mcMincApply: Local multicore mincApply

In Mouse-Imaging-Centre/RMINC: Statistical Tools for Medical Imaging NetCDF (MINC) Files

Local multicore mincApply

Description

Split a minc apply job into batches and process it locally using a fork cluster generated by the parallel package.

Usage

mcMincApply(
  filenames,
  fun,
  ...,
  mask = NULL,
  tinyMask = FALSE,
  slab_sizes = NULL,
  temp_dir = getwd(),
  cores = getOption("mc.cores", parallel::detectCores() - 1),
  return_raw = FALSE,
  cleanup = TRUE,
  mask_vals = NULL,
  collate = simplify2minc
)

Arguments

filenames	Paths to the minc files to apply accross
fun	An arbitrary R function to be applied
...	Additional arguments to pass to fun, see details for a warning
mask	The mask used to select voxels to apply to
tinyMask	Shrink the mask for testing
slab_sizes	A 3 element vector indicating large a chunk of data to read from each minc file at a time defaults to one slice along the first dimension.
temp_dir	A directory to hold mask files used in the job batching
cores	the number of cores to use, defaults to the option mc.cores or one less than the number of detected cores if mc.cores is unset.
return_raw	An internal use argument that prevents the resulting object from being reordered and expanded.
cleanup	Whether to delete temporary parallelization masks
mask_vals	values of the mask over which to parallelize, defaults to subdividing all masked voxels into the specified number of batches
collate	A function to collate the list into another object type

Mouse-Imaging-Centre/RMINC documentation built on Feb. 24, 2025, 1:22 a.m.