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Last data update: 2014.03.03

R: Functions for remapping, changing the resolution, and...

Map and extract data

R Documentation

Functions for remapping, changing the resolution, and extracting from 2-D or 3-D data.

Description

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 NAs where the depth-coordinates extend beyond the sigma values.

Usage

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, ...)

Arguments

`var`	Matrix or array with values to be mapped to other coordinates (`remap`), or to lower or higher resolution (`changeres`), or whose values have to be extracted (`extract`, `transect`), or which has to be mapped from sigma to depth coordinates (`mapsigma`). For `transect` and `transectsigma`, `var` has to be an array.
`x`	Vector with original x-coordinates of the matrix or array `var` to be mapped. Length should be = first dimension of `var`.
`y`	Vector with original y-coordinates of the matrix or array `var` to be mapped. Length should be = second dimension of `var`.
`z`	Vector with original z-coordinates of the array `var` to be mapped. Length should be = third dimension of `var`.
`xto`	Vector with x-coordinates to which `var` should be mapped. The elements in `xto` should be embraced by the elements in `x` (it is not allowed to extrapolate outside of the region). If `NULL` then the range of `x` is covered, with the same number of points.
`yto`	Vector with y-coordinates to which `var` should be mapped. The elements in `yto` should be embraced by the elements in `y` (it is not allowed to extrapolate outside of the region). If `NULL` then the range of `y` is covered, with the same number of points.
`zto`	Vector with z-coordinates to which `var` should be mapped. The elements in `zto` should be embraced by the elements in `z` (it is not allowed to extrapolate outside of the region). If `NULL` then the range of `z` is covered, with the same number of points.
`xyto`	Two-columned matrix, with first and second column specifying the x- respectively y-coordinates to which the matrix `var` should be mapped. The elements should be embraced by the elements in `x` (first column) and `y` (second column) (it is not allowed to extrapolate outside of the region).
`xyzto`	Three-columned matrix, specifying the x-, y- and z-coordinates to which the array `var` should be mapped. The elements should be embraced by the elements in `x`, `y` and `z` (it is not allowed to extrapolate outside of the region).
`to`	Two-columned matrix, specifying the values along the `margin` coordinates of the transect to be taken on the array `var`. The elements should be embraced by the elements in `x`, `y` and `z` (it is not allowed to extrapolate outside of the region).
`margin`	String with the names of the coordinates in the matrix `to`, and along which the transect is to be taken on the array `var`. One of `"xy", "xz", "yz"`. If `"xy"`, then the first and second column in input `to` represent `x` and `y` values respectively, and the transect will select all `z` values corresponding with these inputs.
`sigma`	The sigma coordinates, a matrix or array with the same dimension as `var`. The sigma coordinates should refer to the column as defined by `signr`.
`signr`	The position of the sigma coordinates, in the matrix or array. The default is the second or third dimension in `var` for a `matrix` and `array` respectively.
`depth`	The depth (often referred to as 'z') coordinates to which matrix `var` has to be mapped. If `NULL` then `seq(min(sigma), max(sigma), length.out = numdepth)`.
`numdepth`	Only used when `depth= NULL`, the length of the `depth` vector to which the matrix `var` has to be mapped. If `NULL` then the length will be equal to `ncol(var)` (if `var` is a matrix), or `dim(var)[3]` in case `var` is an array.
`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 `resfac` equals `3` then for each adjacent pair of x- and y- values, `var` will be interpolated to two intermediary points. This uses simple linear interpolation. If `resfac` is one number then the resolution will be increased similarly in x, y- and z-direction. In case of `mapsigma`, `resfac` is overruled if `xto`, `yto` or `zto` is specified.
`na.rm`	How to treat `NA`s in the matrix or array `var`. If `TRUE`, they are ignored while interpolating; this will make the size of `NA` regions smaller; if `FALSE`, the size of the `NA` region will increase.
`...`	any other arguments.

Details

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.

Value

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.

Examples

# 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, 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 = 1:10))
 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

# 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)

Results


R version 3.3.1 (2016-06-21) -- "Bug in Your Hair"
Copyright (C) 2016 The R Foundation for Statistical Computing
Platform: x86_64-pc-linux-gnu (64-bit)

R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.

R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.

Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.

> library(OceanView)
Loading required package: plot3D
Loading required package: plot3Drgl
Loading required package: rgl
> png(filename="/home/ddbj/snapshot/RGM3/R_CC/result/OceanView/mapping.Rd_%03d_medium.png", width=480, height=480)
> ### Name: Map and extract data
> ### Title: Functions for remapping, changing the resolution, and extracting
> ###   from 2-D or 3-D data.
> ### Aliases: remap remap.matrix remap.array changeres changeres.matrix
> ###   changeres.array extract extract.matrix extract.array transect
> ###   mapsigma mapsigma.matrix mapsigma.array transectsigma
> ### Keywords: hplot
> 
> ### ** Examples
> 
> # 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) 
$var
     [,1] [,2]
[1,]  1.0  3.0
[2,]  1.5  3.5
[3,]  2.0  4.0

$x
[1] 1.0 1.5 2.0

$y
[1] 1 2

>  
>  changeres(M, x = 1:2, y = 1:2, resfac = c(2, 1))
$var
     [,1] [,2]
[1,]  1.0  3.0
[2,]  1.5  3.5
[3,]  2.0  4.0

$x
[1] 1.0 1.5 2.0

$y
[1] 1 2

>  changeres(M, x = 1:2, y = 1:2, resfac = 2)
$var
     [,1] [,2] [,3]
[1,]  1.0  2.0  3.0
[2,]  1.5  2.5  3.5
[3,]  2.0  3.0  4.0

$x
[1] 1.0 1.5 2.0

$y
[1] 1.0 1.5 2.0

> 
> # x and or y are a matrix.
>  changeres(var = M, x = M, y = 1:2, resfac = c(2, 1))
$var
     [,1] [,2]
[1,]  1.0  3.0
[2,]  1.5  3.5
[3,]  2.0  4.0

$x
     [,1] [,2]
[1,]  1.0  3.0
[2,]  1.5  3.5
[3,]  2.0  4.0

$y
     [,1] [,2]
[1,]  1.0  1.0
[2,]  1.5  1.5
[3,]  2.0  2.0

>  changeres(M, x = M, y = 1:2, resfac = 2)
$var
     [,1] [,2] [,3]
[1,]  1.0  2.0  3.0
[2,]  1.5  2.5  3.5
[3,]  2.0  3.0  4.0

$x
     [,1] [,2] [,3]
[1,]  1.0  2.0  3.0
[2,]  1.5  2.5  3.5
[3,]  2.0  3.0  4.0

$y
     [,1] [,2] [,3]
[1,]  1.0  1.0  1.0
[2,]  1.5  1.5  1.5
[3,]  2.0  2.0  2.0

> 
>   
> ## =======================================================================
> ## 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: 
> ##D    plotrgl()
> ##D  
> ## End(Not run)
> 
> # extract specific values from 3-D data
>  xyzto <- matrix(nrow = 2, data = c(1, 1, 1, 2, 2, 2), byrow = TRUE)
>  extract(var = p, x, y, z, xyzto = xyzto)
$var
[1] -0.1828839  0.1735051

$xyz
     x y z
[1,] 1 1 1
[2,] 2 2 2

> 
> # 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)
$var
      [,1]
[1,] 102.5

$x
[1] 2.5

$y
[1] 5

>  remap(volcano, x, y, xto = c(2, 5), yto = c(5, 10))
$var
     [,1] [,2]
[1,]  102  101
[2,]  105  103

$x
[1] 2 5

$y
[1]  5 10

> 
> # Specific values
>  extract(volcano, x, y, xyto = cbind(c(2, 5), c(5, 10)))
$var
[1] 102 103

$xy
     x  y
[1,] 2  5
[2,] 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))
$var
 [1] 102.00000 112.86427 140.64820 170.94737 191.73684 173.60665 153.63989
 [8] 156.84211 165.26316 149.41551 133.15235 130.52632 120.45983 110.00000
[15] 105.21053 100.00000  96.00000  96.00000  94.52632  94.00000

$xy
              x        y
 [1,]  1.000000 20.00000
 [2,]  5.526316 22.15789
 [3,] 10.052632 24.31579
 [4,] 14.578947 26.47368
 [5,] 19.105263 28.63158
 [6,] 23.631579 30.78947
 [7,] 28.157895 32.94737
 [8,] 32.684211 35.10526
 [9,] 37.210526 37.26316
[10,] 41.736842 39.42105
[11,] 46.263158 41.57895
[12,] 50.789474 43.73684
[13,] 55.315789 45.89474
[14,] 59.842105 48.05263
[15,] 64.368421 50.21053
[16,] 68.894737 52.36842
[17,] 73.421053 54.52632
[18,] 77.947368 56.68421
[19,] 82.473684 58.84211
[20,] 87.000000 61.00000

> 
>  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 = 1:10))
>  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))$y
> 
> # Sigma coordinates
>  sigma <- t(matrix(nrow = 10, ncol = 10, data = Depth, byrow = TRUE) * 
+                 seq(from = 0, to = 1, length = 10))
>  matplot(sigma, type = "l", main = "sigma coordinates", 
+          xlab = "sigma", ylab = "depth", ylim = c(10, 0))
> 
> # Mapping to the default depth coordinates
>  varz <- mapsigma(var = var, sigma = sigma)
>  image2D(varz$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)
[1] 50 21 21
> 
> # 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$var)
[1] 99 43  9
> 
>  image2D(visc$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)
> 
> 
> 
> 
> 
> dev.off()
null device 
          1 
>