Your "for example" text doesn't seem to match with finding how long it takes to decrease from 25 to 15.
So for example, I would record 10/ ('3/25/2024 15:02:46' - '3/25/2024 15:02:30') or 10/(16 seconds)
The next rate would be 10 / ('3/25/2024 15:02:59' - '3/25/2024 15:02:50') or 10/(9 seconds).
Your first example points are for an increase from 15 to 25 and your second example is for an decrease from 24 to 23.
If you really are wanting to find the average slopes of the decreases from 25 to 15. Try this approach (as a starting point at least). Note that I added an extra point to your data so there would be more than one decrease from 25 to 15.
D1= {'3/25/2024 15:01:10' 15
'3/25/2024 15:01:26' 25
'3/25/2024 15:01:42' 25
'3/25/2024 15:01:58' 23
'3/25/2024 15:02:14' 22
'3/25/2024 15:02:30' 15
'3/25/2024 15:02:46' 25
'3/25/2024 15:02:50' 24
'3/25/2024 15:02:59' 23
'3/25/2024 15:03:05' 15}; % << added a data point so there are two 25 to 15 transitions
T = cell2table(D1, 'VariableNames', {'timestamp', 'data'});
T.timestamp = datetime(T.timestamp(:))
Since we only care about the cases where the data decreases from 25 to 15, eliminate any data that is not 25 or 15 and also eliminate any data before the first occurrence of 25.
idx = T.data == 25 | T.data == 15;
T = T(idx,:);
if T.data(1) ~= 25
T(1,:) = []
end
slope = diff(T.data) ./ seconds(diff(T.timestamp));
We only care about negative slopes so throw away any non-negative slopes.
slope = slope(slope < 0) % slopes in data units per second
