number_timeseries: Create a number time-series.
In rorynolan/nandb: Number and Brightness Image Analysis

Description Usage Arguments Details Value See Also Examples

Given a stack of images img, use the first frames_per_set of them to create one number image, the next frames_per_set of them to create the next number image and so on to get a time-series of number images.

number_timeseries(
  img,
  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
)

`img`	A 4-dimensional array of images indexed by `img[y, x, channel, frame]` (an object of class ijtiff::ijtiff_img). The image to perform the calculation on. To perform this on a file that has not yet been read in, set this argument to the path to that file (a string).
`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`.

This may discard some images, for example if 175 frames are in the input and frames_per_set = 50, then the last 25 are discarded. If detrending is selected, it is performed on the whole image stack before the sectioning is done for calculation of numbers.

An object of class number_ts_img.

number().

1 2	img <- ijtiff::read_tif(system.file("extdata", "50.tif", package = "nandb")) nts <- number_timeseries(img, "n", frames_per_set = 20, thresh = "Huang")