Description Usage Arguments Details Value See Also Examples
Use write_block
to write a block of array data to a RealizationSink
object. The function is typically used in the context of block processing
of array-like objects (typically DelayedArray objects but
not necessarily).
1 2 3 4 5 6 7 8 9 | write_block(sink, viewport, block)
## Backend-agnostic RealizationSink constructor:
AutoRealizationSink(dim, dimnames=NULL, type="double", as.sparse=FALSE)
## Get/set the "automatic realization backend":
getAutoRealizationBackend()
setAutoRealizationBackend(BACKEND=NULL)
supportedRealizationBackends()
|
sink |
A **writable** array-like object, typically a RealizationSink derivative. Some important notes:
Although |
viewport |
An ArrayViewport object compatible with |
block |
An ordinary (dense) array or SparseArraySeed object of the
same dimensions as |
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. |
as.sparse |
Whether the data should be written as sparse or not to the RealizationSink object to create. Not all realization backends support this. |
BACKEND |
|
*** The RealizationSink API ***
The DelayedArray package provides a simple API for writing blocks of array data to disk (or to memory): the "RealizationSink API". This API allows the developper to write code that is agnostic about the particular on-disk (or in-memory) format being used to store the data.
Here is how to use it:
Create a realization sink.
Write blocks of array data to the realization sink with
one or several calls to write_block()
.
Close the realization sink with close()
.
Coerce the realization sink to DelayedArray.
A realization sink is formally represented by a RealizationSink object. Note that RealizationSink is a virtual class with various concrete subclasses like HDF5RealizationSink from the HDF5Array package, or RleRealizationSink. Each subclass implements the "RealizationSink API" for a specific realization backend.
To create a realization sink, use the specific constructor function.
This function should be named as the class itself e.g.
HDF5RealizationSink()
.
To create a realization sink in a backend-agnostic way, use
AutoRealizationSink()
. It will create a RealizationSink object
for the current automatic realization backend (see below).
Once writing to the realization sink is completed, the RealizationSink
object must be closed (with close(sink)
), then coerced to
DelayedArray (with as(sink, "DelayedArray")
. What
specific DelayedArray derivative this coercion will return
depends on the specific class of the RealizationSink object. For
example, if sink
is an HDF5RealizationSink
object from the HDF5Array package, then as(sink, "DelayedArray")
will return an HDF5Array instance (the
HDF5Array class is a DelayedArray subclass).
*** The automatic realization backend ***
The automatic realization backend is a user-controlled global
setting that indicates what specific RealizationSink object
AutoRealizationSink()
should return.
In the context of block processing of a DelayedArray object,
this controls where/how realization happens e.g. as an ordinary array
if not set (i.e. set to NULL
), or as an HDF5Array
object if set to "HDF5Array"
, or as an RleArray object
if set to "RleArray"
, etc...
Use getAutoRealizationBackend()
or setAutoRealizationBackend()
to get or set the automatic realization backend.
Use supportedRealizationBackends()
to get the list of realization
backends that are currently supported.
*** Cross realization backend compatibility ***
Two important things to keep in mind for developers aiming at writing code that is compatible across realization backends:
Realization backends don't necessarily support concurrent writing.
More precisely: Even though it is safe to assume that any
DelayedArray object will support concurrent
read_block()
calls, it is not so safe to assume that
any RealizationSink derivative will support concurrent calls
to write_block()
. For example, at the moment,
HDF5RealizationSink objects do not
support concurrent writing.
This means that in order to remain compatible across realization
backends, code that contains calls to write_block()
should
NOT be parallelized.
Some realization backends are "linear write only", that is, they don't support random write access, only linear write access.
Such backends will provide a relization sink where the blocks of data must be written in linear order (i.e. by ascending rank). Furthermore, the geometry of the blocks must also be compatible with linear write access, that is, they must have a "first-dim-grows-first" shape. Concretely this means that the grid used to walk on the relization sink must be created with something like:
colAutoGrid(sink)
for a two-dimensional sink, or with something like:
defaultAutoGrid(sink, block.shape="first-dim-grows-first")
for a sink with an arbitrary number of dimensions.
See ?defaultAutoGrid
for more information.
For obvious reasons, "linear write only" realization backends do not support concurrent writing.
For write_block()
, the modified array-like object sink
.
For AutoRealizationSink()
, a RealizationSink object for the
current automatic realization backend.
For getAutoRealizationBackend
, NULL
(no backend set yet)
or a single string specifying the name of the automatic realization
backend currently in use.
For supportedRealizationBackends
, a data frame with 1 row
per supported realization backend.
ArrayViewport objects.
SparseArraySeed objects.
read_block
.
blockApply
and family for convenient block
processing of an array-like object.
defaultAutoGrid
and family to generate automatic
grids to use for block processing of array-like objects.
HDF5RealizationSink objects in the HDF5Array package.
HDF5-dump-management in the HDF5Array package to control the location and physical properties of automatically created HDF5 datasets.
RleArray objects.
DelayedArray objects.
array objects in base R.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 | ## ---------------------------------------------------------------------
## USING THE "RealizationSink API": EXAMPLE 1
## ---------------------------------------------------------------------
## -- STEP 1 --
## Create a realization sink. Note that instead of creating a
## realization sink by calling a backend-specific sink constructor
## (e.g. HDF5Array::HDF5RealizationSink), we set the "automatic
## realization backend" to "HDF5Array" and use backend-agnostic
## constructor AutoRealizationSink():
setAutoRealizationBackend("HDF5Array")
sink <- AutoRealizationSink(c(35L, 50L, 8L))
dim(sink)
## -- STEP 2 --
## Define the grid of viewports to walk on. Here we define a grid made
## of very small viewports on 'sink'. Note that, for real-world use cases,
## block processing will typically use grids made of much bigger
## viewports, usually obtained with defaultAutoGrid() or family.
## Also please note that this grid would not be compatible with "linear
## write only" realization backends. See "Cross realization backend
## compatibility" above in this man page for more information.
sink_grid <- RegularArrayGrid(dim(sink), spacings=c(20, 20, 4))
## -- STEP 3 --
## Walk on the grid, and, for each of viewport, write random data to it.
for (bid in seq_along(sink_grid)) {
viewport <- sink_grid[[bid]]
block <- array(runif(length(viewport)), dim=dim(viewport))
sink <- write_block(sink, viewport, block)
}
## -- An alternative to STEP 3 --
FUN <- function(viewport, sink) {
block <- array(runif(length(viewport)), dim=dim(viewport))
write_block(sink, viewport, block)
}
sink <- viewportReduce(FUN, sink_grid, sink, verbose=TRUE)
## -- STEP 4 --
## Close the sink and turn it into a DelayedArray object:
close(sink)
A <- as(sink, "DelayedArray")
A
setAutoRealizationBackend() # unset automatic realization backend
## ---------------------------------------------------------------------
## USING THE "RealizationSink API": EXAMPLE 2
## ---------------------------------------------------------------------
## Say we have a 3D array and want to collapse its 3rd dimension by
## summing the array elements that are stacked vertically, that is, we
## want to compute the matrix M obtained by doing sum(A[i, j, ]) for all
## valid i and j. This is very easy to do with an ordinary array:
collapse_3rd_dim <- function(a) apply(a, MARGIN=1:2, sum)
## or, in a slightly more efficient way:
collapse_3rd_dim <- function(a) {
m <- matrix(0, nrow=nrow(a), ncol=ncol(a))
for (z in seq_len(dim(a)[[3]]))
m <- m + a[ , , z]
m
}
## With a toy 3D array:
a <- array(runif(8000), dim=c(25, 40, 8))
dim(collapse_3rd_dim(a))
stopifnot(identical(sum(a), sum(collapse_3rd_dim(a)))) # sanity check
## Now say that A is so big that even M wouldn't fit in memory. This is
## a situation where we'd want to compute M block by block:
## -- STEP 1 --
## Create the 2D realization sink:
setAutoRealizationBackend("HDF5Array")
sink <- AutoRealizationSink(dim(a)[1:2])
dim(sink)
## -- STEP 2 --
## Define two grids: one for 'sink' and one for 'a'. Since we're going
## to walk on the two grids simultaneously, read a block from 'a' and
## write it to 'sink', we need to make sure that we define grids that
## are "aligned". More precisely the two grids must have the same number
## of viewports and the viewports in one must correspond to the viewports
## in the other one:
sink_grid <- colAutoGrid(sink, ncol=10)
a_spacings <- c(dim(sink_grid[[1L]]), dim(a)[[3]])
a_grid <- RegularArrayGrid(dim(a), spacings=a_spacings)
dims(sink_grid) # dimensions of the individual viewports
dims(a_grid) # dimensions of the individual viewports
## Here is how to check that the two grids are "aligned":
stopifnot(identical(length(sink_grid), length(a_grid)))
stopifnot(identical(dims(sink_grid), dims(a_grid)[ , -3]))
## -- STEP 3 --
## Walk on the two grids simultaneously:
for (bid in seq_along(sink_grid)) {
## Read block from 'a'.
a_viewport <- a_grid[[bid]]
block <- read_block(a, a_viewport)
## Collapse it.
block <- collapse_3rd_dim(block)
## Write the collapsed block to 'sink'.
sink_viewport <- sink_grid[[bid]]
sink <- write_block(sink, sink_viewport, block)
}
## -- An alternative to STEP 3 --
FUN <- function(sink_viewport, sink) {
## Read block from 'a'.
bid <- currentBlockId()
a_viewport <- a_grid[[bid]]
block <- read_block(a, a_viewport)
## Collapse it.
block <- collapse_3rd_dim(block)
## Write the collapsed block to 'sink'.
write_block(sink, sink_viewport, block)
}
sink <- viewportReduce(FUN, sink_grid, sink, verbose=TRUE)
## -- STEP 4 --
## Close the sink and turn it into a DelayedArray object:
close(sink)
M <- as(sink, "DelayedArray")
M
## Sanity check:
stopifnot(identical(collapse_3rd_dim(a), as.array(M)))
setAutoRealizationBackend() # unset automatic realization backend
## ---------------------------------------------------------------------
## USING THE "RealizationSink API": AN ADVANCED EXAMPLE
## ---------------------------------------------------------------------
## 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
## two grids must contain the same number of columns. We arbitrarily
## choose to use blocks of 150 columns:
R_grid <- colAutoGrid(R, ncol=150)
G_grid <- colAutoGrid(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:
setAutoRealizationBackend("HDF5Array")
U_sink <- AutoRealizationSink(c(200L, 1000L))
## 4. Finally, we create a grid on U_sink with viewports that contain
## the same number of columns as the corresponding blocks in R and G:
U_grid <- colAutoGrid(U_sink, ncol=150)
## Note that the three grids should have the same number of viewports:
stopifnot(length(U_grid) == length(R_grid))
stopifnot(length(U_grid) == length(G_grid))
## 5. Now we can proceed. We use a "for" loop to walk on R and G
## simultaneously, 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)
U_sink <- write_block(U_sink, U_grid[[bid]], U_block)
}
## An alternative to the "for" loop is to use viewportReduce():
FUN <- function(U_viewport, U_sink) {
bid <- currentBlockId()
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_viewport, U_block)
}
U_sink <- viewportReduce(FUN, U_grid, U_sink, verbose=TRUE)
close(U_sink)
U <- as(U_sink, "DelayedArray")
U
setAutoRealizationBackend() # unset automatic realization backend
## ---------------------------------------------------------------------
## VERY BASIC (BUT ALSO VERY ARTIFICIAL) USAGE OF THE
## read_block()/write_block() COMBO
## ---------------------------------------------------------------------
###### On an ordinary matrix ######
m1 <- matrix(1:30, ncol=5)
## Define a viewport on 'm1':
block1_dim <- c(4, 3)
viewport1 <- ArrayViewport(dim(m1), IRanges(c(3, 2), width=block1_dim))
## Read/tranform/write:
block1 <- read_block(m1, viewport1)
write_block(m1, viewport1, block1 + 1000L)
## Define another viewport on 'm1':
viewport1b <- ArrayViewport(dim(m1), IRanges(c(1, 3), width=block1_dim))
## Read/tranform/write:
write_block(m1, viewport1b, block1 + 1000L)
## No-op:
m <- write_block(m1, viewport1, read_block(m1, viewport1))
stopifnot(identical(m1, m))
########## On a 3D array ##########
a3 <- array(1:60, 5:3)
## Define a viewport on 'a3':
block3_dim <- c(2, 4, 1)
viewport3 <- ArrayViewport(dim(a3), IRanges(c(1, 1, 3), width=block3_dim))
## Read/tranform/write:
block3 <- read_block(a3, viewport3)
write_block(a3, viewport3, block3 + 1000L)
## Define another viewport on 'a3':
viewport3b <- ArrayViewport(dim(a3), IRanges(c(3, 1, 3), width=block3_dim))
## Read/tranform/write:
write_block(a3, viewport3b, block3 + 1000L)
## No-op:
a <- write_block(a3, viewport3, read_block(a3, viewport3))
stopifnot(identical(a3, a))
## ---------------------------------------------------------------------
## LESS BASIC (BUT STILL VERY ARTIFICIAL) USAGE OF THE
## read_block()/write_block() COMBO
## ---------------------------------------------------------------------
grid1 <- RegularArrayGrid(dim(m1), spacings=c(3L, 2L))
grid1
length(grid1) # number of blocks defined by the grid
read_block(m1, grid1[[3L]]) # read 3rd block
read_block(m1, grid1[[1L, 3L]])
## Walk on the grid, colum by column:
m1a <- m1
for (bid in seq_along(grid1)) {
viewport <- grid1[[bid]]
block <- read_block(m1a, viewport)
block <- bid * 1000L + block
m1a <- write_block(m1a, viewport, block)
}
m1a
## Walk on the grid, row by row:
m1b <- m1
for (i in seq_len(dim(grid1)[[1]])) {
for (j in seq_len(dim(grid1)[[2]])) {
viewport <- grid1[[i, j]]
block <- read_block(m1b, viewport)
block <- (i * 10L + j) * 1000L + block
m1b <- write_block(m1b, viewport, block)
}
}
m1b
## ---------------------------------------------------------------------
## supportedRealizationBackends() AND FAMILY
## ---------------------------------------------------------------------
getAutoRealizationBackend() # no backend set yet
supportedRealizationBackends()
setAutoRealizationBackend("HDF5Array")
getAutoRealizationBackend() # backend is set to "HDF5Array"
supportedRealizationBackends()
getHDF5DumpChunkLength()
setHDF5DumpChunkLength(500L)
getHDF5DumpChunkShape()
sink <- AutoRealizationSink(c(120L, 50L))
class(sink) # HDF5-specific realization sink
dim(sink)
chunkdim(sink)
grid <- defaultAutoGrid(sink, block.length=600)
for (bid in seq_along(grid)) {
viewport <- grid[[bid]]
block <- 101 * bid + runif(length(viewport))
dim(block) <- dim(viewport)
sink <- write_block(sink, viewport, block)
}
close(sink)
A <- as(sink, "DelayedArray")
A
setAutoRealizationBackend() # unset automatic realization backend
|
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.