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Detect temporal dimension based on "refsys" in st_extract() #677

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merged 3 commits into from
Mar 26, 2024
Merged

Detect temporal dimension based on "refsys" in st_extract() #677

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b943d24
Match time dimension by refsys
loreabad6 Mar 21, 2024
77b6db8
Assume only one temporal dimension
loreabad6 Mar 21, 2024
f02c58a
Fail when no temporal dimension is present
loreabad6 Mar 26, 2024
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18 changes: 12 additions & 6 deletions R/extract.R
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Original file line number Diff line number Diff line change
Expand Up @@ -9,7 +9,7 @@ st_extract = function(x, ...) UseMethod("st_extract")
#' @param x object of class \code{stars} or \code{stars_proxy}
#' @param at object of class \code{sf} or \code{sfc} with geometries, or two-column matrix with coordinate points in rows, indicating where to extract values of \code{x}
#' @param bilinear logical; use bilinear interpolation rather than nearest neighbour?
#' @param time_column character or integer; name or index of a column with time or date values that will be matched to values of the dimension "time" in \code{x}, after which this dimension is reduced. This is useful to extract data cube values along a trajectory; see https://github.com/r-spatial/stars/issues/352 .
#' @param time_column character or integer; name or index of a column with time or date values that will be matched to values of the first temporal dimension (matching classes \code{POSIXct}, \code{POSIXt}, \code{Date}, or \code{PCICt}), in \code{x}, after which this dimension is reduced. This is useful to extract data cube values along a trajectory; see https://github.com/r-spatial/stars/issues/352 .
#' @param interpolate_time logical; should time be interpolated? if FALSE, time instances are matched using the coinciding or the last preceding time in the data cube.
#' @param FUN function used to aggregate pixel values when geometries of \code{at} intersect with more than one pixel
#' @param ... passed on to \link{aggregate.stars} when geometries are not exclusively POINT geometries
Expand Down Expand Up @@ -89,11 +89,17 @@ st_extract.stars = function(x, at, ..., bilinear = FALSE, time_column =
}
# match times:
if (!is.null(time_column)) {
tm = match("time", names(st_dimensions(x))) # FIXME: select based on refsys in time classes
refsys_time = c("POSIXct", "POSIXt", "Date", "PCICt")
## If there are more than two temporal dimensions, the first one is taken
tm = names(which(sapply(
st_dimensions(x),
function(i) any(i$refsys %in% refsys_time))))[1]
if (is.na(tm))
stop("cannot match times: x does not have a dimension called 'time'")
tm_cube = st_dimensions(x)$time$values %||% st_get_dimension_values(x, "time")
tm_ix = match_time(tm_pts, tm_cube, !st_dimensions(x)$time$point, interpolate_time)
stop("cannot match times: x does not have a temporal dimension")
tm_cube = st_dimensions(x)[[tm]]$values %||% st_get_dimension_values(x, tm)
tm_ix = match_time(tm_pts, tm_cube,
intervals = !st_dimensions(x)[[tm]]$point,
interpolate_time)
if (!interpolate_time)
m = lapply(m, function(p) p[cbind(seq_along(at), tm_ix)])
else {
Expand Down Expand Up @@ -134,7 +140,7 @@ st_extract.stars = function(x, at, ..., bilinear = FALSE, time_column =
if (!is.null(time_column)) { # add time columns of both cube and at:
if (inherits(tm_cube, "intervals"))
tm_cube = as.list(tm_cube)
df$time = tm_cube[tm_ix]
df[[tm]] = tm_cube[tm_ix]
df[[time_column]] = tm_pts
}
sf = st_as_sf(df)
Expand Down