Brightness time-series calculations for every image in a folder.

Perform brightness_timeseries() calculations on all tif images in a folder and save the resulting number images to disk.

brightness_timeseries_folder(
  folder_path = ".",
  def,
  frames_per_set,
  overlap = FALSE,
  thresh = NULL,
  detrend = FALSE,
  quick = FALSE,
  filt = NULL,
  s = 1,
  offset = 0,
  readout_noise = 0,
  parallel = FALSE
)

Arguments

folder_path	The path (relative or absolute) to the folder you wish to process.
def	A character. Which definition of brightness do you want to use, `"B"` or `"epsilon"`?
frames_per_set	The number of frames with which to calculate the successive brightnesses.
overlap	A boolean. If `TRUE`, the windows used to calculate number are overlapped, if `FALSE`, they are not. For example, for a 20-frame image series with 5 frames per set, if the windows are not overlapped, then the frame sets used are 1-5, 6-10, 11-15 and 16-20; whereas if they are overlapped, the frame sets are 1-5, 2-6, 3-7, 4-8 and so on up to 16-20.
thresh	The threshold or thresholding method (see `autothresholdr::mean_stack_thresh()`) to use on the image prior to detrending and brightness calculations.
detrend	Detrend your data with `detrendr::img_detrend_rh()`. This is the best known detrending method for brightness analysis. For more fine-grained control over your detrending, use the `detrendr` package. If there are many channels, this may be specified as a vector, one element for each channel.
quick	If `FALSE` (the default), the swap finding routine is run several times to get a consensus for the best parameter. If `TRUE`, the swap finding routine is run only once.
filt	Do you want to smooth (`filt = 'mean'`) or median (`filt = 'median'`) filter the number image using `smooth_filter()` or `median_filter()` respectively? If selected, these are invoked here with a filter radius of 1 (with corners included, so each median is the median of 9 elements) and with the option `na_count = TRUE`. If you want to smooth/median filter the number image in a different way, first calculate the numbers without filtering (`filt = NULL`) using this function and then perform your desired filtering routine on the result. If there are many channels, this may be specified as a vector, one element for each channel.
s	A positive number. The \(S\)-factor of microscope acquisition.
offset	Microscope acquisition parameters. See reference Dalal et al.
readout_noise	Microscope acquisition parameters. See reference Dalal et al.
parallel	Would you like to use multiple cores to speed up this function? If so, set the number of cores here, or to use all available cores, use `parallel = TRUE`.

Examples

if (FALSE) {
setwd(tempdir())
img <- ijtiff::read_tif(system.file("extdata", "50.tif", package = "nandb"))
ijtiff::write_tif(img, "img1.tif")
ijtiff::write_tif(img, "img2.tif")
brightness_timeseries_folder(def = "e", thresh = "tri", frames_per_set = 20)
}

Arguments

See also

Examples