R/transformation.R
shortest_bridging_seq.Rd
These functions are designed for expert use. In general it is
recommended to use xform_brain
.
bridging_graph
creates an igraph::graph representing all known
template brains (vertices) and the bridging registrations connecting them
(edges).
shortest_bridging_seq
finds the shortest bridging
sequence on a graph of all available bridging registrations, subject to
constraints defined by graph connectivity and the reciprocal
parameter
.
bridging_graph(
regdirs = getOption("nat.templatebrains.regdirs"),
reciprocal = NA
)
shortest_bridging_seq(
sample,
reference,
via = NULL,
checkboth = TRUE,
imagedata = FALSE,
reciprocal = NA,
...
)
Character vector of directories to search for registrations (see details)
Sets the weight of reciprocal edges in the graph (and thereby whether inverse registrations will be considered).
Source template brain (e.g. IS2) that data is currently in.
Specified either as character vector or a templatebrain
object.
Target template brain (e.g. IS2) that data should be transformed into.
(optional) intermediate template brain that the registration sequence must pass through.
When TRUE
will look for registrations in both
directions. See details.
Whether x
should be treated as image data (presently
only supported as a file on disk) or 3D object vertices - see details.
additional arguments passed on to bridging_graph
When reciprocal != NA
we create a graph where each forward
transformation is matched by a corresponding inverse transformation with
the specified edge weight. The edge weight for forward transforms will
always be 1.0.
By default regdirs
is set to getOption('nat.templatebrains.regdirs')
if (FALSE) {
plot(bridging_graph(), vertex.size=25, edge.arrow.size=0.5)
# with reciprocal edges
plot(bridging_graph(reciprocal=3), vertex.size=25)
}
if (FALSE) {
shortest_bridging_seq(FCWB, IS2)
# or
shortest_bridging_seq('FCWB', 'IS2')
shortest_bridging_seq(sample='FCWB', reference='IS2', via="JFRC2")
}