How can I write a loop that will normalise each column of data to zero?

Hi everyone,

I have a data set where I am trying to normalise each column of data to start at 0 degrees, and be in a positive direction (angular data). As the data flips when it reaches 180 degrees, I also need to account for this, so each column should move from approximately 0-90 degrees. The data also needs to be interpolated and transposed for further analysis.

I am currently stuck trying to write a loop to normalise each data column to start at 0 degrees. This is what I have tried so far:

head_rot=readmatrix('Head_Rotation_Data');
sho_rot=readmatrix('Shoulder_Rotation_Data');

head_rot_unwrap=unwrap(head_rot*pi/180)*180/pi;
sho_rot_unwrap=unwrap(sho_rot*pi/180)*180/pi;

for normalise_loop=1:size(head_rot_unwrap,1) %do separately for each time series (i.e. each participant or each trial)
    hr_norm(:,normalise_loop)= (head_rot_unwrap(:,normalise_loop) - head_rot_unwrap(1,normalise_loop));
    sr_norm(:,normalise_loop)= (sho_rot_unwrap(:,normalise_loop) - sho_rot_unwrap(1,normalise_loop));
end

head_rot_interp=inpaint_nans(hr_norm);
sho_rot_interp=inpaint_nans(sr_norm);

head_rot_transpose=transpose(head_rot_interp);
sho_rot_transpose=transpose(sho_rot_interp);

It is coming up with the error message:

Error using size

Not enough input arguments.

Error in normalise_to_baseline90 (line 8)

hr_norm(:,size)= 0-(head_rot_unwrap(:,size) - head_rot_unwrap(1,size));

I would appreciate any input as to what is going wrong/some suggestions on how I can change and improve this! I still get a bit stuck writing loops, so any hints and tips you've got would also be great if you have any. I've included the sample dataset but you probably don't need to look at is specifically.

Answer 1

[Mathieu NOE]

hello

IMHO there was only this bug

then you can see we can plot the data (unwrapped) without any issue

for normalise_loop=1:size(head_rot_unwrap,1)

must be

for normalise_loop=1:size(head_rot_unwrap,2)

now what do we do next? the data do not have same length for each measurement , so I suspect you would like to extraoplate , or we cut the longest ones to be of same length as the shortest one ?

full code

    clc
clearvars

head_rot=readmatrix('Head_Rotation_Data');
sho_rot=readmatrix('Shoulder_Rotation_Data');
head_rot_unwrap=unwrap(head_rot*pi/180)*180/pi;
sho_rot_unwrap=unwrap(sho_rot*pi/180)*180/pi;

for normalise_loop=1:size(head_rot_unwrap,2) %do separately for each time series (i.e. each participant or each trial)
    hr_norm(:,normalise_loop)= (head_rot_unwrap(:,normalise_loop) - head_rot_unwrap(1,normalise_loop));
    sr_norm(:,normalise_loop)= (sho_rot_unwrap(:,normalise_loop) - sho_rot_unwrap(1,normalise_loop));
end

figure(1),
subplot(211),plot(hr_norm)
title('Head Rotation Data');
subplot(212),plot(sr_norm)
title('Shoulder Rotation Data');

[Mathieu NOE]

Now the full code tested also with interpolation with

I don't see the point of transposing the data, but maybe there is a good reason for that

clc
clearvars

head_rot=readmatrix('Head_Rotation_Data');
sho_rot=readmatrix('Shoulder_Rotation_Data');
head_rot_unwrap=unwrap(head_rot*pi/180)*180/pi;
sho_rot_unwrap=unwrap(sho_rot*pi/180)*180/pi;
[m,n] = size(head_rot_unwrap);

for normalise_loop=1:n %do separately for each time series (i.e. each participant or each trial)
    hr_norm(:,normalise_loop)= (head_rot_unwrap(:,normalise_loop) - head_rot_unwrap(1,normalise_loop));
    sr_norm(:,normalise_loop)= (sho_rot_unwrap(:,normalise_loop) - sho_rot_unwrap(1,normalise_loop));
end

figure(1),
subplot(211),plot(hr_norm)
title('Head Rotation Data');
subplot(212),plot(sr_norm)
title('Shoulder Rotation Data');

head_rot_interp=inpaint_nans(hr_norm);
sho_rot_interp=inpaint_nans(sr_norm);
head_rot_transpose=transpose(head_rot_interp);
sho_rot_transpose=transpose(sho_rot_interp);

figure(2),
subplot(211),plot((1:m),head_rot_interp)
title('Head Rotation Data');
subplot(212),plot((1:m),sho_rot_interp)
title('Shoulder Rotation Data');

figure(3),
subplot(211),plot((1:m),head_rot_transpose)
title('Head Rotation Data');
subplot(212),plot((1:m),sho_rot_transpose)
title('Shoulder Rotation Data');

[pkll201]

You're amazing, thank you so much for your help, and your advice regarding the rest of the code! The reason I need it to be transposed is for further analysis that needs to be done :)

[Mathieu NOE]

My pleasure !

Good luck for the future