% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/Compute.R
\name{Compute}
\alias{Compute}
\title{Specify the execution parameters and trigger the execution}
\usage{
Compute(workflow, chunks = "auto", threads_load = 1, threads_compute = 1,
  cluster = NULL, ecflow_suite_dir = NULL, ecflow_server = NULL,
  silent = FALSE, debug = FALSE, wait = TRUE)
}
\arguments{
\item{workflow}{A list of the class 'startR_workflow' returned by function 
AddSteop() or of class 'startR_cube' returned by function Start(). It 
contains all the objects needed for the execution.}

\item{chunks}{A named list of dimensions which to split the data along and 
the number of chunks to make for each. The chunked dimension can only be 
those not required as the target dimension in function Step(). The default
value is 'auto', which lists all the non-target dimensions and each one has
one chunk.}

\item{threads_load}{An integer indicating the number of execution threads to 
use for the data retrieval stage. The default value is 1.}

\item{threads_compute}{An integer indicating the number of execution threads
to use for the computation. The default value is 1.}

\item{cluster}{A list of components that define the configuration of the 
machine to be run on. The comoponents vary from the different machines.
Check \href{https://earth.bsc.es/gitlab/es/startR/}{startR GitLab} for more 
details and examples. Only needed when the computation is not run locally. 
The default value is NULL.}

\item{ecflow_suite_dir}{A character string indicating the path to a folder in
the local workstation where to store temporary files generated for the 
automatic management of the workflow. Only needed when the execution is run
remotely. The default value is NULL.}

\item{ecflow_server}{A named vector indicating the host and port of the 
EC-Flow server. The vector form should be 
\code{c(host = 'hostname', port = port_number)}. Only needed when the 
execution is run#'  remotely. The default value is NULL.}

\item{silent}{A logical value deciding whether to print the computation 
progress (FALSE) on the R session or not (TRUE). It only works when the 
execution runs locally or the parameter 'wait' is TRUE. The default value
is FALSE.}

\item{debug}{A logical value deciding whether to return detailed messages on 
the progress and operations in a Compute() call (TRUE) or not (FALSE). 
Automatically changed to FALSE if parameter 'silent' is TRUE. The default 
value is FALSE.}

\item{wait}{A logical value deciding whether the R session waits for the 
Compute() call to finish (TRUE) or not (FALSE). If FALSE, it will return an
object with all the information of the startR execution that can be stored
in your disk. After that, the R session can be closed and the results can
be collected later with the Collect() function. The default value is TRUE.}
}
\value{
A list of data arrays for the output returned by the last step in the
 specified workflow (wait = TRUE), or an object with information about the 
 startR execution (wait = FALSE). The configuration details and profiling 
 information are attached as attributes to the returned list of arrays.
}
\description{
The step of the startR workflow after the complete workflow is defined by 
AddStep(). This function specifies the execution parameters and triggers the
execution. The execution can be operated locally or on a remote machine. If 
it is the latter case, the configuration of the machine needs to be 
sepecified in the function, and the EC-Flow server is required to be 
installed.\cr\cr
The execution can be operated by chunks to avoid overloading the RAM memory.
After all the chunks are finished, Compute() will gather and merge them, and 
return a single data object, including one or multiple multidimensional data 
arrays and additional metadata.
}
\examples{
 data_path <- system.file('extdata', package = 'startR')
 path_obs <- file.path(data_path, 'obs/monthly_mean/$var$/$var$_$sdate$.nc')
 sdates <- c('200011', '200012')
 data <- Start(dat = list(list(path = path_obs)),
               var = 'tos',
               sdate = sdates,
               time = 'all',
               latitude = 'all',
               longitude = 'all',
               return_vars = list(latitude = 'dat',
                                  longitude = 'dat',
                                  time = 'sdate'),
               retrieve = FALSE)
 fun <- function(x) {
           lat = attributes(x)$Variables$dat1$latitude
           weight = sqrt(cos(lat * pi / 180))
           corrected = Apply(list(x), target_dims = "latitude",
                             fun = function(x) {x * weight})
         }
 step <- Step(fun = fun,
              target_dims = 'latitude',
              output_dims = 'latitude',
              use_libraries = c('multiApply'),
              use_attributes = list(data = "Variables"))
 wf <- AddStep(data, step)
 res <- Compute(wf, chunks = list(longitude = 4, sdate = 2))

}