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90 lines
3.6 KiB
R
90 lines
3.6 KiB
R
% Generated by roxygen2: do not edit by hand
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% Please edit documentation in R/selection.R
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\name{gdalcubes_selection}
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\alias{gdalcubes_selection}
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\alias{$.cube}
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\alias{[.cube}
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\title{Subsetting data cubes}
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\usage{
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\method{$}{cube}(x, name)
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\method{[}{cube}(x, ib = TRUE, it = TRUE, iy = TRUE, ix = TRUE, ...)
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}
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\arguments{
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\item{x}{source data cube}
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\item{name}{character; name of selected band}
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\item{ib}{first selector (optional), object of type character, list, Date, POSIXt, numeric, \link[sf]{st_bbox}, or \link[sf]{st_sfc}, see Details and examples}
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\item{it}{second selector (optional), see \code{ib}}
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\item{iy}{third selector (optional), see \code{ib}}
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\item{ix}{fourth selector (optional), see \code{ib}}
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\item{...}{further arguments, not used}
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}
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\description{
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Subset data cube dimensions and bands / variables.
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}
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\details{
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The \code{[]} operator allows for flexible subsetting of data cubes by date, datetime,
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bounding box, spatial points, and band names. Depending on the arguments, it supports slicing
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(selecting one element of a dimension), cropping (selecting a subinterval of a dimension) and combinations
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thereof (e.g., selecting a spatial window and a temporal slice). Dimension subsets can
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be specified by integer indexes or coordinates / datetime values. Arguments are matched by type and order.
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For example, if the first argument is a length-two vector of type Date, the function will understand to
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subset the time dimension. Otherwise, arguments are treated in the order band, time, y, x.
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}
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\note{
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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="P3M", aggregation = "median")
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L8.cube = raster_cube(L8.col, v, mask=image_mask("BQA", bits=4, values=16))
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L8.red = L8.cube$B04
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\donttest{
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plot(L8.red)
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}
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v = cube_view(extent=list(left=388941.2, right=766552.4,
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bottom=4345299, top=4744931, t0="2018-01-01", t1="2018-12-31"),
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srs="EPSG:32618", nx = 497, ny=526, dt="P1D", aggregation = "median")
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L8.cube = raster_cube(L8.col, v, mask=image_mask("BQA", bits=4, values=16))
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L8.cube[c("B05","B04")] # select bands
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L8.cube[as.Date(c("2018-01-10", "2018-01-20"))] # crop by time
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L8.cube[as.Date("2018-01-10")] # slice by time
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L8.cube["B05", "2018-01-10"] # select bands and slice by time
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L8.cube["B05", c("2018-01-10","2018-01-17")] # select bands and crop by time
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L8.cube[, c("2018-01-10","2018-01-17")] # crop by time
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# crop by space (coordinates and integer indexes respectively)
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L8.cube[list(left=388941.2 + 1e5, right=766552.4 - 1e5, bottom=4345299 + 1e5, top=4744931 - 1e5)]
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L8.cube[,,c(1,100), c(1,100)]
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L8.cube[,c(1,2),,] # crop by time (integer indexes)
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# subset by spatial point or bounding box
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if (requireNamespace("sf", quietly = TRUE)) {
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s = sf::st_sfc(sf::st_point(c(500000, 4500000)), crs = "EPSG:32618")
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L8.cube[s]
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bbox = sf::st_bbox(c(xmin = 388941.2 + 1e5, xmax = 766552.4 - 1e5,
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ymax = 4744931 - 1e5, ymin = 4345299 + 1e5), crs = sf::st_crs(32618))
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L8.cube[bbox]
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}
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}
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