raster: stackApply problems..
Thank you! The problem is not with the resulting values ??but with the index mapping. Values ??are correct in all three cases. As I wrote in a previous post in the thread (https://stat.ethz.ch/pipermail/r-sig-geo/2019-November/027821.html) , stackApply behaves inconsistently depending on whether the exported stack will remain in memory or it will be stored, due to its large size, on the hard disk. In the first case the indices are identical to my function (ver_mean) and the lubridate::wday indexing system (and are correct) while in the second they are shuffled. So, while Sunday has index 1 and while in the first case (when the result is in memory) stackApply returns the correct index, in the second case (when the result is stored on the hard disk) it returns index_4! So how can one be sure if index_1 corresponds to Sunday or another day using stackApply since it sometimes enumerates it with index_1 and sometimes index_4? Try to run this example (when the resulting stack remains in memory) to see that the indexes are identical (stackApply = ver_median = lubridate::wday) https://gist.github.com/kokkytos/5d554b5a725bb48d2189e2d1fa0e2206 Thank you again
On 11/26/19 9:00 PM, Vijay Lulla wrote:
I'm sorry for the miscommunication.? What I meant to say is that the
output from stackApply and zApply are the same (because zApply uses
stackApply internally) except the names.? The behavior of stackApply
makes sense because AFAIUI R doesn't automatically reorder
vectors/indices that it receives.? Your observation about inconsistent
result with ver_mean is very valid though!? And, that's what I meant
with my comment that using sapply with the explicit ordering that you
want is the best way to control what raster package will output.? In R
the input order should be maintained (this is the prime difference
between SQL and R) but packages/tools do not always adhere to
this...so it's never clear how the output will be ordered.? Sorry for
the confusion.
On Tue, Nov 26, 2019 at 12:22 PM Leonidas Liakos
<leonidas_liakos at yahoo.gr <mailto:leonidas_liakos at yahoo.gr>> wrote:
Why do they seem logical since they do not match?
Check for example index 1 (Sunday). The results are different for
the three processes
> stackapply_mean
class????? : RasterBrick
dimensions : 300, 300, 90000, 7? (nrow, ncol, ncell, nlayers)
resolution : 500, 500? (x, y)
extent???? : 0, 150000, 0, 150000? (xmin, xmax, ymin, ymax)
crs??????? : NA
source???? :
/tmp/RtmpkRMXLb/raster/r_tmp_2019-11-26_191359_7710_20324.grd
names????? :? index_5,? index_6,? index_7,? index_1,? index_2,?
index_3,? index_4
min values : 440.0467, 444.9182, 437.1589, 444.6946, 440.2028,
429.6900, 442.7436
max values : 563.8341, 561.7687, 560.4509, 565.8671, 560.1375,
561.7972, 556.2471
> ver_mean
class????? : RasterStack
dimensions : 300, 300, 90000, 7? (nrow, ncol, ncell, nlayers)
resolution : 500, 500? (x, y)
extent???? : 0, 150000, 0, 150000? (xmin, xmax, ymin, ymax)
crs??????? : NA
names????? :? layer.1,? layer.2,? layer.3,? layer.4,? layer.5,?
layer.6,? layer.7
min values : 442.7436, 440.0467, 444.9182, 437.1589, 444.6946,
440.2028, 429.6900
max values : 556.2471, 563.8341, 561.7687, 560.4509, 565.8671,
560.1375, 561.7972
> z
class????? : RasterBrick
dimensions : 300, 300, 90000, 7? (nrow, ncol, ncell, nlayers)
resolution : 500, 500? (x, y)
extent???? : 0, 150000, 0, 150000? (xmin, xmax, ymin, ymax)
crs??????? : NA
source???? :
/tmp/RtmpkRMXLb/raster/r_tmp_2019-11-26_191439_7710_04780.grd
names????? :?????? X1,?????? X2,?????? X3,?????? X4,??????
X5,?????? X6,?????? X7
min values : 440.0467, 444.9182, 437.1589, 444.6946, 440.2028,
429.6900, 442.7436
max values : 563.8341, 561.7687, 560.4509, 565.8671, 560.1375,
561.7972, 556.2471
?????????? : 1, 2, 3, 4, 5, 6, 7
On 11/26/19 7:03 PM, Vijay Lulla wrote:
If you read the code/help for `stackApply` and `zApply` you'll
see that the results that you obtain make sense (at least they
seem sensible/reasonable to me).? IMO, if you want to control the
ordering of your layers then just use sapply, like how you've
used for ver_mean.? IMO, this is the only reliable (safe?), and
quite a readable, way to accomplish what you're trying to do.
Just my 2 cents.
-- Vijay.
On Tue, Nov 26, 2019 at 11:19 AM Leonidas Liakos via R-sig-Geo
<r-sig-geo at r-project.org <mailto:r-sig-geo at r-project.org>> wrote:
I added raster::zApply in my tests to validate the results.
However, the
indices of the names of the results are different now. Recall
that the
goal is to calculate from a raster stack time series the mean
per day of
the week. And that problem I have is that stackApply, zApply and
calc/sapply return different indices in the result names. New
code is
available here:
https://gist.github.com/kokkytos/93f315a5ecf59c0b183f9788754bc170
I'm really curious about missing something.
On 11/20/19 3:30 AM, Frederico Faleiro wrote:
> 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 <mailto:r-sig-geo at r-project.org>
<mailto:r-sig-geo at r-project.org
<mailto: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
>
>
>? ? ?? ? ? ? [[alternative HTML version deleted]]
>
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