Vectorize averaging a timerange in a large timetable

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Poison Idea fan
Poison Idea fan 2023-11-2
评论： Poison Idea fan 2023-11-2
I have a timetable of data and a counter of sample times.
When there is a non NaN "sample_times" variable, I want to average the previous four minutes of data.
currently, I loop through the times when there is a sample_times variable but this ends up taking longer than I would like.
Is there a way to do this without using a loop?
The current code using a for loop is below.
clc
load question_TT.mat
sample_times = ~isnan(question_tt.counter_array); % when is the counter a number?
sample_times = question_tt.Time(sample_times); % get times when a sample period begins
dt_1 = question_tt.Time(sample_times)-minutes(5);
dt_2 = question_tt.Time(sample_times)-minutes(1); % get beginning and end of averaging time
for n = 1:height(dt_1)
S0(n,:) = mean(question_tt(dt_1(n):dt_2(n),1:4),"omitmissing"); % average the window of background time
end
  2 个评论
Dyuman Joshi
Dyuman Joshi 2023-11-2
Ran in:
Do you want to get the mean for all the times between dt_1(n) and dt_2(n)?
Also, the code is a fair bit fast here.
You should pre-allocate the output variable for increased code performance.
clc
load question_TT.mat
tic
sample_times = ~isnan(question_tt.counter_array); % when is the counter a number?
sample_times = question_tt.Time(sample_times); % get times when a sample period begins
dt_1 = question_tt.Time(sample_times)-minutes(5);
dt_2 = question_tt.Time(sample_times)-minutes(1); % get beginning and end of averaging time
for n = 1:height(dt_1)
S0(n,:) = mean(question_tt(dt_1(n):dt_2(n),1:4),"omitmissing"); % average the window of background time
end
toc
Elapsed time is 0.115147 seconds.
Poison Idea fan
Poison Idea fan 2023-11-2
编辑：Poison Idea fan 2023-11-2
On this reduced size timetable it runs pretty well. My actual data table is quite large though. The elapsed time when I run it on the real data is >10 minutes.
Yes the goal is to average the data in that window.

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采纳的回答

Taylor
Taylor 2023-11-2
Preallocating your variables before a for loop is a great way to help with performance. arrayfun, cellfun, and structfun are also useful tools for vectorizing code. Try replacing the for loop with this:
window_indices = arrayfun(@(n) dt_1(n):dt_2(n), 1:height(dt_1), 'UniformOutput', false);
S0 = cellfun(@(indices) mean(question_tt(indices, 1:4), 'omitmissing'), window_indices, 'UniformOutput', false);
S0 = cat(1, S0{:});
  1 个评论
Poison Idea fan
Poison Idea fan 2023-11-2
Using this method, there is a ~50 second improvement. But still takes a while. Maybe I am just asking for too much!
Thanks.

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