Helper functions for programming

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Description

These are low level functions that can be used by programmers to develop new functions. If in doubt, it is almost certain that you do not need these functions as these are already embedded in all other functions in the raster package.

canProcessInMemory is typically used within functions. In the raster package this function is used to determine if the amount of memory needed for the function is available. If there is not enough memory available, the function returns FALSE, and the function that called it will write the results to a temporary file.

readStart opens file connection(s) for reading, readStop removes it.

pbCreate creates a progress bar, pbStep sets the progress, and pbClose closes it.

Usage

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canProcessInMemory(x, n=4)
pbCreate(nsteps, progress, style=3, label='Progress', ...)
pbStep(pb, step=NULL, label='')
pbClose(pb, timer)
readStart(x, ...)
readStop(x, ...)
getCluster()
returnCluster()

Arguments

x

RasterLayer or RasterBrick object (for connections) or RasterStack object (canProcessInMemory)

n

integer. The number of copies of the Raster* object cell values that a function needs to be able to have in memory

nsteps

integer. Number of steps the progress bar will make from start to end (e.g. nrow(raster))

progress

character. 'text', 'window', or ”

style

style for text progress bar. See txtProgressBar

label

character. Label for the window type prograss bar

...

additional arguments (None implemented, except for 'silent=TRUE' for readStart for files read with gdal, and other arguments passed to gdal.open)

pb

progress bar object created with pbCreate

step

which step is this ( 1 <= step <= nsteps ). If step is NULL, a single step is taken

timer

logical. If TRUE, time to completion will be printed. If missing, the value will be taken from the rasterOptions

Value

canProcessInMemory: logical

closeConnection: RasterLayer or RasterBrick object

getCluster: snow cluster object

Examples

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r <- raster(nrow=100, ncol=100)
canProcessInMemory(r, 4)
r <- raster(nrow=100000, ncol=100000)
canProcessInMemory(r, 2)

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