Parallelize spatial computation over the computational grids

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)

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.

Value

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

Note

In dynamic dots (...), fun_dist arguments should include x and y where sf/terra class objects or file paths are accepted. Virtually any sf/terra functions that accept two arguments can be put in fun_dist, but please be advised that some spatial operations do not necessarily give the exact result from what would have been done single-thread. For example, distance calculated through this function may return the lower value than actual because the computational region was reduced. This would be the case especially where the target features are spatially sparsely distributed.

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()

Author

Insang Song geoissong@gmail.com

Examples

library(sf)
library(mirai)
daemons(4, dispatcher = "process")
#> [1] 4
ncpath <- system.file("shape/nc.shp", package = "sf")
ncpoly <- sf::st_read(ncpath)
#> Reading layer `nc' from data source 
#>   `/usr/local/lib/R/site-library/sf/shape/nc.shp' using driver `ESRI Shapefile'
#> Simple feature collection with 100 features and 14 fields
#> Geometry type: MULTIPOLYGON
#> Dimension:     XY
#> Bounding box:  xmin: -84.32385 ymin: 33.88199 xmax: -75.45698 ymax: 36.58965
#> Geodetic CRS:  NAD27
# sf object
ncpnts <-
  readRDS(
    system.file("extdata/nc_random_point.rds", package = "chopin")
  )
# file path
ncelev <-
    system.file("extdata/nc_srtm15_otm.tif", package = "chopin")

nccompreg <-
  chopin::par_pad_grid(
    input = ncpnts,
    mode = "grid",
    nx = 4L,
    ny = 2L,
    padding = 5e3L
  )
#> Switch sf class to terra...
#> Switch terra class to sf...
res <-
  par_grid_mirai(
    grids = nccompreg,
    fun_dist = extract_at,
    x = ncelev,
    y = ncpnts,
    qsegs = 90L,
    radius = 5e3L,
    id = "pid"
  )
#> ℹ Input is not a character.
mirai::daemons(0L)
#> [1] 0