Breaking and spliting vector into groups and averaging

2021 3 10
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更新时间：2021 3 11
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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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Adam Danz
Adam Danz 2021-3-10
编辑：Adam Danz 2021-3-10
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Ran in:
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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Albert
Albert 2021-3-10
Excellent! Fast and effective, thanks!
Albert
Albert 2021-3-11
I had another question. How could I update the code above so that I group on every period? So instead of taking groups every change in the chunk vector, taking them every time it's back to 0. For example:
timevec = [1 2 3 4 5 6 7 8 9 10 11 12];
chunk = [0 0 0 1 1 1 1 0 0 0 0 1];
should do the means in two groups for [1 2 3 4 5 6 7] and for [8 9 10 11 12]
thanks!
Albert
Albert 2021-3-11
And there is another point, what if I would like to apply a condition to the mean? Once the chunks are ready, in the splitapply part I would like to apply the mean of each chunk but only to the indexes fulfilling a certain condition expressed by another vector cond of same length...
thanks again!
timevec = [0 2 3 4 5 0 9 3 4];
chunk = [0 0 0 1 1 1 1 0 0];
cond = [1 1 1 1 0 0 1 1 1];
g = cumsum([1, diff(chunk)]~=0); % assuming 'chunk' is a row vector
groupMeans = splitapply(@mean, timevec, g)
Adam Danz
Adam Danz 2021-3-11
Ran in:
Ran in:
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
Albert 2021-3-11
编辑：Albert 2021-3-11
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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