number_timeseries_folder: Number time-series calculations for every image in a folder.
In nandb: Number and Brightness Image Analysis

Description Usage Arguments Note See Also Examples

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
)

`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`.

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.

number_timeseries()

## Not run: 
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)

## End(Not run)