BigNeuroVec-methods: Create a Memory-Mapped Neuroimaging Vector

In bbuchsbaum/neuroim2: Data Structures for Brain Imaging Data

Create a Memory-Mapped Neuroimaging Vector

Description

Creates a BigNeuroVec object, which represents a large neuroimaging vector using memory-mapped file storage. This allows working with neuroimaging data that is too large to fit in memory.

Usage

BigNeuroVec(
  data,
  space,
  mask,
  label = "",
  type = c("double", "float", "integer"),
  backingfile = tempfile()
)

Arguments

data	The input data to be stored
space	A NeuroSpace object defining the spatial properties
mask	A logical mask indicating which voxels contain data
label	Optional character string label for the vector
type	Storage type, one of "double", "float", or "integer"
backingfile	Path to the file used for memory mapping (defaults to tempfile())

Value

A new BigNeuroVec object that provides memory-efficient access to large neuroimaging data through memory mapping. The object contains the spatial properties, mask, and memory-mapped data storage.

Examples

# Load an example 4D brain image
example_file <- system.file("extdata", "global_mask_v4.nii", package = "neuroim2")
example_4d_image <- read_vec(example_file)

# Create a mask (e.g., selecting voxels with values > 0)
mask <- array(as.vector(example_4d_image[,,,1]) > 0,
             dim = dim(example_4d_image)[1:3])

if(requireNamespace("bigstatsr", quietly = TRUE)) {
  # Create a BigNeuroVec with memory mapping
  big_vec <- BigNeuroVec(data = example_4d_image@.Data,
                         space = space(example_4d_image),
                         mask = mask,
                         label = "Example BigNeuroVec")
  print(big_vec)
}

bbuchsbaum/neuroim2 documentation built on Feb. 26, 2025, 3:49 p.m.