[R-sig-Geo] raster: stackApply problems..
Vijay Lulla
v|j@y|u||@ @end|ng |rom gm@||@com
Tue Nov 26 21:58:34 CET 2019
Hmm...it appears that stackApply is using different conditions which might
be causing this problem. Below is the snippet of the code which I think
might be the problem.
## For canProcessInMemory
if (rowcalc) {
v <- lapply(uin, function(i) fun(x[, ind == i, drop = FALSE], na.rm =
na.rm))
}
else {
v <- lapply(uin, function(i, ...) apply(x[, ind == i, drop = FALSE], 1,
fun, na.rm = na.rm))
}
## If canProcessInMemory is not TRUE
if (rowcalc) {
v <- lapply(uin, function(i) fun(a[, ind == uin[i], drop = FALSE], na.rm
= na.rm))
}
else {
v <- lapply(uin, function(i, ...) apply(a[, ind == uin[i], drop = FALSE],
1, fun, na.rm = na.rm))
}
I think they should both be same but it appears that they aren't and that's
what you've discovered. Maybe you can try fix(stackApply) to see if it
really is the problem and can tell us what you find. Anyways, good
catch...and...sorry for wasting your time.
Cordially,
Vijay.
On Tue, Nov 26, 2019 at 2:53 PM Leonidas Liakos <leonidas_liakos using yahoo.gr>
wrote:
> 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 using 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 using 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 using r-project.org <mailto:r-sig-geo using 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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>>
>>
>>
>
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