This package automates parallelization
in spatial operations with chopin functions as well as sf/terra functions. With GDAL-compatible files and database tables,
chopin functions help to calculate spatial variables from
vector and raster data with no external software requirements. All who
need to perform geospatial operations with large datasets may find this
package useful to accelerate the covariate calculation process for
further analysis and modeling may find the main functions useful. We
assume that users have basic knowledge of geographic information
system data models, coordinate systems and
transformations, spatial operations,
and raster-vector
overlay.
chopin encapsulates the parallel processing of spatial
computation into three steps. First, users will define
the parallelization strategy, which is one of many supported in
future and future.mirai packages. Users always
need to register parallel workers with future before
running the par_*() functions that will be introduced
below.
future::plan(future.mirai::mirai_multisession, workers = 4L)
# future::multisession, future::cluster are available,
# See future.batchtools and future.callr for other options
# the number of workers are up to users' choiceSecond, users choose the proper data parallelization
configuration by creating a grid partition of the processing extent,
defining the field name with values that are hierarchically coded, or
entering multiple raster file paths into
par_multirasters(). Finally, users run
par_*() function with the configurations set above to
compute spatial variables from input data in parallel:
par_grid: parallelize over artificial grid polygons
that are generated from the maximum extent of inputs.
par_pad_grid is used to generate the grid polygons before
running this function.
par_hierarchy: parallelize over hierarchy coded in
identifier fields (for example, census blocks in each county in the
US)
par_multirasters: parallelize over multiple raster
files
Each of the par_* functions introduced above has
mirai version with a suffix _mirai after the
function names: par_grid_mirai,
par_hierarchy_mirai, and par_multirasters.
These functions will work properly after creating daemons with
mirai::daemons.
mirai::daemons(4L, dispatcher = "process")For grid partitioning, the entire study area will be divided into partly overlapped grids. We suggest two flowcharts to help which function to use for parallel processing below. The upper flowchart is raster-oriented and the lower is vector-oriented. They are supplementary to each other. When a user follows the raster-oriented one, they might visit the vector-oriented flowchart at each end of the raster-oriented flowchart.
Processing functions accept terra/sf classes for spatial data.
Raster-vector overlay is done with exactextractr. Three
helper functions encapsulate multiple geospatial data calculation steps
over multiple CPU threads.
extract_at: extract raster values with point buffers
or polygons with or without kernel weights
summarize_sedc: calculate sums of exponentially
decaying contributions
summarize_aw: area-weighted covariates based on
target and reference polygons
We provide two flowcharts to help users choose the right function for parallel processing. The raster-oriented flowchart is for users who want to start with raster data, and the vector-oriented flowchart is for users with large vector data.
In raster-oriented selection, we suggest four factors to consider:
par_multirasters is recommended. When there are multiple
rasters that share the same extent and resolution, consider stacking the
rasters into multilayer SpatRaster object by calling
terra::rast(filenames).exactextractr::exact_extract().SpatRaster in
exactextractr::exact_extract() is often minimally affected
by the raster extent.max_cells_in_memory argument value of
exactextractr::exact_extract(), raster resolution, and the
number of layers in SpatRaster are multiplicatively related
to the memory usage.
For vector-oriented selection, we suggest three factors to consider:
par_grid or par_hierarchy to
split the data into smaller chunks.par_hierarchy to parallelize the
operation.par_pad_balanced or
par_pad_grid with mode = "grid_quantile".
chopin can be installed using
remotes::install_github (also possible with
pak::pak or devtools::install_github).
rlang::check_installed("remotes")
remotes::install_github("ropensci/chopin")or you can also set repos in
install.packages() as ROpenSci repository:
install.packages("chopin", repos = "https://ropensci.r-universe.dev")Examples will navigate par_grid,
par_hierarchy, and par_multirasters functions
in chopin to parallelize geospatial operations.
# check and install packages to run examples
pkgs <- c("chopin", "dplyr", "sf", "terra", "future", "future.mirai", "mirai")
# install packages if anything is unavailable
rlang::check_installed(pkgs)
library(chopin)
library(dplyr)
#>
#> Attaching package: 'dplyr'
#> The following objects are masked from 'package:stats':
#>
#> filter, lag
#> The following objects are masked from 'package:base':
#>
#> intersect, setdiff, setequal, union
library(sf)
#> Linking to GEOS 3.12.1, GDAL 3.8.4, PROJ 9.4.0; sf_use_s2() is TRUE
library(terra)
#> terra 1.8.54
library(future)
library(future.mirai)
library(mirai)
# disable spherical geometries
sf::sf_use_s2(FALSE)
#> Spherical geometry (s2) switched off
# parallelization-safe random number generator
set.seed(2024, kind = "L'Ecuyer-CMRG")par_grid:
parallelize over artificial grid polygonsPlease refer to a small example below for extracting mean altitude
values at circular point buffers and census tracts in North Carolina.
Before running code chunks below, set the cloned chopin
repository as your working directory with setwd()
ncpoly <- system.file("shape/nc.shp", package = "sf")
ncsf <- sf::read_sf(ncpoly)
ncsf <- sf::st_transform(ncsf, "EPSG:5070")
plot(sf::st_geometry(ncsf))
Ten thousands random point locations were generated inside the counties of North Carolina.
ncpoints <- sf::st_sample(ncsf, 1e4)
ncpoints <- sf::st_as_sf(ncpoints)
ncpoints$pid <- sprintf("PID-%05d", seq(1, 1e4))
plot(sf::st_geometry(ncpoints))
We use an elevation dataset with and a moderate spatial resolution (approximately 400 meters or 0.25 miles).
# data preparation
srtm_path <- file.path(tempdir(check = TRUE), "nc_srtm15_otm.tif")
srtm_url <-
paste0(
"https://raw.githubusercontent.com/",
"ropensci/chopin/refs/heads/main/",
"tests/testdata/nc_srtm15_otm.tif"
)
download.file(srtm_url, srtm_path, mode = "wb")
# terra SpatRaster objects are wrapped when exported to rds file
srtm_ras <- terra::rast(srtm_path)
terra::crs(srtm_ras) <- "EPSG:5070"
srtm_ras
#> class : SpatRaster
#> size : 1534, 2281, 1 (nrow, ncol, nlyr)
#> resolution : 391.5026, 391.5026 (x, y)
#> extent : 1012872, 1905890, 1219961, 1820526 (xmin, xmax, ymin, ymax)
#> coord. ref. : NAD83 / Conus Albers (EPSG:5070)
#> source : nc_srtm15_otm.tif
#> name : srtm15
#> min value : -3589.291
#> max value : 1946.400
terra::plot(srtm_ras)
# ncpoints_tr <- terra::vect(ncpoints)
system.time(
ncpoints_srtm <-
chopin::extract_at(
x = srtm_ras,
y = ncpoints,
id = "pid",
mode = "buffer",
radius = 1e4L # 10,000 meters (10 km)
)
)
#> user system elapsed
#> 5.010 0.087 5.098chopin::par_pad_grid() takes a spatial dataset to
generate regular grid polygons with nx and ny
arguments with padding. Users will have both overlapping (by the degree
of radius) and non-overlapping grids, both of which will be
utilized to split locations and target datasets into sub-datasets for
efficient processing.
compregions <-
chopin::par_pad_grid(
ncpoints,
mode = "grid",
nx = 2L,
ny = 2L,
padding = 1e4L
)
#> Switch sf class to terra...
#> Switch terra class to sf...compregions is a list object with two elements named
original (non-overlapping grid polygons) and
padded (overlapping by padding). The figures
below illustrate the grid polygons with and without overlaps.
names(compregions)
#> [1] "original" "padded"
oldpar <- par()
par(mfrow = c(2, 1))
terra::plot(
terra::vect(compregions$original),
main = "Original grids"
)
terra::plot(
terra::vect(compregions$padded),
main = "Padded grids"
)
Using the grid polygons, we distribute the task of averaging
elevations at 10,000 circular buffer polygons, which are generated from
the random locations, with 10 kilometers radius by
chopin::par_grid(). Users always need to
register multiple CPU threads (logical cores) for
parallelization. chopin::par_*() functions are flexible in
terms of supporting generic spatial operations in sf and
terra, especially where two datasets are involved. Users
can inject generic functions’ arguments (parameters) by writing them in
the ellipsis (...) arguments, as demonstrated below:
future::plan(future.mirai::mirai_multisession, workers = 4L)
system.time(
ncpoints_srtm_mthr <-
par_grid(
grids = compregions,
fun_dist = extract_at,
x = srtm_ras,
y = ncpoints,
id = "pid",
radius = 1e4L,
.standalone = FALSE
)
)
#> ℹ SpatRaster class input is detected.
#> Attempt to track the data source file path...
#> ℹ Input is not a character.
#> Input is a character. Attempt to read it with terra::rast...
#> ℹ Task at CGRIDID: 1 is successfully dispatched.
#>
#> Input is a character. Attempt to read it with terra::rast...
#> ℹ Task at CGRIDID: 2 is successfully dispatched.
#>
#> Input is a character. Attempt to read it with terra::rast...
#> ℹ Task at CGRIDID: 3 is successfully dispatched.
#>
#> Input is a character. Attempt to read it with terra::rast...
#> ℹ Task at CGRIDID: 4 is successfully dispatched.
#> user system elapsed
#> 1.768 0.200 10.594
ncpoints_srtm <-
extract_at(
x = srtm_ras,
y = ncpoints,
id = "pid",
radius = 1e4L
)
future::plan(future::sequential)
mirai::daemons(0L)
#> [1] 0colnames(ncpoints_srtm_mthr)[2] <- "mean_par"
ncpoints_compar <- merge(ncpoints_srtm, ncpoints_srtm_mthr)
# Are the calculations equal?
all.equal(ncpoints_compar$mean, ncpoints_compar$mean_par)
#> [1] TRUEncpoints_s <-
merge(ncpoints, ncpoints_srtm)
ncpoints_m <-
merge(ncpoints, ncpoints_srtm_mthr)
plot(ncpoints_s[, "mean"], main = "Single-thread", pch = 19, cex = 0.33)
plot(ncpoints_m[, "mean_par"], main = "Multi-thread", pch = 19, cex = 0.33)
The same workflow operates on mirai dispatchers.
mirai::daemons(n = 4L, dispatcher = "process")
#> [1] 4
system.time(
ncpoints_srtm_mthr <-
par_grid_mirai(
grids = compregions,
fun_dist = extract_at,
x = srtm_ras,
y = ncpoints,
id = "pid",
radius = 1e4L,
.standalone = FALSE
)
)
#> ℹ SpatRaster class input is detected.
#> Attempt to track the data source file path...
#> ℹ Input is not a character.
#> ■■■■■■■■■ 25% | ETA: 24s
#> ■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■ 100% | ETA: 0s
#> user system elapsed
#> 0.830 0.171 9.507
# remove mirai::daemons
mirai::daemons(0L)
#> [1] 0chopin::par_hierarchy():
parallelize geospatial computations using intrinsic data hierarchyWe usually have nested/exhaustive hierarchies in real-world datasets.
For example, land is organized by administrative/jurisdictional borders
where multiple levels exist. In the U.S. context, a state consists of
several counties, counties are split into census tracts, and they have a
group of block groups. chopin::par_hierarchy() leverages
such hierarchies to parallelize geospatial operations, which means that
a group of lower-level geographic units in a higher-level geography is
assigned to a process. A demonstration below shows that census tracts
are grouped by their counties then each county will be processed in a
CPU thread.
# nc_hierarchy.gpkg includes two layers: county and tracts
hierarchy_path <- file.path(tempdir(check = TRUE), "nc_hierarchy.gpkg")
hierarchy_url <-
paste0(
"https://raw.githubusercontent.com/",
"ropensci/chopin/refs/heads/main/",
"tests/testdata/nc_hierarchy.gpkg"
)
download.file(hierarchy_url, hierarchy_path, mode = "wb")
nc_data <- hierarchy_path
nc_county <- sf::st_read(nc_data, layer = "county")
#> Reading layer `county' from data source `/tmp/RtmpOD6Egx/nc_hierarchy.gpkg' using driver `GPKG'
#> Simple feature collection with 100 features and 1 field
#> Geometry type: POLYGON
#> Dimension: XY
#> Bounding box: xmin: 1054155 ymin: 1341756 xmax: 1838923 ymax: 1690176
#> Projected CRS: NAD83 / Conus Albers
nc_tracts <- sf::st_read(nc_data, layer = "tracts")
#> Reading layer `tracts' from data source `/tmp/RtmpOD6Egx/nc_hierarchy.gpkg' using driver `GPKG'
#> Simple feature collection with 2672 features and 1 field
#> Geometry type: MULTIPOLYGON
#> Dimension: XY
#> Bounding box: xmin: 1054155 ymin: 1341756 xmax: 1838923 ymax: 1690176
#> Projected CRS: NAD83 / Conus Albers
# reproject to Conus Albers Equal Area
nc_county <- sf::st_transform(nc_county, "EPSG:5070")
nc_tracts <- sf::st_transform(nc_tracts, "EPSG:5070")
nc_tracts$COUNTY <- substr(nc_tracts$GEOID, 1, 5)future::plan(future.mirai::mirai_multisession, workers = 4L)
# single-thread
system.time(
nc_elev_tr_single <-
chopin::extract_at(
x = srtm_ras,
y = nc_tracts,
id = "GEOID"
)
)
#> user system elapsed
#> 0.512 0.001 0.513
# hierarchical parallelization
system.time(
nc_elev_tr_distr <-
chopin::par_hierarchy(
regions = nc_county, # higher level geometry
regions_id = "GEOID", # higher level unique id
fun_dist = extract_at,
x = srtm_ras,
y = nc_tracts, # lower level geometry
id = "GEOID", # lower level unique id
func = "mean"
)
)
#> ℹ SpatRaster class input is detected.
#> Attempt to track the data source file path...
#> ℹ Input is not a character.
#> ℹ GEOID is used to stratify the process.
#> Input is a character. Attempt to read it with terra::rast...
#> ℹ Your input function at 37037 is dispatched.
#>
#> Input is a character. Attempt to read it with terra::rast...
#> ℹ Your input function at 37001 is dispatched.
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#> ℹ Your input function at 37057 is dispatched.
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#> ℹ Your input function at 37069 is dispatched.
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#> ℹ Your input function at 37155 is dispatched.
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#> ℹ Your input function at 37109 is dispatched.
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#> ℹ Your input function at 37027 is dispatched.
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#> ℹ Your input function at 37115 is dispatched.
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#> ℹ Your input function at 37151 is dispatched.
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#> ℹ Your input function at 37131 is dispatched.
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#> ℹ Your input function at 37031 is dispatched.
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#> ℹ Your input function at 37005 is dispatched.
#>
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#> ℹ Your input function at 37139 is dispatched.
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#> ℹ Your input function at 37193 is dispatched.
#>
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#> ℹ Your input function at 37003 is dispatched.
#>
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#> ℹ Your input function at 37083 is dispatched.
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#> ℹ Your input function at 37163 is dispatched.
#>
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#> ℹ Your input function at 37189 is dispatched.
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#> ℹ Your input function at 37173 is dispatched.
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#> ℹ Your input function at 37045 is dispatched.
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#> ℹ Your input function at 37125 is dispatched.
#>
#> Input is a character. Attempt to read it with terra::rast...
#> ℹ Your input function at 37067 is dispatched.
#>
#> Input is a character. Attempt to read it with terra::rast...
#> ℹ Your input function at 37077 is dispatched.
#>
#> Input is a character. Attempt to read it with terra::rast...
#> ℹ Your input function at 37185 is dispatched.
#>
#> Input is a character. Attempt to read it with terra::rast...
#> ℹ Your input function at 37137 is dispatched.
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#> ℹ Your input function at 37033 is dispatched.
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#> ℹ Your input function at 37107 is dispatched.
#>
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#> ℹ Your input function at 37075 is dispatched.
#>
#> Input is a character. Attempt to read it with terra::rast...
#> ℹ Your input function at 37073 is dispatched.
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#> ℹ Your input function at 37161 is dispatched.
#>
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#> ℹ Your input function at 37187 is dispatched.
#>
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#> ℹ Your input function at 37007 is dispatched.
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#> ℹ Your input function at 37135 is dispatched.
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#> ℹ Your input function at 37049 is dispatched.
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#> ℹ Your input function at 37195 is dispatched.
#>
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#> ℹ Your input function at 37061 is dispatched.
#>
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#> ℹ Your input function at 37087 is dispatched.
#>
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#> ℹ Your input function at 37081 is dispatched.
#>
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#> ℹ Your input function at 37099 is dispatched.
#>
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#> ℹ Your input function at 37097 is dispatched.
#>
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#> ℹ Your input function at 37091 is dispatched.
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#> ℹ Your input function at 37165 is dispatched.
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#> ℹ Your input function at 37085 is dispatched.
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#> ℹ Your input function at 37105 is dispatched.
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#> ℹ Your input function at 37177 is dispatched.
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#> ℹ Your input function at 37113 is dispatched.
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#> ℹ Your input function at 37143 is dispatched.
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#> Input is a character. Attempt to read it with terra::rast...
#> ℹ Your input function at 37095 is dispatched.
#>
#> Input is a character. Attempt to read it with terra::rast...
#> ℹ Your input function at 37071 is dispatched.
#>
#> Input is a character. Attempt to read it with terra::rast...
#> ℹ Your input function at 37101 is dispatched.
#>
#> Input is a character. Attempt to read it with terra::rast...
#> ℹ Your input function at 37015 is dispatched.
#>
#> Input is a character. Attempt to read it with terra::rast...
#> ℹ Your input function at 37167 is dispatched.
#>
#> Input is a character. Attempt to read it with terra::rast...
#> ℹ Your input function at 37079 is dispatched.
#>
#> Input is a character. Attempt to read it with terra::rast...
#> ℹ Your input function at 37129 is dispatched.
#>
#> Input is a character. Attempt to read it with terra::rast...
#> ℹ Your input function at 37147 is dispatched.
#>
#> Input is a character. Attempt to read it with terra::rast...
#> ℹ Your input function at 37141 is dispatched.
#>
#> Input is a character. Attempt to read it with terra::rast...
#> ℹ Your input function at 37179 is dispatched.
#>
#> Input is a character. Attempt to read it with terra::rast...
#> ℹ Your input function at 37121 is dispatched.
#>
#> Input is a character. Attempt to read it with terra::rast...
#> ℹ Your input function at 37133 is dispatched.
#>
#> Input is a character. Attempt to read it with terra::rast...
#> ℹ Your input function at 37065 is dispatched.
#>
#> Input is a character. Attempt to read it with terra::rast...
#> ℹ Your input function at 37119 is dispatched.
#>
#> Input is a character. Attempt to read it with terra::rast...
#> ℹ Your input function at 37199 is dispatched.
#>
#> Input is a character. Attempt to read it with terra::rast...
#> ℹ Your input function at 37197 is dispatched.
#>
#> Input is a character. Attempt to read it with terra::rast...
#> ℹ Your input function at 37023 is dispatched.
#>
#> Input is a character. Attempt to read it with terra::rast...
#> ℹ Your input function at 37191 is dispatched.
#>
#> Input is a character. Attempt to read it with terra::rast...
#> ℹ Your input function at 37059 is dispatched.
#>
#> Input is a character. Attempt to read it with terra::rast...
#> ℹ Your input function at 37111 is dispatched.
#>
#> Input is a character. Attempt to read it with terra::rast...
#> ℹ Your input function at 37183 is dispatched.
#>
#> Input is a character. Attempt to read it with terra::rast...
#> ℹ Your input function at 37053 is dispatched.
#>
#> Input is a character. Attempt to read it with terra::rast...
#> ℹ Your input function at 37103 is dispatched.
#>
#> Input is a character. Attempt to read it with terra::rast...
#> ℹ Your input function at 37041 is dispatched.
#>
#> Input is a character. Attempt to read it with terra::rast...
#> ℹ Your input function at 37021 is dispatched.
#>
#> Input is a character. Attempt to read it with terra::rast...
#> ℹ Your input function at 37157 is dispatched.
#>
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#> ℹ Your input function at 37117 is dispatched.
#>
#> Input is a character. Attempt to read it with terra::rast...
#> ℹ Your input function at 37089 is dispatched.
#>
#> Input is a character. Attempt to read it with terra::rast...
#> ℹ Your input function at 37127 is dispatched.
#>
#> Input is a character. Attempt to read it with terra::rast...
#> ℹ Your input function at 37009 is dispatched.
#>
#> Input is a character. Attempt to read it with terra::rast...
#> ℹ Your input function at 37019 is dispatched.
#>
#> Input is a character. Attempt to read it with terra::rast...
#> ℹ Your input function at 37123 is dispatched.
#>
#> Input is a character. Attempt to read it with terra::rast...
#> ℹ Your input function at 37181 is dispatched.
#>
#> Input is a character. Attempt to read it with terra::rast...
#> ℹ Your input function at 37175 is dispatched.
#>
#> Input is a character. Attempt to read it with terra::rast...
#> ℹ Your input function at 37171 is dispatched.
#>
#> Input is a character. Attempt to read it with terra::rast...
#> ℹ Your input function at 37043 is dispatched.
#>
#> Input is a character. Attempt to read it with terra::rast...
#> ℹ Your input function at 37055 is dispatched.
#>
#> Input is a character. Attempt to read it with terra::rast...
#> ℹ Your input function at 37047 is dispatched.
#> user system elapsed
#> 1.992 0.469 10.553par_multirasters():
parallelize over multiple rastersThere is a common case of having a large group of raster files at
which the same operation should be performed.
chopin::par_multirasters() is for such cases. An example
below demonstrates where we have five elevation raster files to
calculate the average elevation at counties in North Carolina.
# nccnty <- sf::st_read(nc_data, layer = "county")
ncelev <- terra::rast(srtm_path)
terra::crs(ncelev) <- "EPSG:5070"
names(ncelev) <- c("srtm15")
tdir <- tempdir(check = TRUE)
terra::writeRaster(ncelev, file.path(tdir, "test1.tif"), overwrite = TRUE)
terra::writeRaster(ncelev, file.path(tdir, "test2.tif"), overwrite = TRUE)
terra::writeRaster(ncelev, file.path(tdir, "test3.tif"), overwrite = TRUE)
terra::writeRaster(ncelev, file.path(tdir, "test4.tif"), overwrite = TRUE)
terra::writeRaster(ncelev, file.path(tdir, "test5.tif"), overwrite = TRUE)
# check if the raster files were exported as expected
testfiles <- list.files(tdir, pattern = "*.tif$", full.names = TRUE)
testfiles
#> [1] "/tmp/RtmpOD6Egx/nc_srtm15_otm.tif" "/tmp/RtmpOD6Egx/test1.tif"
#> [3] "/tmp/RtmpOD6Egx/test2.tif" "/tmp/RtmpOD6Egx/test3.tif"
#> [5] "/tmp/RtmpOD6Egx/test4.tif" "/tmp/RtmpOD6Egx/test5.tif"system.time(
res <-
chopin::par_multirasters(
filenames = testfiles,
fun_dist = extract_at,
x = ncelev,
y = nc_county,
id = "GEOID",
func = "mean"
)
)
#> ℹ Input is not a character.
#> Input is a character. Attempt to read it with terra::rast...
#> ℹ Your input function at /tmp/RtmpOD6Egx/nc_srtm15_otm.tif is dispatched.
#>
#> Input is a character. Attempt to read it with terra::rast...
#> ℹ Your input function at /tmp/RtmpOD6Egx/test1.tif is dispatched.
#>
#> Input is a character. Attempt to read it with terra::rast...
#> ℹ Your input function at /tmp/RtmpOD6Egx/test2.tif is dispatched.
#>
#> Input is a character. Attempt to read it with terra::rast...
#> ℹ Your input function at /tmp/RtmpOD6Egx/test3.tif is dispatched.
#>
#> Input is a character. Attempt to read it with terra::rast...
#> ℹ Your input function at /tmp/RtmpOD6Egx/test4.tif is dispatched.
#>
#> Input is a character. Attempt to read it with terra::rast...
#> ℹ Your input function at /tmp/RtmpOD6Egx/test5.tif is dispatched.
#> user system elapsed
#> 1.446 0.286 3.239
knitr::kable(head(res))| mean | base_raster |
|---|---|
| 136.80203 | /tmp/RtmpOD6Egx/nc_srtm15_otm.tif |
| 189.76170 | /tmp/RtmpOD6Egx/nc_srtm15_otm.tif |
| 231.16968 | /tmp/RtmpOD6Egx/nc_srtm15_otm.tif |
| 98.03845 | /tmp/RtmpOD6Egx/nc_srtm15_otm.tif |
| 41.23463 | /tmp/RtmpOD6Egx/nc_srtm15_otm.tif |
| 270.96933 | /tmp/RtmpOD6Egx/nc_srtm15_otm.tif |
# remove temporary raster files
file.remove(testfiles)
#> [1] TRUE TRUE TRUE TRUE TRUE TRUEOther than chopin processing functions,
chopin::par_*() functions support generic geospatial
operations. An example below uses terra::nearest(), which
gets the nearest feature’s attributes, inside
chopin::par_grid().
ncrd1_path <- file.path(tempdir(check = TRUE), "ncroads_first.gpkg")
ncrd1_url <-
paste0(
"https://raw.githubusercontent.com/",
"ropensci/chopin/refs/heads/main/",
"tests/testdata/ncroads_first.gpkg"
)
download.file(ncrd1_url, ncrd1_path, mode = "wb")
# Generate 5000 random points
pnts <- sf::st_sample(nc_county, 5000)
pnts <- sf::st_as_sf(pnts)
# assign identifiers
pnts$pid <- sprintf("RPID-%04d", seq(1, 5000))
rd1 <- sf::st_read(ncrd1_path)
#> Reading layer `ncroads_first' from data source
#> `/tmp/RtmpOD6Egx/ncroads_first.gpkg' using driver `GPKG'
#> Simple feature collection with 620 features and 4 fields
#> Geometry type: MULTILINESTRING
#> Dimension: XY
#> Bounding box: xmin: 1152512 ymin: 1390719 xmax: 1748367 ymax: 1662294
#> Projected CRS: NAD83 / Conus Albers
# reproject
pntst <- sf::st_transform(pnts, "EPSG:5070")
rd1t <- sf::st_transform(rd1, "EPSG:5070")
# generate grids
nccompreg <-
chopin::par_pad_grid(
input = pntst,
mode = "grid",
nx = 4L,
ny = 2L,
padding = 5e4L
)
#> Switch sf class to terra...
#> Switch terra class to sf...The figure below shows the padded grids (50 kilometers), primary
roads, and points. Primary roads will be selected by a padded grid per
iteration and used to calculate the distance from each point to the
nearest primary road. Padded grids and their overlapping areas will look
different according to padding argument in
chopin::par_pad_grid().
# plot
terra::plot(nccompreg$padded, border = "orange")
terra::plot(terra::vect(ncsf), add = TRUE)
terra::plot(rd1t, col = "blue", add = TRUE)
#> Warning in plot.sf(rd1t, col = "blue", add = TRUE): ignoring all but the first
#> attribute
terra::plot(pntst, add = TRUE, cex = 0.3)
legend(1.02e6, 1.72e6,
legend = c("Computation grids (50km padding)", "Major roads"),
lty = 1, lwd = 1, col = c("orange", "blue"),
cex = 0.5)
# terra::nearest run
system.time(
restr <- terra::nearest(x = terra::vect(pntst), y = terra::vect(rd1t))
)
#> user system elapsed
#> 0.429 0.000 0.430
pnt_path <- file.path(tdir, "pntst.gpkg")
sf::st_write(pntst, pnt_path)
#> Writing layer `pntst' to data source `/tmp/RtmpOD6Egx/pntst.gpkg' using driver `GPKG'
#> Writing 5000 features with 1 fields and geometry type Point.
# we use four threads that were configured above
system.time(
resd <-
chopin::par_grid(
grids = nccompreg,
fun_dist = nearest,
x = pnt_path,
y = ncrd1_path,
pad_y = TRUE
)
)
#> ℹ Input is a character. Trying to read with terra .
#> ℹ Input is a character. Trying to read with terra .
#> ℹ Task at CGRIDID: 1 is successfully dispatched.
#>
#> ℹ Input is a character. Trying to read with terra .
#> ℹ Input is a character. Trying to read with terra .
#> ℹ Task at CGRIDID: 2 is successfully dispatched.
#>
#> ℹ Input is a character. Trying to read with terra .
#> ℹ Input is a character. Trying to read with terra .
#> ℹ Task at CGRIDID: 3 is successfully dispatched.
#>
#> ℹ Input is a character. Trying to read with terra .
#> ℹ Input is a character. Trying to read with terra .
#> ℹ Task at CGRIDID: 4 is successfully dispatched.
#>
#> ℹ Input is a character. Trying to read with terra .
#> ℹ Input is a character. Trying to read with terra .
#> ℹ Task at CGRIDID: 5 is successfully dispatched.
#>
#> ℹ Input is a character. Trying to read with terra .
#> ℹ Input is a character. Trying to read with terra .
#> ℹ Task at CGRIDID: 6 is successfully dispatched.
#>
#> ℹ Input is a character. Trying to read with terra .
#> ℹ Input is a character. Trying to read with terra .
#> ℹ Task at CGRIDID: 7 is successfully dispatched.
#>
#> ℹ Input is a character. Trying to read with terra .
#> ℹ Input is a character. Trying to read with terra .
#> ℹ Task at CGRIDID: 8 is successfully dispatched.
#> user system elapsed
#> 1.093 0.251 2.859resj <- merge(restr, resd, by = c("from_x", "from_y"))
all.equal(resj$distance.x, resj$distance.y)
#> [1] TRUEUsers should be mindful of caveats in the parallelization of nearest
feature search, which may result in no or excess distance depending on
the distribution of the target dataset to which the nearest feature is
searched. For example, when one wants to calculate the nearest
interstate from rural homes with fine grids, some grids may have no
interstates then homes in such grids will not get any distance to the
nearest interstate. Such problems can be avoided by choosing
nx, ny, and padding values in
par_pad_grid() meticulously.
chopin parallelizationIf users’ custom function with two main raster/vector arguments whose
names are not x or y, users can use
par_convert_f to map two arguments to x and
y.
sf_intersection_buffer <- function(a, b, buffer_dist = 100) {
intersected <- st_intersection(a, b)
buffered <- st_buffer(intersected, dist = buffer_dist)
return(buffered)
}
# Convert the function to remap arguments dynamically
custom_intersect_buffer <- par_convert_f(sf_intersection_buffer, x = "a", y = "b", dist = "buffer_dist")Parallelization may underperform when the datasets are too small to take advantage of divide-and-compute approach, where parallelization overhead is involved. Overhead here refers to the required amount of computational resources for transferring objects to multiple processes. Since the demonstrations above use quite small datasets, the advantage of parallelization was not as noticeable as it was expected. Should a large amount of data (spatial/temporal resolution or number of files, for example) be processed, users could find the efficiency of this package. A vignette in this package demonstrates use cases extracting various climate/weather datasets.
chopin works best with two-dimensional
(planar) geometries. Users should disable
s2 spherical geometry mode in sf by setting
sf::sf_use_s2(FALSE). Running any chopin
functions at spherical or three-dimensional (e.g., including M/Z
dimensions) geometries may produce incorrect or unexpected results.