Breaking and spliting vector into groups and averaging

Question

Albert 2021-3-10

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https://ww2.mathworks.cn/matlabcentral/answers/768582-breaking-and-spliting-vector-into-groups-and-averaging

编辑： Albert 2021-3-11

Hi,

I have a long one-dimensional time vector that I want to split in chunks and average each chunk. I have another logical vector (same length) of 0s and 1s that changes the sign to signal each chunk transition. How could I easily use the information on the logical vector to select each chunk of the time vector?

Thanks!

timevec = [0 2 3 4 5 0 9 3 4];

chunk = [0 0 0 1 1 1 1 0 0];

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Answer 1

Adam Danz 2021-3-10

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https://ww2.mathworks.cn/matlabcentral/answers/768582-breaking-and-spliting-vector-into-groups-and-averaging#answer_644142

编辑：Adam Danz 2021-3-10

在 MATLAB Online 中打开

timevec = [0 2 3 4 5 0 9 3 4];
chunk = [0 0 0 1 1 1 1 0 0];
g = cumsum([1, diff(chunk)]~=0); % assuming 'chunk' is a row vector
groupMeans = splitapply(@mean, timevec, g)
groupMeans = 1×3
    1.6667    4.5000    3.5000

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Adam Danz 2021-3-11

在 MATLAB Online 中打开

To define groups by the chunk vector resetting to 0,

chunk =   [0 0 0 1 1 1 1 0 0 0 0 1]
chunk = 1×12
     0     0     0     1     1     1     1     0     0     0     0     1
g = cumsum(diff([1,chunk])==-1)
g = 1×12
     1     1     1     1     1     1     1     2     2     2     2     2

For the second question, do you mean this?

timevec = [0 2 3 4 5 0 9 3 4];
chunk = [0 0 0 1 1 1 1 0 0];
cond =  logical([1 1 1 1 0 0 1 1 1]);
X = timevec(cond); 
Y = chunk(cond); 
g = cumsum([1, diff(Y)]~=0); 
groupMeans = splitapply(@mean, X, g)

Albert 2021-3-11

编辑：Albert 2021-3-11

在 MATLAB Online 中打开

For the first question, great, I didn't think it was so easy the adaptation!

For the second question it's a bit trickier because the Y array, after applying the condition in cond may totally mess around the chunks computation in the lines later on (transitions may be lost after applying the condition!). As a workaround I computed the chunks for all the values and applied the condition afterwards when computing the mean through a custom function as:

timevec = [0 2 3 4 5 0 9 3 4];
chunk = [0 0 0 1 1 1 1 0 0];
cond =  logical([1 1 1 1 0 0 1 1 1]);
g = cumsum([1, diff(chunk)]~=0); 
groupMeans = splitapply(@(vec,con)mean(vec(con)), timevec, cond, g)

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