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#' Aggregate data cube time series to lower temporal resolution
#'
#' Create a proxy data cube, which applies an aggregation function over pixel time series to lower temporal resolution.
#'
#' @examples
#' # create image collection from example Landsat data only
#' # if not already done in other examples
#' if (!file.exists(file.path(tempdir(), "L8.db"))) {
#' L8_files <- list.files(system.file("L8NY18", package = "gdalcubes"),
#' ".TIF", recursive = TRUE, full.names = TRUE)
#' create_image_collection(L8_files, "L8_L1TP", file.path(tempdir(), "L8.db"), quiet = TRUE)
#' }
#' L8.col = image_collection(file.path(tempdir(), "L8.db"))
#' v = cube_view(extent=list(left=388941.2, right=766552.4,
#' bottom=4345299, top=4744931, t0="2018-01", t1="2018-12"),
#' srs="EPSG:32618", nx = 497, ny=526, dt="P3M", aggregation = "median")
#' L8.cube = raster_cube(L8.col, v, mask=image_mask("BQA", bits=4, values=16))
#' L8.rgb = select_bands(L8.cube, c("B02", "B03", "B04"))
#' L8.two_monthly = aggregate_time(L8.rgb, "P6M", "min")
#' L8.two_monthly
#'
#' \donttest{
#' plot(L8.two_monthly, rgb=3:1, zlim=c(5000,12000))
#' }
#'
#' @details
#' This function can be used to aggregate time series to lower resolution or to regularize
#' a data cube with irregular (labeled) time axis. It is possible to change the unit of the temporal resolution (e.g. to create monthly composites from daily images).
#' The size of the cube may be expanded automatically if the original temporal extent is not divisible by the new temporal size of pixels.
#'
#'
#' @param cube source data cube
#' @param dt character; new temporal resolution, datetime period string, e.g. "P1M"
#' @param method aggregation method, one of "mean", "min", "max", "median", "count", "sum", "prod", "var", and "sd"
#' @param fact simple integer factor defining how many cells become aggregated to a single new cell, can be used instead of dt
#'
#' @note This function returns a proxy object, i.e., it will not start any computations besides deriving the shape of the result.
#'
#' @export
aggregate_time <- function(cube, dt, method="mean", fact=NULL) {
stopifnot(is.cube(cube))
if (is.null(fact) && missing(dt)) {
stop("Missing required argument: either dt or fact must be provided")
}
if (!is.null(fact) && !missing(dt)) {
warning("Argument fact will be ignored because dt has been provided")
fact = NULL
}
if (!is.null(fact)) {
if (fact %% 1 != 0) {
stop("Invalid argument: fact must be an integer number > 1")
}
if (fact <= 1) {
stop("Invalid argument: fact must be an integer number > 1")
}
x = gc_create_aggregate_time_cube(cube, "", method, as.integer(fact))
}
else {
if (!is.character(dt)) {
stop("Invalid argument: dt must be of type character")
}
x = gc_create_aggregate_time_cube(cube, dt, method, 0)
}
class(x) <- c("aggregate_time_cube", "cube", "xptr")
return(x)
}
is.aggregate_time_cube <- function(obj) {
if(!("aggregate_time_cube" %in% class(obj))) {
return(FALSE)
}
if (gc_is_null(obj)) {
warning("GDAL data cube proxy object is invalid")
return(FALSE)
}
return(TRUE)
}
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