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73 lines
2.8 KiB
R
73 lines
2.8 KiB
R
% Generated by roxygen2: do not edit by hand
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% Please edit documentation in R/reduce.R
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\name{reduce_space.cube}
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\alias{reduce_space.cube}
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\title{Reduce a data cube over spatial (x,y or lat,lon) dimensions}
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\usage{
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\method{reduce_space}{cube}(
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x,
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expr,
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...,
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FUN,
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names = NULL,
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load_pkgs = FALSE,
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load_env = FALSE
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)
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}
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\arguments{
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\item{x}{source data cube}
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\item{expr}{either a single string, or a vector of strings defining which reducers will be applied over which bands of the input cube}
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\item{...}{optional additional expressions (if \code{expr} is not a vector)}
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\item{FUN}{a user-defined R function applied over pixel time series (see Details)}
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\item{names}{character vector; names of the output bands, if FUN is provided, the length of names is used as the expected number of output bands}
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\item{load_pkgs}{logical or character; if TRUE, all currently attached packages will be attached automatically before executing FUN in spawned R processes, specific packages can alternatively be provided as a character vector.}
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\item{load_env}{logical or environment; if TRUE, the current global environment will be restored automatically before executing FUN in spawned R processes, can be set to a custom environment.}
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}
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\value{
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proxy data cube object
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}
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\description{
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Create a proxy data cube, which applies one or more reducer functions to selected bands over spatial slices of a data cube
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}
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\details{
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Notice that expressions have a very simple format: the reducer is followed by the name of a band in parentheses. You cannot add
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more complex functions or arguments.
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Possible reducers currently include "min", "max", "sum", "prod", "count", "mean", "median", "var", and "sd".
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For more details and examples on how to write user-defined functions, please refer to the gdalcubes website
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at \url{https://gdalcubes.github.io/source/concepts/udfs.html}.
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}
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\note{
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Implemented reducers will ignore any NAN values (as na.rm=TRUE does).
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This function returns a proxy object, i.e., it will not start any computations besides deriving the shape of the result.
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}
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\examples{
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# create image collection from example Landsat data only
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# if not already done in other examples
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if (!file.exists(file.path(tempdir(), "L8.db"))) {
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L8_files <- list.files(system.file("L8NY18", package = "gdalcubes"),
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".TIF", recursive = TRUE, full.names = TRUE)
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create_image_collection(L8_files, "L8_L1TP", file.path(tempdir(), "L8.db"), quiet = TRUE)
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}
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L8.col = image_collection(file.path(tempdir(), "L8.db"))
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v = cube_view(extent=list(left=388941.2, right=766552.4,
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bottom=4345299, top=4744931, t0="2018-01", t1="2018-12"),
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srs="EPSG:32618", nx = 497, ny=526, dt="P1M")
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L8.cube = raster_cube(L8.col, v)
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L8.b02 = select_bands(L8.cube, c("B02"))
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L8.b02.median = reduce_space(L8.b02, "median(B02)")
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L8.b02.median
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\donttest{
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plot(L8.b02.median)
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}
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}
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