3D with value interpolation in R (X, Y, Z, V)

Question

Is there an R package that does X, Y, Z, V interpolation? I see that Akima does X, Y, V but I need one more dimension.

Basically I have X,Y,Z coordinates plus the value (V) that I want to interpolate. This is all GIS data but my GIS does not do voxel interpolation

So if I have a point cloud of XYZ coordinates with a value of V, how can I interpolate what V would be at XYZ coordinate (15,15,-12) ? Some test data would look like this:

X <-rbind(10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,20,20,20,20,20,20,20,20,20,20,20,20,20,20,20,20,20,20,20,20,20,20,20,20,20,30,30,30,30,30,30,30,30,30,30,30,30,30,30,30,30,30,30,30,30,30,30,30,30,30,40,40,40,40,40,40,40,40,40,40,40,40,40,40,40,40,40,40,40,40,40,40,40,40,40,50,50,50,50,50,50,50,50,50,50,50,50,50,50,50,50,50,50,50,50,50,50,50,50,50,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,20,20,20,20,20,20,20,20,20,20,20,20,20,20,20,20,20,20,20,20,20,20,20,20,20,30,30,30,30,30,30,30,30,30,30,30,30,30,30,30,30,30,30,30,30,30,30,30,30,30,40,40,40,40,40,40,40,40,40,40,40,40,40,40,40,40,40,40,40,40,40,40,40,40,40,50,50,50,50,50,50,50,50,50,50,50,50,50,50,50,50,50,50,50,50,50,50,50,50,50,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,20,20,20,20,20,20,20,20,20,20,20,20,20,20,20,20,20,20,20,20,20,20,20,20,20,30,30,30,30,30,30,30,30,30,30,30,30,30,30,30,30,30,30,30,30,30,30,30,30,30,40,40,40,40,40,40,40,40,40,40,40,40,40,40,40,40,40,40,40,40,40,40,40,40,40,50,50,50,50,50,50,50,50,50,50,50,50,50,50,50,50,50,50,50,50,50,50,50,50,50)

Y <- rbind(10,10,10,10,10,20,20,20,20,20,30,30,30,30,30,40,40,40,40,40,50,50,50,50,50,10,10,10,10,10,20,20,20,20,20,30,30,30,30,30,40,40,40,40,40,50,50,50,50,50,10,10,10,10,10,20,20,20,20,20,30,30,30,30,30,40,40,40,40,40,50,50,50,50,50,10,10,10,10,10,20,20,20,20,20,30,30,30,30,30,40,40,40,40,40,50,50,50,50,50,10,10,10,10,10,20,20,20,20,20,30,30,30,30,30,40,40,40,40,40,50,50,50,50,50,10,10,10,10,10,20,20,20,20,20,30,30,30,30,30,40,40,40,40,40,50,50,50,50,50,10,10,10,10,10,20,20,20,20,20,30,30,30,30,30,40,40,40,40,40,50,50,50,50,50,10,10,10,10,10,20,20,20,20,20,30,30,30,30,30,40,40,40,40,40,50,50,50,50,50,10,10,10,10,10,20,20,20,20,20,30,30,30,30,30,40,40,40,40,40,50,50,50,50,50,10,10,10,10,10,20,20,20,20,20,30,30,30,30,30,40,40,40,40,40,50,50,50,50,50,10,10,10,10,10,20,20,20,20,20,30,30,30,30,30,40,40,40,40,40,50,50,50,50,50,10,10,10,10,10,20,20,20,20,20,30,30,30,30,30,40,40,40,40,40,50,50,50,50,50,10,10,10,10,10,20,20,20,20,20,30,30,30,30,30,40,40,40,40,40,50,50,50,50,50,10,10,10,10,10,20,20,20,20,20,30,30,30,30,30,40,40,40,40,40,50,50,50,50,50,10,10,10,10,10,20,20,20,20,20,30,30,30,30,30,40,40,40,40,40,50,50,50,50,50)

Z  <- rbind(-5,-5,-5,-5,-5,-5,-5,-5,-5,-5,-5,-5,-5,-5,-5,-5,-5,-5,-5,-5,-5,-5,-5,-5,-5,-5,-5,-5,-5,-5,-5,-5,-5,-5,-5,-5,-5,-5,-5,-5,-5,-5,-5,-5,-5,-5,-5,-5,-5,-5,-5,-5,-5,-5,-5,-5,-5,-5,-5,-5,-5,-5,-5,-5,-5,-5,-5,-5,-5,-5,-5,-5,-5,-5,-5,-5,-5,-5,-5,-5,-5,-5,-5,-5,-5,-5,-5,-5,-5,-5,-5,-5,-5,-5,-5,-5,-5,-5,-5,-5,-5,-5,-5,-5,-5,-5,-5,-5,-5,-5,-5,-5,-5,-5,-5,-5,-5,-5,-5,-5,-5,-5,-5,-5,-5,-17,-17,-17,-17,-17,-17,-17,-17,-17,-17,-17,-17,-17,-17,-17,-17,-17,-17,-17,-17,-17,-17,-17,-17,-17,-17,-17,-17,-17,-17,-17,-17,-17,-17,-17,-17,-17,-17,-17,-17,-17,-17,-17,-17,-17,-17,-17,-17,-17,-17,-17,-17,-17,-17,-17,-17,-17,-17,-17,-17,-17,-17,-17,-17,-17,-17,-17,-17,-17,-17,-17,-17,-17,-17,-17,-17,-17,-17,-17,-17,-17,-17,-17,-17,-17,-17,-17,-17,-17,-17,-17,-17,-17,-17,-17,-17,-17,-17,-17,-17,-17,-17,-17,-17,-17,-17,-17,-17,-17,-17,-17,-17,-17,-17,-17,-17,-17,-17,-17,-17,-17,-17,-17,-17,-17,-29,-29,-29,-29,-29,-29,-29,-29,-29,-29,-29,-29,-29,-29,-29,-29,-29,-29,-29,-29,-29,-29,-29,-29,-29,-29,-29,-29,-29,-29,-29,-29,-29,-29,-29,-29,-29,-29,-29,-29,-29,-29,-29,-29,-29,-29,-29,-29,-29,-29,-29,-29,-29,-29,-29,-29,-29,-29,-29,-29,-29,-29,-29,-29,-29,-29,-29,-29,-29,-29,-29,-29,-29,-29,-29,-29,-29,-29,-29,-29,-29,-29,-29,-29,-29,-29,-29,-29,-29,-29,-29,-29,-29,-29,-29,-29,-29,-29,-29,-29,-29,-29,-29,-29,-29,-29,-29,-29,-29,-29,-29,-29,-29,-29,-29,-29,-29,-29,-29,-29,-29,-29,-29,-29,-29)

V <- rbind(0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,5,25,35,75,25,50,0,0,0,0,0,10,12,17,22,27,32,37,25,13,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,50,125,130,105,110,115,165,180,120,100,80,60,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0)

user_2371031 · Answer

I had the same question and was hoping for an answer in R. My question was: How do I perform 3D (trilinear) interpolation using regular gridded coordinate/value data (x,y,z,v)? For example, CT images, where each image has pixel centers (x, y) and greyscale value (v) and there are multiple image "slices" (z) along the thing being imaged (e.g., head, torso, leg, ...).

There is a slight problem with the given example data.

# original example data (reformatted)
X <- rep( rep( seq(10, 50, by=10), each=25), 3)
Y <- rep( rep( seq(10, 50, by=10), each=5), 15)
Z <- rep(c(-5, -17, -29), each=125)    
V <- rbind(0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,5,25,35,75,25,50,0,0,0,0,0,10,12,17,22,27,32,37,25,13,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,50,125,130,105,110,115,165,180,120,100,80,60,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0)

# the dimensions of the 3D grid described do not match the number of values
(length(unique(X))*length(unique(Y))*length(unique(Z))) == length(V)
## [1] FALSE
## which makes sense since 75 != 375

# visualize this:
library(rgl)
plot3d(x=X, y=Y, z=Z, col=terrain.colors(181)[V])

# examine the example data real quick...
df <- data.frame(x=X,y=Y,z=Z,v=V); 
head(df);  
table(df$x, df$y, df$z);
# there are 5 V values at each X,Y,Z coordinate... duplicates!

# redefine Z so there are 15 unique values 
# making 375 unique coordinate points 
# and matching the length of the given value vector, V
df$z <- seq(-5, -29, length.out=15)
head(df)
table(df$x, df$y, df$z);
# there is now 1 V value at each X,Y,Z coordinate

# that was for testing, now actually redefine the Z vector.
Z <- rep(seq(-5,-29, length.out = 15), 25)

# plot it.
library(rgl)
plot3d(x=X, y=Y, z=Z, col=terrain.colors(181)[V])

I couldn't find any 4D interpolation functions in the usual R packages, so I wrote a quick and dirty one. The following implements (without ANY error checking... caveat emptor!) the technique described at: https://en.wikipedia.org/wiki/Trilinear_interpolation

# convenience function #1:
# define a function that takes a vector of lookup values and a value to lookup
# and returns the two lookup values that the value falls between
between = function(vec, value) {
  # extract list of unique lookup values
  u = unique(vec)

  # difference vector
  dvec = u - value

  vals = c(u[dvec==max(dvec[dvec<0])], u[dvec==min(dvec[dvec>0])])
  return(vals)
}

# convenience function #2:
# return the value (v) from a grid data.frame for given point (x, y, z)
get_value = function(df, xi, yi, zi) {
  # assumes df is data.frame with column names: x, y, z, v
  subset(df, x==xi & y==yi & z==zi)$v
}

# inputs df (x,y,z,v), points to look up (x, y, z)
interp3 = function(dfin, xin, yin, zin) {
  # TODO: check if all(xin, yin, zin) equals a grid point, if so just return the point value
  # TODO: check if any(xin, yin, zin) equals a grid point, if so then do bilinear or linear interp
  cube_x <- between(dfin$x, xin)
  cube_y <- between(dfin$y, yin)
  cube_z <- between(dfin$z, zin)

  # find the two values in each dimension that the lookup value falls within
  # and extract the cube of 8 points
  tmp <- subset(dfin, x %in% cube_x &
                      y %in% cube_y &
                      z %in% cube_z)

  stopifnot(nrow(tmp)==8)

  # define points in a periodic and cubic lattice
  x0 = min(cube_x); x1 = max(cube_x);
  y0 = min(cube_y); y1 = max(cube_y);
  z0 = min(cube_z); z1 = max(cube_z);

  # define differences in each dimension
  xd = (xin-x0)/(x1-x0); # 0.5
  yd = (yin-y0)/(y1-y0); # 0.5
  zd = (zin-z0)/(z1-z0); # 0.9166666

  # interpolate along x:
  v00 = get_value(tmp, x0, y0, z0)*(1-xd) + get_value(tmp,x1,y0,z0)*xd # 2.5
  v01 = get_value(tmp, x0, y0, z1)*(1-xd) + get_value(tmp,x1,y0,z1)*xd # 0
  v10 = get_value(tmp, x0, y1, z0)*(1-xd) + get_value(tmp,x1,y1,z0)*xd # 0
  v11 = get_value(tmp, x0, y1, z1)*(1-xd) + get_value(tmp,x1,y1,z1)*xd # 65

  # interpolate along y:
  v0 = v00*(1-yd) + v10*yd # 1.25
  v1 = v01*(1-yd) + v11*yd # 32.5

  # interpolate along z:
  return(v0*(1-zd) + v1*zd) # 29.89583 (~91.7% between v0 and v1)
}

> interp3(df, 15, 15, -12)    
[1] 29.89583

Testing that same source's assertion that trilinear is simply linear(bilinear(), bilinear()), we can use the base R linear interpolation function, approx(), and the akima package's bilinear interpolation function, interp(), as follows:

library(akima)
approx(x=c(-11.857143,-13.571429),
       y=c(interp(x=df[round(df$z,1)==-11.9,"x"], y=df[round(df$z,1)==-11.9,"y"], z=df[round(df$z,1)==-11.9,"v"], xo=15, yo=15)$z,
           interp(x=df[round(df$z,1)==-13.6,"x"], y=df[round(df$z,1)==-13.6,"y"], z=df[round(df$z,1)==-13.6,"v"], xo=15, yo=15)$z),
       xout=-12)$y
# [1] 0.2083331

Checked another package to triangulate:

library(oce)
Vmat <- array(data = V, dim = c(length(unique(X)), length(unique(Y)), length(unique(Z))))
approx3d(x=unique(X), y=unique(Y), z=unique(Z), f=Vmat, xout=15, yout=15, zout=-12)
[1] 1.666667

So 'oce', 'akima' and my function all give pretty different answers. This is either a mistake in my code somewhere, or due to differences in the underlying Fortran code in the akima interp(), and whatever is in the oce 'approx3d' function that we'll leave for another day.

Not sure what the correct answer is because the MWE is not exactly "minimum" or simple. But I tested the functions with some really simple grids and it seems to give 'correct' answers. Here's one simple 2x2x2 example:

# really, really simple example:
# answer is always the z-coordinate value
sdf <- expand.grid(x=seq(0,1),y=seq(0,1),z=seq(0,1))
sdf$v <- rep(seq(0,1), each=4)

> interp3(sdf,0.25,0.25,.99)
[1] 0.99
> interp3(sdf,0.25,0.25,.4)
[1] 0.4

Trying akima on the simple example, we get the same answer (phew!):

library(akima)
approx(x=unique(sdf$z),
       y=c(interp(x=sdf[sdf$z==0,"x"], y=sdf[sdf$z==0,"y"], z=sdf[sdf$z==0,"v"], xo=.25, yo=.25)$z,
           interp(x=sdf[sdf$z==1,"x"], y=sdf[sdf$z==1,"y"], z=sdf[sdf$z==1,"v"], xo=.25, yo=.25)$z), 
       xout=.4)$y
# [1] 0.4

The new example data in the OP's own, accepted answer was not possible to interpolate with my simple interp3() function above because:

(a) the grid coordinates are not regularly spaced, and
(b) the coordinates to lookup (x1, y1, z1) lie outside of the grid.

# for completeness, here's the attempt: 
options(scipen = 999)

XCoor=c(78121.6235,78121.6235,78121.6235,78121.6235,78136.723,78136.723,78136.723,78136.8969,78136.8969,78136.8969,78137.4595,78137.4595,78137.4595,78125.061,78125.061,78125.061,78092.4696,78092.4696,78092.4696,78092.7683,78092.7683,78092.7683,78092.7683,78075.1171,78075.1171,78064.7462,78064.7462,78064.7462,78052.771,78052.771,78052.771,78032.1179,78032.1179,78032.1179)
YCoor=c(5213642.173,523642.173,523642.173,523642.173,523594.495,523594.495,523594.495,523547.475,523547.475,523547.475,523503.462,523503.462,523503.462,523426.33,523426.33,523426.33,523656.953,523656.953,523656.953,523607.157,523607.157,523607.157,523607.157,523514.671,523514.671,523656.81,523656.81,523656.81,523585.232,523585.232,523585.232,523657.091,523657.091,523657.091)
ZCoor=c(-3.0,-5.0,-10.0,-13.0,-3.5,-6.5,-10.5,-3.5,-6.5,-9.5,-3.5,-5.5,-10.5,-3.5,-5.5,-7.5,-3.5,-6.5,-11.5,-3.0,-5.0,-9.0,-12.0,-6.5,-10.5,-2.5,-3.5,-8.0,-3.5,-6.5,-9.5,-2.5,-6.5,-8.5)
V=c(2.4000,30.0,620.0,590.0,61.0,480.0,0.3700,0.0,0.3800,0.1600,0.1600,0.9000,0.4100,0.0,0.0,0.0061,6.0,52.0,0.3400,33.0,235.0,350.0,9300.0,31.0,2100.0,0.0,0.0,10.5000,3.8000,0.9000,310.0,0.2800,8.3000,18.0)

adf = data.frame(x=XCoor, y=YCoor, z=ZCoor, v=V)
# the first y value looks like a typo?
> head(adf)
         x         y     z     v
1 78121.62 5213642.2  -3.0   2.4
2 78121.62  523642.2  -5.0  30.0
3 78121.62  523642.2 -10.0 620.0
4 78121.62  523642.2 -13.0 590.0
5 78136.72  523594.5  -3.5  61.0
6 78136.72  523594.5  -6.5 480.0    

x1=198130.000
y1=1913590.000
z1=-8
> interp3(adf, x1,y1,z1)
numeric(0)
Warning message:
In min(dvec[dvec > 0]) : no non-missing arguments to min; returning Inf

3D with value interpolation in R (X, Y, Z, V)

2 Answers