Faster function for determining the cells of the 4, 8 or bishop
neighbours of the cells. This is a hybrid function that uses
matrix for small numbers of loci (<1e4) and data.table for larger numbers of loci
adj(
x = NULL,
cells,
directions = 8,
sort = FALSE,
pairs = TRUE,
include = FALSE,
target = NULL,
numCol = NULL,
numCell = NULL,
match.adjacent = FALSE,
cutoff.for.data.table = 2000,
torus = FALSE,
id = NULL,
numNeighs = NULL,
returnDT = FALSE
)SpatRaster object for which adjacency will be calculated.
vector of cell numbers for which adjacent cells should be found. Cell numbers start with 1 in the upper-left corner and increase from left to right and from top to bottom.
the number of directions in which cells should be connected:
4 (rook's case), 8 (queen's case), or "bishop" to connect
cells with one-cell diagonal moves.
Or a neighbourhood matrix (see Details).
logical. Whether the outputs should be sorted or not, using cell ids
of the from cells (and to cells, if match.adjacent
is TRUE).
logical. If TRUE, a matrix of pairs of adjacent cells is returned.
If FALSE, a vector of cells adjacent to cells is returned
logical. Should the focal cells be included in the result?
a vector of cells that can be spread to. This is the inverse of a mask.
numeric indicating number of columns in the raster.
Using this with numCell is a bit faster execution time.
numeric indicating number of cells in the raster.
Using this with numCol is a bit faster execution time.
logical. Should the returned object be the same as
raster::adjacent.
Default FALSE, which is faster.
numeric. If the number of cells is above this value, the function uses data.table which is faster with large numbers of cells. Default is 5000, which appears to be the turning point where data.table becomes faster.
Logical. Should the spread event wrap around to the other side of the raster?
Default is FALSE.
numeric If not NULL (default), then function will return "id" column.
A numeric scalar, indicating how many neighbours to return. Must be
less than or equal to directions; which neighbours are random
with equal probabilities.
A logical. If TRUE, then the function will return the result
as a data.table, if the internals used data.table,
i.e., if number of cells is greater than cutoff.for.data.table.
User should be warned that this will therefore cause the output
format to change depending cutoff.for.data.table.
This will be faster for situations where cutoff.for.data.table = TRUE.
Either a matrix (if more than 1 column, i.e., pairs = TRUE,
and/or id is provided), a vector (if only one column), or a data.table
(if cutoff.for.data.table is less than length(cells)
and
returnDT is TRUE.
To get a consistent output, say a matrix, it would be wise to test the output
for its class.
The variable output is done to minimize coercion to maintain speed.
The columns will be one or more of id, from, to.
Between 4x (large number loci) to 200x (small number loci) speed gains over
adjacent in raster package. There is some extra speed gain if
NumCol and NumCells are passed rather than a raster.
Efficiency gains come from:
use data.table internally
no need to remove NAs because wrapped or outside points are just removed directly with data.table
use data.table to sort and fast select (though not fastest possible)
don't make intermediate objects; just put calculation into return statement
The steps used in the algorithm are:
Calculate indices of neighbouring cells
Remove "to" cells that are
< 1 or > numCells (i.e., they are above or below raster), using a single modulo
calculation
where the modulo of "to" cells is equal to 1 if "from" cells are 0 (wrapped right to left)
or where the modulo of the "to" cells is equal to 0 if "from" cells are 1 (wrapped left to right)
library(terra)
#> terra 1.7.78
#>
#> Attaching package: ‘terra’
#> The following object is masked from ‘package:SpaDES.tools’:
#>
#> wrap
origDTThreads <- data.table::setDTthreads(2L)
origNcpus <- options(Ncpus = 2L)
a <- rast(ext(0, 1000, 0, 1000), res = 1)
sam <- sample(1:ncell(a), 1e4)
numCol <- ncol(a)
numCell <- ncell(a)
adj.new <- adj(numCol = numCol, numCell = numCell, cells = sam, directions = 8)
adj.new <- adj(numCol = numCol, numCell = numCell, cells = sam, directions = 8,
include = TRUE)
# clean up
data.table::setDTthreads(origDTThreads)
options(Ncpus = origNcpus)