Leonidas,
I see that you are not happy with the output, but it is not so clear what you
actually expect to see.
If you use stackApply directly, the indices are used in the names. Layer 1 and
8 belong to the group with index 4. It is the first group in the list of
indexes, so the first layer of the output is then referred to as index_4. Then
comes index_5 with layers 2, 10 and 15 of your input. The order of these names
will follow the order of the first appearance of your indices. The indices gets
lost with the use of clusterR, so it gives you the same output, but with names
layer.1 - layer.7.
You could change the names of the result from clusterR with:
names(ResClusterR) = paste0("index_", unique(indices))
If you want your result (from stackApply or clusterR) to follow the order of
your indices, you should be able to get this with:
sResClusterR = ResClusterR[[order(names(ResClusterR))]]
Does this help you further?
Jon
--
Jon Olav Skøien
European Commission
Joint Research Centre – JRC.E.1
Disaster Risk Management Unit
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________________________________
From: R-sig-Geo <r-sig-geo-boun...@r-project.org> on behalf of Leonidas Liakos
via R-sig-Geo <r-sig-geo@r-project.org>
Sent: 21 November 2019 08:52
To: Ben Tupper; r-sig-geo@r-project.org
Subject: Re: [R-sig-Geo] raster: stackApply problems..
Unfortunately the names are not always in ascending order. This is the
result of my data.
names : index_4, index_5, index_6, index_7, index_1, index_2, index_3
min values : 3, 3, 3, 3, 3, 3, 3
max values : 307.0, 297.5, 311.0, 313.0, 468.0, 290.0, 302.0
And worst of all, it is not a proper match with indices.
If I run it with clusterR then the result is different:
names : layer.1, layer.2, layer.3, layer.4, layer.5, layer.6, layer.7
min values : 3, 3, 3, 3, 3, 3, 3
max values : 307.0, 297.5, 311.0, 313.0, 468.0, 290.0, 302.0
The solution is to reorder the layers of the stack so that the
stackApply indices are in ascending order e.g. 1,1,1,2,2,2,3,3,3 ...
My indices of my data was like that:
4 5 6 7 1 2 3 4 5 6 7 1 2 3 5 6 7
I've reported this behavior here
https://urldefense.com/v3/__https://github.com/rspatial/raster/issues/82__;!NW73rmyV52c!SiZfwLn8F-IC_xeeUNNjzf8STJX1LMbYaoJKqfWo5ImGWi_dEhB7ilEG9835G-KjNZJC$
On 11/20/19 3:05 PM, Ben Tupper wrote:
> 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@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.
>>
>>
>> Στις 20/11/2019 3:30 π.μ., ο Frederico Faleiro έγραψε:
>>> 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@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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>>>>
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>>>>
>>
>> --
>> Λιάκος Λεωνίδας, Γεωγράφος
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