First, you would load your mat file data using the load
function, placing it in a structure. Let's say that gave you the following sample data:
>> s = struct('a1',1:5,'b',[],'a2',1:5,'a3',1:5,'c1',1:3,'c2',3:5)
s =
a1: [1 2 3 4 5]
b: []
a2: [1 2 3 4 5]
a3: [1 2 3 4 5]
c1: [1 2 3]
c2: [3 4 5]
Then, we'll order the fields alphabetically using orderfields
, find the field names using fieldnames
, use regexprep
to strip all trailing numbers off of the field names, then get the unique substrings using unique
:
>> s = orderfields(s);
>> [strs, inds] = unique(regexprep(fieldnames(s), '\d*$', ''), 'last')
strs =
'a'
'b'
'c'
inds =
3
4
6
Using the indices returned by unique
, we can calculate how many times each substring appears by doing diff([0; inds])
. Then, we group the structure data into a cell array using these counts and struct2cell
and mat2cell
:
>> data = mat2cell(struct2cell(s), diff([0; inds]))
data =
{3x1 cell}
{1x1 cell}
{2x1 cell}
Notice that we have a cell array of cell arrays. If you know for sure that each set of entries for each substring will concatenate properly (as in our example), you can concatenate them using cellfun
and cell2mat
as follows:
>> data = cellfun(@cell2mat, data, 'UniformOutput', false)
data =
[3x5 double]
[]
[2x3 double]
Now we have a cell array of matrices, and a new structure can be made using cell2struct
:
>> snew = cell2struct(data, strs)
snew =
a: [3x5 double]
b: []
c: [2x3 double]