chopin (version 0.9.4)

par_grid_mirai: Parallelize spatial computation over the computational grids

Description

mirai::daemons will set the parallel backend then mirai::mirai_map will parallelize the work in each grid. For details of the terminology in mirai package, refer to mirai::mirai. This function assumes that users have one raster file and a sizable and spatially distributed target locations. Each thread will process the nearest integer of $|N_g| / |N_t|$ grids where $|N_g|$ denotes the number of grids and $|N_t|$ denotes the number of threads.

Usage

par_grid_mirai(grids, fun_dist, ..., pad_y = FALSE, .debug = TRUE)

Value

a data.frame object with computation results. For entries of the results, consult the documentation of the function put in fun_dist argument.

Arguments

grids

List of two sf/SpatVector objects. Computational grids. It takes a strict assumption that the grid input is an output of par_pad_grid.

fun_dist

sf, terra or chopin functions. This function should have x and y arguments.

...

Arguments passed to the argument fun_dist.

pad_y

logical(1). Whether to filter y with the padded grid. Should be TRUE when x is where the values are calculated. Default is FALSE. In the reverse case, like terra::extent or exactextractr::exact_extract, the raster (x) extent should be set with the padded grid.

.debug

logical(1). Default is FALSE. Otherwise, if a unit computation fails, the error message and the CGRIDID value where the error occurred will be included in the output.

Author

Insang Song

See Also

mirai::daemons, mirai::mirai_map, par_convert_f

Other Parallelization: par_cut_coords(), par_grid(), par_hierarchy(), par_hierarchy_mirai(), par_make_grid(), par_merge_grid(), par_multirasters(), par_multirasters_mirai(), par_pad_balanced(), par_pad_grid(), par_split_list()

Examples

# \donttest{
lastpar <- par(mfrow = c(1, 1))
library(sf)
library(mirai)
options(sf_use_s2 = FALSE)
daemons(4, dispatcher = "process")
ncpath <- system.file("shape/nc.shp", package = "sf")
ncpoly <- sf::st_read(ncpath)
ncpoly <- sf::st_transform(ncpoly, "EPSG:5070")

# sf object
ncpnts <-
  sf::st_sample(ncpoly, 2000)
ncpnts <- sf::st_as_sf(ncpnts)
ncpnts$pid <- seq_len(nrow(ncpnts))

# file path
rrast <- terra::rast(ncpoly, nrow = 600, ncol = 1320)
terra::values(rrast) <- rgamma(7.92e5, 4, 2)
# Using raster path
rastpath <- file.path(tempdir(), "ncelev.tif")
terra::writeRaster(rrast, rastpath, overwrite = TRUE)

nccompreg <-
  chopin::par_pad_grid(
    input = ncpnts,
    mode = "grid",
    nx = 4L,
    ny = 2L,
    padding = 5e3L
  )
res <-
  par_grid_mirai(
    grids = nccompreg,
    fun_dist = extract_at,
    x = rastpath,
    y = ncpnts,
    qsegs = 90L,
    radius = 5e3L,
    id = "pid"
  )
mirai::daemons(0L)
par(lastpar)
# }