MATLAB: How to use nested functions to share variables? (Detailed code included)

nested functions

I am struggling with structuring some code and sharing variables between functions and scripts.I want to use nested functions or a similar method but cannot resolve my issue so far. To allow you to understand my intent for the script I have created the same effect using global variables but I absolutely do not want to use these. This is only for illustrative purposes.
I have included xlsread in the main script so that it only runs once and creates data_matrix. The function meanfun then calculates the mean of every column within the data_matrix based on if data in column 2 a given speed1 value. The output is a 17xC matrix, which is perfect. I then use xls write to output the file the result to excel.
I only want to run xlswrite / read once due to the processing time.
I am only creating the structure here. I will need to create various other functions for std deviation and other analytics which will be output as well.
The files I am processing are sometimes large say (1000 columns x 100 rows) sometimes more. So efficiency is important for me.
I can't share the data but column 2 is filled of values matching (600:100:2200).
if true
  % code

input_file = 'inputdata_1.xlsx';
output_file= 'outputdata_1.xlsx';
global data_matrix speed1
speed1 = (600:100:2200);
data_matrix = xlsread(input_file);
[r c] = size(data_matrix)
n=2;
m=3:1:c;
res=meanfun(n,m);
xlswrite(output_file,res);
here is the function I have created
if true
  % code
end
function [ output ] = meanfun(ai,bi)
%Function calculates the average of each column when the speed is located
%matches a value in the vector location specified. 
global data_matrix speed1
output = [
mean(data_matrix(data_matrix(:,ai) == speed1(1), bi))
mean(data_matrix(data_matrix(:,ai) == speed1(2), bi))
mean(data_matrix(data_matrix(:,ai) == speed1(3), bi))
mean(data_matrix(data_matrix(:,ai) == speed1(4), bi))
mean(data_matrix(data_matrix(:,ai) == speed1(5), bi))
mean(data_matrix(data_matrix(:,ai) == speed1(6), bi))
mean(data_matrix(data_matrix(:,ai) == speed1(7), bi))
mean(data_matrix(data_matrix(:,ai) == speed1(8), bi))
mean(data_matrix(data_matrix(:,ai) == speed1(9), bi))
mean(data_matrix(data_matrix(:,ai) == speed1(10), bi))
mean(data_matrix(data_matrix(:,ai) == speed1(11), bi))
mean(data_matrix(data_matrix(:,ai) == speed1(12), bi))
mean(data_matrix(data_matrix(:,ai) == speed1(13), bi))
mean(data_matrix(data_matrix(:,ai) == speed1(14), bi))
mean(data_matrix(data_matrix(:,ai) == speed1(15), bi))
mean(data_matrix(data_matrix(:,ai) == speed1(16), bi))
mean(data_matrix(data_matrix(:,ai) == speed1(17), bi))
]
end

Best Answer

You could simply use the two variables as inputs:
res = meanfun(n, m, data_matrix, speed1);
and
function output = meanfun(ai, bi, data_matrix, speed1)
I do not see the need to share variables, when they can be provided as inputs directly.
By the way, I'd use a loop:
function output = meanfun(ai, bi, data, v)
output = zeros(numel(v), numel(bi));
vdata  = data(:, ai);
for k = 1:numel(v)
  index        = (vdata == v(k));
  output(k, :) = mean(data(index, bi), 1);
end
end
Specifying the dimension to operate on in mean(x,1) is safer: when the data contain a single row for a specific v(k) only, mean(x) would operator alow the row.
There might be a single accumarray or splitapply command also, such that there is no need for a subfunction at all.
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