);
func(c{:});
Maybe you could try with nargin - a variable in a function that has the value of the number of input arguments. Since you have a need for different length input, I believe this can best be handled with varargin, which can be set as the last input variable and will then group together all the extra input arguments..
function result = func(varargin)
if nargin == 5: % this is every element separately
x1 = varargin{1}
x2 = varargin{2}
x3 = varargin{3}
x4 = varargin{4}
x5 = varargin{5}
else if nargin == 1: % and one vectorized input
[x1 x2 x3 x4 x5] = varargin{1}
I've written x1...x5 for your input variables
Another method would be to create a separate inline function. Say you have a function f which takes multiple parameters:
f = f(x1,x2,x3)
You can call this with an array of parameter values by defining a separate function g:
g = #(x) f(x(1),x(2),x(3))
Now, if you have a vector of parameters values v = [1,2,3], you will be able to call f(v(1),v(2),v(3)) using g(v).
Just make the function take a single argument.
function result = func(a)
if ~isvector(a)
error('Input must be a vector')
end
end
Since arguments to functions in Matlab can themselves be vectoes (or even matrices) you cannot replace several arguments with a single vector.
If func expects 5 arguments, you cannot pass a single vector and expect matlab to understand that all five arguments are elements in the vector. How can Matlab tell the difference between this case and a case where the first argument is a 5-vector?
So, I would suggest this solution
s.type = '()';
s.subs = {1:5};
func( subsref( num2cell( otherfunc(b) ), s ) )
I'm not sure if this works (I don't have matlab here), but the rationale is to convert the 5-vector a (the output of otherfunc(b)) into a cell array and then expand it as 5 different arguments to func.
Please not the difference between a{:} and a(:) in this subsref.
You could create a function of the following form:
function [ out ] = funeval( f, x )
string = 'f(';
for I = 1:length(x)
string = strcat( string, 'x(' , num2str(I), '),' );
end
string( end ) = ')';
out = eval( string );
end
In which case, funeval( func, a ) gives the required output.
Use eval:
astr = [];
for i=1:length(a)
astr = [astr,'a(',num2str(i),'),']; % a(1),a(2),...
end
astr = astr(1:end-1);
eval(['func(' astr ');']);
I have a function that takes optional arguments as name/value pairs.
function example(varargin)
% Lots of set up stuff
vargs = varargin;
nargs = length(vargs);
names = vargs(1:2:nargs);
values = vargs(2:2:nargs);
validnames = {'foo', 'bar', 'baz'};
for name = names
validatestring(name{:}, validnames);
end
% Do something ...
foo = strmatch('foo', names);
disp(values(foo))
end
example('foo', 1:10, 'bar', 'qwerty')
It seems that there is a lot of effort involved in extracting the appropriate values (and it still isn't particularly robust again badly specified inputs). Is there a better way of handling these name/value pairs? Are there any helper functions that come with MATLAB to assist?
I prefer using structures for my options. This gives you an easy way to store the options and an easy way to define them. Also, the whole thing becomes rather compact.
function example(varargin)
%# define defaults at the beginning of the code so that you do not need to
%# scroll way down in case you want to change something or if the help is
%# incomplete
options = struct('firstparameter',1,'secondparameter',magic(3));
%# read the acceptable names
optionNames = fieldnames(options);
%# count arguments
nArgs = length(varargin);
if round(nArgs/2)~=nArgs/2
error('EXAMPLE needs propertyName/propertyValue pairs')
end
for pair = reshape(varargin,2,[]) %# pair is {propName;propValue}
inpName = lower(pair{1}); %# make case insensitive
if any(strcmp(inpName,optionNames))
%# overwrite options. If you want you can test for the right class here
%# Also, if you find out that there is an option you keep getting wrong,
%# you can use "if strcmp(inpName,'problemOption'),testMore,end"-statements
options.(inpName) = pair{2};
else
error('%s is not a recognized parameter name',inpName)
end
end
InputParser helps with this. See Parse Function Inputs for more information.
I could yack for hours about this, but still don't have a good gestalt view of general Matlab signature handling. But here's a couple pieces of advice.
First, take a laissez faire approach to validating input types. Trust the caller. If you really want strong type testing, you want a static language like Java. Try to enforce type safety every where in Matlab, and you'll end up with a good part of your LOC and execution time devoted to run time type tests and coercion in userland, which trades in a lot of the power and development speed of Matlab. I learned this the hard way.
For API signatures (functions intended to be called from other functions, instead of from the command lines), consider using a single Args argument instead of varargin. Then it can be passed around between multiple arguments without having to convert it to and from a comma-separated list for varargin signatures. Structs, like Jonas says, are very convenient. There's also a nice isomorphism between structs and n-by-2 {name,value;...} cells, and you could set up a couple functions to convert between them inside your functions to whichever it wants to use internally.
function example(args)
%EXAMPLE
%
% Where args is a struct or {name,val;...} cell array
Whether you use inputParser or roll your own name/val parser like these other fine examples, package it up in a separate standard function that you'll call from the top of your functions that have name/val signatures. Have it accept the default value list in a data structure that's convenient to write out, and your arg-parsing calls will look sort of like function signature declarations, which helps readability, and avoid copy-and-paste boilerplate code.
Here's what the parsing calls could look like.
function out = my_example_function(varargin)
%MY_EXAMPLE_FUNCTION Example function
% No type handling
args = parsemyargs(varargin, {
'Stations' {'ORD','SFO','LGA'}
'Reading' 'Min Temp'
'FromDate' '1/1/2000'
'ToDate' today
'Units' 'deg. C'
});
fprintf('\nArgs:\n');
disp(args);
% With type handling
typed_args = parsemyargs(varargin, {
'Stations' {'ORD','SFO','LGA'} 'cellstr'
'Reading' 'Min Temp' []
'FromDate' '1/1/2000' 'datenum'
'ToDate' today 'datenum'
'Units' 'deg. C' []
});
fprintf('\nWith type handling:\n');
disp(typed_args);
% And now in your function body, you just reference stuff like
% args.Stations
% args.FromDate
And here's a function to implement the name/val parsing that way. You could hollow it out and replace it with inputParser, your own type conventions, etc. I think the n-by-2 cell convention makes for nicely readable source code; consider keeping that. Structs are typically more convenient to deal with in the receiving code, but the n-by-2 cells are more convenient to construct using expressions and literals. (Structs require the ",..." continuation at each line, and guarding cell values from expanding to nonscalar structs.)
function out = parsemyargs(args, defaults)
%PARSEMYARGS Arg parser helper
%
% out = parsemyargs(Args, Defaults)
%
% Parses name/value argument pairs.
%
% Args is what you pass your varargin in to. It may be
%
% ArgTypes is a list of argument names, default values, and optionally
% argument types for the inputs. It is an n-by-1, n-by-2 or n-by-3 cell in one
% of these forms forms:
% { Name; ... }
% { Name, DefaultValue; ... }
% { Name, DefaultValue, Type; ... }
% You may also pass a struct, which is converted to the first form, or a
% cell row vector containing name/value pairs as
% { Name,DefaultValue, Name,DefaultValue,... }
% Row vectors are only supported because it's unambiguous when the 2-d form
% has at most 3 columns. If there were more columns possible, I think you'd
% have to require the 2-d form because 4-element long vectors would be
% ambiguous as to whether they were on record, or two records with two
% columns omitted.
%
% Returns struct.
%
% This is slow - don't use name/value signatures functions that will called
% in tight loops.
args = structify(args);
defaults = parse_defaults(defaults);
% You could normalize case if you want to. I recommend you don't; it's a runtime cost
% and just one more potential source of inconsistency.
%[args,defaults] = normalize_case_somehow(args, defaults);
out = merge_args(args, defaults);
%%
function out = parse_defaults(x)
%PARSE_DEFAULTS Parse the default arg spec structure
%
% Returns n-by-3 cellrec in form {Name,DefaultValue,Type;...}.
if isstruct(x)
if ~isscalar(x)
error('struct defaults must be scalar');
end
x = [fieldnames(s) struct2cell(s)];
end
if ~iscell(x)
error('invalid defaults');
end
% Allow {name,val, name,val,...} row vectors
% Does not work for the general case of >3 columns in the 2-d form!
if size(x,1) == 1 && size(x,2) > 3
x = reshape(x, [numel(x)/2 2]);
end
% Fill in omitted columns
if size(x,2) < 2
x(:,2) = {[]}; % Make everything default to value []
end
if size(x,2) < 3
x(:,3) = {[]}; % No default type conversion
end
out = x;
%%
function out = structify(x)
%STRUCTIFY Convert a struct or name/value list or record list to struct
if isempty(x)
out = struct;
elseif iscell(x)
% Cells can be {name,val;...} or {name,val,...}
if (size(x,1) == 1) && size(x,2) > 2
% Reshape {name,val, name,val, ... } list to {name,val; ... }
x = reshape(x, [2 numel(x)/2]);
end
if size(x,2) ~= 2
error('Invalid args: cells must be n-by-2 {name,val;...} or vector {name,val,...} list');
end
% Convert {name,val, name,val, ...} list to struct
if ~iscellstr(x(:,1))
error('Invalid names in name/val argument list');
end
% Little trick for building structs from name/vals
% This protects cellstr arguments from expanding into nonscalar structs
x(:,2) = num2cell(x(:,2));
x = x';
x = x(:);
out = struct(x{:});
elseif isstruct(x)
if ~isscalar(x)
error('struct args must be scalar');
end
out = x;
end
%%
function out = merge_args(args, defaults)
out = structify(defaults(:,[1 2]));
% Apply user arguments
% You could normalize case if you wanted, but I avoid it because it's a
% runtime cost and one more chance for inconsistency.
names = fieldnames(args);
for i = 1:numel(names)
out.(names{i}) = args.(names{i});
end
% Check and convert types
for i = 1:size(defaults,1)
[name,defaultVal,type] = defaults{i,:};
if ~isempty(type)
out.(name) = needa(type, out.(name), type);
end
end
%%
function out = needa(type, value, name)
%NEEDA Check that a value is of a given type, and convert if needed
%
% out = needa(type, value)
% HACK to support common 'pseudotypes' that aren't real Matlab types
switch type
case 'cellstr'
isThatType = iscellstr(value);
case 'datenum'
isThatType = isnumeric(value);
otherwise
isThatType = isa(value, type);
end
if isThatType
out = value;
else
% Here you can auto-convert if you're feeling brave. Assumes that the
% conversion constructor form of all type names works.
% Unfortunately this ends up with bad results if you try converting
% between string and number (you get Unicode encoding/decoding). Use
% at your discretion.
% If you don't want to try autoconverting, just throw an error instead,
% with:
% error('Argument %s must be a %s; got a %s', name, type, class(value));
try
out = feval(type, value);
catch err
error('Failed converting argument %s from %s to %s: %s',...
name, class(value), type, err.message);
end
end
It is so unfortunate that strings and datenums are not first-class types in Matlab.
MathWorks has revived this beaten horse, but with very useful functionality that answers this need, directly. It's called Function Argument Validation (a phrase one can and should search for in the documentation) and comes with release R2019b+. MathWorks created a video about it, also. Validation works much like the "tricks" people have come up with over the years. Here is an example:
function ret = example( inputDir, proj, options )
%EXAMPLE An example.
% Do it like this.
% See THEOTHEREXAMPLE.
arguments
inputDir (1, :) char
proj (1, 1) projector
options.foo char {mustBeMember(options.foo, {'bar' 'baz'})} = 'bar'
options.Angle (1, 1) {double, integer} = 45
options.Plot (1, 1) logical = false
end
% Code always follows 'arguments' block.
ret = [];
switch options.foo
case 'bar'
ret = sind(options.Angle);
case 'baz'
ret = cosd(options.Angle);
end
if options.Plot
plot(proj.x, proj.y)
end
end
Here's the unpacking:
The arguments block must come before any code (OK after help block) and must follow the positional order defined in the function definition, and I believe every argument requires a mention. Required arguments go first, followed by optional arguments, followed by name-value pairs. MathWorks also recommends to no longer use the varargin keyword, but nargin and nargout are still useful.
Class requirements can be custom classes, such as projector, in this case.
Required arguments may not have a default value (i.e. they are known because they don't have a default value).
Optional arguments must have a default value (i.e. they are known because they have a default value).
Default values must be able to pass the same argument validation. In other words, a default value of zeros(3) won't work as a default value for an argument that's supposed to be a character vector.
Name-value pairs are stored in an argument that are internally converted to a struct, which I'm calling options, here (hinting to us that we can use structs to pass keyword arguments, like kwargs in Python).
Very nicely, name-value arguments will now show up as argument hints when you hit tab in a function call. (If completion hints interest you, I encourage you to also look up MATLAB's functionSignatures.json functionality).
So in the example, inputDir is a required argument because it's given no default value. It also must be a 1xN character vector. As if to contradict that statement, note that MATLAB will try to convert the supplied argument to see if the converted argument passes. If you pass 97:122 as inputDir, for example, it will pass and inputDir == char(97:122) (i.e. inputDir == 'abcdefghijklmnopqrstuvwxyz'). Conversely, zeros(3) won't work on account of its not being a vector. And forget about making strings fail when you specify characters, making doubles fail when you demand uint8, etc. Those will be converted. You'd need to dig deeper to circumvent this "flexibility."
Moving on, 'foo' specifies a name-value pair whose value may be only 'bar' or 'baz'.
MATLAB has a number of mustBe... validation functions (start typing
mustBe and hit tab to see what's available), and it's easy enough to
create your own. If you create your own, the validation function must
give an error if the input doesn't match, unlike, say, uigetdir,
which returns 0 if the user cancels the dialog. Personally, I
follow MATLAB's convention and call my validation functions
mustBe..., so I have functions like mustBeNatural for natural
numbers, and mustBeFile to ensure I passed a file that actually
exists.
'Angle' specifies a name-value pair whose value must be a scalar double or integer, so, for example, example(pwd, 'foo', 'baz', 'Angle', [30 70]) won't work since you passed a vector for the Angle argument.
You get the idea. There is a lot of flexibility with the arguments block -- too much and too little, I think -- but for simple functions, it's fast and easy. You still might rely on one or more of inputParser, validateattributes, assert, and so on for addressing greater validation complexity, but I always try to stuff things into an arguments block, first. If it's becoming unsightly, maybe I'll do an arguments block and some assertions, etc.
Personally I use a custom function derived from a private method used by many Statistics Toolbox functions (like kmeans, pca, svmtrain, ttest2, ...)
Being an internal utility function, it changed and was renamed many times over the releases. Depending on your MATLAB version, try looking for one of the following files:
%# old versions
which -all statgetargs
which -all internal.stats.getargs
which -all internal.stats.parseArgs
%# current one, as of R2014a
which -all statslib.internal.parseArgs
As with any undocumented function, there are no guarantees and it could be removed from MATLAB in subsequent releases without any notice... Anyways, I believe someone posted an old version of it as getargs on the File Exchange..
The function processes parameters as name/value pairs, using a set of valid parameter names along with their default values. It returns the parsed parameters as separate output variables. By default, unrecognized name/value pairs raise an error, but we could also silently capture them in an extra output. Here is the function description:
$MATLABROOT\toolbox\stats\stats\+internal\+stats\parseArgs.m
function varargout = parseArgs(pnames, dflts, varargin)
%
% [A,B,...] = parseArgs(PNAMES, DFLTS, 'NAME1',VAL1, 'NAME2',VAL2, ...)
% PNAMES : cell array of N valid parameter names.
% DFLTS : cell array of N default values for these parameters.
% varargin : Remaining arguments as name/value pairs to be parsed.
% [A,B,...]: N outputs assigned in the same order as the names in PNAMES.
%
% [A,B,...,SETFLAG] = parseArgs(...)
% SETFLAG : structure of N fields for each parameter, indicates whether
% the value was parsed from input, or taken from the defaults.
%
% [A,B,...,SETFLAG,EXTRA] = parseArgs(...)
% EXTRA : cell array containing name/value parameters pairs not
% specified in PNAMES.
Example:
function my_plot(x, varargin)
%# valid parameters, and their default values
pnames = {'Color', 'LineWidth', 'LineStyle', 'Title'};
dflts = { 'r', 2, '--', []};
%# parse function arguments
[clr,lw,ls,txt] = internal.stats.parseArgs(pnames, dflts, varargin{:});
%# use the processed values: clr, lw, ls, txt
%# corresponding to the specified parameters
%# ...
end
Now this example function could be called as any of the following ways:
>> my_plot(data) %# use the defaults
>> my_plot(data, 'linestyle','-', 'Color','b') %# any order, case insensitive
>> my_plot(data, 'Col',[0.5 0.5 0.5]) %# partial name match
Here are some invalid calls and the errors thrown:
%# unrecognized parameter
>> my_plot(x, 'width',0)
Error using [...]
Invalid parameter name: width.
%# bad parameter
>> my_plot(x, 1,2)
Error using [...]
Parameter name must be text.
%# wrong number of arguments
>> my_plot(x, 'invalid')
Error using [...]
Wrong number of arguments.
%# ambiguous partial match
>> my_plot(x, 'line','-')
Error using [...]
Ambiguous parameter name: line.
inputParser:
As others have mentioned, the officially recommended approach to parsing functions inputs is to use inputParser class. It supports various schemes such as specifying required inputs, optional positional arguments, and name/value parameters. It also allows to perform validation on the inputs (such as checking the class/type and the size/shape of the arguments)
Read Loren's informative post on this issue. Don't forget to read the comments section... - You will see that there are quite a few different approaches to this topic. They all work, so selecting a prefered method is really a matter of personal taste and maintainability.
I'm a bigger fan of home-grown boiler plate code like this:
function TestExample(req1, req2, varargin)
for i = 1:2:length(varargin)
if strcmpi(varargin{i}, 'alphabet')
ALPHA = varargin{i+1};
elseif strcmpi(varargin{i}, 'cutoff')
CUTOFF = varargin{i+1};
%we need to remove these so seqlogo doesn't get confused
rm_inds = [rm_inds i, i+1]; %#ok<*AGROW>
elseif strcmpi(varargin{i}, 'colors')
colors = varargin{i+1};
rm_inds = [rm_inds i, i+1];
elseif strcmpi(varargin{i}, 'axes_handle')
handle = varargin{i+1};
rm_inds = [rm_inds i, i+1];
elseif strcmpi(varargin{i}, 'top-n')
TOPN = varargin{i+1};
rm_inds = [rm_inds i, i+1];
elseif strcmpi(varargin{i}, 'inds')
npos = varargin{i+1};
rm_inds = [rm_inds i, i+1];
elseif strcmpi(varargin{i}, 'letterfile')
LETTERFILE = varargin{i+1};
rm_inds = [rm_inds i, i+1];
elseif strcmpi(varargin{i}, 'letterstruct')
lo = varargin{i+1};
rm_inds = [rm_inds i, i+1];
end
end
This way I can simulate the 'option', value pair that's nearly identical to how most Matlab functions take their arguments.
Hope that helps,
Will
Here's the solution I'm trialling, based upon Jonas' idea.
function argStruct = NameValuePairToStruct(defaults, varargin)
%NAMEVALUEPAIRTOSTRUCT Converts name/value pairs to a struct.
%
% ARGSTRUCT = NAMEVALUEPAIRTOSTRUCT(DEFAULTS, VARARGIN) converts
% name/value pairs to a struct, with defaults. The function expects an
% even number of arguments to VARARGIN, alternating NAME then VALUE.
% (Each NAME should be a valid variable name.)
%
% Examples:
%
% No defaults
% NameValuePairToStruct(struct, ...
% 'foo', 123, ...
% 'bar', 'qwerty', ...
% 'baz', magic(3))
%
% With defaults
% NameValuePairToStruct( ...
% struct('bar', 'dvorak', 'quux', eye(3)), ...
% 'foo', 123, ...
% 'bar', 'qwerty', ...
% 'baz', magic(3))
%
% See also: inputParser
nArgs = length(varargin);
if rem(nArgs, 2) ~= 0
error('NameValuePairToStruct:NotNameValuePairs', ...
'Inputs were not name/value pairs');
end
argStruct = defaults;
for i = 1:2:nArgs
name = varargin{i};
if ~isvarname(name)
error('NameValuePairToStruct:InvalidName', ...
'A variable name was not valid');
end
argStruct = setfield(argStruct, name, varargin{i + 1}); %#ok<SFLD>
end
end
Inspired by Jonas' answer, but more compact:
function example(varargin)
defaults = struct('A',1, 'B',magic(3)); %define default values
params = struct(varargin{:});
for f = fieldnames(defaults)',
if ~isfield(params, f{1}),
params.(f{1}) = defaults.(f{1});
end
end
%now just access them as params.A, params.B
There is a nifty function called parsepvpairs that takes care of this nicely, provided you have access to MATLAB's finance toolbox. It takes three arguments, expected field names, default field values, and the actual arguments received.
For example, here's a function that creates an HTML figure in MATLAB and can take the optional field value pairs named 'url', 'html', and 'title'.
function htmldlg(varargin)
names = {'url','html','title'};
defaults = {[],[],'Padaco Help'};
[url, html,titleStr] = parsepvpairs(names,defaults,varargin{:});
%... code to create figure using the parsed input values
end
Since ages I am using process_options.m. It is stable, easy to use and has been included in various matlab frameworks. Don't know anything about performance though – might be that there are faster implementations.
Feature I like most with process_options is the unused_args return value, that can be used to split input args in groups of args for, e.g., subprocesses.
And you can easily define default values.
Most importantly: using process_options.m usually results in readable and maintainable option definitions.
Example code:
function y = func(x, y, varargin)
[u, v] = process_options(varargin,
'u', 0,
'v', 1);
If you are using MATLAB 2019b or later, the best way to deal with name-value pairs in your function is to use "Declare function argument validation".
function result = myFunction(NameValueArgs)
arguments
NameValueArgs.Name1
NameValueArgs.Name2
end
% Function code
result = NameValueArgs.Name1 * NameValueArgs.Name2;
end
see: https://www.mathworks.com/help/matlab/ref/arguments.html
function argtest(varargin)
a = 1;
for ii=1:length(varargin)/2
[~] = evalc([varargin{2*ii-1} '=''' num2str(varargin{2*ii}) '''']);
end;
disp(a);
who
This does of course not check for correct assignments, but it's simple and any useless variable will be ignored anyway. It also only works for numerics, strings and arrays, but not for matrices, cells or structures.
I ended up writing this today, and then found these mentions.
Mine uses struct's and struct 'overlays' for options. It essentially mirrors the functionality of setstructfields() except that new parameters can not be added. It also has an option for recursing, whereas setstructfields() does it automatically.
It can take in a cell array of paired values by calling struct(args{:}).
% Overlay default fields with input fields
% Good for option management
% Arguments
% $opts - Default options
% $optsIn - Input options
% Can be struct(), cell of {name, value, ...}, or empty []
% $recurseStructs - Applies optOverlay to any existing structs, given new
% value is a struct too and both are 1x1 structs
% Output
% $opts - Outputs with optsIn values overlayed
function [opts] = optOverlay(opts, optsIn, recurseStructs)
if nargin < 3
recurseStructs = false;
end
isValid = #(o) isstruct(o) && length(o) == 1;
assert(isValid(opts), 'Existing options cannot be cell array');
assert(isValid(optsIn), 'Input options cannot be cell array');
if ~isempty(optsIn)
if iscell(optsIn)
optsIn = struct(optsIn{:});
end
assert(isstruct(optsIn));
fields = fieldnames(optsIn);
for i = 1:length(fields)
field = fields{i};
assert(isfield(opts, field), 'Field does not exist: %s', field);
newValue = optsIn.(field);
% Apply recursion
if recurseStructs
curValue = opts.(field);
% Both values must be proper option structs
if isValid(curValue) && isValid(newValue)
newValue = optOverlay(curValue, newValue, true);
end
end
opts.(field) = newValue;
end
end
end
I'd say that using the naming convention 'defaults' and 'new' would probably be better :P
I have made a function based on Jonas and Richie Cotton. It implements both functionalities (flexible arguments or restricted, meaning that only variables existing in the defaults are allowed), and a few other things like syntactic sugar and sanity checks.
function argStruct = getnargs(varargin, defaults, restrict_flag)
%GETNARGS Converts name/value pairs to a struct (this allows to process named optional arguments).
%
% ARGSTRUCT = GETNARGS(VARARGIN, DEFAULTS, restrict_flag) converts
% name/value pairs to a struct, with defaults. The function expects an
% even number of arguments in VARARGIN, alternating NAME then VALUE.
% (Each NAME should be a valid variable name and is case sensitive.)
% Also VARARGIN should be a cell, and defaults should be a struct().
% Optionally: you can set restrict_flag to true if you want that only arguments names specified in defaults be allowed. Also, if restrict_flag = 2, arguments that aren't in the defaults will just be ignored.
% After calling this function, you can access your arguments using: argstruct.your_argument_name
%
% Examples:
%
% No defaults
% getnargs( {'foo', 123, 'bar', 'qwerty'} )
%
% With defaults
% getnargs( {'foo', 123, 'bar', 'qwerty'} , ...
% struct('foo', 987, 'bar', magic(3)) )
%
% See also: inputParser
%
% Authors: Jonas, Richie Cotton and LRQ3000
%
% Extract the arguments if it's inside a sub-struct (happens on Octave), because anyway it's impossible that the number of argument be 1 (you need at least a couple, thus two)
if (numel(varargin) == 1)
varargin = varargin{:};
end
% Sanity check: we need a multiple of couples, if we get an odd number of arguments then that's wrong (probably missing a value somewhere)
nArgs = length(varargin);
if rem(nArgs, 2) ~= 0
error('NameValuePairToStruct:NotNameValuePairs', ...
'Inputs were not name/value pairs');
end
% Sanity check: if defaults is not supplied, it's by default an empty struct
if ~exist('defaults', 'var')
defaults = struct;
end
if ~exist('restrict_flag', 'var')
restrict_flag = false;
end
% Syntactic sugar: if defaults is also a cell instead of a struct, we convert it on-the-fly
if iscell(defaults)
defaults = struct(defaults{:});
end
optionNames = fieldnames(defaults); % extract all default arguments names (useful for restrict_flag)
argStruct = defaults; % copy over the defaults: by default, all arguments will have the default value.After we will simply overwrite the defaults with the user specified values.
for i = 1:2:nArgs % iterate over couples of argument/value
varname = varargin{i}; % make case insensitive
% check that the supplied name is a valid variable identifier (it does not check if the variable is allowed/declared in defaults, just that it's a possible variable name!)
if ~isvarname(varname)
error('NameValuePairToStruct:InvalidName', ...
'A variable name was not valid: %s position %i', varname, i);
% if options are restricted, check that the argument's name exists in the supplied defaults, else we throw an error. With this we can allow only a restricted range of arguments by specifying in the defaults.
elseif restrict_flag && ~isempty(defaults) && ~any(strmatch(varname, optionNames))
if restrict_flag ~= 2 % restrict_flag = 2 means that we just ignore this argument, else we show an error
error('%s is not a recognized argument name', varname);
end
% else alright, we replace the default value for this argument with the user supplied one (or we create the variable if it wasn't in the defaults and there's no restrict_flag)
else
argStruct = setfield(argStruct, varname, varargin{i + 1}); %#ok<SFLD>
end
end
end
Also available as a Gist.
And for those interested in having real named arguments (with a syntax similar to Python, eg: myfunction(a=1, b='qwerty'), use InputParser (only for Matlab, Octave users will have to wait until v4.2 at least or you can try a wrapper called InputParser2).
Also as a bonus, if you don't want to always have to type argstruct.yourvar but directly use yourvar, you can use the following snippet by Jason S:
function varspull(s)
% Import variables in a structures into the local namespace/workspace
% eg: s = struct('foo', 1, 'bar', 'qwerty'); varspull(s); disp(foo); disp(bar);
% Will print: 1 and qwerty
%
%
% Author: Jason S
%
for n = fieldnames(s)'
name = n{1};
value = s.(name);
assignin('caller',name,value);
end
end