Description Usage Arguments Details Value See Also Examples
S3 functions remap
maps a variable (var
) (a matrix
or array
)
with x
, y
(and z
) coordinates
to a matrix
or array
with coordinates given by xto
, yto
(and zto
).
x, y, z, xto, yto
and zto
are all vectors.
The functions interpolate to all combinations of xto, yto
and zto
.
Simple 2-D linear interpolation is used.
Result is a matrix
or array
.
Function changeres
changes the resolution of a variable (var
) (a matrix
or array
)
with x
, y
(and z
) coordinates.
If var
is a matrix, then x, y
can be either a vector or a matrix; if
var
is an array, then x, y, z
should all be vectors.
Simple 2-D linear interpolation is used.
Result is a matrix
or array
.
S3-functions extract
map a variable (var
) from a matrix with (x, y) coordinates
or from an array with (x, y, z) coordinates to the xy
coordinate pair xyto
or xyz coordinate triplets xyzto
by linear interpolation. Result is a vector.
transect
takes a cross section across an array (var
).
Result is a matrix.
mapsigma
maps a matrix or array var
containing values defined at (x, sigma) (or (x, y, sigma)) coordinates
to (x, depth) (or (x, y, depth)) coordinates.
The depths corresponding to the sigma values in var
are in an input matrix or array called sigma
with same dimensions as var
.
The result is a matrix or array which will contain NA
s where the depth-coordinates
extend beyond the sigma values.
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 41 | remap (var, ...)
## S3 method for class 'matrix'
remap(var, x, y, xto = NULL, yto = NULL,
na.rm = TRUE, ...)
## S3 method for class 'array'
remap(var, x, y, z, xto = NULL, yto = NULL, zto = NULL,
na.rm = TRUE, ...)
changeres (var, ...)
## S3 method for class 'matrix'
changeres(var, x, y, resfac, na.rm = TRUE, ...)
## S3 method for class 'array'
changeres(var, x, y, z, resfac, na.rm = TRUE, ...)
extract (var, ...)
## S3 method for class 'matrix'
extract(var, x, y, xyto, ...)
## S3 method for class 'array'
extract(var, x, y, z, xyzto, ...)
transect(var, x, y, z, to, margin = "xy", ...)
mapsigma (var, ...)
## S3 method for class 'matrix'
mapsigma(var = NULL, sigma, signr = 2, x = NULL,
depth = NULL, numdepth = NULL, xto = NULL, resfac = 1, ...)
## S3 method for class 'array'
mapsigma(var = NULL, sigma, signr = 3, x = NULL, y = NULL,
depth = NULL, numdepth = NULL, xto = NULL, yto = NULL,
resfac = 1, ...)
transectsigma(var = NULL, sigma, x, y, to, depth = NULL,
numdepth = NULL, resfac = 1, ...)
|
var |
Matrix or array with values to be mapped to other coordinates ( |
x |
Vector with original x-coordinates of the matrix or array |
y |
Vector with original y-coordinates of the matrix or array |
z |
Vector with original z-coordinates of the array |
xto |
Vector with x-coordinates to which |
yto |
Vector with y-coordinates to which |
zto |
Vector with z-coordinates to which |
xyto |
Two-columned matrix, with first and second column specifying the
x- respectively y-coordinates to which the matrix |
xyzto |
Three-columned matrix, specifying the x-, y- and z-coordinates
to which the array |
to |
Two-columned matrix, specifying the values along the |
margin |
String with the names of the coordinates in the matrix |
sigma |
The sigma coordinates, a matrix or array with the same dimension
as |
signr |
The position of the sigma coordinates, in the matrix or array.
The default is the second or third dimension in |
depth |
The depth (often referred to as 'z') coordinates to which matrix
|
numdepth |
Only used when |
resfac |
Resolution factor, one value or a vector of two or three numbers,
for the x, y- and z- values respectively.
A value > 1 will increase the resolution. For instance, if |
na.rm |
How to treat |
... |
any other arguments. |
S3-function remap
can be used to increase or decrease
the resolution of a matrix or array var
, or to zoom in on a certain area.
It returns an object of the same class as var
(i.e. a matrix or array).
S3-function transect
takes a slice from an array; it returns a matrix.
S3-function extract
returns a vector with one value
corresponding to each row in xyto
or xyzto
.
mapsigma
should be used to make images from data that are in sigma
coordinates.
remap.matrix
:
var The higher or lower resolution matrix with dimension = c(length(xto), length(yto)).
x The x coordinates, corresponding to first dimension of var
(input argument xto
).
y The y coordinates, corresponding to second dimension of var
(input argument yto
).
remap.array
:
var The higher or lower resolution array, with dimension = c(length(xto), length(yto), length(zto)).
x The x coordinates, corresponding to first dimension of var
(input argument xto
).
y The y coordinates, corresponding to second dimension of var
(input argument yto
).
z The z coordinates, corresponding to third dimension of var
(input argument zto
).
extract.matrix
:
var The higher or lower resolution object, with dimension = c(nrow(xyto), dim(var)[3]).
xy The pairs of (x,y) coordinates
(input argument xyto
).
extract.array
:
var The higher or lower resolution object, with dimension = c(nrow(xyzto), dim(var)[3]).
xyz The triplets of (x,y,z) coordinates
(input argument xyzto
).
mapsigma
:
var A matrix with columns in depth-coordinates.
depth The depth-coordinates, also known as 'z'-coordinates,
referring to the dimension of var
as specified by signr
.
x The 'x'-coordinates referring to the first dimension of var
, except for the depth.
y Only if var
is an array, the 'y'-coordinates referring to the second dimension of var
, except for the depth.
Sylt3D for other examples of mapping.
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 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 | # save plotting parameters
pm <- par("mfrow")
## =======================================================================
## Simple examples
## =======================================================================
M <- matrix(nrow = 2, data = 1:4)
remap(M, x = 1:2, y = 1:2,
xto = seq(1, 2, length.out = 3), yto = 1:2)
changeres(M, x = 1:2, y = 1:2, resfac = c(2, 1))
changeres(M, x = 1:2, y = 1:2, resfac = 2)
# x and or y are a matrix.
changeres(var = M, x = M, y = 1:2, resfac = c(2, 1))
changeres(M, x = M, y = 1:2, resfac = 2)
## =======================================================================
## Use remap to add more detail to a slice3D plot
## =======================================================================
par(mfrow = c(1, 1))
x <- y <- z <- seq(-4, 4, by = 0.5)
M <- mesh(x, y, z)
R <- with (M, sqrt(x^2 + y^2 + z^2))
p <- sin(2*R) /(R+1e-3)
slice3D(x, y, z, ys = seq(-4, 4, by = 2), theta = 85,
colvar = p, pch = ".", clim = range(p))
xto <- yto <- zto <- seq(-1.2, 1.2, 0.3)
Res <- remap (p, x, y, z, xto, yto, zto)
# expand grid for scatterplot
Mt <- mesh(Res$x, Res$y, Res$z)
scatter3D(x = Mt$x, y = Mt$y, z = Mt$z, colvar = Res$var,
pch = ".", add = TRUE, cex = 3, clim = range(p))
# same in rgl:
## Not run:
plotrgl()
## End(Not run)
# extract specific values from 3-D data
xyzto <- matrix(nrow = 2, ncol = 3, data = c(1,1,1,2,2,2), byrow = TRUE)
extract(var = p, x, y, z, xyzto = xyzto)
# a transect
to <- cbind(seq(-4, 4, length.out = 20), seq(-4, 4, length.out = 20))
image2D( transect(p, x, y, z, to = to)$var)
## =======================================================================
## change the resolution of a 2-D image
## =======================================================================
par(mfrow = c(2, 2))
nr <- nrow(volcano)
nc <- ncol(volcano)
x <- 1 : nr
y <- 1 : nc
image2D(x = x, y = y, volcano, main = "original")
# increasing the resolution
x2 <- seq(from = 1, to = nr, by = 0.5)
y2 <- seq(from = 1, to = nc, by = 0.5)
VOLC1 <- remap(volcano, x = x, y = y, xto = x2, yto = y2)$var
image2D(x = x2, y = y2, z = VOLC1, main = "high resolution")
# low resolution
xb <- seq(from = 1, to = nr, by = 2)
yb <- seq(from = 1, to = nc, by = 3)
VOLC2 <- remap(volcano, x, y, xb, yb)$var
image2D(VOLC2, main = "low resolution")
# zooming in high resolution
xc <- seq(10, 40, 0.1)
yc <- seq(10, 40, 0.1)
VOLC3 <- remap(volcano,x, y, xc, yc)$var
image2D(VOLC3, main = "zoom")
# Get one value or a grid of values
remap(volcano, x, y, xto = 2.5, yto = 5)
remap(volcano, x, y, xto = c(2, 5), yto = c(5, 10))
# Specific values
extract(volcano, x, y, xyto = cbind(c(2, 5), c(5, 10)))
## =======================================================================
## take a cross section or transect of volcano
## =======================================================================
par(mfrow = c(2, 1))
image2D(volcano, x = 1:nr, y = 1:nc)
xyto <- cbind(seq(from = 1, to = nr, length.out = 20),
seq(from = 20, to = nc, length.out = 20))
points(xyto[,1], xyto[,2], pch = 16)
(Crossection <- extract (volcano, x = 1:nr, y = 1:nc,
xyto = xyto))
scatter2D(xyto[, 1], Crossection$var, colvar = Crossection$var,
type = "b", cex = 2, pch = 16)
## =======================================================================
## mapsigma: changing from sigma coordinates into depth-coordinates
## =======================================================================
par(mfrow = c(2, 2))
var <- t(matrix (nrow = 10, ncol = 10, data = rep(1:10, times = 10)))
image2D(var, ylab = "sigma", main = "values in sigma coordinates",
clab = "var")
# The depth at each 'column'
Depth <- approx(x = 1:5, y = c(10, 4, 5, 6, 4),
xout = seq(1,5, length.out = 10))$y
Depth <- rep(Depth, times = 10)
# Sigma coordinates
sigma <- t(matrix(nrow = 10, ncol = 10, data = Depth, byrow = TRUE) *
seq(from = 0, to = 1, length = 10))
matplot(sigma, type = "l", main = "sigma coordinates",
xlab = "sigma", ylab = "depth", ylim = c(10, 0))
# Mapping to the default depth coordinates
varz <- mapsigma(var = var, sigma = sigma)
image2D(varz$var, y = varz$depth, NAcol = "black", ylim = c(10, 0),
clab = "var", ylab = "depth",
main = "depth-coord, low resolution")
# Mapping at higher resolution of depth coordinates
varz <- mapsigma(var, sigma = sigma, resfac = 10)
image2D(varz$var, y = varz$depth, NAcol = "black", ylim = c(10, 0),
clab = "var", ylab = "depth",
main = "depth-coord, high resolution")
## =======================================================================
## mapsigma: mapping to depth for data Sylttran (x, sigma, time)
## =======================================================================
# depth values
D <- seq(-1, 20, by = 0.5)
dim(Sylttran$visc)
# sigma coordinates are the second dimension (signr)
# resolution is increased for 'x' and decreased for 'time'
visc <- mapsigma(Sylttran$visc, x = Sylttran$x, y = Sylttran$time,
sigma = Sylttran$sigma, signr = 2, depth = D, resfac = c(2, 1, 0.4))
# changed dimensions
dim(visc$var)
image2D(visc$var, x = visc$x, y = -visc$depth, ylim = c(-20, 1),
main = paste("eddy visc,", format(visc$y, digits = 2), " hr"),
ylab = "m", xlab = "x", clab = c("","m2/s"),
clim = range(visc$var, na.rm = TRUE))
par(mfrow = c(1, 1))
# make depth the last dimension
cv <- aperm(visc$var, c(1, 3, 2))
# visualise as slices
slice3D(colvar = cv, x = visc$x, y = visc$y, z = -visc$depth,
phi = 10, theta = 60, ylab = "time",
xs = NULL, zs = NULL, ys = visc$y, NAcol = "transparent")
# restore plotting parameters
par(mfrow = pm)
|
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