Last updated on 2025-02-16 08:50:49 CET.
| Flavor | Version | Tinstall | Tcheck | Ttotal | Status | Flags |
|---|---|---|---|---|---|---|
| r-devel-linux-x86_64-debian-clang | 2.1-3 | 20.50 | 184.94 | 205.44 | OK | |
| r-devel-linux-x86_64-debian-gcc | 2.1-3 | 15.31 | 127.96 | 143.27 | OK | |
| r-devel-linux-x86_64-fedora-clang | 2.1-3 | 358.84 | OK | |||
| r-devel-linux-x86_64-fedora-gcc | 2.1-3 | 3995.43 | ERROR | |||
| r-devel-macos-arm64 | 2.1-3 | 89.00 | OK | |||
| r-devel-macos-x86_64 | 2.1-3 | 230.00 | OK | |||
| r-devel-windows-x86_64 | 2.1-3 | 35.00 | 247.00 | 282.00 | OK | |
| r-patched-linux-x86_64 | 2.1-3 | 21.77 | 173.49 | 195.26 | OK | |
| r-release-linux-x86_64 | 2.1-3 | 20.05 | 176.30 | 196.35 | OK | |
| r-release-macos-arm64 | 2.1-3 | 121.00 | OK | |||
| r-release-macos-x86_64 | 2.1-3 | 246.00 | OK | |||
| r-release-windows-x86_64 | 2.1-3 | 34.00 | 248.00 | 282.00 | OK | |
| r-oldrel-macos-arm64 | 2.1-2 | 126.00 | OK | |||
| r-oldrel-macos-x86_64 | 2.1-3 | 237.00 | OK | |||
| r-oldrel-windows-x86_64 | 2.1-3 | 39.00 | 294.00 | 333.00 | OK |
Version: 2.1-3
Check: tests
Result: ERROR
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’ [32m/26m]
Running ‘unproj.R’
Comparing ‘unproj.Rout’ to ‘unproj.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’ [30m/17m]
Running the tests in ‘tests/stars.R’ failed.
Complete output:
> Sys.setenv(TZ = "UTC")
>
> # 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. NA's
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. :0.0855 (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
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. NA's
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...
Running the tests in ‘tests/windst.R’ failed.
Complete output:
> suppressPackageStartupMessages(library(sp))
> suppressPackageStartupMessages(library(spacetime))
> suppressPackageStartupMessages(library(gstat))
> suppressPackageStartupMessages(library(stars))
>
> 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"))
+ }
Flavor: r-devel-linux-x86_64-fedora-gcc
These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.