How can I make multiple vectors the same length?

Hi all,

I am struggeling with an interpolation task and dont make any process.

I have calculated muscle thickness values across the total muscle length of several subjects. Now I want to compare the region specific differences between subjects but of course all subjects have different muscle length (and thus I have vectors with more values (if muscle has greater length) and smaller vectors (for short muscles).

How could I interpolate these data so that each vector has the same length? I have attached a data sheet (each subjects has an own column).

Thank you for your help!!

[Walter Roberson]

Do you want the interpolation to be done at intervals that are the same for each muscle (for example 2mm), or do you want the interpolation to be at intervals that are equal proportions of the muscle (e.g., every 1/8th of the length) ?

[Christoph Centner]

Hey Walter,

thank you for your quick reply. Ideally, I would like to have it done at equal portions (10%, 20%, 30%,..) of muscle length.

Do you know any solution?

Thanks so much!

Answer 1

[Stephen23]

Because each of the input columns has their own domain, you will have to loop over the columns and interpolate each column separately, something like this:

inp = xlsread('Sample.xlsx');
out = inp; % preallocate
num = size(out,1);
for k = 1:size(out,2)
    idx = ~isnan(inp(:,k));
    xi  = 1:nnz(idx);
    xo  = linspace(1,nnz(idx),num);
    out(:,k) = interp1(xi,inp(idx,k),xo);
end

Giving:

>> inp % raw data
inp =
    0.3487    0.6447    0.8550    0.6123    0.4617    0.7680    0.8120    0.4800    0.9830
    0.7033    0.5983    0.8143    0.7540    0.6167    0.6230    0.6570    0.6357    0.7357
    0.7700    0.6753    0.2363    0.7473    0.7600    0.6143    0.8490    0.8643    0.6917
    0.7583    0.7740    0.7683    0.9083    0.7167    0.5497    0.8620    0.8520    0.8227
    0.8963    0.8700    0.8983    0.9853    0.8053    0.6887    0.9170    0.9657    0.9020
    0.8990    1.0077    0.9497    0.9460    0.8480    0.7230    0.8993    1.0130    0.9747
    0.9040    0.9130    0.9560    1.0037    0.8913    0.7640    0.9340    1.1217    1.0367
    0.8550    0.9693    0.9565    1.0513    0.8900    0.7763    1.0707    1.1203    1.0160
    0.9277    0.9963    0.9220       NaN    0.9037    0.8657    1.1093    1.1407    1.0457
    0.9290    1.0790    0.8823       NaN    0.8880    0.8247    1.0657    1.1763    1.0410
       NaN    0.9413    0.7607       NaN    1.0247       NaN    1.0347    1.1750    1.0693
       NaN       NaN       NaN       NaN    0.9120       NaN    1.0880    1.2743    1.0937
       NaN       NaN       NaN       NaN       NaN       NaN    1.1810    1.0443    1.1110
       NaN       NaN       NaN       NaN       NaN       NaN    1.2476       NaN       NaN
       NaN       NaN       NaN       NaN       NaN       NaN    1.3207       NaN       NaN
>> out % interpolated data
out =
    0.3487    0.6447    0.8550    0.6123    0.4617    0.7680    0.8120    0.4800    0.9830
    0.5767    0.6116    0.8260    0.6832    0.5835    0.6748    0.6570    0.6134    0.7710
    0.7224    0.6313    0.5666    0.7540    0.6986    0.6205    0.8490    0.7990    0.7042
    0.7652    0.6894    0.3123    0.7507    0.7445    0.6150    0.8620    0.8573    0.7665
    0.7633    0.7599    0.6923    0.7473    0.7293    0.5774    0.9170    0.9007    0.8567
    0.7879    0.8289    0.8426    0.8278    0.7990    0.5795    0.8993    0.9792    0.9228
    0.8766    0.9093    0.9130    0.9083    0.8358    0.6688    0.9340    1.0285    0.9835
    0.8977    1.0077    0.9497    0.9468    0.8697    0.7058    1.0707    1.1217    1.0367
    0.8997    0.9400    0.9542    0.9853    0.8910    0.7289    1.1093    1.1205    1.0190
    0.9029    0.9371    0.9562    0.9657    0.8910    0.7552    1.0657    1.1349    1.0372
    0.8830    0.9732    0.9515    0.9460    0.9017    0.7693    1.0347    1.1610    1.0430
    0.8602    0.9925    0.9269    0.9748    0.8936    0.7827    1.0880    1.1758    1.0531
    0.9069    1.0436    0.8993    1.0037    0.9466    0.8401    1.1810    1.2034    1.0763
    0.9281    1.0397    0.8476    1.0275    1.0005    0.8510    1.2476    1.2415    1.0961
    0.9290    0.9413    0.7607    1.0513    0.9120    0.8247    1.3207    1.0443    1.1110
>> 

[Christoph Centner]

WOW that was quick! This already helps a lot!

@ Stephen: Do you also know how to make them all a length of 10 data points? Then I could easily compare all subjects on 10%, 20%, 30% - 100% of muscle length? (see comment from Walter above)

THANK YOU!!! Its so much appreciated and you helped me so much so far!

[Ameer Hamza]

Just change these two lines

out = inp; % preallocate
num = size(out,1);

with

num = 10;
out = zeros(num, size(inp,2)); % preallocate

[Christoph Centner]

Thank you Steven! Much appreciated!!!!

[Stephen23]

"Thank you Steven!"

Thank you Ameer Hamza.

"Do you also know how to make them all a length of 10 data points? Then I could easily compare all subjects on 10%, 20%, 30% - 100% of muscle length?"

If you sample every 10% along the length, i.e. at 0%, 10%, 20%, ... 90%, 100% of the length, then this requires eleven sample points, not ten. If you sample the length with only ten points, then this gives samples at 0%, 11.111..%, 22.222..%, ... 88.888...%, 100% of the length.