| Title: | Illustrate Various Methods of Interpolation for Irregular Data |
|---|---|
| Description: | Examples of interpolating irregular data, to illustrate the mechanics of various methods and some easy tools to run them. |
| Authors: | Michael Sumner [aut, cre], Marcelino de la Cruz [ctb] (wrote maybetin example function on R-Sig-Geo), Tomas Remenyi [ctb] |
| Maintainer: | Michael Sumner <[email protected]> |
| License: | GPL-3 |
| Version: | 0.2.0.9001 |
| Built: | 2024-11-05 04:44:14 UTC |
| Source: | https://github.com/hypertidy/guerrilla |
bathy is a simple polygon region layer to sit over the SST data.
A raster
Title
defaultgrid( xy, ncols = 60, nrows = 50, prj = "+proj=longlat +ellps=WGS84 +datum=WGS84 +no_defs +towgs84=0,0,0" )defaultgrid( xy, ncols = 60, nrows = 50, prj = "+proj=longlat +ellps=WGS84 +datum=WGS84 +no_defs +towgs84=0,0,0" )
xy |
coordinates |
ncols |
number of columns |
nrows |
number of rows |
prj |
projection metadata |
raster
Title
facets( X, nx, ny, x = NULL, y = NULL, na.v = 0, method = c("dirichlet", "delaunay") )facets( X, nx, ny, x = NULL, y = NULL, na.v = 0, method = c("dirichlet", "delaunay") )
X |
spatstat object |
nx |
number of x coords |
ny |
number of y coords |
x |
option input x values |
y |
optional input y values |
na.v |
na value |
method |
dirichlet or delaunay |
ppp object
Create a raster by interpolating across triangles
mesh_raster(x, grid = NULL, n = 128) ## S3 method for class 'mesh3d' mesh_raster(x, grid = NULL, n = 128) ## S3 method for class 'matrix' mesh_raster(x, grid = NULL, n = 128) ## S3 method for class 'data.frame' mesh_raster(x, grid = NULL, n = 128)mesh_raster(x, grid = NULL, n = 128) ## S3 method for class 'mesh3d' mesh_raster(x, grid = NULL, n = 128) ## S3 method for class 'matrix' mesh_raster(x, grid = NULL, n = 128) ## S3 method for class 'data.frame' mesh_raster(x, grid = NULL, n = 128)
x |
matrix of points, or a mesh3d |
grid |
raster to populate |
n |
grid size of raster if 'grid' not supplied |
At the moment, mesh_raster is identical to tri_fun for the matrix x-y-z case, but adds capability for a mesh3d object (of triangles). Barycentric interpolation is used to efficiently obtain a within-triangle estimate of a field of values
Raster
data("humface", package = "Rvcg") x <- humface grid <- mesh_raster(x, n = 256) raster::plot(grid, col = grey.colors(21), breaks = quantile(grid, seq(0, 1, length = 22), na.rm = TRUE)) anglr::plot3d(grid) ## interpolate from raw points xyz <- quakes[c("long", "lat", "depth")] xyz$depth <- -xyz$depth gx <- mesh_raster(xyz) rat <- 1/cos(mean(xyz[["lat"]]) * pi/180) raster::image(gx, asp = rat, col = hcl.colors(12, "YlOrRd")) maps::map(add = TRUE) points(xyz, pch = "+", cex = 0.3) ## add some dummy points (we aren't modelling the world) xex <- cbind(expand.grid(long = range(xyz$long), lat = range(xyz$lat)), depth = 0) g2 <- mesh_raster(rbind(xex, xyz)) raster::image(g2, asp = rat) maps::map(add = TRUE) points(xyz, pch = "+", cex = 0.3) anglr::plot3d(g2); rgl::aspect3d(1, rat, 0.1) rgl::points3d(xyz$long, xyz$lat, xyz$depth + 30)data("humface", package = "Rvcg") x <- humface grid <- mesh_raster(x, n = 256) raster::plot(grid, col = grey.colors(21), breaks = quantile(grid, seq(0, 1, length = 22), na.rm = TRUE)) anglr::plot3d(grid) ## interpolate from raw points xyz <- quakes[c("long", "lat", "depth")] xyz$depth <- -xyz$depth gx <- mesh_raster(xyz) rat <- 1/cos(mean(xyz[["lat"]]) * pi/180) raster::image(gx, asp = rat, col = hcl.colors(12, "YlOrRd")) maps::map(add = TRUE) points(xyz, pch = "+", cex = 0.3) ## add some dummy points (we aren't modelling the world) xex <- cbind(expand.grid(long = range(xyz$long), lat = range(xyz$lat)), depth = 0) g2 <- mesh_raster(rbind(xex, xyz)) raster::image(g2, asp = rat) maps::map(add = TRUE) points(xyz, pch = "+", cex = 0.3) anglr::plot3d(g2); rgl::aspect3d(1, rat, 0.1) rgl::points3d(xyz$long, xyz$lat, xyz$depth + 30)
Interpolation to a regular grid via triangulation
tri_fun(xy, value, grid = NULL, ...)tri_fun(xy, value, grid = NULL, ...)
xy |
coordinates |
value |
value to interpolate |
grid |
grid to use |
... |
ignored |
raster
zero_extent <- raster::extent(0, ncol(volcano), 0, nrow(volcano)) r <- raster::setExtent(raster::raster(volcano), zero_extent) xy <- raster::sampleRandom(r, size = 150, xy = TRUE)[, 1:2, drop = FALSE] tri_est <- tri_fun(xy, raster::extract(r, xy)) grd <- raster::raster(raster::extent(xy) ,res = 0.1) tri_est2 <- tri_fun(xy, raster::extract(r, xy), grid = grd)zero_extent <- raster::extent(0, ncol(volcano), 0, nrow(volcano)) r <- raster::setExtent(raster::raster(volcano), zero_extent) xy <- raster::sampleRandom(r, size = 150, xy = TRUE)[, 1:2, drop = FALSE] tri_est <- tri_fun(xy, raster::extract(r, xy)) grd <- raster::raster(raster::extent(xy) ,res = 0.1) tri_est2 <- tri_fun(xy, raster::extract(r, xy), grid = grd)
This functio was used by an early version of tri_fun.
tri_pip(tri, pts)tri_pip(tri, pts)
tri |
list P n2 coordinates and T matrix of n3 indices defining triangles |
pts |
input points |