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\name{RealizationSink}
\alias{class:RealizationSink}
\alias{RealizationSink-class}
\alias{close,RealizationSink-method}
\alias{class:arrayRealizationSink}
\alias{arrayRealizationSink-class}
\alias{dim,arrayRealizationSink-method}
\alias{write_block,arrayRealizationSink-method}
\alias{coerce,arrayRealizationSink,DelayedArray-method}
\alias{supportedRealizationBackends}
\alias{getRealizationBackend}
\alias{setRealizationBackend}
\alias{RealizationSink}
\title{RealizationSink objects}
\description{
Get or set the \emph{realization backend} for the current session with
\code{getRealizationBackend} or \code{setRealizationBackend}.
Advanced users: Create a RealizationSink object with the backend-agnostic
\code{RealizationSink()} constructor. Use this object to write an
array-like object block by block to disk (or to memory).
}
\usage{
supportedRealizationBackends()
getRealizationBackend()
setRealizationBackend(BACKEND=NULL)
RealizationSink(dim, dimnames=NULL, type="double")
}
\arguments{
\item{BACKEND}{
\code{NULL} (the default), or a single string specifying the name of
a \emph{realization backend}.
}
\item{dim}{
The dimensions (specified as an integer vector) of the RealizationSink
object to create.
}
\item{dimnames}{
The dimnames (specified as a list of character vectors or NULLs) of
the RealizationSink object to create.
}
\item{type}{
The type of the data that will be written to the RealizationSink
object to create.
}
}
\details{
The \emph{realization backend} controls where/how realization happens e.g.
as an ordinary array if set to \code{NULL}, as an \link{RleArray} object
if set to \code{"RleArray"}, or as an \link[HDF5Array]{HDF5Array} object
if set to \code{"HDF5Array"}.
}
\value{
\code{supportedRealizationBackends}: A data frame with 1 row per
supported \emph{realization backend}.
\code{getRealizationBackend}: \code{NULL} or a single string specifying
the name of the \emph{realization backend} currently in use.
\code{RealizationSink}: A RealizationSink object for the current
\emph{realization backend}.
}
\seealso{
\itemize{
\item \code{\link{write_block}}.
\item \code{\link{blockGrid}} to define grids to use in the context
of block processing of array-like objects.
\item \link{DelayedArray} objects.
\item \link{RleArray} objects.
\item \link[HDF5Array]{HDF5Array} objects in the \pkg{HDF5Array} package.
\item \link[HDF5Array]{HDF5-dump-management} in the \pkg{HDF5Array}
package to control the location and physical properties of
automatically created HDF5 datasets.
\item \link[base]{array} objects in base R.
}
}
\examples{
## ---------------------------------------------------------------------
## A. supportedRealizationBackends() AND FAMILY
## ---------------------------------------------------------------------
supportedRealizationBackends()
getRealizationBackend() # backend is set to NULL
setRealizationBackend("HDF5Array")
supportedRealizationBackends()
getRealizationBackend() # backend is set to "HDF5Array"
## ---------------------------------------------------------------------
## B. A SIMPLE (AND VERY ARTIFICIAL) RealizationSink() EXAMPLE
## ---------------------------------------------------------------------
getHDF5DumpChunkLength()
setHDF5DumpChunkLength(500L)
getHDF5DumpChunkShape()
sink <- RealizationSink(c(120L, 50L))
dim(sink)
chunkdim(sink)
grid <- blockGrid(sink, block.length=600)
for (b in seq_along(grid)) {
viewport <- grid[[b]]
block <- 101 * b + runif(length(viewport))
dim(block) <- dim(viewport)
write_block(sink, viewport, block)
}
## Always close the RealizationSink object when you are done writing to
## it and before coercing it to DelayedArray:
close(sink)
A <- as(sink, "DelayedArray")
A
## ---------------------------------------------------------------------
## C. AN ADVANCED EXAMPLE OF USER-IMPLEMENTED BLOCK PROCESSING USING
## colGrid() AND A REALIZATION SINK
## ---------------------------------------------------------------------
## Say we have 2 matrices with the same number of columns. Each column
## represents a biological sample:
library(HDF5Array)
R <- as(matrix(runif(75000), ncol=1000), "HDF5Array") # 75 rows
G <- as(matrix(runif(250000), ncol=1000), "HDF5Array") # 250 rows
## Say we want to compute the matrix U obtained by applying the same
## binary functions FUN() to all samples i.e. U is defined as:
##
## U[ , j] <- FUN(R[ , j], G[ , j]) for 1 <= j <= 1000
##
## Note that FUN() should return a vector of constant length, say 200,
## so U will be a 200x1000 matrix. A naive implementation would be:
##
## pFUN <- function(r, g) {
## stopifnot(ncol(r) == ncol(g)) # sanity check
## sapply(seq_len(ncol(r)), function(j) FUN(r[ , j], g[ , j]))
## }
##
## But because U is going to be too big to fit in memory, we can't
## just do pFUN(R, G). So we want to compute U block by block and
## write the blocks to disk as we go. The blocks will be made of full
## columns. Also since we need to walk on 2 matrices at the same time
## (R and G), we can't use blockApply() or blockReduce() so we'll use
## a "for" loop.
## Before we can write the "for" loop, we need 4 things:
## 1) Two grids of blocks, one on R and one on G. The blocks in the
## 2 grids must contain the same number of columns. We arbitrarily
## choose to use blocks of 150 columns:
R_grid <- colGrid(R, ncol=150)
G_grid <- colGrid(G, ncol=150)
## 2) The function pFUN(). It will take 2 blocks as input, 1 from R
## and 1 from G, apply FUN() to all the samples in the blocks,
## and return a matrix with one columns per sample:
pFUN <- function(r, g) {
stopifnot(ncol(r) == ncol(g)) # sanity check
## Return a matrix with 200 rows with random values. Completely
## artificial sorry. A realistic example would actually need to
## apply the same binary function to r[ ,j] and g[ , j] for
## 1 <= j <= ncol(r).
matrix(runif(200 * ncol(r)), nrow=200)
}
## 3) A RealizationSink object where to write the matrices returned
## by pFUN() as we go. Note that instead of creating a realization
## sink by calling a backend-specific sink constructor (e.g.
## HDF5Array:::HDF5RealizationSink), we use the backend-agnostic
## constructor RealizationSink() and set the current realization
## backend to HDF5:
setRealizationBackend("HDF5Array")
U_sink <- RealizationSink(c(200L, 1000L))
## 4) Finally, we create a grid on U_sink with blocks that contain the
## same number of columns as the corresponding blocks in R and G:
U_grid <- colGrid(U_sink, ncol=150)
## Note that the 3 grids should have the same number of blocks:
stopifnot(length(U_grid) == length(R_grid))
stopifnot(length(U_grid) == length(G_grid))
## Now we can procede. We write a loop where we walk on R and G at
## the same time, block by block, apply pFUN(), and write the output
## of pFUN() to U_sink:
for (b in seq_along(U_grid)) {
R_block <- read_block(R, R_grid[[b]])
G_block <- read_block(G, G_grid[[b]])
U_block <- pFUN(R_block, G_block)
write_block(U_sink, U_grid[[b]], U_block)
}
close(U_sink)
U <- as(U_sink, "DelayedArray")
## A note about parallelization: even though concurrent block reading
## from the same object is supported, concurrent writing to a sink is
## not supported yet. So the above code cannot be parallelized at the
## moment.
}
\keyword{utilities}
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