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

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

number_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,
  gamma = 1,
  parallel = FALSE
)

Arguments

folder_path	The path (relative or absolute) to the folder you wish to process.
def	A character. Which definition of number do you want to use, "n" or "N"?
frames_per_set	The number of frames with which to calculate the successive numbers.
overlap	A boolean. If TRUE, the windows used to calculate brightness 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 number calculations. If there are many channels, this may be specified as a vector or list, one element for each channel.
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	FALSE repeats the detrending procedure (which has some inherent randomness) a few times to hone in on the best detrend. TRUE is quicker, performing the routine only once. FALSE is better.
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.
gamma	Factor for correction of number \(n\) due to the illumination profile. The default (gamma = 1) has no effect. Changing gamma will have the effect of dividing the result by gamma, so the result with gamma = 0.5 is two times the result with gamma = 1. For a Gaussian illumination profile, use gamma = 0.3536; for a Gaussian-Lorentzian illumination profile, use gamma = 0.0760.
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.

Note

Extreme number values (of magnitude greater than 3.40282e+38) will be written to the TIFF file as NA, since TIFF files cannot handle such huge numbers.

See also

number_timeseries()

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")
number_timeseries_folder(def = "n", thresh = "Huang", frames_per_set = 20)
}