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Consider a time-stack image series img of 100 frames. An ordinary call brightness(img, def = "epsilon") will use all 100 frames to calculate the brightness image.
Sometimes we want to see how the brightness is changing over the course of the acquisition. To do this, we could break up img into sequences of say 25 consecutive frames, getting 4 sets of frames (1-25, 26-50, 51-75 and 76-100) and calculate 4 brightness images. Ordinarily this would be quite laborious but brightness_timeseries(img, def = "epsilon", frames_per_set = 25) does this with ease.
To get more fine-grained time information, we could overlap the windows to get 76 sets of frames (1-25, 2-26, 3-27, …, 76-100). This can be done with brightness_timeseries(img, def = "epsilon", frames_per_set = 25, overlap = TRUE). Beware when calculating overlapped timeseries like this that the resulting frames are correlated because e.g. the calculations on frames 1-25 and 2-26 use almost all of the same data.
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