gstat

*

tests

  Running ‘allier.R’ [1s/1s]
  Comparing ‘allier.Rout’ to ‘allier.Rout.save’ ... OK
  Running ‘blockkr.R’ [1s/1s]
  Comparing ‘blockkr.Rout’ to ‘blockkr.Rout.save’ ... OK
  Running ‘covtable.R’ [1s/1s]
  Comparing ‘covtable.Rout’ to ‘covtable.Rout.save’ ... OK
  Running ‘cv.R’ [1s/1s]
  Comparing ‘cv.Rout’ to ‘cv.Rout.save’ ... OK
  Running ‘cv3d.R’ [1s/2s]
  Comparing ‘cv3d.Rout’ to ‘cv3d.Rout.save’ ... OK
  Running ‘fit.R’ [1s/1s]
  Comparing ‘fit.Rout’ to ‘fit.Rout.save’ ... OK
  Running ‘krige0.R’ [2s/2s]
  Comparing ‘krige0.Rout’ to ‘krige0.Rout.save’ ... OK
  Running ‘line.R’ [1s/2s]
  Comparing ‘line.Rout’ to ‘line.Rout.save’ ... OK
  Running ‘merge.R’ [1s/1s]
  Comparing ‘merge.Rout’ to ‘merge.Rout.save’ ... OK
  Running ‘na.action.R’ [1s/1s]
  Comparing ‘na.action.Rout’ to ‘na.action.Rout.save’ ... OK
  Running ‘rings.R’ [1s/2s]
  Comparing ‘rings.Rout’ to ‘rings.Rout.save’ ... OK
  Running ‘sim.R’ [1s/1s]
  Comparing ‘sim.Rout’ to ‘sim.Rout.save’ ... OK
  Running ‘stars.R’ [10s/13s]
  Comparing ‘stars.Rout’ to ‘stars.Rout.save’ ...
149c149
<         AXIS["easting (x)",east,
---
>         AXIS["easting (X)",east,
152c152
<         AXIS["northing (y)",north,
---
>         AXIS["northing (Y)",north,
156,158c156,158
<         SCOPE["Cadastre, engineering survey, topographic mapping."],
<         AREA["Netherlands - onshore and offshore."],
<         BBOX[50.75,2.53,55.77,7.22]],
---
>         SCOPE["Engineering survey, topographic mapping."],
>         AREA["Netherlands - onshore, including Waddenzee, Dutch Wadden Islands and 12-mile offshore coastal zone."],
>         BBOX[50.75,3.2,53.7,7.22]],
  Running ‘variogram.R’ [1s/2s]
  Comparing ‘variogram.Rout’ to ‘variogram.Rout.save’ ... OK
  Running ‘vdist.R’ [1s/1s]
  Comparing ‘vdist.Rout’ to ‘vdist.Rout.save’ ... OK
  Running ‘windst.R’ [5s/7s]
  Comparing ‘windst.Rout’ to ‘windst.Rout.save’ ... OK

tests

  Running ‘allier.R’
  Comparing ‘allier.Rout’ to ‘allier.Rout.save’ ... OK
  Running ‘blockkr.R’
  Comparing ‘blockkr.Rout’ to ‘blockkr.Rout.save’ ... OK
  Running ‘covtable.R’
  Comparing ‘covtable.Rout’ to ‘covtable.Rout.save’ ... OK
  Running ‘cv.R’
  Comparing ‘cv.Rout’ to ‘cv.Rout.save’ ... OK
  Running ‘cv3d.R’
  Comparing ‘cv3d.Rout’ to ‘cv3d.Rout.save’ ... OK
  Running ‘fit.R’
  Comparing ‘fit.Rout’ to ‘fit.Rout.save’ ... OK
  Running ‘krige0.R’
  Comparing ‘krige0.Rout’ to ‘krige0.Rout.save’ ... OK
  Running ‘line.R’
  Comparing ‘line.Rout’ to ‘line.Rout.save’ ... OK
  Running ‘merge.R’
  Comparing ‘merge.Rout’ to ‘merge.Rout.save’ ... OK
  Running ‘na.action.R’
  Comparing ‘na.action.Rout’ to ‘na.action.Rout.save’ ... OK
  Running ‘rings.R’
  Comparing ‘rings.Rout’ to ‘rings.Rout.save’ ... OK
  Running ‘sim.R’
  Comparing ‘sim.Rout’ to ‘sim.Rout.save’ ... OK
  Running ‘stars.R’ [26s/39s]
  Comparing ‘stars.Rout’ to ‘stars.Rout.save’ ... OK
  Running ‘variogram.R’
  Comparing ‘variogram.Rout’ to ‘variogram.Rout.save’ ... OK
  Running ‘vdist.R’
  Comparing ‘vdist.Rout’ to ‘vdist.Rout.save’ ... OK
  Running ‘windst.R’ [90m/81m]
Running the tests in ‘tests/windst.R’ failed.
Complete output:
  > suppressPackageStartupMessages(library(sp))
  > suppressPackageStartupMessages(library(spacetime))
  > suppressPackageStartupMessages(library(gstat))
  > suppressPackageStartupMessages(library(stars))
  > 
  > Sys.unsetenv("KMP_DEVICE_THREAD_LIMIT")
  > Sys.unsetenv("KMP_ALL_THREADS")
  > Sys.unsetenv("KMP_TEAMS_THREAD_LIMIT")
  > Sys.unsetenv("OMP_THREAD_LIMIT")
  > 
  > data(wind)
  > wind.loc$y = as.numeric(char2dms(as.character(wind.loc[["Latitude"]])))
  > wind.loc$x = as.numeric(char2dms(as.character(wind.loc[["Longitude"]])))
  > coordinates(wind.loc) = ~x+y
  > proj4string(wind.loc) = "+proj=longlat +datum=WGS84 +ellps=WGS84"
  > 
  > wind$time = ISOdate(wind$year+1900, wind$month, wind$day)
  > wind$jday = as.numeric(format(wind$time, '%j'))
  > stations = 4:15
  > windsqrt = sqrt(0.5148 * wind[stations]) # knots -> m/s
  > Jday = 1:366
  > daymeans = colMeans(
  + 	sapply(split(windsqrt - colMeans(windsqrt), wind$jday), colMeans))
  > meanwind = lowess(daymeans ~ Jday, f = 0.1)$y[wind$jday]
  > velocities = apply(windsqrt, 2, function(x) { x - meanwind })
  > # match order of columns in wind to Code in wind.loc;
  > # convert to utm zone 29, to be able to do interpolation in
  > # proper Euclidian (projected) space:
  > pts = coordinates(wind.loc[match(names(wind[4:15]), wind.loc$Code),])
  > pts = SpatialPoints(pts)
  > if (require(sp, quietly = TRUE) && require(maps, quietly = TRUE)) {
  + proj4string(pts) = "+proj=longlat +datum=WGS84 +ellps=WGS84"
  + utm29 = "+proj=utm +zone=29 +datum=WGS84 +ellps=WGS84"
  + pts = as(st_transform(st_as_sfc(pts), utm29), "Spatial")
  + # note the t() in:
  + w = STFDF(pts, wind$time, data.frame(values = as.vector(t(velocities))))
  + 
  + library(mapdata)
  + mp = map("worldHires", xlim = c(-11,-5.4), ylim = c(51,55.5), plot=FALSE)
  + sf = st_transform(st_as_sf(mp, fill = FALSE), utm29)
  + m = as(sf, "Spatial")
  + 
  + # setup grid
  + grd = SpatialPixels(SpatialPoints(makegrid(m, n = 300)),
  + 	proj4string = m@proj4string)
  + # grd$t = rep(1, nrow(grd))
  + #coordinates(grd) = ~x1+x2
  + #gridded(grd)=TRUE
  + 
  + # select april 1961:
  + w = w[, "1961-04"]
  + 
  + covfn = function(x, y = x) { 
  + 	du = spDists(coordinates(x), coordinates(y))
  + 	t1 = as.numeric(index(x)) # time in seconds
  + 	t2 = as.numeric(index(y)) # time in seconds
  + 	dt = abs(outer(t1, t2, "-"))
  + 	# separable, product covariance model:
  + 	0.6 * exp(-du/750000) * exp(-dt / (1.5 * 3600 * 24))
  + }
  + 
  + n = 10
  + tgrd = seq(min(index(w)), max(index(w)), length=n)
  + pred = krige0(sqrt(values)~1, w, STF(grd, tgrd), covfn)
  + layout = list(list("sp.points", pts, first=F, cex=.5),
  + 	list("sp.lines", m, col='grey'))
  + wind.pr0 = STFDF(grd, tgrd, data.frame(var1.pred = pred))
  + 
  + v = vgmST("separable",
  +           space = vgm(1, "Exp", 750000), 
  +           time = vgm(1, "Exp", 1.5 * 3600 * 24),
  +           sill = 0.6)
  + wind.ST = krigeST(sqrt(values)~1, w, STF(grd, tgrd), v)
  + 
  + all.equal(wind.pr0, wind.ST)
  + 
  + # stars:
  + df = data.frame(a = rep(NA, 324*10))
  + s = STF(grd, tgrd)
  + newd = addAttrToGeom(s, df)
  + wind.sta = krigeST(sqrt(values)~1, st_as_stars(w), st_as_stars(newd), v)
  + # 1
  + plot(stars::st_as_stars(wind.ST), breaks = "equal", col = sf.colors())
  + # 2
  + stplot(wind.ST)
  + # 3
  + plot(wind.sta, breaks = "equal", col = sf.colors())
  + st_as_stars(wind.ST)[[1]][1:3,1:3,1]
  + (wind.sta)[[1]][1:3,1:3,1]
  + st_bbox(wind.sta)
  + bbox(wind.ST)
  + all.equal(wind.sta, stars::st_as_stars(wind.ST), check.attributes = FALSE)
  + 
  + # 4: roundtrip wind.sta->STFDF->stars
  + rt = stars::st_as_stars(as(wind.sta, "STFDF"))
  + plot(rt, breaks = "equal", col = sf.colors())
  + # 5:
  + stplot(as(wind.sta, "STFDF"))
  + st_bbox(rt)
  + 
  + # 6:
  + stplot(as(st_as_stars(wind.ST), "STFDF"))
  + }
  OMP: Warning #96: Cannot form a team with 24 threads, using 2 instead.
  OMP: Hint Consider unsetting KMP_DEVICE_THREAD_LIMIT (KMP_ALL_THREADS), KMP_TEAMS_THREAD_LIMIT, and OMP_THREAD_LIMIT (if any are set).

tests

  Running ‘allier.R’
  Comparing ‘allier.Rout’ to ‘allier.Rout.save’ ... OK
  Running ‘blockkr.R’
  Comparing ‘blockkr.Rout’ to ‘blockkr.Rout.save’ ... OK
  Running ‘covtable.R’
  Comparing ‘covtable.Rout’ to ‘covtable.Rout.save’ ... OK
  Running ‘cv.R’
  Comparing ‘cv.Rout’ to ‘cv.Rout.save’ ... OK
  Running ‘cv3d.R’ [3s/13s]
  Comparing ‘cv3d.Rout’ to ‘cv3d.Rout.save’ ... OK
  Running ‘fit.R’
  Comparing ‘fit.Rout’ to ‘fit.Rout.save’ ... OK
  Running ‘krige0.R’ [5s/14s]
  Comparing ‘krige0.Rout’ to ‘krige0.Rout.save’ ... OK
  Running ‘line.R’ [3s/10s]
  Comparing ‘line.Rout’ to ‘line.Rout.save’ ... OK
  Running ‘merge.R’
  Comparing ‘merge.Rout’ to ‘merge.Rout.save’ ... OK
  Running ‘na.action.R’
  Comparing ‘na.action.Rout’ to ‘na.action.Rout.save’ ... OK
  Running ‘rings.R’ [3s/14s]
  Comparing ‘rings.Rout’ to ‘rings.Rout.save’ ... OK
  Running ‘sim.R’
  Comparing ‘sim.Rout’ to ‘sim.Rout.save’ ... OK
  Running ‘stars.R’ [0m/89m]
  Running ‘variogram.R’
Running the tests in ‘tests/stars.R’ failed.
Complete output:
  > Sys.setenv(TZ = "UTC")
  > 
  > Sys.unsetenv("KMP_DEVICE_THREAD_LIMIT")
  > Sys.unsetenv("KMP_ALL_THREADS")
  > Sys.unsetenv("KMP_TEAMS_THREAD_LIMIT")
  > Sys.unsetenv("OMP_THREAD_LIMIT")
  > 
  > # 0. using sp:
  > 
  > suppressPackageStartupMessages(library(sp))
  > demo(meuse, ask = FALSE)
  
  
  	demo(meuse)
  	---- ~~~~~
  
  > require(sp)
  
  > crs = CRS("EPSG:28992")
  
  > data("meuse")
  
  > coordinates(meuse) <- ~x+y
  
  > proj4string(meuse) <- crs
  
  > data("meuse.grid")
  
  > coordinates(meuse.grid) <- ~x+y
  
  > gridded(meuse.grid) <- TRUE
  
  > proj4string(meuse.grid) <- crs
  
  > data("meuse.riv")
  
  > meuse.riv <- SpatialPolygons(list(Polygons(list(Polygon(meuse.riv)),"meuse.riv")))
  
  > proj4string(meuse.riv) <- crs
  
  > data("meuse.area")
  
  > meuse.area = SpatialPolygons(list(Polygons(list(Polygon(meuse.area)), "area")))
  
  > proj4string(meuse.area) <- crs
  > suppressPackageStartupMessages(library(gstat))
  > v = variogram(log(zinc)~1, meuse)
  > (v.fit = fit.variogram(v, vgm(1, "Sph", 900, 1)))
    model      psill    range
  1   Nug 0.05066243   0.0000
  2   Sph 0.59060780 897.0209
  > k_sp = krige(log(zinc)~1, meuse[-(1:5),], meuse[1:5,], v.fit)
  [using ordinary kriging]
  > k_sp_grd = krige(log(zinc)~1, meuse, meuse.grid, v.fit)
  [using ordinary kriging]
  > 
  > # 1. using sf:
  > suppressPackageStartupMessages(library(sf))
  > demo(meuse_sf, ask = FALSE, echo = FALSE)
  > # reloads meuse as data.frame, so
  > demo(meuse, ask = FALSE)
  
  
  	demo(meuse)
  	---- ~~~~~
  
  > require(sp)
  
  > crs = CRS("EPSG:28992")
  
  > data("meuse")
  
  > coordinates(meuse) <- ~x+y
  
  > proj4string(meuse) <- crs
  
  > data("meuse.grid")
  
  > coordinates(meuse.grid) <- ~x+y
  
  > gridded(meuse.grid) <- TRUE
  
  > proj4string(meuse.grid) <- crs
  
  > data("meuse.riv")
  
  > meuse.riv <- SpatialPolygons(list(Polygons(list(Polygon(meuse.riv)),"meuse.riv")))
  
  > proj4string(meuse.riv) <- crs
  
  > data("meuse.area")
  
  > meuse.area = SpatialPolygons(list(Polygons(list(Polygon(meuse.area)), "area")))
  
  > proj4string(meuse.area) <- crs
  > 
  > v = variogram(log(zinc)~1, meuse_sf)
  > (v.fit = fit.variogram(v, vgm(1, "Sph", 900, 1)))
    model      psill    range
  1   Nug 0.05066243   0.0000
  2   Sph 0.59060780 897.0209
  > k_sf = krige(log(zinc)~1, meuse_sf[-(1:5),], meuse_sf[1:5,], v.fit)
  [using ordinary kriging]
  > 
  > all.equal(k_sp, as(k_sf, "Spatial"), check.attributes = FALSE)
  [1] TRUE
  > all.equal(k_sp, as(k_sf, "Spatial"), check.attributes = TRUE)
  [1] "Attributes: < Component \"bbox\": Attributes: < Component \"dimnames\": Component 1: 2 string mismatches > >"  
  [2] "Attributes: < Component \"coords\": Attributes: < Component \"dimnames\": Component 2: 2 string mismatches > >"
  [3] "Attributes: < Component \"coords.nrs\": Numeric: lengths (2, 0) differ >"                                      
  > 
  > # 2. using stars for grid:
  > 
  > suppressPackageStartupMessages(library(stars))
  > st = st_as_stars(meuse.grid)
  > st_crs(st)
  Coordinate Reference System:
    User input: Amersfoort / RD New 
    wkt:
  PROJCRS["Amersfoort / RD New",
      BASEGEOGCRS["Amersfoort",
          DATUM["Amersfoort",
              ELLIPSOID["Bessel 1841",6377397.155,299.1528128,
                  LENGTHUNIT["metre",1]]],
          PRIMEM["Greenwich",0,
              ANGLEUNIT["degree",0.0174532925199433]],
          ID["EPSG",4289]],
      CONVERSION["RD New",
          METHOD["Oblique Stereographic",
              ID["EPSG",9809]],
          PARAMETER["Latitude of natural origin",52.1561605555556,
              ANGLEUNIT["degree",0.0174532925199433],
              ID["EPSG",8801]],
          PARAMETER["Longitude of natural origin",5.38763888888889,
              ANGLEUNIT["degree",0.0174532925199433],
              ID["EPSG",8802]],
          PARAMETER["Scale factor at natural origin",0.9999079,
              SCALEUNIT["unity",1],
              ID["EPSG",8805]],
          PARAMETER["False easting",155000,
              LENGTHUNIT["metre",1],
              ID["EPSG",8806]],
          PARAMETER["False northing",463000,
              LENGTHUNIT["metre",1],
              ID["EPSG",8807]]],
      CS[Cartesian,2],
          AXIS["easting (X)",east,
              ORDER[1],
              LENGTHUNIT["metre",1]],
          AXIS["northing (Y)",north,
              ORDER[2],
              LENGTHUNIT["metre",1]],
      USAGE[
          SCOPE["Engineering survey, topographic mapping."],
          AREA["Netherlands - onshore, including Waddenzee, Dutch Wadden Islands and 12-mile offshore coastal zone."],
          BBOX[50.75,3.2,53.7,7.22]],
      ID["EPSG",28992]]
  > 
  > # compare inputs:
  > sp = as(st, "Spatial")
  > fullgrid(meuse.grid) = TRUE
  > all.equal(sp, meuse.grid["dist"], check.attributes = FALSE)
  [1] "Names: Lengths (5, 1) differ (string compare on first 1)"
  [2] "Names: 1 string mismatch"                                
  > all.equal(sp, meuse.grid["dist"], check.attributes = TRUE, use.names = FALSE)
  [1] "Names: Lengths (5, 1) differ (string compare on first 1)"                                      
  [2] "Names: 1 string mismatch"                                                                      
  [3] "Attributes: < Component 3: Names: 1 string mismatch >"                                         
  [4] "Attributes: < Component 3: Length mismatch: comparison on first 1 components >"                
  [5] "Attributes: < Component 3: Component 1: Mean relative difference: 1.08298 >"                   
  [6] "Attributes: < Component 4: Attributes: < Component 2: names for current but not for target > >"
  [7] "Attributes: < Component 4: Attributes: < Component 3: names for current but not for target > >"
  > 
  > # kriging:
  > st_crs(st) = st_crs(meuse_sf) = NA # GDAL roundtrip messes them up!
  > k_st = if (Sys.getenv("USER") == "travis") {
  + 	try(krige(log(zinc)~1, meuse_sf, st, v.fit))
  + } else {
  + 	krige(log(zinc)~1, meuse_sf, st, v.fit)
  + }
  [using ordinary kriging]
  > k_st
  stars object with 2 dimensions and 2 attributes
  attribute(s):
                  Min.   1st Qu.    Median      Mean   3rd Qu.      Max.  NAs
  var1.pred  4.7765547 5.2376293 5.5728839 5.7072287 6.1717619 7.4399911 5009
  var1.var   0.0854949 0.1372864 0.1621838 0.1853319 0.2116152 0.5002756 5009
  dimension(s):
    from  to offset delta x/y
  x    1  78 178440    40 [x]
  y    1 104 333760   -40 [y]
  > 
  > # handle factors, when going to stars?
  > k_sp_grd$cls = cut(k_sp_grd$var1.pred, c(0, 5, 6, 7, 8, 9))
  > st_as_stars(k_sp_grd)
  stars object with 2 dimensions and 3 attributes
  attribute(s):
     var1.pred       var1.var           cls      
   Min.   :4.777   Min.   :0.08549   (0,5]: 316  
   1st Qu.:5.238   1st Qu.:0.13729   (5,6]:1778  
   Median :5.573   Median :0.16218   (6,7]: 962  
   Mean   :5.707   Mean   :0.18533   (7,8]:  47  
   3rd Qu.:6.172   3rd Qu.:0.21162   (8,9]:   0  
   Max.   :7.440   Max.   :0.50028   NAs  :5009  
   NAs    :5009    NAs    :5009                  
  dimension(s):
    from  to offset delta              refsys x/y
  x    1  78 178440    40 Amersfoort / RD New [x]
  y    1 104 333760   -40 Amersfoort / RD New [y]
  > if (require(raster, quietly = TRUE)) {
  +  print(st_as_stars(raster::stack(k_sp_grd))) # check
  +  print(all.equal(st_redimension(st_as_stars(k_sp_grd)), st_as_stars(raster::stack(k_sp_grd)), check.attributes=FALSE))
  + }
  stars object with 3 dimensions and 1 attribute
  attribute(s):
                  Min.   1st Qu. Median     Mean  3rd Qu.     Max.   NAs
  var1.pred  0.0854949 0.2116778      2 2.710347 5.237542 7.439991 15027
  dimension(s):
       from  to offset delta              refsys                          values
  x       1  78 178440    40 Amersfoort / RD New                            NULL
  y       1 104 333760   -40 Amersfoort / RD New                            NULL
  band    1   3     NA    NA                  NA var1.pred, var1.var , cls      
       x/y
  x    [x]
  y    [y]
  band    
  [1] TRUE
  > 
  > suppressPackageStartupMessages(library(spacetime))
  > 
  > tm = as.POSIXct("2019-02-25 15:37:24 CET")
  > n = 4
  > s = stars:::st_stars(list(foo = array(1:(n^3), rep(n,3))),
  + stars:::create_dimensions(list(
  +   x = stars:::create_dimension(from = 1, to = n, offset = 10, delta = 0.5),
  +   y = stars:::create_dimension(from = 1, to = n, offset = 0, delta = -0.7),
  +   time = stars:::create_dimension(values = tm + 1:n)),
  +   raster = stars:::get_raster(dimensions = c("x", "y")))
  +   )
  > s
  stars object with 3 dimensions and 1 attribute
  attribute(s):
       Min. 1st Qu. Median Mean 3rd Qu. Max.
  foo     1   16.75   32.5 32.5   48.25   64
  dimension(s):
       from to                  offset  delta  refsys x/y
  x       1  4                      10    0.5      NA [x]
  y       1  4                       0   -0.7      NA [y]
  time    1  4 2019-02-25 15:37:25 UTC 1 secs POSIXct    
  > 
  > as.data.frame(s)
         x     y                time foo
  1  10.25 -0.35 2019-02-25 15:37:25   1
  2  10.75 -0.35 2019-02-25 15:37:25   2
  3  11.25 -0.35 2019-02-25 15:37:25   3
  4  11.75 -0.35 2019-02-25 15:37:25   4
  5  10.25 -1.05 2019-02-25 15:37:25   5
  6  10.75 -1.05 2019-02-25 15:37:25   6
  7  11.25 -1.05 2019-02-25 15:37:25   7
  8  11.75 -1.05 2019-02-25 15:37:25   8
  9  10.25 -1.75 2019-02-25 15:37:25   9
  10 10.75 -1.75 2019-02-25 15:37:25  10
  11 11.25 -1.75 2019-02-25 15:37:25  11
  12 11.75 -1.75 2019-02-25 15:37:25  12
  13 10.25 -2.45 2019-02-25 15:37:25  13
  14 10.75 -2.45 2019-02-25 15:37:25  14
  15 11.25 -2.45 2019-02-25 15:37:25  15
  16 11.75 -2.45 2019-02-25 15:37:25  16
  17 10.25 -0.35 2019-02-25 15:37:26  17
  18 10.75 -0.35 2019-02-25 15:37:26  18
  19 11.25 -0.35 2019-02-25 15:37:26  19
  20 11.75 -0.35 2019-02-25 15:37:26  20
  21 10.25 -1.05 2019-02-25 15:37:26  21
  22 10.75 -1.05 2019-02-25 15:37:26  22
  23 11.25 -1.05 2019-02-25 15:37:26  23
  24 11.75 -1.05 2019-02-25 15:37:26  24
  25 10.25 -1.75 2019-02-25 15:37:26  25
  26 10.75 -1.75 2019-02-25 15:37:26  26
  27 11.25 -1.75 2019-02-25 15:37:26  27
  28 11.75 -1.75 2019-02-25 15:37:26  28
  29 10.25 -2.45 2019-02-25 15:37:26  29
  30 10.75 -2.45 2019-02-25 15:37:26  30
  31 11.25 -2.45 2019-02-25 15:37:26  31
  32 11.75 -2.45 2019-02-25 15:37:26  32
  33 10.25 -0.35 2019-02-25 15:37:27  33
  34 10.75 -0.35 2019-02-25 15:37:27  34
  35 11.25 -0.35 2019-02-25 15:37:27  35
  36 11.75 -0.35 2019-02-25 15:37:27  36
  37 10.25 -1.05 2019-02-25 15:37:27  37
  38 10.75 -1.05 2019-02-25 15:37:27  38
  39 11.25 -1.05 2019-02-25 15:37:27  39
  40 11.75 -1.05 2019-02-25 15:37:27  40
  41 10.25 -1.75 2019-02-25 15:37:27  41
  42 10.75 -1.75 2019-02-25 15:37:27  42
  43 11.25 -1.75 2019-02-25 15:37:27  43
  44 11.75 -1.75 2019-02-25 15:37:27  44
  45 10.25 -2.45 2019-02-25 15:37:27  45
  46 10.75 -2.45 2019-02-25 15:37:27  46
  47 11.25 -2.45 2019-02-25 15:37:27  47
  48 11.75 -2.45 2019-02-25 15:37:27  48
  49 10.25 -0.35 2019-02-25 15:37:28  49
  50 10.75 -0.35 2019-02-25 15:37:28  50
  51 11.25 -0.35 2019-02-25 15:37:28  51
  52 11.75 -0.35 2019-02-25 15:37:28  52
  53 10.25 -1.05 2019-02-25 15:37:28  53
  54 10.75 -1.05 2019-02-25 15:37:28  54
  55 11.25 -1.05 2019-02-25 15:37:28  55
  56 11.75 -1.05 2019-02-25 15:37:28  56
  57 10.25 -1.75 2019-02-25 15:37:28  57
  58 10.75 -1.75 2019-02-25 15:37:28  58
  59 11.25 -1.75 2019-02-25 15:37:28  59
  60 11.75 -1.75 2019-02-25 15:37:28  60
  61 10.25 -2.45 2019-02-25 15:37:28  61
  62 10.75 -2.45 2019-02-25 15:37:28  62
  63 11.25 -2.45 2019-02-25 15:37:28  63
  64 11.75 -2.45 2019-02-25 15:37:28  64
  > plot(s, col = sf.colors(), axes = TRUE)
  > (s.stfdf = as(s, "STFDF"))
  An object of class "STFDF"
  Slot "data":
     foo
  1    1
  2    2
  3    3
  4    4
  5    5
  6    6
  7    7
  8    8
  9    9
  10  10
  11  11
  12  12
  13  13
  14  14
  15  15
  16  16
  17  17
  18  18
  19  19
  20  20
  21  21
  22  22
  23  23
  24  24
  25  25
  26  26
  27  27
  28  28
  29  29
  30  30
  31  31
  32  32
  33  33
  34  34
  35  35
  36  36
  37  37
  38  38
  39  39
  40  40
  41  41
  42  42
  43  43
  44  44
  45  45
  46  46
  47  47
  48  48
  49  49
  50  50
  51  51
  52  52
  53  53
  54  54
  55  55
  56  56
  57  57
  58  58
  59  59
  60  60
  61  61
  62  62
  63  63
  64  64
  
  Slot "sp":
  Object of class SpatialPixels
  Grid topology:
    cellcentre.offset cellsize cells.dim
  x             10.25      0.5         4
  y             -2.45      0.7         4
  SpatialPoints:
            x     y
   [1,] 10.25 -0.35
   [2,] 10.75 -0.35
   [3,] 11.25 -0.35
   [4,] 11.75 -0.35
   [5,] 10.25 -1.05
   [6,] 10.75 -1.05
   [7,] 11.25 -1.05
   [8,] 11.75 -1.05
   [9,] 10.25 -1.75
  [10,] 10.75 -1.75
  [11,] 11.25 -1.75
  [12,] 11.75 -1.75
  [13,] 10.25 -2.45
  [14,] 10.75 -2.45
  [15,] 11.25 -2.45
  [16,] 11.75 -2.45
  Coordinate Reference System (CRS) arguments: NA 
  
  Slot "time":
                      timeIndex
  2019-02-25 15:37:25         1
  2019-02-25 15:37:26         2
  2019-02-25 15:37:27         3
  2019-02-25 15:37:28         4
  
  Slot "endTime":
  [1] "2019-02-25 15:37:26 UTC" "2019-02-25 15:37:27 UTC"
  [3] "2019-02-25 15:37:28 UTC" "2019-02-25 15:37:29 UTC"
  
  > stplot(s.stfdf, scales = list(draw = TRUE))
  > 
  > (s2 = st_as_stars(s.stfdf))
  stars object with 3 dimensions and 1 attribute
  attribute(s):
       Min. 1st Qu. Median Mean 3rd Qu. Max.
  foo     1   16.75   32.5 32.5   48.25   64
  dimension(s):
       from to                  offset  delta  refsys x/y
  x       1  4                      10    0.5      NA [x]
  y       1  4               -1.11e-16   -0.7      NA [y]
  time    1  4 2019-02-25 15:37:25 UTC 1 secs POSIXct    
  > plot(s2, col = sf.colors(), axes = TRUE)
  > all.equal(s, s2, check.attributes = FALSE)
  [1] TRUE
  > 
  > # multiple simulations:
  > data(meuse, package = "sp")
  > data(meuse.grid, package = "sp")
  > coordinates(meuse.grid) <- ~x+y
  > gridded(meuse.grid) <- TRUE
  > meuse.grid = st_as_stars(meuse.grid)
  > meuse_sf = st_as_sf(meuse, coords = c("x", "y"))
  > g = gstat(NULL, "zinc", zinc~1, meuse_sf, model = vgm(1, "Exp", 300), nmax = 10)
  > g = gstat(g, "lead", lead~1, meuse_sf, model = vgm(1, "Exp", 300), nmax = 10, fill.cross = TRUE)
  > set.seed(123)
  > ## IGNORE_RDIFF_BEGIN
  > (p = predict(g, meuse.grid, nsim = 5))
  drawing 5 multivariate GLS realisations of beta...

*

*

*

*

*

tests

  Running ‘allier.R’ [1s/2s]
  Comparing ‘allier.Rout’ to ‘allier.Rout.save’ ... OK
  Running ‘blockkr.R’ [1s/2s]
  Comparing ‘blockkr.Rout’ to ‘blockkr.Rout.save’ ... OK
  Running ‘covtable.R’ [1s/2s]
  Comparing ‘covtable.Rout’ to ‘covtable.Rout.save’ ... OK
  Running ‘cv.R’ [2s/3s]
  Comparing ‘cv.Rout’ to ‘cv.Rout.save’ ... OK
  Running ‘cv3d.R’ [2s/2s]
  Comparing ‘cv3d.Rout’ to ‘cv3d.Rout.save’ ... OK
  Running ‘fit.R’ [1s/2s]
  Comparing ‘fit.Rout’ to ‘fit.Rout.save’ ... OK
  Running ‘krige0.R’ [3s/4s]
  Comparing ‘krige0.Rout’ to ‘krige0.Rout.save’ ... OK
  Running ‘line.R’ [2s/2s]
  Comparing ‘line.Rout’ to ‘line.Rout.save’ ... OK
  Running ‘merge.R’ [1s/2s]
  Comparing ‘merge.Rout’ to ‘merge.Rout.save’ ... OK
  Running ‘na.action.R’ [1s/2s]
  Comparing ‘na.action.Rout’ to ‘na.action.Rout.save’ ... OK
  Running ‘rings.R’ [1s/2s]
  Comparing ‘rings.Rout’ to ‘rings.Rout.save’ ... OK
  Running ‘sim.R’ [1s/2s]
  Comparing ‘sim.Rout’ to ‘sim.Rout.save’ ... OK
  Running ‘stars.R’ [14s/16s]
  Comparing ‘stars.Rout’ to ‘stars.Rout.save’ ...187c187
<                 Min.   1st Qu.    Median      Mean   3rd Qu.      Max. NA's
---
>                 Min.   1st Qu.    Median      Mean   3rd Qu.      Max.  NAs
201,207c201,207
<  Min.   :4.777   Min.   :0.0855   (0,5]: 316  
<  1st Qu.:5.238   1st Qu.:0.1373   (5,6]:1778  
<  Median :5.573   Median :0.1622   (6,7]: 962  
<  Mean   :5.707   Mean   :0.1853   (7,8]:  47  
<  3rd Qu.:6.172   3rd Qu.:0.2116   (8,9]:   0  
<  Max.   :7.440   Max.   :0.5003   NA's :5009  
<  NA's   :5009    NA's   :5009                 
---
>  Min.   :4.777   Min.   :0.08549   (0,5]: 316  
>  1st Qu.:5.238   1st Qu.:0.13729   (5,6]:1778  
>  Median :5.573   Median :0.16218   (6,7]: 962  
>  Mean   :5.707   Mean   :0.18533   (7,8]:  47  
>  3rd Qu.:6.172   3rd Qu.:0.21162   (8,9]:   0  
>  Max.   :7.440   Max.   :0.50028   NAs  :5009  
>  NAs    :5009    NAs    :5009                  
218c218
<                 Min.   1st Qu. Median     Mean  3rd Qu.     Max.  NA's
---
>                 Min.   1st Qu. Median     Mean  3rd Qu.     Max.   NAs
  Running ‘variogram.R’ [1s/2s]
  Comparing ‘variogram.Rout’ to ‘variogram.Rout.save’ ... OK
  Running ‘vdist.R’ [1s/1s]
  Comparing ‘vdist.Rout’ to ‘vdist.Rout.save’ ... OK
  Running ‘windst.R’ [8s/10s]
  Comparing ‘windst.Rout’ to ‘windst.Rout.save’ ... OK

tests

  Running ‘allier.R’ [1s/2s]
  Comparing ‘allier.Rout’ to ‘allier.Rout.save’ ... OK
  Running ‘blockkr.R’ [1s/2s]
  Comparing ‘blockkr.Rout’ to ‘blockkr.Rout.save’ ... OK
  Running ‘covtable.R’ [1s/2s]
  Comparing ‘covtable.Rout’ to ‘covtable.Rout.save’ ... OK
  Running ‘cv.R’ [2s/2s]
  Comparing ‘cv.Rout’ to ‘cv.Rout.save’ ... OK
  Running ‘cv3d.R’ [2s/3s]
  Comparing ‘cv3d.Rout’ to ‘cv3d.Rout.save’ ... OK
  Running ‘fit.R’ [1s/2s]
  Comparing ‘fit.Rout’ to ‘fit.Rout.save’ ... OK
  Running ‘krige0.R’ [3s/5s]
  Comparing ‘krige0.Rout’ to ‘krige0.Rout.save’ ... OK
  Running ‘line.R’ [2s/2s]
  Comparing ‘line.Rout’ to ‘line.Rout.save’ ... OK
  Running ‘merge.R’ [1s/2s]
  Comparing ‘merge.Rout’ to ‘merge.Rout.save’ ... OK
  Running ‘na.action.R’ [1s/2s]
  Comparing ‘na.action.Rout’ to ‘na.action.Rout.save’ ... OK
  Running ‘rings.R’ [2s/2s]
  Comparing ‘rings.Rout’ to ‘rings.Rout.save’ ... OK
  Running ‘sim.R’ [1s/2s]
  Comparing ‘sim.Rout’ to ‘sim.Rout.save’ ... OK
  Running ‘stars.R’ [14s/17s]
  Comparing ‘stars.Rout’ to ‘stars.Rout.save’ ...187c187
<                 Min.   1st Qu.    Median      Mean   3rd Qu.      Max. NA's
---
>                 Min.   1st Qu.    Median      Mean   3rd Qu.      Max.  NAs
201,207c201,207
<  Min.   :4.777   Min.   :0.0855   (0,5]: 316  
<  1st Qu.:5.238   1st Qu.:0.1373   (5,6]:1778  
<  Median :5.573   Median :0.1622   (6,7]: 962  
<  Mean   :5.707   Mean   :0.1853   (7,8]:  47  
<  3rd Qu.:6.172   3rd Qu.:0.2116   (8,9]:   0  
<  Max.   :7.440   Max.   :0.5003   NA's :5009  
<  NA's   :5009    NA's   :5009                 
---
>  Min.   :4.777   Min.   :0.08549   (0,5]: 316  
>  1st Qu.:5.238   1st Qu.:0.13729   (5,6]:1778  
>  Median :5.573   Median :0.16218   (6,7]: 962  
>  Mean   :5.707   Mean   :0.18533   (7,8]:  47  
>  3rd Qu.:6.172   3rd Qu.:0.21162   (8,9]:   0  
>  Max.   :7.440   Max.   :0.50028   NAs  :5009  
>  NAs    :5009    NAs    :5009                  
218c218
<                 Min.   1st Qu. Median     Mean  3rd Qu.     Max.  NA's
---
>                 Min.   1st Qu. Median     Mean  3rd Qu.     Max.   NAs
  Running ‘variogram.R’ [1s/2s]
  Comparing ‘variogram.Rout’ to ‘variogram.Rout.save’ ... OK
  Running ‘vdist.R’ [1s/2s]
  Comparing ‘vdist.Rout’ to ‘vdist.Rout.save’ ... OK
  Running ‘windst.R’ [8s/11s]
  Comparing ‘windst.Rout’ to ‘windst.Rout.save’ ... OK

tests

  Running ‘allier.R’ [0s/0s]
  Comparing ‘allier.Rout’ to ‘allier.Rout.save’ ... OK
  Running ‘blockkr.R’ [0s/0s]
  Comparing ‘blockkr.Rout’ to ‘blockkr.Rout.save’ ... OK
  Running ‘covtable.R’ [0s/0s]
  Comparing ‘covtable.Rout’ to ‘covtable.Rout.save’ ... OK
  Running ‘cv.R’ [0s/0s]
  Comparing ‘cv.Rout’ to ‘cv.Rout.save’ ... OK
  Running ‘cv3d.R’ [0s/0s]
  Comparing ‘cv3d.Rout’ to ‘cv3d.Rout.save’ ... OK
  Running ‘fit.R’ [0s/0s]
  Comparing ‘fit.Rout’ to ‘fit.Rout.save’ ... OK
  Running ‘krige0.R’ [1s/1s]
  Comparing ‘krige0.Rout’ to ‘krige0.Rout.save’ ... OK
  Running ‘line.R’ [0s/0s]
  Comparing ‘line.Rout’ to ‘line.Rout.save’ ... OK
  Running ‘merge.R’ [0s/0s]
  Comparing ‘merge.Rout’ to ‘merge.Rout.save’ ... OK
  Running ‘na.action.R’ [0s/0s]
  Comparing ‘na.action.Rout’ to ‘na.action.Rout.save’ ... OK
  Running ‘rings.R’ [0s/0s]
  Comparing ‘rings.Rout’ to ‘rings.Rout.save’ ... OK
  Running ‘sim.R’ [0s/0s]
  Comparing ‘sim.Rout’ to ‘sim.Rout.save’ ... OK
  Running ‘stars.R’ [3s/3s]
  Comparing ‘stars.Rout’ to ‘stars.Rout.save’ ...187c187
<                 Min.   1st Qu.    Median      Mean   3rd Qu.      Max. NA's
---
>                 Min.   1st Qu.    Median      Mean   3rd Qu.      Max.  NAs
201,207c201,207
<  Min.   :4.777   Min.   :0.0855   (0,5]: 316  
<  1st Qu.:5.238   1st Qu.:0.1373   (5,6]:1778  
<  Median :5.573   Median :0.1622   (6,7]: 962  
<  Mean   :5.707   Mean   :0.1853   (7,8]:  47  
<  3rd Qu.:6.172   3rd Qu.:0.2116   (8,9]:   0  
<  Max.   :7.440   Max.   :0.5003   NA's :5009  
<  NA's   :5009    NA's   :5009                 
---
>  Min.   :4.777   Min.   :0.08549   (0,5]: 316  
>  1st Qu.:5.238   1st Qu.:0.13729   (5,6]:1778  
>  Median :5.573   Median :0.16218   (6,7]: 962  
>  Mean   :5.707   Mean   :0.18533   (7,8]:  47  
>  3rd Qu.:6.172   3rd Qu.:0.21162   (8,9]:   0  
>  Max.   :7.440   Max.   :0.50028   NAs  :5009  
>  NAs    :5009    NAs    :5009                  
218c218
<                 Min.   1st Qu. Median     Mean  3rd Qu.     Max.  NA's
---
>                 Min.   1st Qu. Median     Mean  3rd Qu.     Max.   NAs
  Running ‘variogram.R’ [0s/0s]
  Comparing ‘variogram.Rout’ to ‘variogram.Rout.save’ ... OK
  Running ‘vdist.R’ [0s/0s]
  Comparing ‘vdist.Rout’ to ‘vdist.Rout.save’ ... OK
  Running ‘windst.R’ [2s/2s]
  Comparing ‘windst.Rout’ to ‘windst.Rout.save’ ... OK

tests

  Running ‘allier.R’ [1s/2s]
  Comparing ‘allier.Rout’ to ‘allier.Rout.save’ ... OK
  Running ‘blockkr.R’ [1s/2s]
  Comparing ‘blockkr.Rout’ to ‘blockkr.Rout.save’ ... OK
  Running ‘covtable.R’ [1s/1s]
  Comparing ‘covtable.Rout’ to ‘covtable.Rout.save’ ... OK
  Running ‘cv.R’ [1s/2s]
  Comparing ‘cv.Rout’ to ‘cv.Rout.save’ ... OK
  Running ‘cv3d.R’ [1s/2s]
  Comparing ‘cv3d.Rout’ to ‘cv3d.Rout.save’ ... OK
  Running ‘fit.R’ [1s/1s]
  Comparing ‘fit.Rout’ to ‘fit.Rout.save’ ... OK
  Running ‘krige0.R’ [2s/4s]
  Comparing ‘krige0.Rout’ to ‘krige0.Rout.save’ ... OK
  Running ‘line.R’ [1s/3s]
  Comparing ‘line.Rout’ to ‘line.Rout.save’ ... OK
  Running ‘merge.R’ [1s/2s]
  Comparing ‘merge.Rout’ to ‘merge.Rout.save’ ... OK
  Running ‘na.action.R’ [1s/2s]
  Comparing ‘na.action.Rout’ to ‘na.action.Rout.save’ ... OK
  Running ‘rings.R’ [1s/2s]
  Comparing ‘rings.Rout’ to ‘rings.Rout.save’ ... OK
  Running ‘sim.R’ [1s/2s]
  Comparing ‘sim.Rout’ to ‘sim.Rout.save’ ... OK
  Running ‘stars.R’ [10s/20s]
  Comparing ‘stars.Rout’ to ‘stars.Rout.save’ ...187c187
<                 Min.   1st Qu.    Median      Mean   3rd Qu.      Max. NA's
---
>                 Min.   1st Qu.    Median      Mean   3rd Qu.      Max.  NAs
201,207c201,207
<  Min.   :4.777   Min.   :0.0855   (0,5]: 316  
<  1st Qu.:5.238   1st Qu.:0.1373   (5,6]:1778  
<  Median :5.573   Median :0.1622   (6,7]: 962  
<  Mean   :5.707   Mean   :0.1853   (7,8]:  47  
<  3rd Qu.:6.172   3rd Qu.:0.2116   (8,9]:   0  
<  Max.   :7.440   Max.   :0.5003   NA's :5009  
<  NA's   :5009    NA's   :5009                 
---
>  Min.   :4.777   Min.   :0.08549   (0,5]: 316  
>  1st Qu.:5.238   1st Qu.:0.13729   (5,6]:1778  
>  Median :5.573   Median :0.16218   (6,7]: 962  
>  Mean   :5.707   Mean   :0.18533   (7,8]:  47  
>  3rd Qu.:6.172   3rd Qu.:0.21162   (8,9]:   0  
>  Max.   :7.440   Max.   :0.50028   NAs  :5009  
>  NAs    :5009    NAs    :5009                  
218c218
<                 Min.   1st Qu. Median     Mean  3rd Qu.     Max.  NA's
---
>                 Min.   1st Qu. Median     Mean  3rd Qu.     Max.   NAs
  Running ‘variogram.R’ [1s/2s]
  Comparing ‘variogram.Rout’ to ‘variogram.Rout.save’ ... OK
  Running ‘vdist.R’ [1s/2s]
  Comparing ‘vdist.Rout’ to ‘vdist.Rout.save’ ... OK
  Running ‘windst.R’ [7s/13s]
  Comparing ‘windst.Rout’ to ‘windst.Rout.save’ ... OK

tests

  Running 'allier.R' [1s]
  Comparing 'allier.Rout' to 'allier.Rout.save' ... OK
  Running 'blockkr.R' [1s]
  Comparing 'blockkr.Rout' to 'blockkr.Rout.save' ... OK
  Running 'covtable.R' [1s]
  Comparing 'covtable.Rout' to 'covtable.Rout.save' ... OK
  Running 'cv.R' [1s]
  Comparing 'cv.Rout' to 'cv.Rout.save' ... OK
  Running 'cv3d.R' [1s]
  Comparing 'cv3d.Rout' to 'cv3d.Rout.save' ... OK
  Running 'fit.R' [1s]
  Comparing 'fit.Rout' to 'fit.Rout.save' ... OK
  Running 'krige0.R' [3s]
  Comparing 'krige0.Rout' to 'krige0.Rout.save' ... OK
  Running 'line.R' [2s]
  Comparing 'line.Rout' to 'line.Rout.save' ... OK
  Running 'merge.R' [1s]
  Comparing 'merge.Rout' to 'merge.Rout.save' ... OK
  Running 'na.action.R' [1s]
  Comparing 'na.action.Rout' to 'na.action.Rout.save' ... OK
  Running 'rings.R' [1s]
  Comparing 'rings.Rout' to 'rings.Rout.save' ... OK
  Running 'sim.R' [1s]
  Comparing 'sim.Rout' to 'sim.Rout.save' ... OK
  Running 'stars.R' [12s]
  Comparing 'stars.Rout' to 'stars.Rout.save' ...187c187
<                 Min.   1st Qu.    Median      Mean   3rd Qu.      Max. NA's
---
>                 Min.   1st Qu.    Median      Mean   3rd Qu.      Max.  NAs
201,207c201,207
<  Min.   :4.777   Min.   :0.0855   (0,5]: 316  
<  1st Qu.:5.238   1st Qu.:0.1373   (5,6]:1778  
<  Median :5.573   Median :0.1622   (6,7]: 962  
<  Mean   :5.707   Mean   :0.1853   (7,8]:  47  
<  3rd Qu.:6.172   3rd Qu.:0.2116   (8,9]:   0  
<  Max.   :7.440   Max.   :0.5003   NA's :5009  
<  NA's   :5009    NA's   :5009                 
---
>  Min.   :4.777   Min.   :0.08549   (0,5]: 316  
>  1st Qu.:5.238   1st Qu.:0.13729   (5,6]:1778  
>  Median :5.573   Median :0.16218   (6,7]: 962  
>  Mean   :5.707   Mean   :0.18533   (7,8]:  47  
>  3rd Qu.:6.172   3rd Qu.:0.21162   (8,9]:   0  
>  Max.   :7.440   Max.   :0.50028   NAs  :5009  
>  NAs    :5009    NAs    :5009                  
218c218
<                 Min.   1st Qu. Median     Mean  3rd Qu.     Max.  NA's
---
>                 Min.   1st Qu. Median     Mean  3rd Qu.     Max.   NAs
  Running 'variogram.R' [1s]
  Comparing 'variogram.Rout' to 'variogram.Rout.save' ... OK
  Running 'vdist.R' [1s]
  Comparing 'vdist.Rout' to 'vdist.Rout.save' ... OK
  Running 'windst.R' [8s]
  Comparing 'windst.Rout' to 'windst.Rout.save' ... OK