[R-sig-Geo] parallel raster processing with calc and mc2d monte carlo simulation
Sean Kearney
sean.kearney at alumni.ubc.ca
Tue Mar 17 17:21:36 CET 2015
Hi Robert,
Thanks for the advice. So I tried exporting the objects defined in the function (lm.final and lambda_DV) with the following code:
library(raster)
library(rgdal)
library(mc2d)
brick <- brick(BC_BA, BC_BA_SE, SlopePer) ## Stack three rasters into one RasterBrick
testbrick <- crop(brick, extent(299700, 300100, 1553550, 1553650)) ##Crop Raster Brick to manageable size
####
#### Predict Biomass using final linear model with Monte Carlo Simulation
ndunc(101)
fun.CROP_AGWBC <- function(x) {
require(mc2d)
dBC_BA <- mcdata(x[[1]], type="0")
dBC_BA_SE <- mcdata(x[[2]], type = "0")
SlopePer <-x[[3]]
stBA <- mcstoc(rnorm, type = "U", rtrunc = TRUE,
mean = dBC_BA, sd = dBC_BA_SE, linf = 0, lhs = FALSE)
BC_AGWBC <- lm.final$coefficients[1] +
lm.final$coefficients[2]*stBA +
lm.final$coefficients[3]*SlopePer
AGWBC <- (lambda_DV * BC_AGWBC + 1)^(1/lambda_DV)-1
quantile(AGWBC[], c(0.025, 0.5, 0.975), na.rm=TRUE)
}
####
#### Check function using calc
CROP_AGWBC <- calc(testbrick, fun.CROP_AGWBC)
plot(CROP_AGWBC)
CROP_AGWBC
####
#### Run function using parallel processing
library(snow)
beginCluster(8)
library(parallel)
cl <- getCluster()
clusterExport(cl, list("lm.final", "lambda_DV"))
clusterR(x = testbrick, fun = fun.CROP_AGWBC)
endCluster()
RESULT: Again, the calc process works fine to produce object CROP_AGWBC but when I try to run the last bit with parallel processing, I still get the same error:
> clusterR(x = testbrick, fun = fun.CROP_AGWBC)
[1] "data should be numeric or logical"
attr(,"class")
[1] "snow-try-error" "try-error"
Error in clusterR(x = testbrick, fun = fun.CROP_AGWBC) : cluster error
I was suspicious that maybe the issue was still with the export because while “lambda_DV" is a constant, lm.final is actually an lm model, so I replaced these objects in the function with constants (see code below) but still get the same error:
REVISED CODE WITH CONSTANTS:
library(raster)
library(rgdal)
library(mc2d)
brick <- brick(BC_BA, BC_BA_SE, SlopePer) ## Stack three rasters into one RasterBrick
testbrick <- crop(brick, extent(299700, 300100, 1553550, 1553650)) ##Crop Raster Brick to manageable size
####
#### Predict Biomass using final linear model with Monte Carlo Simulation
ndunc(101)
fun2.CROP_AGWBC <- function(x) {
require(mc2d)
dBC_BA <- mcdata(x[[1]], type="0")
dBC_BA_SE <- mcdata(x[[2]], type = "0")
SlopePer <-x[[3]]
stBA <- mcstoc(rnorm, type = "U", rtrunc = TRUE,
mean = dBC_BA, sd = dBC_BA_SE, linf = 0, lhs = FALSE)
BC_AGWBC <- 0.6419 +
0.9307*stBA +
(-0.0176)*SlopePer
AGWBC <- (0.2626263 * BC_AGWBC + 1)^(1/0.2626263)-1
quantile(AGWBC[], c(0.025, 0.5, 0.975), na.rm=TRUE)
}
####
#### Check function using calc
CROP_AGWBC_2 <- calc(testbrick, fun2.CROP_AGWBC)
plot(CROP_AGWBC_2)
CROP_AGWBC_2
####
#### Run function using parallel processing
library(snow)
beginCluster(8)
clusterR(x = testbrick, fun = fun2.CROP_AGWBC)
endCluster()
RESULT: same error:
> clusterR(x = testbrick, fun = fun2.CROP_AGWBC)
[1] "data should be numeric or logical"
attr(,"class")
[1] "snow-try-error" "try-error"
Error in clusterR(x = testbrick, fun = fun2.CROP_AGWBC) : cluster error
Any other thoughts? Many thanks,
sean
On Mar 17, 2015, at 8:14 AM, Robert J. Hijmans <r.hijmans at gmail.com> wrote:
> Sean,
>
> fun.CROP_AGWBC refers to objects that are not defined inside the
> function ("lm.final" and "lambda_DV"). I assume that this is
> intentional and that these represent constants; and that they are
> available in your global environment. If so, you need to export these
> objects to the cluster nodes. See the 'export' argument in clusterR.
> You also need to load necessary packages before calling beginCluster
>
> Robert
>
> On Mon, Mar 16, 2015 at 1:46 PM, spkearney <sean.kearney at alumni.ubc.ca> wrote:
>> Hello all, and thanks in advance for any and all help you can give on this:
>>
>> I have set up a function to extract the 2.5%, 50% and 97.5% percentiles from
>> a monte carlo simulation on three rasters that is to be called up using
>> calc() in the raster package and it works great on a test-sized stack/brick,
>> thanks to suggestions at this post here:
>> http://grokbase.com/t/r/r-sig-geo/123cb3daaq/apply-monte-carlo-simulation-for-each-cell-in-a-matrix-originally-raster
>>
>> My problem, is that I want to run this function on a much larger Raster
>> Brick that, as written, takes hours to process. I need to do this multiple
>> times, so I am trying to speed up the processing using clusterR (or another
>> option such as rasterEngine with multi-core processing). However, I can't
>> get it to work! Here is the code that works on the test raster brick:
>>
>> brick <- brick(BC_BA, BC_BA_SE, SlopePer) ## Stack three rasters into one
>> Raster Brick
>> testbrick <- crop(brick, extent(299700, 300100, 1553550, 1553650)) ## Crop
>> brick to manageable size
>>
>> ndunc(101)
>> fun.CROP_AGWBC <- function(x) {
>> dBC_BA <- mcdata(x[[1]], type="0")
>> dBC_BA_SE <- mcdata(x[[2]], type = "0")
>> SlopePer <-x[[3]]
>> stBA <- mcstoc(rnorm, type = "U", rtrunc = TRUE,
>> mean = dBC_BA, sd = dBC_BA_SE, linf = 0, lhs = FALSE)
>> BC_AGWBC <- lm.final$coefficients[1] +
>> lm.final$coefficients[2]*stBA +
>> lm.final$coefficients[3]*SlopePer
>> AGWBC <- (lambda_DV * BC_AGWBC + 1)^(1/lambda_DV)-1
>> quantile(AGWBC[], c(0.025, 0.5, 0.975), na.rm=TRUE)
>> }
>>
>> CROP_AGWBC <- calc(teststack, fun.CROP_AGWBC) ##Run the calc function
>> CROP_AGWBC ##Check the result
>> plot(CROP_AGWBC) ##Plot the three-raster brick result
>>
>> ##Extract the individual raster layers
>> CROP_AGWBC_PRED <- CROP_AGWBC[[2]]
>> CROP_AGWBC_LWR <- CROP_AGWBC[[1]]
>> CROP_AGWBC_UPR <- CROP_AGWBC[[3]]
>>
>> As I mentioned, the code works great on a small sample. I tried to speed it
>> up using clusterR as follows, first testing it on the 'testbrick' Raster
>> Brick with hopes to use it on the whole Raster Brick:
>>
>> beginCluster(8)
>> clusterR(x = testbrick, fun = fun.CROP_AGWBC)
>>
>> and I get the following error:
>> [1] "data should be numeric or logical"
>> attr(,"class")
>> [1] "snow-try-error" "try-error"
>> Error in clusterR(x = testbrick, fun = fun.CROP_AGWBC) : cluster error
>>
>> It is interesting because, if I try to run it again, I get this error
>> instead:
>> Error in as.vector((x[, 1] - 1) * ncol(object) + x[, 2]) :
>> error in evaluating the argument 'x' in selecting a method for function
>> 'as.vector': Error in x[, 2] : subscript out of bounds
>>
>> I have tried this many different ways, including along the lines of:
>> f <- function (x) calc(x, fun.CROP_AGWBC)
>> y <- clusterR(testbrick, f)
>>
>> which gives me the same error (more or less) of:
>> Error in checkForRemoteErrors(lapply(cl, recvResult)) :
>> 2 nodes produced errors; first error: data should be numeric or logical
>>
>> And I have tried using the rasterEngine() function (first without parallel
>> processing) by changing up the code in two ways, the first being:
>> ndunc(101)
>> fun.CROP_AGWBC <- function(x) {
>> dBC_BA <- mcdata(x[[1]], type="0")
>> dBC_BA_SE <- mcdata(x[[2]], type = "0")
>> SlopePer <-x[[3]]
>> stBA <- mcstoc(rnorm, type = "U", rtrunc = TRUE,
>> mean = dBC_BA, sd = dBC_BA_SE, linf = 0, lhs = FALSE)
>> BC_AGWBC <- lm.final$coefficients[1] +
>> lm.final$coefficients[2]*stBA +
>> lm.final$coefficients[3]*SlopePer
>> AGWBC <- (lambda_DV * BC_AGWBC + 1)^(1/lambda_DV)-1
>> output <- quantile(AGWBC[], c(0.025, 0.5, 0.975), na.rm=TRUE)
>> output_array <- array(output,dim=c(dim(x)[1],dim(x)[2],3))
>> return(output_array)
>> }
>> re <- rasterEngine(x = testbrick, fun = fun.CROP_AGWBC)
>>
>> which runs but gives me a 3-layer Raster Brick all with NA's or Inf. The
>> second thing I tried used the same fun.CROP_AGWBC function as above, but
>> with the following rasterEngine code to call up the calc formula:
>> f <- function(x) {reout <- calc(x, fun.CROP_AGWBC)
>> reout_array <- array(getValues(reout),
>> dim=c(dim(x)[1],dim(x)[2],3))
>> return(reout_array)
>> }
>> re <- rasterEngine(x = testbrick, fun = f, chunk_format = "raster")
>>
>> which gives me the following error, even though I thought I converted the
>> output to an array:
>> chunk processing units require array vector outputs. Please check your
>> function.
>> Error in focal_hpc_test(x, fun, window_center, window_dims, args,
>> layer_names, :
>>
>> So, my questions are as follows:
>> *1) Does anyone know why the clusterR does not work for the calc() function
>> in my first attempt? I imagine it has something to do with the conversion
>> of rasters to mcnodes in the function, but can't figure it out! Any
>> suggestions?
>>
>> 2) Any thoughts on why I can't get this to work with the rasterEngine()
>> function? I am converting the outputs to arrays with the same dimensions as
>> the input file, but still no luck.*
>>
>> Again, any help is much appreciated. Any suggestions for improving this
>> question are welcome and I'll do my best to update it - this is my first
>> post!
>>
>> Kind Regards,
>> sean
>>
>>
>>
>>
>>
>> --
>> View this message in context: http://r-sig-geo.2731867.n2.nabble.com/parallel-raster-processing-with-calc-and-mc2d-monte-carlo-simulation-tp7587901.html
>> Sent from the R-sig-geo mailing list archive at Nabble.com.
>>
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