raster: stackApply problems..

Hi,

That is certainly is unexpected to have two different naming styles.
It's not really solution to take to the bank, but you could simply
compose your own names assuming that the layer orders are always
returned in ascending index order.
Would that work for you

### start
library(raster)

# Compute layer names for stackApply output
#
# @param index numeric, 1-based layer indices used for stackApply function
# @param prefix character, prefix for names
# @return character layers names in index order
layer_names <- function(index = c(2,2,3,3,1,1), prefix = c("layer.",
"index_")[1]){
  paste0(prefix, sort(unique(index)))
}

indices <- c(2,2,3,3,1,1)

r <- raster()
values(r) <- 1
# simple sequential stack from 1 to 6 in all cells
s <- stack(r, r*2, r*3, r*4, r*5, r*6)
s

beginCluster(2)
res <- clusterR(s, stackApply, args = list(indices=indices, fun = mean))
raster::endCluster()
names(res) <- layer_names(indices, prefix = "foobar.")
res

res2 <- stackApply(s, indices, mean)
names(res2) <- layer_names(indices, prefix = "foobar.")
res2
### end


On Wed, Nov 20, 2019 at 1:36 AM Leonidas Liakos via R-sig-Geo
<r-sig-geo at r-project.org> wrote:

This is not a reasonable solution. It is not efficient to run stackapply
twice to get the right names. Each execution can take hours.


???? 20/11/2019 3:30 ?.?., ? Frederico Faleiro ??????:

Hi Leonidas,

both results are in the same order, but the name is different.
You can rename the first as in the second:
names(res) <- names(res2)

I provided an example to help you understand the logic.

library(raster)
beginCluster(2)
r <- raster()
values(r) <- 1
# simple sequential stack from 1 to 6 in all cells
s <- stack(r, r*2, r*3, r*4, r*5, r*6)
s
res <- clusterR(s, stackApply, args = list(indices=c(2,2,3,3,1,1), fun =
mean))
res
res2 <- stackApply(s, c(2,2,3,3,1,1), mean)
res2
dif <- res - res2
# exatly the same order because the difference is zero for all layers
dif
# rename
names(res) <- names(res2)

Best regards,

Frederico Faleiro

On Tue, Nov 19, 2019 at 4:15 PM Leonidas Liakos via R-sig-Geo <
r-sig-geo at r-project.org> wrote:

I run the example with clusterR:

no_cores <- parallel::detectCores() -1
raster::beginCluster(no_cores)
?????? res <- raster::clusterR(inp, raster::stackApply, args =
list(indices=c(2,2,3,3,1,1),fun = mean))
raster::endCluster()

And the result is:

res

class?????????? : RasterBrick
dimensions : 180, 360, 64800, 3?? (nrow, ncol, ncell, nlayers)
resolution : 1, 1?? (x, y)
extent???????? : -180, 180, -90, 90?? (xmin, xmax, ymin, ymax)
crs?????????????? : +proj=longlat +datum=WGS84 +ellps=WGS84 +towgs84=0,0,0
source???????? : memory
names?????????? : layer.1, layer.2, layer.3
min values :???????? 1.5,???????? 3.5,???????? 5.5
max values :???????? 1.5,???????? 3.5,???????? 5.5??


layer.1, layer.2, layer.3 (?)

So what corrensponds to what?


If I run:

res2 <- stackApply(inp,c(2,2,3,3,1,1),mean)

The result is:

res2

class      : RasterBrick
dimensions : 180, 360, 64800, 3  (nrow, ncol, ncell, nlayers)
resolution : 1, 1  (x, y)
extent     : -180, 180, -90, 90  (xmin, xmax, ymin, ymax)
crs        : +proj=longlat +datum=WGS84 +ellps=WGS84 +towgs84=0,0,0
source     : memory
names      : index_2, index_3, index_1
min values :     1.5,     3.5,     5.5
max values :     1.5,     3.5,     5.5

There is no consistency with the names of the output and obscure
correspondence with the indices in the case of clusterR


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