Read skeletons for segments by extracting from the corresponding zip file(s)

read_segments2 is a reworked version of read_segments that reads skeletons straight from zip files to memory.

Usage

read_segments(x, voxdims = c(32, 32, 40), ...)

read_segments2(
  x,
  voxdims = c(32, 32, 40),
  minfilesize = 80,
  datafrac = NULL,
  coordsonly = FALSE,
  ...
)

Arguments

x: A vector of segment ids or any Neuroglancer scene specification that includes segments ids (see examples and ngl_segments for details).
voxdims: The voxel dimensions in nm of the skeletonised data
...: additional arguments passed to read.neurons
minfilesize: The uncompressed size of the swc file must be >= this. A cheap way to insist that we have >1 point.
datafrac: Fraction of the data to read based on uncompressed file size (see details)
coordsonly: Only read in XYZ coordinates of neurons.

Value

A neuronlist containing one

neuron for each fragment

Details

I would recommend read_segments2 at this point. read_segments has the potential benefit of caching SWC files on disk rather than extracting every time. However there is a large slowdown on many filesystems as the number of extracted files enters the thousands - something that I have hit a few times. Furthermore read_segments2 makes it easier to select fragment files before extracting them.

datafrac a number in the range 0-1 specifies a fraction of the data to read. Skeleton fragments will be placed in descending size order and read in until the number of bytes exceeds datafrac * sum(all file sizes). We have noticed that the time taken to read a neuron from a zip file seems to depend largely on the number of fragments that are read in, rather than the amount of data in each fragment! Reading 90 can take < 10

Examples

if (FALSE) {
# read neuron using raw segment identifier
n <- read_segments2(22427007374)

# read a neuron from a scene specification copied from Neuroglancer window
# after clicking on the {} icon at top right
n <- read_segments2(clipr::read_clip())

summary(n)

n2 <- read_segments2(22427007374, datafrac=0.9)
summary(n2)
}