cc_brightness_timeseries_folder: Cross-correlated brightness time-series calculations for...
In rorynolan/nandb: Number and Brightness Image Analysis

cc_brightness_timeseries_folder

R Documentation

Cross-correlated brightness time-series calculations for every image in a folder.

Description

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

Usage

cc_brightness_timeseries_folder(
  folder_path = ".",
  frames_per_set,
  overlap = FALSE,
  ch1 = 1,
  ch2 = 2,
  thresh = NULL,
  detrend = detrend,
  quick = quick,
  filt = NULL,
  parallel = FALSE
)

Arguments

`folder_path`	The path (relative or absolute) to the folder you wish to process.
`frames_per_set`	The number of frames with which to calculate the successive cross-correlated brightnesses. 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 bleaching or/and thresholding are selected, they are performed on the whole image stack before the sectioning is done for calculation of cross-correlated brightnesses.
`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.
`ch1`	A natural number. The index of the first channel to use.
`ch2`	A natural number. The index of the second channel to use.
`thresh`	Do you want to apply an intensity threshold prior to calculating cross-correlated brightness (via `autothresholdr::mean_stack_thresh()`)? If so, set your thresholding method here. If this is a single value, that same threshold will be applied to both channels. If this is a length-2 vector or list, then these two thresholds will be applied to channels 1 and 2 respectively. A value of `NA` for either channel gives no thresholding for that 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. To detrend one channel and not the other, specify this as a length 2 vector.
`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 = 'smooth'`) or median (`filt = 'median'`) filter the cross-correlated brightness image using `smooth_filter()` or `median_filter()` respectively? If selected, these are invoked here with a filter radius of 1 and with the option `na_count = TRUE`. A value of `NA` for either channel gives no thresholding for that channel. If you want to smooth/median filter the cross-correlated brightness image in a different way, first calculate the cross-correlated brightnesses without filtering (`filt = NULL`) using this function and then perform your desired filtering routine on the result.
`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

## Not run: 
setwd(tempdir())
ijtiff::write_tif(img, "a.tif")
ijtiff::write_tif(img, "ab.tif")
cc_brightness_timeseries_folder(frames_per_set = 25)
list.files()

## End(Not run)

rorynolan/nandb documentation built on Jan. 28, 2025, 10:22 a.m.