adj {SpaDES.tools} | R Documentation |
Fast adjacent
function, and Just In Time compiled version
Description
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
Usage
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
)
Arguments
x |
|
cells |
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. |
directions |
the number of directions in which cells should be connected:
4 (rook's case), 8 (queen's case), or |
sort |
logical. Whether the outputs should be sorted or not, using cell ids
of the |
pairs |
logical. If |
include |
logical. Should the focal cells be included in the result? |
target |
a vector of cells that can be spread to. This is the inverse of a mask. |
numCol |
numeric indicating number of columns in the raster.
Using this with |
numCell |
numeric indicating number of cells in the raster.
Using this with |
match.adjacent |
logical. Should the returned object be the same as
|
cutoff.for.data.table |
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. |
torus |
Logical. Should the spread event wrap around to the other side of the raster?
Default is |
id |
numeric If not |
numNeighs |
A numeric scalar, indicating how many neighbours to return. Must be
less than or equal to |
returnDT |
A logical. If TRUE, then the function will return the result
as a |
Details
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
internallyno 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)
-
Value
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
.
Author(s)
Eliot McIntire
See Also
Examples
library(terra)
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)