neuroim2 (version 0.8.5)

MappedNeuroVec-class: MappedNeuroVec Class

Description

A class representing a four-dimensional brain image backed by a memory-mapped file. This class provides efficient access to large brain images without loading the entire dataset into memory.

The MappedNeuroVec class provides memory-efficient access to large neuroimaging datasets through memory mapping. This allows processing of datasets larger than available RAM by keeping data on disk and only loading requested portions into memory.

Creates a MappedNeuroVec object that provides efficient, memory-mapped access to large neuroimaging datasets. This allows processing of data larger than available RAM by keeping it on disk and only loading requested portions into memory.

Usage

MappedNeuroVec(file_name, label = basename(file_name))

Value

A new MappedNeuroVec object providing:

• Memory-mapped access to the data

• Spatial and temporal indexing

• Efficient data extraction

• Automatic memory management

Arguments

file_name

Character string specifying the path to the neuroimaging file. Supported formats include NIFTI (.nii) and ANALYZE (.hdr/.img).

label

Optional character string providing a label for the vector

Slots

filemap

An object of class mmap representing the memory-mapped file containing the brain image data.

offset

An integer representing the byte offset within the memory-mapped file where the brain image data starts.

Methods

This class inherits methods from NeuroVec and implements the ArrayLike4D interface. Additional methods specific to memory-mapped operations may be available.

Implementation Details

The class uses the mmap package to establish a memory mapping between the file and memory space. Key features include:

• Zero-copy access to file data

• Automatic memory management

• Support for large datasets

• Efficient random access

Details

MappedNeuroVec objects use memory-mapped files to store and access large 4D brain images efficiently. This approach allows for rapid access to specific portions of the data without requiring the entire dataset to be loaded into memory at once.

Create a Memory-Mapped Neuroimaging Vector

The function implements several key features:

• Zero-copy access to file data

• Automatic memory management

• Support for large datasets

• Efficient random access

• Proper cleanup on object deletion

Memory mapping is particularly useful when:

• Working with large datasets

• Only portions of data are needed at once

• Random access is required

• Multiple processes need to share data

See Also

NeuroVec-class for the parent class. mmap for details on memory-mapped file objects.

MappedNeuroVec for creating instances of this class

mmap for memory mapping details

Examples

# Create a MappedNeuroVec object (pseudo-code)
file_path <- system.file("extdata", "global_mask_v4.nii", package = "neuroim2")
mapped_vec <- MappedNeuroVec(file_path)

# Access a subset of the data
subset <- mapped_vec[,,, 1:2]


# Create mapped vector from NIFTI file
mvec <- MappedNeuroVec(system.file("extdata", "global_mask_v4.nii", package = "neuroim2"))

# Extract first volume
vol1 <- mvec[[1]]

# Get dimensions
dim(mvec)

# Access specific timepoint
timepoint <- mvec[, , , 2]