Read L2 skeleton for crant neurons using pcg_skel

This function reads level 2 (L2) node data for neuron segmentations in the crant ant brain connectome dataset. It uses the pcg_skel Python library to create a skeleton object, which is then converted into a nat neuron object in R.

Usage

crant_read_l2skel(
  ids,
  OmitFailures = TRUE,
  datastack_name = crant_datastack_name(),
  ...
)

Arguments

ids

A vector of one or more neuron segment IDs to read.

OmitFailures

Logical; if TRUE, any segments that fail to be read are omitted from the results without an error (default=TRUE).

datastack_name

An optional CAVE dataset name (expert use only, by default will choose the standard crant dataset). See details.

...

Additional arguments passed to internal functions.

Value

A neuronlist containing one or more neuron objects. Note that neurons will be calibrated in nanometers (nm).

Details

crant_read_l2skel works by:

1. Validating the input IDs using crant_ids.

2. Using pcg_skel to generate a skeleton for each neuron segment.

3. Converting the pcg_skel skeleton to an SWC format DataFrame.

4. Converting the SWC DataFrame into a nat neuron object.

This function relies on the pcg_skel Python library (https://github.com/AllenInstitute/pcg_skel) for skeleton generation. Ensure that pcg_skel is installed in your Python environment.

References

pcg_skel Python library: https://github.com/AllenInstitute/pcg_skel

See also

crant_ids for ID validation neuron for details on the neuron object structure neuronlist for details on the neuronlist object structure

Examples

if (FALSE) { # \dontrun{
# One-time installation of necessary Python packages
fafbseg::simple_python('none', pkgs='numpy~=1.23.5')
fafbseg::simple_python(pkgs="pcg_skel")

# Read a single neuron
ant.neuron <- crant_read_l2skel("576460752653449509")

# Plot the neuron
plot3d(ant.neuron)

# Plot with crant surface (assuming crant.surf is available)
plot3d(crant.surf, alpha = 0.1, col = "lightgrey")
} # }