Description Usage Arguments Details Value Note Author(s) Examples
View source: R/singleIterationFunctions.R
Default returns total 'road length' by connecting given new locations (xyStarts
) and
known existing locations (roadNodes
) using Euclidean distance. Wrapper function for a call to
nn2
which uses a K-D tree from the RANN
package.
1 |
xyStarts |
Matrix of easting (1st column) and northing (2nd column) of new pad center locations. Corresponding eastings and northings assumed to be in the same row. |
roadNodes |
Matrix of existing vertices of current road network. Easting (1st column) and northing (2nd column) pairs. Corresponding eastings and northings assumed to be in the same row. |
totalLength |
Default value is |
A K-D tree, or K-dimensional tree, is a binary search tree data structure used for organizing points that can be used for quick and efficient sorting and nearest neighbor searching of large sets of points.
If totalLength
is TRUE
, the total length of road using a
Euclidean distance between starting and ending locations is returned.
If totalLength
is FALSE
, a list xyEnds
containing the ending xy coordinates
from the roadNodes
are returned for each pad in addition to the total length of roads.
Edited by CDMartinez 15 Mar 16
Created by CDMartinez 15 Mar 16
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 | library(raster)
set.seed(46)
OGasmt <- continuousAssessment(auMC = 5,
auType = 'Gas',
auProbability = 1,
auAreaProductive = c(100,400,800),
auAreaDrainage = c(10,20,40),
auPercAreaUntested = c(93,96,99),
auPercAreaSweet = c(100,100,100),
auPercFutureSS = c(20,40,50),
auEURss = c(0.15,0.4,0.65),
auLGR = c(.08,.5,1),
year = 2016)
OGasmt <- convertAcre2sqMeter(OGasmt)
rBase <- raster(resolution = c(10,10), xmn = 0, xmx = 2000, ymn = 0, ymx = 2000)
values(rBase) <- sample(1:10, 40000, replace = TRUE)
points <- rbind(c(250,250),c(250,1750),c(1750,1750),c(1750,250),c(250,250))
shape <- SpatialPolygons(list(Polygons(list(Polygon(points)), 'auOutline')))
plot(rBase, xlim = c(0,2000), ylim = c(0,2000))
lines(shape)
spatialPrep <- prepareSimSpatial(surfaceRaster = rBase, shape, OGasmt)
distributionPrep <- prepareSimDistributions(spatialPrep,wellsPerPad = 3,
padArea = 500, EA = OGasmt, numIterations=5)
# Create road network
nVertices <- 500
road1 <- cbind(seq(0, 2000, length.out = nVertices),
seq(0, 100, length.out = nVertices)*sin(seq(-pi, 1.5*pi, length.out = nVertices)) + 600)
road2 <- cbind(200*cos(seq(-pi, 1.5*pi, length.out = nVertices)) +
seq(200, 1800, length.out = nVertices), seq(0, 2000, length.out = nVertices))
# Prepare road input: a two-column matrix of (Easting, Northing)
prepRoads <- rbind(road1, road2, cbind(road1[,1],rev(road1[,2]) + 700))
pads <- placePads(distributionPrep, 5)
roadLength <- makeRoads(xyStarts = pads$xyPadCenter, roadNodes = prepRoads)
|
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