RealizationSink {DelayedArray} | R Documentation

## RealizationSink objects

### Description

Get or set the realization backend for the current session with `getRealizationBackend` or `setRealizationBackend`.

Advanced users: Create a RealizationSink object with the backend-agnostic `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

`BACKEND` | `NULL` (the default), or a single string specifying the name of a realization backend.
`dim` | The dimensions (specified as an integer vector) of the RealizationSink object to create.
`dimnames` | The dimnames (specified as a list of character vectors or NULLs) of the RealizationSink object to create.
`type` | The type of the data that will be written to the RealizationSink object to create.

### Details

The realization backend controls where/how realization happens e.g. as an ordinary array if set to `NULL`, as an RleArray object if set to `"RleArray"`, or as an HDF5Array object if set to `"HDF5Array"`.

### Value

`supportedRealizationBackends`: A data frame with 1 row per supported realization backend.

`getRealizationBackend`: `NULL` or a single string specifying the name of the realization backend currently in use.

`RealizationSink`: A RealizationSink object for the current realization backend.

### See Also

- `write_block`.

- `blockGrid` to define grids to use in the context of block processing of array-like objects.

- DelayedArray objects.

- RleArray objects.

- HDF5Array objects in the HDF5Array package.

- HDF5-dump-management in the HDF5Array package to control the location and physical properties of automatically created HDF5 datasets.

- 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 (bid in seq_along(grid)) {
        viewport <- grid[[bid]]
        block <- 101 * bid + 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 get to 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 use a "for" 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 (bid in seq_along(U_grid)) {
        R_block <- read_block(R, R_grid[[bid]])
        G_block <- read_block(G, G_grid[[bid]])
        U_block <- pFUN(R_block, G_block)
        write_block(U_sink, U_grid[[bid]], 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.

[Package DelayedArray version 0.14.1 Index]