How to replace all “string” type by “char” type in a cell array?

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Category:Languages

Context

In R2016b, MATLAB introduced a new string datatype, in addition to the usual char datatype. So far, so good, but it is now giving me a lot of issues with the JSONlab toolbox I'm using.

For instance, in R2015b, loadjson returns a 1x2 cell character array:

dd = loadjson('["Titi", "Toto"]')  dd =       'Titi'    'Toto' 

But in R2018a, loadjson returns a 1x2 string array:

dd = loadjson('["Titi", "Toto"]')  dd =    1×2 cell array      {["Titi"]}    {["Toto"]} 

Problem

For not having to change my code everywhere, I'd like to patch the loadjson routine to replace all string types it may return with char types. For instance, in the following cell array:

test = { 'hello', "world", 0.3; 'how', 'are', "you"}  test =    2×3 cell array      {'hello'}    {["world"]}    {[0.3000]}     {'how'  }    {'are'    }    {["you" ]} 

I'd like to replace all strings:

cellfun(@isstring, test)  ans =    2×3 logical array     0   1   0    0   0   1 

Is there a way I can do it quickly (i.e. without looping through all elements) ?

PS: I know of jsondecode and jsonencode to replace JSONLab in the future, but so far I just want to quickly patch things.

 


You can use cellstr (confusingly, despite "str" suggesting string) to convert strings to character arrays without looping or cellfun... the docs state the following:

C = cellstr(A) converts A to a cell array of character vectors. The input array A can be a character array, a categorical array, or, starting in R2016b, a string array.

test = {'hello', "world", 0.3; 'how', 'are', "you"}; ind = cellfun(@isstring, test); test(ind) = cellstr(test(ind)) 

A cellfun performance note for class checks:

In both mine and Luis' answers, cellfun is used to determine which elements are strings. You can improve the performance of cellfun for this task...

Per the cellfun docs, there are some character array options which are much quicker than their function-handle counterparts. For the isstring indexing, it's likely a lot faster to run the first of these:

% rapid ind = cellfun('isclass', test, 'string'); % akin to looping ind = cellfun(@isstring, test); 

They have the same output, in a simple test I see a 4x speed improvement:

% Create large test array of random strings c = cell(100,1000); c = cellfun(@(x) string(char(randi([65,122],1,10))), c, 'uni', 0);  % Create functions for benchmarking  f=@()cellfun('isclass', c, 'string'); g=@()cellfun(@isstring,c);  % Timing on MATLAB R2017b timeit( f ) % >> 0.017sec timeit( g ) % >> 0.066sec  

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