Skip to contents

Evaluate an expression after temporarily setting malevnc options

Source: R/urls.R
with_mcns.Rd

malecns is a thin wrapper around malevnc. This function temporarily changes the server/dataset options for the malevnc while running your expression.

choose_mcns swaps out the male vnc dataset for the male cns. This means that all functions from the malevnc package should target the male cns instead. It is recommended that you use the with_mcns function to do this temporarily unless you have no intention of using the male vnc dataset. To switch the default malecns dataset please see choose_mcns_dataset.

Usage

with_mcns(
  expr,
  dataset = getOption("malecns.dataset", default = "male-cns:v0.9")
)

choose_mcns(
  dataset = getOption("malecns.dataset", default = "male-cns:v0.9"),
  set = TRUE,
  use_clio = NA
)

Arguments

expr

An expression involving malecns/malevnc functions to evaluate with the specified autosegmentation. .

dataset

The name of the dataset as reported in Clio e.g. CNS, male-cns:v0.9 etc.

set

Whether to set the relevant package options or just to return a list of the required options.

use_clio

Whether to use clio to list datasets (expert use only; default of use_clio=NA should do the right thing).

Details

Note that as of 11 Aug 2025 it also switches out the active dataset for the malecns package if you specify something different using the dataset argument. This is probably what people always expected and allows you to easily run the same expression for e.g. production vs snapshot malecns datasets.

See also

Other malecns-package: choose_mcns_dataset(), dr_malecns(), malecns-package

Examples

if (FALSE) { # \dontrun{
with_mcns(malevnc::manc_dvid_node(type = 'clio'))
} # }
# \donttest{
# This should work for both clio and neuprint calls, here clio:
# this body was typed after the v0.9 snapshot
with_mcns(mcns_body_annotations(194965), dataset = "male-cns:v0.9")
#>   bodyid celltype_predicted_nt celltype_predicted_nt_confidence
#> 1 194965         acetylcholine                         0.952291
#>   celltype_total_nt_predictions  consensus_nt flywire_type group instance
#> 1                         22356 acetylcholine         Sm01 29457    Cm2_L
#>    predicted_nt predicted_nt_confidence soma_side   status   superclass
#> 1 acetylcholine               0.9597166         L Reviewed ol_intrinsic
#>   total_nt_predictions type  auto
#> 1                   66  Cm2 FALSE
with_mcns(mcns_body_annotations(194965), dataset = "CNS")
#> switching CNS dataset from `male-cns:v0.9` to `CNS`
#>   bodyid celltype_predicted_nt celltype_predicted_nt_confidence
#> 1 194965         acetylcholine                        0.9478553
#>   celltype_total_nt_predictions  consensus_nt flywire_type group instance
#> 1                         22356 acetylcholine         Sm01 29457    Cm2_L
#>    predicted_nt predicted_nt_confidence soma_side   status   superclass
#> 1 acetylcholine               0.9552417         L Reviewed ol_intrinsic
#>   total_nt_predictions type  user  auto
#> 1                   66  Cm2 bergs FALSE
# }