Read skeleton(s) from brainmaps API into a nat::neuron object

Read skeleton(s) from brainmaps API into a nat::neuron object

Usage

read.neuron.brainmaps(x, ...)

read.neurons.brainmaps(x, OmitFailures = NA, df = NULL, ...)

Arguments

x

A vector of segment ids or any Neuroglancer scene specification that includes segments ids (see examples and ngl_segments for details).

...

Additional arguments passed to brainmaps_skeleton and then on to brainmaps_fetch. These may include a skeletonuri argument, a brainmaps URI specifying the remote source of the skeletons. See brainmaps_skeleton for details.

OmitFailures

Whether to omit neurons for which FUN gives an error. The default value (NA) will result in nlapply stopping with an error message the moment there is an error. For other values, see details.

df

Optional data frame containing information about each neuron

Value

a nat::neuron object

Details

When OmitFailures is not NA, FUN will be wrapped in a call to try to ensure that failure for any single neuron does not abort the nlapply call. When OmitFailures=TRUE the resultant neuronlist will be subsetted down to return values for which FUN evaluated successfully. When OmitFailures=FALSE, "try-error" objects will be left in place. In either of the last 2 cases error messages will not be printed because the call is wrapped as try(expr, silent=TRUE).

The optional dataframe (df) detailing each neuron should have rownames that match the names of each neuron. It would also make sense if the same key was present in a column of the data frame. If the dataframe contains more rows than neurons, the superfluous rows are dropped with a warning. If the dataframe is missing rows for some neurons an error is generated. If SortOnUpdate=TRUE then updating an existing neuronlist should result in a new neuronlist with ordering identical to reading all neurons from scratch.

Examples

if (FALSE) {
n=read.neuron.brainmaps(22427007374)
nm=read.neurons.brainmaps(find_merged_segments(7186840767))

# you would specify a particular skeleton source like so
read.neurons.brainmaps(find_merged_segments(7186840767),
   skeletonuri="brainmaps://<volume>/<meshName>")
}