stars/0000755000176200001440000000000014645437762011430 5ustar liggesusersstars/tests/0000755000176200001440000000000014644756220012562 5ustar liggesusersstars/tests/rectilinear.Rout.save0000644000176200001440000000545214644736040016677 0ustar liggesusers R Under development (unstable) (2024-07-13 r86895 ucrt) -- "Unsuffered Consequences" Copyright (C) 2024 The R Foundation for Statistical Computing Platform: x86_64-w64-mingw32/x64 R is free software and comes with ABSOLUTELY NO WARRANTY. You are welcome to redistribute it under certain conditions. Type 'license()' or 'licence()' for distribution details. R is a collaborative project with many contributors. Type 'contributors()' for more information and 'citation()' on how to cite R or R packages in publications. Type 'demo()' for some demos, 'help()' for on-line help, or 'help.start()' for an HTML browser interface to help. Type 'q()' to quit R. > suppressPackageStartupMessages(library(stars)) > tif = system.file("tif/L7_ETMs.tif", package = "stars") > x = read_stars(tif) > (xx = x[,c(1:347,349),c(1:350,352),]) # rectilinear: one-but-last row & col missing stars object with 3 dimensions and 1 attribute attribute(s): Min. 1st Qu. Median Mean 3rd Qu. Max. L7_ETMs.tif 1 54 69 68.9915 86 255 dimension(s): from to refsys point x 1 348 SIRGAS 2000 / UTM zone 25S FALSE y 1 351 SIRGAS 2000 / UTM zone 25S FALSE band 1 6 NA NA values x/y x [288790.5,288819),...,[298708.5,298737) [x] y [9120747,9120718),...,[9110743,9110715) [y] band NULL > image(xx) # chooses useRaster itself > # see also tidy.R for a ggplot example, leaving out the row. > > m = matrix(1:16, 4, 4) > x = 1:4 > y = 2:5 > (st = st_as_stars(list(m = m), dimensions = st_dimensions(x = x, y = y))) stars object with 2 dimensions and 1 attribute attribute(s): Min. 1st Qu. Median Mean 3rd Qu. Max. m 1 4.75 8.5 8.5 12.25 16 dimension(s): from to offset delta point x/y x 1 4 1 1 FALSE [x] y 1 4 2 1 FALSE [y] > st = st_set_dimensions(st, 1, values = c(1,2,3,4,6)) > st = st_set_dimensions(st, 2, values = c(2,3,4,5,8)) > try(st_set_dimensions(st, 2, values = c(2,3,4,5,8,20))) # one too many Error in st_set_dimensions(st, 2, values = c(2, 3, 4, 5, 8, 20)) : length of values ( 6 ) does not match length of dimension 2 ( 4 ) > > # $<-.stars: > st$foo = st$m * 2 > st stars object with 2 dimensions and 2 attributes attribute(s): Min. 1st Qu. Median Mean 3rd Qu. Max. m 1 4.75 8.5 8.5 12.25 16 foo 2 9.50 17.0 17.0 24.50 32 dimension(s): from to point values x/y x 1 4 FALSE [1,2),...,[4,6) [x] y 1 4 FALSE [2,3),...,[5,8) [y] > > # st_as_sf: > > if (utils::packageVersion("sf") > "0.7.2") { + plot(st_as_sf(st, as_points = FALSE), axes = TRUE) + } > > image(st, text_values = TRUE, axes = TRUE) > > proc.time() user system elapsed 1.10 0.17 1.26 stars/tests/plot.R0000644000176200001440000000155614453011313013653 0ustar liggesuserssuppressPackageStartupMessages(library(stars)) if (suppressPackageStartupMessages(require(sp, quietly = TRUE)) && require(spacetime, quietly = TRUE)) { data(air) # this loads several datasets in .GlobalEnv dim(air) d = st_dimensions(station = st_as_sfc(stations), time = dates) aq = st_as_stars(list(PM10 = air), dimensions = d) image(aperm(log(aq), 2:1), main = "NA pattern (white) in PM10 station time series") st_geometry(aq) } csv = system.file("tif/ESACCI-LC-Legend.csv", package = "starsdata") if (csv != "") { rgb = read.csv(csv, header = TRUE, sep = ";") luc = read_stars(system.file("tif/LUC3.tif", package = "starsdata")) plot(luc, rgb = rgb, axes = TRUE, key.pos = NULL) } # https://github.com/r-spatial/stars/issues/233 : nc = read_sf(system.file("shape/nc.shp", package = "sf")) output = st_as_stars(st_bbox(nc), nx = 1, ny = 1, values = 1) plot(output) stars/tests/rasterize.R0000644000176200001440000000237714144045432014715 0ustar liggesuserssuppressPackageStartupMessages(library(stars)) nc = read_sf(system.file("gpkg/nc.gpkg", package="sf")) (x = st_rasterize(nc)) # default grid: plot(x, axes = TRUE) # a bit more customized grid: (x = st_rasterize(nc, st_as_stars(st_bbox(nc), nx = 100, ny = 50, values = NA_real_))) plot(x, axes = TRUE) (ls = st_sf(a = 1:2, st_sfc(st_linestring(rbind(c(0.1, 0), c(1.1, 1))), st_linestring(rbind(c(0, 0.05), c(1, 0.05)))))) (grd = st_as_stars(st_bbox(ls), nx = 10, ny = 10, xlim = c(0, 1.0), ylim = c(0, 1), values = NA_real_)) # the following two plots confirm that (only) the # upper-left corner is part of the grid cell (when dy is negative), leading # to a seemingly half-gridcell-shift problem: ## sf_extSoftVersion()["GDAL"] plot(st_rasterize(ls, grd), axes = TRUE, reset = FALSE) # ALL_TOUCHED=FALSE; plot(ls, add = TRUE, col = "red") plot(st_rasterize(ls, grd, options = "ALL_TOUCHED=TRUE"), axes = TRUE, reset = FALSE) plot(ls, add = TRUE, col = "red") # add lines to existing 0 values, summing values in case of multiple lines: (grd = st_as_stars(st_bbox(ls), nx = 10, ny = 10, xlim = c(0, 1.0), ylim = c(0, 1), values = 0)) r = st_rasterize(ls, grd, options = c("MERGE_ALG=ADD", "ALL_TOUCHED=TRUE")) plot(r, axes = TRUE, reset = FALSE) plot(ls, add = TRUE, col = "red") stars/tests/spatstat.R0000644000176200001440000000043314376467445014562 0ustar liggesuserssuppressPackageStartupMessages(library(sf)) library(stars) if (suppressPackageStartupMessages(require(spatstat.geom, quietly = TRUE))) { tif = system.file("tif/L7_ETMs.tif", package = "stars") s = adrop(read_stars(tif)[,,,1]) > 70 plot(s) m = as.owin(s) plot(m) print(table(m$m)) } stars/tests/datasets.Rout.save0000644000176200001440000000316014644736040016200 0ustar liggesusers R Under development (unstable) (2024-07-13 r86895 ucrt) -- "Unsuffered Consequences" Copyright (C) 2024 The R Foundation for Statistical Computing Platform: x86_64-w64-mingw32/x64 R is free software and comes with ABSOLUTELY NO WARRANTY. You are welcome to redistribute it under certain conditions. Type 'license()' or 'licence()' for distribution details. R is a collaborative project with many contributors. Type 'contributors()' for more information and 'citation()' on how to cite R or R packages in publications. Type 'demo()' for some demos, 'help()' for on-line help, or 'help.start()' for an HTML browser interface to help. Type 'q()' to quit R. > suppressPackageStartupMessages(library(stars)) > bcsd_obs stars_proxy object with 2 attributes in 2 file(s): $foo [1] "[...]/bcsd_obs_1999.nc:pr" $bar [1] "[...]/bcsd_obs_1999.nc:tas" dimension(s): from to offset delta refsys values x/y x 1 81 -85 0.125 NA NULL [x] y 1 33 37.12 -0.125 NA NULL [y] time 1 12 NA NA POSIXct 1999-01-31,...,1999-12-31 > L7_ETMs stars_proxy object with 1 attribute in 1 file(s): $L7_ETMs [1] "[...]/L7_ETMs.tif" dimension(s): from to offset delta refsys point x/y x 1 349 288776 28.5 SIRGAS 2000 / UTM zone 25S FALSE [x] y 1 352 9120761 -28.5 SIRGAS 2000 / UTM zone 25S FALSE [y] band 1 6 NA NA NA NA > if (require(starsdata, quietly = TRUE)) { + print(stars_sentinel2) + } > > proc.time() user system elapsed 0.62 0.21 0.78 stars/tests/spatstat.Rout.save0000644000176200001440000000212414644736040016232 0ustar liggesusers R Under development (unstable) (2024-07-13 r86895 ucrt) -- "Unsuffered Consequences" Copyright (C) 2024 The R Foundation for Statistical Computing Platform: x86_64-w64-mingw32/x64 R is free software and comes with ABSOLUTELY NO WARRANTY. You are welcome to redistribute it under certain conditions. Type 'license()' or 'licence()' for distribution details. R is a collaborative project with many contributors. Type 'contributors()' for more information and 'citation()' on how to cite R or R packages in publications. Type 'demo()' for some demos, 'help()' for on-line help, or 'help.start()' for an HTML browser interface to help. Type 'q()' to quit R. > suppressPackageStartupMessages(library(sf)) > > library(stars) Loading required package: abind > if (suppressPackageStartupMessages(require(spatstat.geom, quietly = TRUE))) { + tif = system.file("tif/L7_ETMs.tif", package = "stars") + s = adrop(read_stars(tif)[,,,1]) > 70 + plot(s) + m = as.owin(s) + plot(m) + print(table(m$m)) + } FALSE TRUE 39494 83354 > > proc.time() user system elapsed 2.00 0.17 2.17 stars/tests/extract.Rout.save0000644000176200001440000001101514644736040016040 0ustar liggesusers R Under development (unstable) (2024-07-13 r86895 ucrt) -- "Unsuffered Consequences" Copyright (C) 2024 The R Foundation for Statistical Computing Platform: x86_64-w64-mingw32/x64 R is free software and comes with ABSOLUTELY NO WARRANTY. You are welcome to redistribute it under certain conditions. Type 'license()' or 'licence()' for distribution details. R is a collaborative project with many contributors. Type 'contributors()' for more information and 'citation()' on how to cite R or R packages in publications. Type 'demo()' for some demos, 'help()' for on-line help, or 'help.start()' for an HTML browser interface to help. Type 'q()' to quit R. > # Create 'stars' object > set.seed(1331) > suppressPackageStartupMessages(library(stars)) > volcano = rbind(volcano, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA) # add NA rows > d = st_dimensions(x = 1:ncol(volcano), y = 1:nrow(volcano)) > (r = st_as_stars(t(volcano))) stars object with 2 dimensions and 1 attribute attribute(s): Min. 1st Qu. Median Mean 3rd Qu. Max. NA's A1 94 108 124 130.1879 150 195 732 dimension(s): from to offset delta point x/y X1 1 61 0 1 FALSE [x] X2 1 99 0 1 FALSE [y] > r = st_set_dimensions(r, 1, offset = 0, delta = 1) > r = st_set_dimensions(r, 2, offset = nrow(volcano), delta = -1) > > # Create points > pnt = st_sample(st_as_sfc(st_bbox(r)), 100) > pnt = st_as_sf(pnt) > > # Extract - 'st_join' > x = st_join(pnt, st_as_sf(r)) > > # Extract - 'st_extract' > y = st_extract(r, pnt) > > # check there are NA's: > any(is.na(x)) [1] TRUE > # Compare > all.equal(x$A1, y[[1]]) [1] TRUE > > ################################################ > if (FALSE) { + + ## tic: segfaults + # check equal results with stars_proxy: + #x = st_extract(stars:::st_as_stars_proxy(r), pnt) + #all.equal(x$A1, y[[1]]) + #all.equal(x, y) + + r = c(r, 2*r, 10*r) + x = st_join(pnt, st_as_sf(r)) + y = st_as_sf(st_extract(r, pnt)) + all.equal(x, y) + + ## tic: segfaults + #x = st_extract(stars:::st_as_stars_proxy(merge(r)), pnt) + #all.equal(st_as_sf(x), y) + + tif = system.file("tif/L7_ETMs.tif", package = "stars") + xp = read_stars(tif, proxy = TRUE) + xm = read_stars(tif, proxy = FALSE) + pts = st_sample(st_as_sfc(st_bbox(xp)), 10) + pts = c(pts, st_as_sfc("POINT(0 0)"), pts) + em = st_extract(xm, pts) + if (utils::packageVersion("sf") >= "0.9-7") { + ep = st_extract(xp, pts) + print(all.equal(ep, em, check.attributes = TRUE)) + } + + # two-attribute objects: + library(stars) + tif = system.file("tif/L7_ETMs.tif", package = "stars") + xp = read_stars(c(tif, tif), proxy = TRUE) + xm = read_stars(c(tif, tif), proxy = FALSE) + pts = st_sample(st_as_sfc(st_bbox(xp)), 10) + pts = c(pts, st_as_sfc("POINT(0 0)"), pts) + em = st_extract(xm, pts) + if (utils::packageVersion("sf") >= "0.9-7") { + ep = st_extract(xp, pts) + print(all.equal(ep, em, check.attributes = TRUE)) + } + + # single-attribute, single raster objects: + tif1 = paste0(tempfile(), ".tif") + write_stars(xm[1,,,1], "x.tif") + xp = read_stars("x.tif", proxy = TRUE) + xm = read_stars("x.tif", proxy = FALSE) + em = st_extract(xm, pts) + if (utils::packageVersion("sf") >= "0.9-7") { + ep = st_extract(xp, pts) + print(all.equal(ep, em, check.attributes = TRUE)) + } + + # multiple-file attributes: + x = c( + "avhrr-only-v2.19810901.nc", + "avhrr-only-v2.19810902.nc", + "avhrr-only-v2.19810903.nc", + "avhrr-only-v2.19810904.nc", + "avhrr-only-v2.19810905.nc", + "avhrr-only-v2.19810906.nc", + "avhrr-only-v2.19810907.nc", + "avhrr-only-v2.19810908.nc", + "avhrr-only-v2.19810909.nc" + ) + file_list = system.file(paste0("netcdf/", x), package = "starsdata") + if (!identical(file_list, "")) { + y = read_stars(file_list, quiet = TRUE) + print(y) + st_crs(y) = "OGC:CRS84" + pts = st_sample(st_as_sfc(st_bbox(y)), 10) + em = st_extract(y, pts) + + (y = read_stars(file_list, quiet = TRUE, proxy = TRUE)) + print(y) + st_crs(y) = "OGC:CRS84" + if (utils::packageVersion("sf") >= "0.9-7") { + ep = st_extract(y, pts) + print(all.equal(em, ep)) + } + } + + # nearest & bilinear comparison: + if (utils::packageVersion("sf") >= "0.9-7") { + set.seed(12331) + s = st_as_stars(matrix(rnorm(16), 4)) + pts = st_sample(st_as_sfc(st_bbox(s)), 10000, type = "regular") + s1 = st_extract(s, pts, bilinear = FALSE) + s2 = st_extract(s, pts, bilinear = TRUE) + s1$s2 = s2[[1]] + names(s1)[c(1,3)] = c("nearest", "bilinear") + print(s1[sample(10000, 5),]) + } + } > > proc.time() user system elapsed 0.60 0.14 0.73 stars/tests/align.Rout.save0000644000176200001440000000424114644736040015463 0ustar liggesusers R Under development (unstable) (2024-07-13 r86895 ucrt) -- "Unsuffered Consequences" Copyright (C) 2024 The R Foundation for Statistical Computing Platform: x86_64-w64-mingw32/x64 R is free software and comes with ABSOLUTELY NO WARRANTY. You are welcome to redistribute it under certain conditions. Type 'license()' or 'licence()' for distribution details. R is a collaborative project with many contributors. Type 'contributors()' for more information and 'citation()' on how to cite R or R packages in publications. Type 'demo()' for some demos, 'help()' for on-line help, or 'help.start()' for an HTML browser interface to help. Type 'q()' to quit R. > suppressPackageStartupMessages(library(stars)) > set.seed(1331) > st_sample(st_as_sfc(st_bbox(L7_ETMs)), 1) Geometry set for 1 feature Geometry type: POINT Dimension: XY Bounding box: xmin: 291430.8 ymin: 9111430 xmax: 291430.8 ymax: 9111430 Projected CRS: SIRGAS 2000 / UTM zone 25S POINT (291430.8 9111430) > b = st_buffer(st_sample(st_as_sfc(st_bbox(L7_ETMs)), 1), 335) > bba = stars:::st_align(st_bbox(b), st_dimensions(L7_ETMs)) > > plot(L7_ETMs[,,,1], extent = st_buffer(b, 20), reset = FALSE, key.pos = NULL) > plot(b, add = TRUE, col = NA, border = 'red') > plot(st_as_sfc(st_bbox(b)), col = NA, border = 'red', add = TRUE) > plot(st_as_sfc(bba), col = NA, border = 'blue', add = TRUE) > st_bbox(b) xmin ymin xmax ymax 297116.8 9119404.1 297786.8 9120074.1 > bba xmin ymin xmax ymax 297098.3 9119392.8 297810.8 9120076.8 > > plot(L7_ETMs[,,,1], reset = FALSE, key.pos = NULL) > plot(b, col = 'red', add = TRUE) > plot(st_as_sfc(bba), col = NA, border = 'blue', add = TRUE) > > b0 = st_buffer(b, -100) > L7 = st_as_stars(L7_ETMs)[b0][,,,1] |> st_normalize() > bbb = stars:::st_align(st_bbox(b), st_dimensions(L7)) > plot(L7, extent = st_buffer(b, 120), reset = FALSE, key.pos = NULL) > plot(st_as_sfc(st_bbox(L7)), col = NA, border = 'black', add = TRUE) > plot(st_as_sfc(bbb), col = NA, border = 'blue', add = TRUE) > plot(b, col = NA, border = 'red', add = TRUE) > > all.equal(bbb, bba) [1] TRUE > > proc.time() user system elapsed 1.15 0.18 1.32 stars/tests/warp.R0000644000176200001440000000404314603253262013650 0ustar liggesuserssuppressPackageStartupMessages(library(stars)) geomatrix = system.file("tif/geomatrix.tif", package = "stars") x = read_stars(geomatrix) # can stars reproduce what gdal does, by default? x2 = st_warp(x, use_gdal = TRUE, no_data_value = -9999) y = st_warp(x, x2) if (interactive()) { plot(x2, breaks = "equal", axes=TRUE) } if (interactive()) { plot(y, breaks = "equal", axes=TRUE) } names(x2) = names(y) all.equal(x2, y) # yes? # does gdal reproduce with stars template object? (x2 = setNames(st_warp(x, y, use_gdal = TRUE, no_data_value=-1), "file.tif")) # does gdal reproduce what stars does, default cell size? (x2 = st_warp(x, crs = st_crs(x), use_gdal = FALSE)) (y = setNames(st_warp(x, x2, use_gdal = TRUE, debug = FALSE, no_data_value=-1), "file.tif")) # try with multiple bands: tif = system.file("tif/L7_ETMs.tif", package = "stars") (x1 = read_stars(tif)) tifcp = tempfile(fileext = ".tif") file.copy(tif, tifcp) x1p = read_stars(tifcp, proxy = TRUE) st_dimensions(x1p) (x1a = st_warp(x1, crs = st_crs(4326))) (x1b = setNames(st_warp(x1, x1p, use_gdal = TRUE, no_data_value=-1), "file.tif")) # does gdal reproduce what stars does? Smaller grid: x2 = st_warp(x, crs = st_crs(x), use_gdal = FALSE, cellsize = 3) # x2 = x2[,2:43,2:43] if (interactive()) { plot(x2, breaks = "equal", axes=TRUE, reset = FALSE) } if (interactive()) { plot(st_as_sfc(st_bbox(x2)), add = TRUE, col = NA, border = 'red') } ### doesn't work: FIXME: check with more recent GDAL: (y = setNames(st_warp(x, x2, use_gdal = TRUE, debug = FALSE, no_data_value=-1), "file.tif")) if (interactive()) { plot(y, breaks = "equal") } names(x2) = names(y) # isTRUE(all.equal(x2, y, check.attributes=FALSE)) m = mean(as.vector(x2[[1]]==y[[1]]), na.rm = TRUE) u = unique(as.vector(x2[[1]]-y[[1]])) g = st_as_stars() attr(g, "dimensions")$x$offset = 0 g$values = as.vector(t(matrix(seq_len(prod(dim(g))), 180, 360))) if (interactive()) { plot(g, axes=TRUE) } a = st_warp(g, st_as_stars(), use_gdal=FALSE) b = st_warp(g, st_as_stars(), use_gdal=TRUE, no_data_value = -1) all.equal(a, b, check.attributes = FALSE) stars/tests/mdim.R0000644000176200001440000000174014603251236013625 0ustar liggesusersSys.setenv(TZ="") suppressPackageStartupMessages(library(stars)) matrix(1:4,2) m = matrix(1:4,2) s = st_as_stars(m) s = st_set_dimensions(s, st_dimensions(s), xy = NULL) sfc = st_sfc(st_point(0:1), st_point(3:2)) s = st_set_dimensions(s, 1, sfc) |> st_set_crs(4326) tm = as.POSIXct("2023-03-04 12:35") + c(0, 3600) s = st_set_dimensions(s, 2, tm) f = tempfile(fileext = ".nc") write_mdim(s, f, as_float = FALSE) s2 = read_mdim(f) #print(s2) print(all.equal(s, s2, check.attributes = FALSE)) print(all.equal(s, s2)) st_crs(sfc) = st_crs(s2) st_crs(s) = st_crs(s2) tm = as.Date("2023-02-25") + 0:1 sd = st_set_dimensions(s, 2, tm) print(sd) sd = st_set_dimensions(sd, 1, sfc) print(sd) f = tempfile(fileext = ".nc") write_mdim(sd, f) sd2 = read_mdim(f) print(sd2) print(all.equal(sd, sd2, check.attributes = FALSE)) print(all.equal(sd, sd2)) # if (.Platform$OS.type == "unix") { # print(read_mdim(paste0("/vsizip/", (system.file("zarr/sic_daily_sample.zarr.zip", package = "stars")))) # } stars/tests/transform.R0000644000176200001440000000243014604056237014714 0ustar liggesuserssuppressPackageStartupMessages(library(stars)) #suppressPackageStartupMessages(library(lwgeom)) geomatrix = system.file("tif/geomatrix.tif", package = "stars") (x = read_stars(geomatrix)) new = st_crs(4326) y = st_transform(x, new) plot(st_transform(st_as_sfc(st_bbox(x)), new), col = NA, border = 'red') plot(st_as_sfc(y, as_points=FALSE), col = NA, border = 'green', axes = TRUE, add = TRUE) image(y, add = TRUE) plot(st_as_sfc(y, as_points=TRUE), pch=3, cex=.5, col = 'blue', add = TRUE) plot(st_transform(st_as_sfc(x, as_points=FALSE), new), add = TRUE) tif = system.file("tif/L7_ETMs.tif", package = "stars") x = read_stars(tif)[,1:10,1:10,1:3] x_ = st_transform(x, st_crs(4326)) ## IGNORE_RDIFF_BEGIN if (require(lwgeom, quietly = TRUE)) { x__ = st_transform_proj(x, st_crs(4326)$proj4string) st_crs(x_) = st_crs(x__) print(all.equal(x_,x__)) # x__ = st_transform(x, x_) #now in st_warp # all.equal(x_, x__) x = st_xy2sfc(x, as_points = FALSE) (x_ = st_transform(x, st_crs(4326))) (x__ = st_transform_proj(x, st_crs(4326))) print(all.equal(x_,x__,check.attributes = FALSE)) } ## IGNORE_RDIFF_END # nothing to transform: now errs: try(st_transform(st_as_stars(list(m = matrix(1,10,10))), st_crs(4326))) try(st_transform_proj(st_as_stars(list(m = matrix(1,10,10))), st_crs(4326))) stars/tests/write.R0000644000176200001440000000046013777653456014054 0ustar liggesuserssuppressPackageStartupMessages(library(stars)) tif = system.file("tif/L7_ETMs.tif", package = "stars") (x = read_stars(tif)) # write stars object: write_stars(x, tempfile(), driver = "GTiff") # write stars_proxy object: (x = read_stars(tif, proxy = TRUE)) write_stars(x, tempfile(), driver = "GTiff") stars/tests/raster.R0000644000176200001440000000405414464700704014204 0ustar liggesusersoptions(rgdal_show_exportToProj4_warnings = "none") suppressPackageStartupMessages(library(stars)) if (suppressPackageStartupMessages(require(sp, quietly = TRUE)) && require("raster", quietly = TRUE) && suppressPackageStartupMessages(require("terra", quietly = TRUE))) { tif = system.file("tif/L7_ETMs.tif", package = "stars") (x = read_stars(tif)) (r = as(x, "Raster")) (y = st_as_stars(r)) # single band: x = adrop(x[,,,1]) r = as(x, "Raster") (y = st_as_stars(r)) # proxy: (x = read_stars(tif, proxy = TRUE)) (r = as(x, "Raster")) (y = st_as_stars(r)) (y2 = st_as_stars(r, proxy = TRUE)) } if (require(terra, quietly = TRUE)) { ## terra ------------- stopifnot(require(terra)) (x = read_stars(tif)) (r = as(x, "SpatRaster")) (y = st_as_stars(r)) # single band: x = adrop(x[,,,1]) r = as(x, "SpatRaster") (y = st_as_stars(r)) # proxy: (x = read_stars(tif, proxy = TRUE)) (r = as(x, "SpatRaster")) (y = st_as_stars(r)) (y2 = st_as_stars(r, proxy = TRUE)) f = system.file("tif/lc.tif", package = "stars") lc = read_stars(system.file("tif/lc.tif", package = "stars")) levels(lc[[1]]) r = rast(lc) e = attr(lc[[1]], "exclude") if (packageVersion("terra") >= "1.5.48") { print(all.equal(terra::levels(r)[[1]][,2], terra::levels(rast(f))[[1]][!e,2])) print(all.equal(terra::coltab(r)[[1]][!e,], terra::coltab(rast(f))[[1]][!e,], check.attributes = FALSE)) r<- rast(nrow=10, ncol=10) set.seed(5) values(r)<- sample(1:3, size = ncell(r), replace = TRUE) levels(r) <- data.frame(ID=1:3, category = c("c1", "c2", "c3")) r2<- rast(nrow=10, ncol=10) set.seed(6) values(r2)<- sample(c(0,2,3), size = ncell(r2), replace = TRUE) levels(r2) <- data.frame(ID=c(0,2,3), category = c("x1", "x2", "x3")) r3<- c(r,r2) names(r3)<- c("lyr.1", "lyr.2") r3_stars<- st_as_stars(r3) st_redimension(r3_stars) |> print() rast(r3_stars) |> levels() |> print() m = matrix(1:121, 11, 11) bb = st_bbox(c(xmin=0, xmax=11, ymin=0, ymax=11)) s = st_as_stars(bb, values = m) r = rast(s) aggregate(r, 2) |> st_as_stars() -> r.s stopifnot(isTRUE(all.equal(r.s[,1:5,1:5], st_downsample(s, 1, FUN = mean)))) } } stars/tests/stars.Rout.save0000644000176200001440000004573114644736040015536 0ustar liggesusers R Under development (unstable) (2024-07-13 r86895 ucrt) -- "Unsuffered Consequences" Copyright (C) 2024 The R Foundation for Statistical Computing Platform: x86_64-w64-mingw32/x64 R is free software and comes with ABSOLUTELY NO WARRANTY. You are welcome to redistribute it under certain conditions. Type 'license()' or 'licence()' for distribution details. R is a collaborative project with many contributors. Type 'contributors()' for more information and 'citation()' on how to cite R or R packages in publications. Type 'demo()' for some demos, 'help()' for on-line help, or 'help.start()' for an HTML browser interface to help. Type 'q()' to quit R. > Sys.setenv(TZ="UTC") > suppressPackageStartupMessages(library(stars)) > set.seed(13521) # runif > tif = system.file("tif/L7_ETMs.tif", package = "stars") > (x_ = read_stars(c(tif,tif))) # FIXME: not what you'd expect stars object with 3 dimensions and 2 attributes attribute(s): Min. 1st Qu. Median Mean 3rd Qu. Max. L7_ETMs.tif 1 54 69 68.91242 86 255 L7_ETMs.tif.1 1 54 69 68.91242 86 255 dimension(s): from to offset delta refsys point x/y x 1 349 288776 28.5 SIRGAS 2000 / UTM zone 25S FALSE [x] y 1 352 9120761 -28.5 SIRGAS 2000 / UTM zone 25S FALSE [y] band 1 6 NA NA NA NA > (x = read_stars(tif)) stars object with 3 dimensions and 1 attribute attribute(s): Min. 1st Qu. Median Mean 3rd Qu. Max. L7_ETMs.tif 1 54 69 68.91242 86 255 dimension(s): from to offset delta refsys point x/y x 1 349 288776 28.5 SIRGAS 2000 / UTM zone 25S FALSE [x] y 1 352 9120761 -28.5 SIRGAS 2000 / UTM zone 25S FALSE [y] band 1 6 NA NA NA NA > # image(x) > gdal_crs(tif) Coordinate Reference System: User input: PROJCS["SIRGAS 2000 / UTM zone 25S",GEOGCS["SIRGAS 2000",DATUM["Sistema_de_Referencia_Geocentrico_para_las_AmericaS_2000",SPHEROID["GRS 1980",6378137,298.257222101,AUTHORITY["EPSG","7019"]],AUTHORITY["EPSG","6674"]],PRIMEM["Greenwich",0,AUTHORITY["EPSG","8901"]],UNIT["degree",0.0174532925199433,AUTHORITY["EPSG","9122"]],AUTHORITY["EPSG","4674"]],PROJECTION["Transverse_Mercator"],PARAMETER["latitude_of_origin",0],PARAMETER["central_meridian",-33],PARAMETER["scale_factor",0.9996],PARAMETER["false_easting",500000],PARAMETER["false_northing",10000000],UNIT["metre",1,AUTHORITY["EPSG","9001"]],AXIS["Easting",EAST],AXIS["Northing",NORTH],AUTHORITY["EPSG","31985"]] wkt: PROJCRS["SIRGAS 2000 / UTM zone 25S", BASEGEOGCRS["SIRGAS 2000", DATUM["Sistema de Referencia Geocentrico para las AmericaS 2000", ELLIPSOID["GRS 1980",6378137,298.257222101, LENGTHUNIT["metre",1]]], PRIMEM["Greenwich",0, ANGLEUNIT["degree",0.0174532925199433]], ID["EPSG",4674]], CONVERSION["UTM zone 25S", METHOD["Transverse Mercator", ID["EPSG",9807]], PARAMETER["Latitude of natural origin",0, ANGLEUNIT["degree",0.0174532925199433], ID["EPSG",8801]], PARAMETER["Longitude of natural origin",-33, ANGLEUNIT["degree",0.0174532925199433], ID["EPSG",8802]], PARAMETER["Scale factor at natural origin",0.9996, SCALEUNIT["unity",1], ID["EPSG",8805]], PARAMETER["False easting",500000, LENGTHUNIT["metre",1], ID["EPSG",8806]], PARAMETER["False northing",10000000, LENGTHUNIT["metre",1], ID["EPSG",8807]]], CS[Cartesian,2], AXIS["easting",east, ORDER[1], LENGTHUNIT["metre",1]], AXIS["northing",north, ORDER[2], LENGTHUNIT["metre",1]], ID["EPSG",31985]] > plot(x) > plot(x, join_zlim = FALSE) > x %>% st_set_dimensions(names = c('a', 'b', 'c')) stars object with 3 dimensions and 1 attribute attribute(s): Min. 1st Qu. Median Mean 3rd Qu. Max. L7_ETMs.tif 1 54 69 68.91242 86 255 dimension(s): from to offset delta refsys point x/y a 1 349 288776 28.5 SIRGAS 2000 / UTM zone 25S FALSE [x] b 1 352 9120761 -28.5 SIRGAS 2000 / UTM zone 25S FALSE [y] c 1 6 NA NA NA NA > st_get_dimension_values(x, 3) [1] 1 2 3 4 5 6 > > (x1 = st_set_dimensions(x, "band", values = c(1,2,3,4,5,7), names = "band_number", point = TRUE)) stars object with 3 dimensions and 1 attribute attribute(s): Min. 1st Qu. Median Mean 3rd Qu. Max. L7_ETMs.tif 1 54 69 68.91242 86 255 dimension(s): from to offset delta refsys point values x/y x 1 349 288776 28.5 SIRGAS 2000 / UTM zone 25S FALSE NULL [x] y 1 352 9120761 -28.5 SIRGAS 2000 / UTM zone 25S FALSE NULL [y] band_number 1 6 NA NA NA TRUE 1,...,7 > rbind(c(0.45,0.515), c(0.525,0.605), c(0.63,0.69), c(0.775,0.90), c(1.55,1.75), c(2.08,2.35)) %>% + units::set_units(um) -> bw # units::set_units(µm) -> bw > # set bandwidth midpoint: > (x2 = st_set_dimensions(x, "band", values = 0.5 * (bw[,1]+bw[,2]), + names = "bandwidth_midpoint", point = TRUE)) stars object with 3 dimensions and 1 attribute attribute(s): Min. 1st Qu. Median Mean 3rd Qu. Max. L7_ETMs.tif 1 54 69 68.91242 86 255 dimension(s): from to offset delta refsys point x 1 349 288776 28.5 SIRGAS 2000 / UTM zone 25S FALSE y 1 352 9120761 -28.5 SIRGAS 2000 / UTM zone 25S FALSE bandwidth_midpoint 1 6 NA NA udunits TRUE values x/y x NULL [x] y NULL [y] bandwidth_midpoint 0.4825 [um],...,2.215 [um] > # set bandwidth intervals: > (x3 = st_set_dimensions(x, "band", values = make_intervals(bw), names = "bandwidth")) stars object with 3 dimensions and 1 attribute attribute(s): Min. 1st Qu. Median Mean 3rd Qu. Max. L7_ETMs.tif 1 54 69 68.91242 86 255 dimension(s): from to offset delta refsys point x 1 349 288776 28.5 SIRGAS 2000 / UTM zone 25S FALSE y 1 352 9120761 -28.5 SIRGAS 2000 / UTM zone 25S FALSE bandwidth 1 6 NA NA udunits NA values x/y x NULL [x] y NULL [y] bandwidth [0.45,0.515) [um],...,[2.08,2.35) [um] > > x + x stars object with 3 dimensions and 1 attribute attribute(s): Min. 1st Qu. Median Mean 3rd Qu. Max. L7_ETMs.tif 2 108 138 137.8248 172 510 dimension(s): from to offset delta refsys point x/y x 1 349 288776 28.5 SIRGAS 2000 / UTM zone 25S FALSE [x] y 1 352 9120761 -28.5 SIRGAS 2000 / UTM zone 25S FALSE [y] band 1 6 NA NA NA NA > x * x stars object with 3 dimensions and 1 attribute attribute(s): Min. 1st Qu. Median Mean 3rd Qu. Max. L7_ETMs.tif 1 2916 4761 5512.41 7396 65025 dimension(s): from to offset delta refsys point x/y x 1 349 288776 28.5 SIRGAS 2000 / UTM zone 25S FALSE [x] y 1 352 9120761 -28.5 SIRGAS 2000 / UTM zone 25S FALSE [y] band 1 6 NA NA NA NA > x[,,,1:3] stars object with 3 dimensions and 1 attribute attribute(s): Min. 1st Qu. Median Mean 3rd Qu. Max. L7_ETMs.tif 21 58 70 70.36041 83 255 dimension(s): from to offset delta refsys point x/y x 1 349 288776 28.5 SIRGAS 2000 / UTM zone 25S FALSE [x] y 1 352 9120761 -28.5 SIRGAS 2000 / UTM zone 25S FALSE [y] band 1 3 NA NA NA NA > x[,1:100,100:200,] stars object with 3 dimensions and 1 attribute attribute(s): Min. 1st Qu. Median Mean 3rd Qu. Max. L7_ETMs.tif 13 54 65 67.21531 77 252 dimension(s): from to offset delta refsys point x/y x 1 100 288776 28.5 SIRGAS 2000 / UTM zone 25S FALSE [x] y 100 200 9120761 -28.5 SIRGAS 2000 / UTM zone 25S FALSE [y] band 1 6 NA NA NA NA > sqrt(x) stars object with 3 dimensions and 1 attribute attribute(s): Min. 1st Qu. Median Mean 3rd Qu. Max. L7_ETMs.tif 1 7.348469 8.306624 8.094006 9.273618 15.96872 dimension(s): from to offset delta refsys point x/y x 1 349 288776 28.5 SIRGAS 2000 / UTM zone 25S FALSE [x] y 1 352 9120761 -28.5 SIRGAS 2000 / UTM zone 25S FALSE [y] band 1 6 NA NA NA NA > st_apply(x, 3, min) stars object with 1 dimensions and 1 attribute attribute(s): Min. 1st Qu. Median Mean 3rd Qu. Max. min 1 3 15 18.5 29.25 47 dimension(s): from to band 1 6 > st_apply(x, 1:2, max) stars object with 2 dimensions and 1 attribute attribute(s): Min. 1st Qu. Median Mean 3rd Qu. Max. max 55 85 96 99.36018 113 255 dimension(s): from to offset delta refsys point x/y x 1 349 288776 28.5 SIRGAS 2000 / UTM zone 25S FALSE [x] y 1 352 9120761 -28.5 SIRGAS 2000 / UTM zone 25S FALSE [y] > st_apply(x, 1:2, range) stars object with 3 dimensions and 1 attribute attribute(s): Min. 1st Qu. Median Mean 3rd Qu. Max. L7_ETMs.tif 1 50 71 72.43565 96 255 dimension(s): from to offset delta refsys point x/y range 1 2 NA NA NA NA x 1 349 288776 28.5 SIRGAS 2000 / UTM zone 25S FALSE [x] y 1 352 9120761 -28.5 SIRGAS 2000 / UTM zone 25S FALSE [y] > > geomatrix = system.file("tif/geomatrix.tif", package = "stars") > x = read_stars(geomatrix) > y = st_transform(x, st_crs(4326)) > st_coordinates(x)[1:2,] x y 1 1841000 1144000 2 1841002 1143995 > > nc = system.file("nc/tos_O1_2001-2002.nc", package = "stars") > if (nc != "" && require(PCICt, quietly = TRUE)) { + print(x <- read_stars(nc)) + print(st_bbox(x)) + s = st_as_stars(st_bbox(x)) # inside = NA + print(st_bbox(s)) + s = st_as_stars(st_bbox(x), inside = TRUE) + print(st_bbox(s)) + s = st_as_stars(st_bbox(x), inside = FALSE) + print(st_bbox(s)) + (s = st_as_stars(st_bbox(x), dy = 1)) + print(st_bbox(s)) + print(identical(st_as_stars(st_bbox(x), dx = 1), st_as_stars(st_bbox(x), dy = 1))) + s = st_as_stars(st_bbox(x), dx = 10) + print(st_bbox(s)) + s = st_as_stars(st_bbox(x), dx = 20) + print(st_bbox(s)) + x1 = x + st_crs(x1) = "OGC:CRS84" + print(identical(st_as_stars(st_bbox(x1), dx = 1), st_as_stars(st_bbox(x1), dx = units::set_units(1, degree)))) + + df = as.data.frame(x) + if (require(units, quietly = TRUE)) + print(units::drop_units(x)) + + print(dimnames(x)) + dimnames(x) <- letters[1:3] + print(dimnames(x)) + } # PCICt > > st_as_stars() stars object with 2 dimensions and 1 attribute attribute(s): Min. 1st Qu. Median Mean 3rd Qu. Max. values 0 0 0 0 0 0 dimension(s): from to offset delta refsys x/y x 1 360 -180 1 WGS 84 (CRS84) [x] y 1 180 90 -1 WGS 84 (CRS84) [y] > > # multiple sub-datasets: > nc_red = system.file("nc/reduced.nc", package = "stars") > red = read_stars(nc_red) sst, anom, err, ice, > red stars object with 4 dimensions and 4 attributes attribute(s): Min. 1st Qu. Median Mean 3rd Qu. Max. NA's sst [°C] -1.80 -0.03 13.655 12.9940841 24.8125 32.97 4448 anom [°C] -10.16 -0.58 -0.080 -0.1855812 0.2100 2.99 4448 err [°C] 0.11 0.16 0.270 0.2626872 0.3200 0.84 4448 ice [percent] 0.01 0.47 0.920 0.7178118 0.9600 1.00 13266 dimension(s): from to offset delta refsys x/y x 1 180 -1 2 NA [x] y 1 90 90 -2 NA [y] zlev 1 1 0 [m] NA NA time 1 1 1981-12-31 UTC NA POSIXct > plot(red) > > x = st_xy2sfc(read_stars(tif)[,1:10,1:10,], as_points = FALSE) > st_bbox(x) xmin ymin xmax ymax 288776.3 9120475.8 289061.3 9120760.8 > x = read_stars(tif) > merge(split(x, "band")) stars object with 3 dimensions and 1 attribute attribute(s): Min. 1st Qu. Median Mean 3rd Qu. Max. X1.X2.X3.X4.X5.X6 1 54 69 68.91242 86 255 dimension(s): from to offset delta refsys point values x 1 349 288776 28.5 SIRGAS 2000 / UTM zone 25S FALSE NULL y 1 352 9120761 -28.5 SIRGAS 2000 / UTM zone 25S FALSE NULL attributes 1 6 NA NA NA NA X1,...,X6 x/y x [x] y [y] attributes > > read_stars(c(tif,tif)) # merges as attributes stars object with 3 dimensions and 2 attributes attribute(s): Min. 1st Qu. Median Mean 3rd Qu. Max. L7_ETMs.tif 1 54 69 68.91242 86 255 L7_ETMs.tif.1 1 54 69 68.91242 86 255 dimension(s): from to offset delta refsys point x/y x 1 349 288776 28.5 SIRGAS 2000 / UTM zone 25S FALSE [x] y 1 352 9120761 -28.5 SIRGAS 2000 / UTM zone 25S FALSE [y] band 1 6 NA NA NA NA > read_stars(c(tif,tif), along = "sensor") stars object with 4 dimensions and 1 attribute attribute(s), summary of first 1e+05 cells: Min. 1st Qu. Median Mean 3rd Qu. Max. L7_ETMs.tif 47 65 76 77.3419 87 255 dimension(s): from to offset delta refsys point x/y x 1 349 288776 28.5 SIRGAS 2000 / UTM zone 25S FALSE [x] y 1 352 9120761 -28.5 SIRGAS 2000 / UTM zone 25S FALSE [y] band 1 6 NA NA NA NA sensor 1 2 NA NA NA NA > read_stars(c(tif,tif), along = 4) stars object with 4 dimensions and 1 attribute attribute(s), summary of first 1e+05 cells: Min. 1st Qu. Median Mean 3rd Qu. Max. L7_ETMs.tif 47 65 76 77.3419 87 255 dimension(s): from to offset delta refsys point x/y x 1 349 288776 28.5 SIRGAS 2000 / UTM zone 25S FALSE [x] y 1 352 9120761 -28.5 SIRGAS 2000 / UTM zone 25S FALSE [y] band 1 6 NA NA NA NA new_dim 1 2 NA NA NA NA > read_stars(c(tif,tif), along = "band") stars object with 3 dimensions and 1 attribute attribute(s), summary of first 1e+05 cells: Min. 1st Qu. Median Mean 3rd Qu. Max. L7_ETMs.tif 47 65 76 77.3419 87 255 dimension(s): from to offset delta refsys point x/y x 1 349 288776 28.5 SIRGAS 2000 / UTM zone 25S FALSE [x] y 1 352 9120761 -28.5 SIRGAS 2000 / UTM zone 25S FALSE [y] band 1 12 NA NA NA NA > read_stars(c(tif,tif), along = 3) stars object with 3 dimensions and 1 attribute attribute(s), summary of first 1e+05 cells: Min. 1st Qu. Median Mean 3rd Qu. Max. L7_ETMs.tif 47 65 76 77.3419 87 255 dimension(s): from to offset delta refsys point x/y x 1 349 288776 28.5 SIRGAS 2000 / UTM zone 25S FALSE [x] y 1 352 9120761 -28.5 SIRGAS 2000 / UTM zone 25S FALSE [y] band 1 12 NA NA NA NA > > # cut: > tif = system.file("tif/L7_ETMs.tif", package = "stars") > x = read_stars(tif) > cut(x, c(0, 50, 100, 255)) stars object with 3 dimensions and 1 attribute attribute(s): L7_ETMs.tif (0,50] :156060 (50,100] :503764 (100,255]: 77264 dimension(s): from to offset delta refsys point x/y x 1 349 288776 28.5 SIRGAS 2000 / UTM zone 25S FALSE [x] y 1 352 9120761 -28.5 SIRGAS 2000 / UTM zone 25S FALSE [y] band 1 6 NA NA NA NA > cut(x[,,,1,drop=TRUE], c(0, 50, 100, 255)) stars object with 2 dimensions and 1 attribute attribute(s): L7_ETMs.tif (0,50] : 1 (50,100] :117134 (100,255]: 5713 dimension(s): from to offset delta refsys point x/y x 1 349 288776 28.5 SIRGAS 2000 / UTM zone 25S FALSE [x] y 1 352 9120761 -28.5 SIRGAS 2000 / UTM zone 25S FALSE [y] > plot(cut(x[,,,1,drop=TRUE], c(0, 50, 100, 255))) > > st_bbox(st_dimensions(x)) xmin ymin xmax ymax 288776.3 9110728.8 298722.8 9120760.8 > x[x < 0] = NA > x[is.na(x)] = 0 > > # c: > f = system.file("netcdf/avhrr-only-v2.19810902.nc", package = "starsdata") > if (FALSE && f != "") { + files = c("avhrr-only-v2.19810901.nc", + "avhrr-only-v2.19810902.nc", + "avhrr-only-v2.19810903.nc", + "avhrr-only-v2.19810904.nc", + "avhrr-only-v2.19810905.nc", + "avhrr-only-v2.19810906.nc", + "avhrr-only-v2.19810907.nc", + "avhrr-only-v2.19810908.nc", + "avhrr-only-v2.19810909.nc") + l = list() + for (f in files) { + from = system.file(paste0("netcdf/", f), package = "starsdata") + l[[f]] = read_stars(from, sub = c("sst", "anom")) + } + ret = do.call(c, l) + print(ret) + ret = adrop(c(l[[1]], l[[2]], l[[3]], along = list(times = as.Date("1981-09-01") + 0:2))) + print(ret) + #ret = adrop(adrop(c(l[[1]], l[[2]], l[[3]], along = "times"))) + #print(ret) + } > > st_dimensions(list(matrix(1, 4, 4))) # st_dimensions.default from to point values X1 1 1 FALSE 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1 > > if (FALSE && require("starsdata", quietly = TRUE)) { + # curvilinear: + s5p = system.file( + "sentinel5p/S5P_NRTI_L2__NO2____20180717T120113_20180717T120613_03932_01_010002_20180717T125231.nc", + package = "starsdata") + print(s5p) + lat_ds = paste0("HDF5:\"", s5p, "\"://PRODUCT/latitude") + lon_ds = paste0("HDF5:\"", s5p, "\"://PRODUCT/longitude") + nit_ds = paste0("HDF5:\"", s5p, "\"://PRODUCT/SUPPORT_DATA/DETAILED_RESULTS/nitrogendioxide_summed_total_column") + lat = read_stars(lat_ds) + lon = read_stars(lon_ds) + nit = read_stars(nit_ds) + nit[[1]][nit[[1]] > 9e+36] = NA + + ll = setNames(c(lon, lat), c("x", "y")) + nit.c = st_as_stars(nit, curvilinear = ll) + print(nit.c) + + s5p = system.file( + "sentinel5p/S5P_NRTI_L2__NO2____20180717T120113_20180717T120613_03932_01_010002_20180717T125231.nc", + package = "starsdata") + nit.c2 = read_stars(s5p, + sub = "//PRODUCT/SUPPORT_DATA/DETAILED_RESULTS/nitrogendioxide_summed_total_column", + curvilinear = c("//PRODUCT/latitude", "//PRODUCT/longitude")) + print(all.equal(nit.c, nit.c2)) + } > > proc.time() user system elapsed 6.00 0.54 6.53 stars/tests/area.Rout.save0000644000176200001440000000540314644736040015302 0ustar liggesusers R Under development (unstable) (2024-07-13 r86895 ucrt) -- "Unsuffered Consequences" Copyright (C) 2024 The R Foundation for Statistical Computing Platform: x86_64-w64-mingw32/x64 R is free software and comes with ABSOLUTELY NO WARRANTY. You are welcome to redistribute it under certain conditions. Type 'license()' or 'licence()' for distribution details. R is a collaborative project with many contributors. Type 'contributors()' for more information and 'citation()' on how to cite R or R packages in publications. Type 'demo()' for some demos, 'help()' for on-line help, or 'help.start()' for an HTML browser interface to help. Type 'q()' to quit R. > suppressPackageStartupMessages(library(stars)) > tif = system.file("tif/L7_ETMs.tif", package = "stars") > x = read_stars(tif) > (a = st_area(x[,1:5,1:5])) stars object with 2 dimensions and 1 attribute attribute(s): Min. 1st Qu. Median Mean 3rd Qu. Max. area [m^2] 812.25 812.25 812.25 812.25 812.25 812.25 dimension(s): from to offset delta refsys point x/y x 1 5 288776 28.5 SIRGAS 2000 / UTM zone 25S FALSE [x] y 1 5 9120761 -28.5 SIRGAS 2000 / UTM zone 25S FALSE [y] > > m = matrix(1:20, nrow = 5, ncol = 4) > x = c(0, 0.5, 1, 2, 4, 5) # 6 numbers: boundaries! > y = c(0.3, 0.5, 1, 2, 2.2) # 5 numbers: boundaries! > (r = st_as_stars(list(m = m), dimensions = st_dimensions(x = x, y = y))) stars object with 2 dimensions and 1 attribute attribute(s): Min. 1st Qu. Median Mean 3rd Qu. Max. m 1 5.75 10.5 10.5 15.25 20 dimension(s): from to point values x/y x 1 5 FALSE [0,0.5),...,[4,5) [x] y 1 4 FALSE [0.3,0.5),...,[2,2.2) [y] > # image(r, axes = TRUE, col = grey((1:20)/20)) > (a = st_area(r)) Missing coordinate reference system: assuming Cartesian coordinates stars object with 2 dimensions and 1 attribute attribute(s): Min. 1st Qu. Median Mean 3rd Qu. Max. area 0.1 0.2 0.325 0.475 0.5 2 dimension(s): from to point values x/y x 1 5 FALSE [0,0.5),...,[4,5) [x] y 1 4 FALSE [0.3,0.5),...,[2,2.2) [y] > #plot(a, axes = TRUE) > > s5p = system.file("sentinel5p/S5P_NRTI_L2__NO2____20180717T120113_20180717T120613_03932_01_010002_20180717T125231.nc", package = "starsdata") > if (s5p != "") { + nit.c = read_stars(s5p, sub = "//PRODUCT/SUPPORT_DATA/DETAILED_RESULTS/nitrogendioxide_summed_total_column", + curvilinear = c("//PRODUCT/longitude", "//PRODUCT/latitude"), driver = NULL) + if (inherits(nit.c[[1]], "units")) + nit.c[[1]] = units::drop_units(nit.c[[1]]) + nit.c[[1]][nit.c[[1]] > 9e+36] = NA + st_crs(nit.c) = 4326 + print((a <- st_area(nit.c[,1:5,1:5]))) + # plot(a, axes = TRUE, border = NA) + } > > proc.time() user system elapsed 0.71 0.15 0.84 stars/tests/downsample.Rout.save0000644000176200001440000001027214644736040016543 0ustar liggesusers R Under development (unstable) (2024-07-13 r86895 ucrt) -- "Unsuffered Consequences" Copyright (C) 2024 The R Foundation for Statistical Computing Platform: x86_64-w64-mingw32/x64 R is free software and comes with ABSOLUTELY NO WARRANTY. You are welcome to redistribute it under certain conditions. Type 'license()' or 'licence()' for distribution details. R is a collaborative project with many contributors. Type 'contributors()' for more information and 'citation()' on how to cite R or R packages in publications. Type 'demo()' for some demos, 'help()' for on-line help, or 'help.start()' for an HTML browser interface to help. Type 'q()' to quit R. > suppressPackageStartupMessages(library(stars)) > m = matrix(1:121, 11, 11) > m [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10] [,11] [1,] 1 12 23 34 45 56 67 78 89 100 111 [2,] 2 13 24 35 46 57 68 79 90 101 112 [3,] 3 14 25 36 47 58 69 80 91 102 113 [4,] 4 15 26 37 48 59 70 81 92 103 114 [5,] 5 16 27 38 49 60 71 82 93 104 115 [6,] 6 17 28 39 50 61 72 83 94 105 116 [7,] 7 18 29 40 51 62 73 84 95 106 117 [8,] 8 19 30 41 52 63 74 85 96 107 118 [9,] 9 20 31 42 53 64 75 86 97 108 119 [10,] 10 21 32 43 54 65 76 87 98 109 120 [11,] 11 22 33 44 55 66 77 88 99 110 121 > s = st_as_stars(m) > st_downsample(s, 1) stars object with 2 dimensions and 1 attribute attribute(s): Min. 1st Qu. Median Mean 3rd Qu. Max. A1 1 28.5 61 61 93.5 121 dimension(s): from to offset delta point x/y X1 1 6 0 2 FALSE [x] X2 1 6 0 2 FALSE [y] > st_downsample(s, 1)[[1]] [,1] [,2] [,3] [,4] [,5] [,6] [1,] 1 23 45 67 89 111 [2,] 3 25 47 69 91 113 [3,] 5 27 49 71 93 115 [4,] 7 29 51 73 95 117 [5,] 9 31 53 75 97 119 [6,] 11 33 55 77 99 121 > st_downsample(s, 1, offset = 1) stars object with 2 dimensions and 1 attribute attribute(s): Min. 1st Qu. Median Mean 3rd Qu. Max. A1 13 37 61 61 85 109 dimension(s): from to offset delta point x/y X1 1 5 1 2 FALSE [x] X2 1 5 1 2 FALSE [y] > st_downsample(s, 1, offset = 1)[[1]] [,1] [,2] [,3] [,4] [,5] [1,] 13 35 57 79 101 [2,] 15 37 59 81 103 [3,] 17 39 61 83 105 [4,] 19 41 63 85 107 [5,] 21 43 65 87 109 > st_downsample(s, 1, FUN = mean) stars object with 2 dimensions and 1 attribute attribute(s): Min. 1st Qu. Median Mean 3rd Qu. Max. A1 7 31 55 55 79 103 dimension(s): from to offset delta point x/y X1 1 5 0 2 FALSE [x] X2 1 5 0 2 FALSE [y] > st_downsample(s, 1, FUN = mean)[[1]] [,1] [,2] [,3] [,4] [,5] [1,] 7 29 51 73 95 [2,] 9 31 53 75 97 [3,] 11 33 55 77 99 [4,] 13 35 57 79 101 [5,] 15 37 59 81 103 > st_downsample(s, 1, offset = 1, FUN = mean) stars object with 2 dimensions and 1 attribute attribute(s): Min. 1st Qu. Median Mean 3rd Qu. Max. A1 19 43 67 67 91 115 dimension(s): from to offset delta point x/y X1 1 5 1 2 FALSE [x] X2 1 5 1 2 FALSE [y] > st_downsample(s, 1, offset = c(0,1), FUN = mean)[[1]] [,1] [,2] [,3] [,4] [,5] [1,] 18 40 62 84 106 [2,] 20 42 64 86 108 [3,] 22 44 66 88 110 [4,] 24 46 68 90 112 [5,] 26 48 70 92 114 > st_downsample(s, 1, offset = c(0,1)) stars object with 2 dimensions and 1 attribute attribute(s): Min. 1st Qu. Median Mean 3rd Qu. Max. A1 12 36.5 61 61 85.5 110 dimension(s): from to offset delta point x/y X1 1 6 0 2 FALSE [x] X2 1 5 1 2 FALSE [y] > st_downsample(s, 1, offset = c(0,1))[[1]] [,1] [,2] [,3] [,4] [,5] [1,] 12 34 56 78 100 [2,] 14 36 58 80 102 [3,] 16 38 60 82 104 [4,] 18 40 62 84 106 [5,] 20 42 64 86 108 [6,] 22 44 66 88 110 > > proc.time() user system elapsed 0.68 0.10 0.79 stars/tests/nc.R0000644000176200001440000000233413777653456013324 0ustar liggesusers# examples with the sf::nc polygon dataset NOT ABOUT NETCDF's, see e.g. stars.R netcdf.R suppressPackageStartupMessages(library(sf)) nc = read_sf(system.file("gpkg/nc.gpkg", package="sf")) m = st_set_geometry(nc, NULL) n = as.matrix(m[c("BIR74", "SID74", "NWBIR74", "BIR79", "SID79", "NWBIR79")]) dim(n) = c(county = 100, var = 3, year = 2) dimnames(n) = list(county = nc$NAME, var = c("BIR", "SID", "NWBIR"), year = c(1974, 1979)) suppressPackageStartupMessages(library(stars)) (st = st_as_stars(pop = n)) foo <- st %>% st_set_dimensions(1, st_geometry(nc)) # %>% st_set_dimensions(3, c(1974, 1979)) st %>% st_set_dimensions(1, st_geometry(nc)) %>% st_set_dimensions(names = c("geometries", "var", "year")) foo st_bbox(foo) (x = st_as_sf(foo)) frac = function(x) x[2] / x[1] frac2 = function(x) c(sidsr = x[2] / x[1], nwbr = x[3] / x[1]) frac2an = function(x) c(x[2] / x[1], x[3] / x[1]) st_apply(foo, c(1,3), frac) st_apply(foo, c(1,3), frac2) st_apply(foo, c(1,3), frac2an) library(abind) (x = aperm(st_apply(foo, c(1,3), frac2), c(2,3,1))) y = aperm(st_apply(foo, c(1,3), frac2), c("county","year","frac2")) all.equal(st_dimensions(x), st_dimensions(y)) split(foo, 2) split(foo, 3) st_crs(foo) plot(foo) # subset vector cube: foo[nc[1]] stars/tests/predict.Rout.save0000644000176200001440000000662014644736040016026 0ustar liggesusers R Under development (unstable) (2024-07-13 r86895 ucrt) -- "Unsuffered Consequences" Copyright (C) 2024 The R Foundation for Statistical Computing Platform: x86_64-w64-mingw32/x64 R is free software and comes with ABSOLUTELY NO WARRANTY. You are welcome to redistribute it under certain conditions. Type 'license()' or 'licence()' for distribution details. R is a collaborative project with many contributors. Type 'contributors()' for more information and 'citation()' on how to cite R or R packages in publications. Type 'demo()' for some demos, 'help()' for on-line help, or 'help.start()' for an HTML browser interface to help. Type 'q()' to quit R. > suppressPackageStartupMessages(library(stars)) > # predict: > tif = system.file("tif/L7_ETMs.tif", package = "stars") > (x = read_stars(tif)) stars object with 3 dimensions and 1 attribute attribute(s): Min. 1st Qu. Median Mean 3rd Qu. Max. L7_ETMs.tif 1 54 69 68.91242 86 255 dimension(s): from to offset delta refsys point x/y x 1 349 288776 28.5 SIRGAS 2000 / UTM zone 25S FALSE [x] y 1 352 9120761 -28.5 SIRGAS 2000 / UTM zone 25S FALSE [y] band 1 6 NA NA NA NA > st_crs(x) = st_crs(x) # cleans trailing space > model = lm(x~L7_ETMs.tif, head(as.data.frame(x), 50)) > x = predict(x, model) > > (y = read_stars(tif, proxy = TRUE)) stars_proxy object with 1 attribute in 1 file(s): $L7_ETMs.tif [1] "[...]/L7_ETMs.tif" dimension(s): from to offset delta refsys point x/y x 1 349 288776 28.5 SIRGAS 2000 / UTM zone 25S FALSE [x] y 1 352 9120761 -28.5 SIRGAS 2000 / UTM zone 25S FALSE [y] band 1 6 NA NA NA NA > (yp = predict(y, model)) stars_proxy object with 1 attribute in 1 file(s): $L7_ETMs.tif [1] "[...]/L7_ETMs.tif" dimension(s): from to offset delta refsys point x/y x 1 349 288776 28.5 SIRGAS 2000 / UTM zone 25S FALSE [x] y 1 352 9120761 -28.5 SIRGAS 2000 / UTM zone 25S FALSE [y] band 1 6 NA NA NA NA call_list: [[1]] predict(object = object, model = model) attr(,".Environment") This object has pending lazy operations: dimensions as printed may not reflect this. > all.equal(x, st_as_stars(yp)) [1] TRUE > > yy = st_set_dimensions(y, 3, values = paste0("band", 1:6)) > (y_spl = split(yy, 3)) stars_proxy object with 1 attribute in 1 file(s): $L7_ETMs.tif [1] "[...]/L7_ETMs.tif" dimension(s): from to offset delta refsys point values x 1 349 288776 28.5 SIRGAS 2000 / UTM zone 25S FALSE NULL y 1 352 9120761 -28.5 SIRGAS 2000 / UTM zone 25S FALSE NULL band 1 6 NA NA NA NA band1,...,band6 x/y x [x] y [y] band call_list: [[1]] split(x = x, 3) attr(,".Environment") This object has pending lazy operations: dimensions as printed may not reflect this. > as.data.frame(st_as_stars(split(yy, 3)))[1:3,] x y band1 band2 band3 band4 band5 band6 1 288790.5 9120747 69 56 46 79 86 46 2 288819.0 9120747 69 57 49 75 88 49 3 288847.5 9120747 63 52 45 66 75 41 > > proc.time() user system elapsed 2.18 0.23 2.40 stars/tests/rectilinear.R0000644000176200001440000000142613777653456015226 0ustar liggesuserssuppressPackageStartupMessages(library(stars)) tif = system.file("tif/L7_ETMs.tif", package = "stars") x = read_stars(tif) (xx = x[,c(1:347,349),c(1:350,352),]) # rectilinear: one-but-last row & col missing image(xx) # chooses useRaster itself # see also tidy.R for a ggplot example, leaving out the row. m = matrix(1:16, 4, 4) x = 1:4 y = 2:5 (st = st_as_stars(list(m = m), dimensions = st_dimensions(x = x, y = y))) st = st_set_dimensions(st, 1, values = c(1,2,3,4,6)) st = st_set_dimensions(st, 2, values = c(2,3,4,5,8)) try(st_set_dimensions(st, 2, values = c(2,3,4,5,8,20))) # one too many # $<-.stars: st$foo = st$m * 2 st # st_as_sf: if (utils::packageVersion("sf") > "0.7.2") { plot(st_as_sf(st, as_points = FALSE), axes = TRUE) } image(st, text_values = TRUE, axes = TRUE) stars/tests/aggregate.R0000644000176200001440000000327314603250270014625 0ustar liggesusers## IGNORE_RDIFF_BEGIN library(stars) ## IGNORE_RDIFF_END if (suppressPackageStartupMessages(require(sp, quietly = TRUE)) && require(spacetime, quietly = TRUE)) { data(air) # this loads several datasets in .GlobalEnv dim(air) d = st_dimensions(station = st_set_crs(st_as_sfc(stations), 4326), time = dates) blocks = st_make_grid(st_as_sfc("POLYGON ((5.871619 47.26986, 15.03811 47.26986, 15.03811 55.05653, 5.871619 55.05653, 5.871619 47.26986))", crs = 4326), n = c(3,3)) print(aq <- st_as_stars(list(PM10 = air), dimensions = d)) print(a <- aggregate(aq, blocks, mean, na.rm = TRUE)) } # adapted from ?read_stars: m = array(1:720, dim = c(x = 10, y = 12, t = 6)) # named dim st = st_as_stars(m) attr(st, "dimensions")$y$delta = -1 attr(st, "dimensions")$y$offset = 12 tm = as.Date("2019-02-19") + 1:6 st = st_set_crs(st_set_dimensions(st, 3, values = tm), 4326) tmp = tempfile(fileext = ".tif") write_stars(st, tmp) (red <- setNames(read_stars(tmp, RasterIO = list(nXOff = 1, nYOff = 1, nXsize = 10, nYSize = 12, nBufXSize = 2, nBufYSize = 2)), "foo")) sfc = st_set_crs(st_as_sfc(red, as_points = FALSE), st_crs(st)) (a = aggregate(st, st_sf(a = 1, geom = sfc), mean)) (a = aggregate(st, sfc, mean)) if (require(raster)) { print(a <- aggregate(st, sfc, mean, exact = TRUE)) print(a[[1]]) print(sum(a[[1]])*30 == sum(1:720)) } tm0 = as.Date("2019-02-19") + -1:8 (a = aggregate(st, tm0, mean, na.rm = TRUE)) (a = aggregate(st, "days", mean, na.rm = TRUE)) yd = function(x) as.POSIXlt(x)$yday (a = aggregate(st, yd, mean, na.rm = TRUE)) # with "by" geometry not overlapping x pt = st_point(c(-10,-10)) (sfc = c(sfc, st_sfc(pt, crs = st_crs(sfc)))) (a = aggregate(st, sfc, mean)) (a = aggregate(st, st, mean)) stars/tests/proxy.R0000644000176200001440000000440014353574021014056 0ustar liggesusersSys.setenv(TZ="UTC") suppressPackageStartupMessages(library(sf)) suppressPackageStartupMessages(library(stars)) tif = system.file("tif/L7_ETMs.tif", package = "stars") (r = read_stars(tif, proxy = TRUE)) if (interactive()) { plot(r) } dim(r) r[,,,2:3] r+r st_as_stars(sin(r), downsample = 2) aperm(r, c(3,2,1)) if (interactive()) { plot(r) } (xx = c(r,r)) st_redimension(xx) st_as_stars(r) (y = st_apply(r, 1:2, max)) (yy = st_as_stars(y, downsample = 1)) (y = adrop(st_apply(r, c("x", "y"), max))) (yy = st_as_stars(y, downsample = 1)) plot(yy) (y = adrop(st_apply(r, c("x", "band"), max))) (yy = st_as_stars(y, downsample = 1)) rm(yy) (xx = c(r,r)) names(xx) = c("a", "b") xx["a"] pt = st_point(c(x = 290462.103109179, y = 9114202.32594085)) buf = st_buffer(st_sfc(pt, crs = st_crs(r)), 1500) buf = st_sfc(st_polygon(list(st_buffer(pt, 1500)[[1]], st_buffer(pt, 1000)[[1]])), crs = st_crs(r)) r = r[buf] # crops r = r[buf, epsilon = 1e-5] # crops with shrinked bounding box # c: f = system.file("netcdf/avhrr-only-v2.19810902.nc", package = "starsdata") if (FALSE && f != "") { files = c("avhrr-only-v2.19810901.nc", "avhrr-only-v2.19810902.nc", "avhrr-only-v2.19810903.nc", "avhrr-only-v2.19810904.nc", "avhrr-only-v2.19810905.nc", "avhrr-only-v2.19810906.nc", "avhrr-only-v2.19810907.nc", "avhrr-only-v2.19810908.nc", "avhrr-only-v2.19810909.nc") l = list() for (f in files) { from = system.file(paste0("netcdf/", f), package = "starsdata") l[[f]] = read_stars(from, sub = c("sst", "anom"), proxy = TRUE) } ret = do.call(c, l) print(ret) all = system.file(paste0("netcdf/", files), package = "starsdata") ret = read_stars(all, sub = c("sst", "anom")) print(ret) print(st_redimension(ret)) # collapse the two attributes into new dimension try(ret <- c(l[[1]], l[[2]], l[[3]], along = list(times = as.Date("1981-09-01") + 0:2))) #print(ret) #ret = adrop(adrop(c(l[[1]], l[[2]], l[[3]], along = "times"))) #print(ret) ret <- st_redimension(l[[1]], along = list(times = as.Date("1981-09-01") + 0:1)) print(ret) } # demonstrate that environments work: x = read_stars(tif, proxy = TRUE) f = function(x) { g = function(xx) { ndvi = function(z1, z2, z3, z4, z5, z6) (z2-z1)/(z2+z1) st_apply(xx, 1:2, ndvi) } g(x) } (x = f(x)) st_as_stars(x) stars/tests/dimensions.Rout.save0000644000176200001440000000465614644736040016553 0ustar liggesusers R Under development (unstable) (2024-07-13 r86895 ucrt) -- "Unsuffered Consequences" Copyright (C) 2024 The R Foundation for Statistical Computing Platform: x86_64-w64-mingw32/x64 R is free software and comes with ABSOLUTELY NO WARRANTY. You are welcome to redistribute it under certain conditions. Type 'license()' or 'licence()' for distribution details. R is a collaborative project with many contributors. Type 'contributors()' for more information and 'citation()' on how to cite R or R packages in publications. Type 'demo()' for some demos, 'help()' for on-line help, or 'help.start()' for an HTML browser interface to help. Type 'q()' to quit R. > # https://github.com/r-spatial/stars/issues/295 > suppressPackageStartupMessages(library(stars)) > raw <- read_stars(system.file("nc/bcsd_obs_1999.nc", package = "stars")) pr, tas, > foo <- function(x, idx) stats::lowess(idx, x)$y > > timeline <- st_get_dimension_values(raw, "time") > smooth = st_apply(raw, + MARGIN = c("x", "y"), + FUN = foo, + idx = st_get_dimension_values(raw, "time") + ) > > st_set_dimensions(smooth, + which = "foo", + values = st_get_dimension_values(raw, "time"), + names = "time" + ) stars object with 3 dimensions and 2 attributes attribute(s): Min. 1st Qu. Median Mean 3rd Qu. Max. NA's pr 16.133519 70.371665 84.55774 87.72969 101.94830 278.6427 7116 tas -2.354958 9.632157 15.32671 14.61650 19.99331 24.7976 7116 dimension(s): from to offset delta refsys values x/y time 1 12 NA NA POSIXct 1999-01-31,...,1999-12-31 x 1 81 -85 0.125 NA NULL [x] y 1 33 37.12 -0.125 NA NULL [y] > > raw %>% + st_apply(MARGIN = c("x", "y"), FUN = foo, idx = timeline) %>% + st_set_dimensions("foo", st_dimensions(raw)["time"]) stars object with 3 dimensions and 2 attributes attribute(s): Min. 1st Qu. Median Mean 3rd Qu. Max. NA's pr 16.133519 70.371665 84.55774 87.72969 101.94830 278.6427 7116 tas -2.354958 9.632157 15.32671 14.61650 19.99331 24.7976 7116 dimension(s): from to offset delta refsys values x/y time 1 12 NA NA POSIXct 1999-01-31,...,1999-12-31 x 1 81 -85 0.125 NA NULL [x] y 1 33 37.12 -0.125 NA NULL [y] > > proc.time() user system elapsed 1.46 0.18 1.62 stars/tests/extract.R0000644000176200001440000000641614144045432014355 0ustar liggesusers# Create 'stars' object set.seed(1331) suppressPackageStartupMessages(library(stars)) volcano = rbind(volcano, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA) # add NA rows d = st_dimensions(x = 1:ncol(volcano), y = 1:nrow(volcano)) (r = st_as_stars(t(volcano))) r = st_set_dimensions(r, 1, offset = 0, delta = 1) r = st_set_dimensions(r, 2, offset = nrow(volcano), delta = -1) # Create points pnt = st_sample(st_as_sfc(st_bbox(r)), 100) pnt = st_as_sf(pnt) # Extract - 'st_join' x = st_join(pnt, st_as_sf(r)) # Extract - 'st_extract' y = st_extract(r, pnt) # check there are NA's: any(is.na(x)) # Compare all.equal(x$A1, y[[1]]) ################################################ if (FALSE) { ## tic: segfaults # check equal results with stars_proxy: #x = st_extract(stars:::st_as_stars_proxy(r), pnt) #all.equal(x$A1, y[[1]]) #all.equal(x, y) r = c(r, 2*r, 10*r) x = st_join(pnt, st_as_sf(r)) y = st_as_sf(st_extract(r, pnt)) all.equal(x, y) ## tic: segfaults #x = st_extract(stars:::st_as_stars_proxy(merge(r)), pnt) #all.equal(st_as_sf(x), y) tif = system.file("tif/L7_ETMs.tif", package = "stars") xp = read_stars(tif, proxy = TRUE) xm = read_stars(tif, proxy = FALSE) pts = st_sample(st_as_sfc(st_bbox(xp)), 10) pts = c(pts, st_as_sfc("POINT(0 0)"), pts) em = st_extract(xm, pts) if (utils::packageVersion("sf") >= "0.9-7") { ep = st_extract(xp, pts) print(all.equal(ep, em, check.attributes = TRUE)) } # two-attribute objects: library(stars) tif = system.file("tif/L7_ETMs.tif", package = "stars") xp = read_stars(c(tif, tif), proxy = TRUE) xm = read_stars(c(tif, tif), proxy = FALSE) pts = st_sample(st_as_sfc(st_bbox(xp)), 10) pts = c(pts, st_as_sfc("POINT(0 0)"), pts) em = st_extract(xm, pts) if (utils::packageVersion("sf") >= "0.9-7") { ep = st_extract(xp, pts) print(all.equal(ep, em, check.attributes = TRUE)) } # single-attribute, single raster objects: tif1 = paste0(tempfile(), ".tif") write_stars(xm[1,,,1], "x.tif") xp = read_stars("x.tif", proxy = TRUE) xm = read_stars("x.tif", proxy = FALSE) em = st_extract(xm, pts) if (utils::packageVersion("sf") >= "0.9-7") { ep = st_extract(xp, pts) print(all.equal(ep, em, check.attributes = TRUE)) } # multiple-file attributes: x = c( "avhrr-only-v2.19810901.nc", "avhrr-only-v2.19810902.nc", "avhrr-only-v2.19810903.nc", "avhrr-only-v2.19810904.nc", "avhrr-only-v2.19810905.nc", "avhrr-only-v2.19810906.nc", "avhrr-only-v2.19810907.nc", "avhrr-only-v2.19810908.nc", "avhrr-only-v2.19810909.nc" ) file_list = system.file(paste0("netcdf/", x), package = "starsdata") if (!identical(file_list, "")) { y = read_stars(file_list, quiet = TRUE) print(y) st_crs(y) = "OGC:CRS84" pts = st_sample(st_as_sfc(st_bbox(y)), 10) em = st_extract(y, pts) (y = read_stars(file_list, quiet = TRUE, proxy = TRUE)) print(y) st_crs(y) = "OGC:CRS84" if (utils::packageVersion("sf") >= "0.9-7") { ep = st_extract(y, pts) print(all.equal(em, ep)) } } # nearest & bilinear comparison: if (utils::packageVersion("sf") >= "0.9-7") { set.seed(12331) s = st_as_stars(matrix(rnorm(16), 4)) pts = st_sample(st_as_sfc(st_bbox(s)), 10000, type = "regular") s1 = st_extract(s, pts, bilinear = FALSE) s2 = st_extract(s, pts, bilinear = TRUE) s1$s2 = s2[[1]] names(s1)[c(1,3)] = c("nearest", "bilinear") print(s1[sample(10000, 5),]) } } stars/tests/downsample.R0000644000176200001440000000070614576033423015056 0ustar liggesuserssuppressPackageStartupMessages(library(stars)) m = matrix(1:121, 11, 11) m s = st_as_stars(m) st_downsample(s, 1) st_downsample(s, 1)[[1]] st_downsample(s, 1, offset = 1) st_downsample(s, 1, offset = 1)[[1]] st_downsample(s, 1, FUN = mean) st_downsample(s, 1, FUN = mean)[[1]] st_downsample(s, 1, offset = 1, FUN = mean) st_downsample(s, 1, offset = c(0,1), FUN = mean)[[1]] st_downsample(s, 1, offset = c(0,1)) st_downsample(s, 1, offset = c(0,1))[[1]] stars/tests/nc.Rout.save0000644000176200001440000002473614644736040015004 0ustar liggesusers R Under development (unstable) (2024-07-13 r86895 ucrt) -- "Unsuffered Consequences" Copyright (C) 2024 The R Foundation for Statistical Computing Platform: x86_64-w64-mingw32/x64 R is free software and comes with ABSOLUTELY NO WARRANTY. You are welcome to redistribute it under certain conditions. Type 'license()' or 'licence()' for distribution details. R is a collaborative project with many contributors. Type 'contributors()' for more information and 'citation()' on how to cite R or R packages in publications. Type 'demo()' for some demos, 'help()' for on-line help, or 'help.start()' for an HTML browser interface to help. Type 'q()' to quit R. > # examples with the sf::nc polygon dataset NOT ABOUT NETCDF's, see e.g. stars.R netcdf.R > suppressPackageStartupMessages(library(sf)) > nc = read_sf(system.file("gpkg/nc.gpkg", package="sf")) > m = st_set_geometry(nc, NULL) > n = as.matrix(m[c("BIR74", "SID74", "NWBIR74", "BIR79", "SID79", "NWBIR79")]) > dim(n) = c(county = 100, var = 3, year = 2) > dimnames(n) = list(county = nc$NAME, var = c("BIR", "SID", "NWBIR"), year = c(1974, 1979)) > suppressPackageStartupMessages(library(stars)) > (st = st_as_stars(pop = n)) stars object with 3 dimensions and 1 attribute attribute(s): Min. 1st Qu. Median Mean 3rd Qu. Max. pop 0 8 538 1657.032 1784 30757 dimension(s): from to values county 1 100 Ashe,...,Brunswick var 1 3 BIR , SID , NWBIR year 1 2 1974, 1979 > foo <- st %>% st_set_dimensions(1, st_geometry(nc)) # %>% st_set_dimensions(3, c(1974, 1979)) > st %>% st_set_dimensions(1, st_geometry(nc)) %>% st_set_dimensions(names = c("geometries", "var", "year")) stars object with 3 dimensions and 1 attribute attribute(s): Min. 1st Qu. Median Mean 3rd Qu. Max. pop 0 8 538 1657.032 1784 30757 dimension(s): from to refsys point geometries 1 100 NAD27 FALSE var 1 3 NA NA year 1 2 NA NA values geometries MULTIPOLYGON (((-81.47276...,...,MULTIPOLYGON (((-78.65572... var BIR , SID , NWBIR year 1974, 1979 > foo stars object with 3 dimensions and 1 attribute attribute(s): Min. 1st Qu. Median Mean 3rd Qu. Max. pop 0 8 538 1657.032 1784 30757 dimension(s): from to refsys point county 1 100 NAD27 FALSE var 1 3 NA NA year 1 2 NA NA values county MULTIPOLYGON (((-81.47276...,...,MULTIPOLYGON (((-78.65572... var BIR , SID , NWBIR year 1974, 1979 > st_bbox(foo) xmin ymin xmax ymax -84.32385 33.88199 -75.45698 36.58965 > (x = st_as_sf(foo)) Simple feature collection with 100 features and 6 fields Geometry type: MULTIPOLYGON Dimension: XY Bounding box: xmin: -84.32385 ymin: 33.88199 xmax: -75.45698 ymax: 36.58965 Geodetic CRS: NAD27 First 10 features: BIR.1974 SID.1974 NWBIR.1974 BIR.1979 SID.1979 NWBIR.1979 1 1091 1 10 1364 0 19 2 487 0 10 542 3 12 3 3188 5 208 3616 6 260 4 508 1 123 830 2 145 5 1421 9 1066 1606 3 1197 6 1452 7 954 1838 5 1237 7 286 0 115 350 2 139 8 420 0 254 594 2 371 9 968 4 748 1190 2 844 10 1612 1 160 2038 5 176 county 1 MULTIPOLYGON (((-81.47276 3... 2 MULTIPOLYGON (((-81.23989 3... 3 MULTIPOLYGON (((-80.45634 3... 4 MULTIPOLYGON (((-76.00897 3... 5 MULTIPOLYGON (((-77.21767 3... 6 MULTIPOLYGON (((-76.74506 3... 7 MULTIPOLYGON (((-76.00897 3... 8 MULTIPOLYGON (((-76.56251 3... 9 MULTIPOLYGON (((-78.30876 3... 10 MULTIPOLYGON (((-80.02567 3... > frac = function(x) x[2] / x[1] > frac2 = function(x) c(sidsr = x[2] / x[1], nwbr = x[3] / x[1]) > frac2an = function(x) c(x[2] / x[1], x[3] / x[1]) > st_apply(foo, c(1,3), frac) stars object with 2 dimensions and 1 attribute attribute(s): Min. 1st Qu. Median Mean 3rd Qu. Max. frac 0 0.001197874 0.001930504 0.002042241 0.002599348 0.00955414 dimension(s): from to refsys point county 1 100 NAD27 FALSE year 1 2 NA NA values county MULTIPOLYGON (((-81.47276...,...,MULTIPOLYGON (((-78.65572... year 1974, 1979 > st_apply(foo, c(1,3), frac2) stars object with 3 dimensions and 1 attribute attribute(s): Min. 1st Qu. Median Mean 3rd Qu. Max. pop 0 0.001923331 0.005119673 0.1567576 0.3065298 0.7727273 dimension(s): from to refsys point frac2 1 2 NA NA county 1 100 NAD27 FALSE year 1 2 NA NA values frac2 sidsr, nwbr county MULTIPOLYGON (((-81.47276...,...,MULTIPOLYGON (((-78.65572... year 1974, 1979 > st_apply(foo, c(1,3), frac2an) stars object with 3 dimensions and 1 attribute attribute(s): Min. 1st Qu. Median Mean 3rd Qu. Max. pop 0 0.001923331 0.005119673 0.1567576 0.3065298 0.7727273 dimension(s): from to refsys point frac2an 1 2 NA NA county 1 100 NAD27 FALSE year 1 2 NA NA values frac2an NULL county MULTIPOLYGON (((-81.47276...,...,MULTIPOLYGON (((-78.65572... year 1974, 1979 > library(abind) > (x = aperm(st_apply(foo, c(1,3), frac2), c(2,3,1))) stars object with 3 dimensions and 1 attribute attribute(s): Min. 1st Qu. Median Mean 3rd Qu. Max. pop 0 0.001923331 0.005119673 0.1567576 0.3065298 0.7727273 dimension(s): from to refsys point county 1 100 NAD27 FALSE year 1 2 NA NA frac2 1 2 NA NA values county MULTIPOLYGON (((-81.47276...,...,MULTIPOLYGON (((-78.65572... year 1974, 1979 frac2 sidsr, nwbr > y = aperm(st_apply(foo, c(1,3), frac2), c("county","year","frac2")) > all.equal(st_dimensions(x), st_dimensions(y)) [1] TRUE > > split(foo, 2) stars object with 2 dimensions and 3 attributes attribute(s): Min. 1st Qu. Median Mean 3rd Qu. Max. BIR 248 1176.75 2265 3761.770 4450.75 30757 SID 0 2.00 5 7.515 9.00 57 NWBIR 1 206.00 742 1201.810 1315.75 11631 dimension(s): from to refsys point county 1 100 NAD27 FALSE year 1 2 NA NA values county MULTIPOLYGON (((-81.47276...,...,MULTIPOLYGON (((-78.65572... year 1974, 1979 > split(foo, 3) stars object with 2 dimensions and 2 attributes attribute(s): Min. 1st Qu. Median Mean 3rd Qu. Max. 1974 0 8 501.5 1452.367 1580.50 21588 1979 0 9 614.0 1861.697 2040.25 30757 dimension(s): from to refsys point county 1 100 NAD27 FALSE var 1 3 NA NA values county MULTIPOLYGON (((-81.47276...,...,MULTIPOLYGON (((-78.65572... var BIR , SID , NWBIR > > st_crs(foo) Coordinate Reference System: User input: NAD27 wkt: GEOGCRS["NAD27", DATUM["North American Datum 1927", ELLIPSOID["Clarke 1866",6378206.4,294.978698213898, LENGTHUNIT["metre",1]]], PRIMEM["Greenwich",0, ANGLEUNIT["degree",0.0174532925199433]], CS[ellipsoidal,2], AXIS["geodetic latitude (Lat)",north, ORDER[1], ANGLEUNIT["degree",0.0174532925199433]], AXIS["geodetic longitude (Lon)",east, ORDER[2], ANGLEUNIT["degree",0.0174532925199433]], USAGE[ SCOPE["Geodesy."], AREA["North and central America: Antigua and Barbuda - onshore. Bahamas - onshore plus offshore over internal continental shelf only. Belize - onshore. British Virgin Islands - onshore. Canada onshore - Alberta, British Columbia, Manitoba, New Brunswick, Newfoundland and Labrador, Northwest Territories, Nova Scotia, Nunavut, Ontario, Prince Edward Island, Quebec, Saskatchewan and Yukon - plus offshore east coast. Cuba - onshore and offshore. El Salvador - onshore. Guatemala - onshore. Honduras - onshore. Panama - onshore. Puerto Rico - onshore. Mexico - onshore plus offshore east coast. Nicaragua - onshore. United States (USA) onshore and offshore - Alabama, Alaska, Arizona, Arkansas, California, Colorado, Connecticut, Delaware, Florida, Georgia, Idaho, Illinois, Indiana, Iowa, Kansas, Kentucky, Louisiana, Maine, Maryland, Massachusetts, Michigan, Minnesota, Mississippi, Missouri, Montana, Nebraska, Nevada, New Hampshire, New Jersey, New Mexico, New York, North Carolina, North Dakota, Ohio, Oklahoma, Oregon, Pennsylvania, Rhode Island, South Carolina, South Dakota, Tennessee, Texas, Utah, Vermont, Virginia, Washington, West Virginia, Wisconsin and Wyoming - plus offshore . US Virgin Islands - onshore."], BBOX[7.15,167.65,83.17,-47.74]], ID["EPSG",4267]] > plot(foo) > > # subset vector cube: > foo[nc[1]] stars object with 3 dimensions and 1 attribute attribute(s): Min. 1st Qu. Median Mean 3rd Qu. Max. pop 0 8 538 1657.032 1784 30757 dimension(s): from to refsys point county 1 100 NAD27 FALSE var 1 3 NA NA year 1 2 NA NA values county MULTIPOLYGON (((-81.47276...,...,MULTIPOLYGON (((-78.65572... var BIR , SID , NWBIR year 1974, 1979 > > proc.time() user system elapsed 1.31 0.21 1.50 stars/tests/spacetime.Rout.save0000644000176200001440000001655214644736040016353 0ustar liggesusers R Under development (unstable) (2024-07-13 r86895 ucrt) -- "Unsuffered Consequences" Copyright (C) 2024 The R Foundation for Statistical Computing Platform: x86_64-w64-mingw32/x64 R is free software and comes with ABSOLUTELY NO WARRANTY. You are welcome to redistribute it under certain conditions. Type 'license()' or 'licence()' for distribution details. R is a collaborative project with many contributors. Type 'contributors()' for more information and 'citation()' on how to cite R or R packages in publications. Type 'demo()' for some demos, 'help()' for on-line help, or 'help.start()' for an HTML browser interface to help. Type 'q()' to quit R. > suppressPackageStartupMessages(library(stars)) > if (suppressPackageStartupMessages(require(sp, quietly = TRUE)) && require(spacetime, quietly = TRUE)) { + set.seed(1331) + Sys.setenv(TZ="UTC") + example(STFDF) + + x = st_as_stars(stfdf) + xx = as(x, "STFDF") + + # as.xts.stars: + library(xts) + xts = as.xts(x) + st_as_stars(xts) + st_as_stars(xts, dimensions = st_dimensions(x)) + + library(sp) + pts = SpatialPoints(rbind(c(0,0), c(0,1), c(1,0), c(1,1))) + gridded(pts) = TRUE + + tm = xts(1:10, as.Date("2010-01-01")+0:9) + stfdf = STFDF(pts, tm, data.frame(foo = seq_len(length(pts) * length(tm)))) + x = st_as_stars(stfdf) + xx = as(x, "STFDF") + + x = st_as_stars(as(stfdf, "STSDF")) + } STFDF> sp = cbind(x = c(0,0,1), y = c(0,1,1)) STFDF> row.names(sp) = paste("point", 1:nrow(sp), sep="") STFDF> library(sp) STFDF> sp = SpatialPoints(sp) STFDF> time = as.POSIXct("2010-08-05")+3600*(10:13) STFDF> m = c(10,20,30) # means for each of the 3 point locations STFDF> mydata = rnorm(length(sp)*length(time),mean=rep(m, 4)) STFDF> IDs = paste("ID",1:length(mydata)) STFDF> mydata = data.frame(values = signif(mydata,3), ID=IDs) STFDF> stfdf = STFDF(sp, time, mydata) STFDF> stfdf An object of class "STFDF" Slot "data": values ID 1 9.38 ID 1 2 21.10 ID 2 3 29.80 ID 3 4 11.50 ID 4 5 19.80 ID 5 6 31.20 ID 6 7 10.40 ID 7 8 20.50 ID 8 9 29.80 ID 9 10 10.30 ID 10 11 21.40 ID 11 12 29.40 ID 12 Slot "sp": SpatialPoints: x y point1 0 0 point2 0 1 point3 1 1 Coordinate Reference System (CRS) arguments: NA Slot "time": timeIndex 2010-08-05 10:00:00 1 2010-08-05 11:00:00 2 2010-08-05 12:00:00 3 2010-08-05 13:00:00 4 Slot "endTime": [1] "2010-08-05 11:00:00 UTC" "2010-08-05 12:00:00 UTC" [3] "2010-08-05 13:00:00 UTC" "2010-08-05 14:00:00 UTC" STFDF> stfdf[1:2,] An object of class "STFDF" Slot "data": values ID 1 9.38 ID 1 2 21.10 ID 2 3 11.50 ID 4 4 19.80 ID 5 5 10.40 ID 7 6 20.50 ID 8 7 10.30 ID 10 8 21.40 ID 11 Slot "sp": SpatialPoints: x y point1 0 0 point2 0 1 Coordinate Reference System (CRS) arguments: NA Slot "time": timeIndex 2010-08-05 10:00:00 1 2010-08-05 11:00:00 2 2010-08-05 12:00:00 3 2010-08-05 13:00:00 4 Slot "endTime": [1] "2010-08-05 11:00:00 UTC" "2010-08-05 12:00:00 UTC" [3] "2010-08-05 13:00:00 UTC" "2010-08-05 14:00:00 UTC" STFDF> stfdf[,1:2] An object of class "STFDF" Slot "data": values ID 1 9.38 ID 1 2 21.10 ID 2 3 29.80 ID 3 4 11.50 ID 4 5 19.80 ID 5 6 31.20 ID 6 Slot "sp": SpatialPoints: x y point1 0 0 point2 0 1 point3 1 1 Coordinate Reference System (CRS) arguments: NA Slot "time": timeIndex 2010-08-05 10:00:00 1 2010-08-05 11:00:00 2 Slot "endTime": [1] "2010-08-05 11:00:00 UTC" "2010-08-05 12:00:00 UTC" STFDF> stfdf[,,2] An object of class "STFDF" Slot "data": ID 1 ID 1 2 ID 2 3 ID 3 4 ID 4 5 ID 5 6 ID 6 7 ID 7 8 ID 8 9 ID 9 10 ID 10 11 ID 11 12 ID 12 Slot "sp": SpatialPoints: x y point1 0 0 point2 0 1 point3 1 1 Coordinate Reference System (CRS) arguments: NA Slot "time": timeIndex 2010-08-05 10:00:00 1 2010-08-05 11:00:00 2 2010-08-05 12:00:00 3 2010-08-05 13:00:00 4 Slot "endTime": [1] "2010-08-05 11:00:00 UTC" "2010-08-05 12:00:00 UTC" [3] "2010-08-05 13:00:00 UTC" "2010-08-05 14:00:00 UTC" STFDF> stfdf[,,"values"] An object of class "STFDF" Slot "data": values 1 9.38 2 21.10 3 29.80 4 11.50 5 19.80 6 31.20 7 10.40 8 20.50 9 29.80 10 10.30 11 21.40 12 29.40 Slot "sp": SpatialPoints: x y point1 0 0 point2 0 1 point3 1 1 Coordinate Reference System (CRS) arguments: NA Slot "time": timeIndex 2010-08-05 10:00:00 1 2010-08-05 11:00:00 2 2010-08-05 12:00:00 3 2010-08-05 13:00:00 4 Slot "endTime": [1] "2010-08-05 11:00:00 UTC" "2010-08-05 12:00:00 UTC" [3] "2010-08-05 13:00:00 UTC" "2010-08-05 14:00:00 UTC" STFDF> stfdf[1,] values ID timeIndex 2010-08-05 10:00:00 9.38 ID 1 1 2010-08-05 11:00:00 11.50 ID 4 2 2010-08-05 12:00:00 10.40 ID 7 3 2010-08-05 13:00:00 10.30 ID 10 4 STFDF> stfdf[,2] coordinates values ID 1 (0, 0) 11.5 ID 4 2 (0, 1) 19.8 ID 5 3 (1, 1) 31.2 ID 6 STFDF> as(stfdf[,,1], "xts") point1 point2 point3 2010-08-05 10:00:00 9.38 21.1 29.8 2010-08-05 11:00:00 11.50 19.8 31.2 2010-08-05 12:00:00 10.40 20.5 29.8 2010-08-05 13:00:00 10.30 21.4 29.4 STFDF> as(stfdf[,,2], "xts") point1 point2 point3 2010-08-05 10:00:00 ID 1 ID 2 ID 3 2010-08-05 11:00:00 ID 4 ID 5 ID 6 2010-08-05 12:00:00 ID 7 ID 8 ID 9 2010-08-05 13:00:00 ID 10 ID 11 ID 12 STFDF> # examples for [[, [[<-, $ and $<- STFDF> stfdf[[1]] [1] 9.38 21.10 29.80 11.50 19.80 31.20 10.40 20.50 29.80 10.30 21.40 29.40 STFDF> stfdf[["values"]] [1] 9.38 21.10 29.80 11.50 19.80 31.20 10.40 20.50 29.80 10.30 21.40 29.40 STFDF> stfdf[["newVal"]] <- rnorm(12) STFDF> stfdf$ID [1] "ID 1" "ID 2" "ID 3" "ID 4" "ID 5" "ID 6" "ID 7" "ID 8" "ID 9" [10] "ID 10" "ID 11" "ID 12" STFDF> stfdf$ID = paste("OldIDs", 1:12, sep="") STFDF> stfdf$NewID = paste("NewIDs", 12:1, sep="") STFDF> stfdf An object of class "STFDF" Slot "data": values ID newVal NewID 1 9.38 OldIDs1 -0.3125824 NewIDs12 2 21.10 OldIDs2 -1.0536246 NewIDs11 3 29.80 OldIDs3 -0.7188880 NewIDs10 4 11.50 OldIDs4 -1.0525404 NewIDs9 5 19.80 OldIDs5 -0.8441413 NewIDs8 6 31.20 OldIDs6 1.1420848 NewIDs7 7 10.40 OldIDs7 1.9479070 NewIDs6 8 20.50 OldIDs8 -0.4512836 NewIDs5 9 29.80 OldIDs9 0.6695716 NewIDs4 10 10.30 OldIDs10 0.1269003 NewIDs3 11 21.40 OldIDs11 -0.7273297 NewIDs2 12 29.40 OldIDs12 -0.8151803 NewIDs1 Slot "sp": SpatialPoints: x y point1 0 0 point2 0 1 point3 1 1 Coordinate Reference System (CRS) arguments: NA Slot "time": timeIndex 2010-08-05 10:00:00 1 2010-08-05 11:00:00 2 2010-08-05 12:00:00 3 2010-08-05 13:00:00 4 Slot "endTime": [1] "2010-08-05 11:00:00 UTC" "2010-08-05 12:00:00 UTC" [3] "2010-08-05 13:00:00 UTC" "2010-08-05 14:00:00 UTC" STFDF> x = stfdf[stfdf[1:2,],] STFDF> all.equal(x, stfdf[1:2,]) [1] TRUE STFDF> all.equal(stfdf, stfdf[stfdf,]) # converts character to factor... [1] TRUE Loading required package: zoo Attaching package: 'zoo' The following objects are masked from 'package:base': as.Date, as.Date.numeric only the first attribute will used; maybe use merge() first? > > proc.time() user system elapsed 1.78 0.23 1.95 stars/tests/area.R0000644000176200001440000000177114353574021013615 0ustar liggesuserssuppressPackageStartupMessages(library(stars)) tif = system.file("tif/L7_ETMs.tif", package = "stars") x = read_stars(tif) (a = st_area(x[,1:5,1:5])) m = matrix(1:20, nrow = 5, ncol = 4) x = c(0, 0.5, 1, 2, 4, 5) # 6 numbers: boundaries! y = c(0.3, 0.5, 1, 2, 2.2) # 5 numbers: boundaries! (r = st_as_stars(list(m = m), dimensions = st_dimensions(x = x, y = y))) # image(r, axes = TRUE, col = grey((1:20)/20)) (a = st_area(r)) #plot(a, axes = TRUE) s5p = system.file("sentinel5p/S5P_NRTI_L2__NO2____20180717T120113_20180717T120613_03932_01_010002_20180717T125231.nc", package = "starsdata") if (s5p != "") { nit.c = read_stars(s5p, sub = "//PRODUCT/SUPPORT_DATA/DETAILED_RESULTS/nitrogendioxide_summed_total_column", curvilinear = c("//PRODUCT/longitude", "//PRODUCT/latitude"), driver = NULL) if (inherits(nit.c[[1]], "units")) nit.c[[1]] = units::drop_units(nit.c[[1]]) nit.c[[1]][nit.c[[1]] > 9e+36] = NA st_crs(nit.c) = 4326 print((a <- st_area(nit.c[,1:5,1:5]))) # plot(a, axes = TRUE, border = NA) } stars/tests/sf.Rout.save0000644000176200001440000001401414644736040015000 0ustar liggesusers R Under development (unstable) (2024-07-13 r86895 ucrt) -- "Unsuffered Consequences" Copyright (C) 2024 The R Foundation for Statistical Computing Platform: x86_64-w64-mingw32/x64 R is free software and comes with ABSOLUTELY NO WARRANTY. You are welcome to redistribute it under certain conditions. Type 'license()' or 'licence()' for distribution details. R is a collaborative project with many contributors. Type 'contributors()' for more information and 'citation()' on how to cite R or R packages in publications. Type 'demo()' for some demos, 'help()' for on-line help, or 'help.start()' for an HTML browser interface to help. Type 'q()' to quit R. > suppressPackageStartupMessages(library(stars)) > library(sf) > #jp2 = system.file("jp2/B01.jp2", package = "stars") > #(x = read_stars(jp2, options = c("OVERVIEW_LEVEL=3"))) > tif = system.file("tif/L7_ETMs.tif", package = "stars") > #(x = read_stars(tif, options = c("OVERVIEW_LEVEL=3"))) # FIXME: breaks on windows > (x = read_stars(tif)[,1:20,1:20]) stars object with 3 dimensions and 1 attribute attribute(s): Min. 1st Qu. Median Mean 3rd Qu. Max. L7_ETMs.tif 17 44 58 58.19667 70 130 dimension(s): from to offset delta refsys point x/y x 1 20 288776 28.5 SIRGAS 2000 / UTM zone 25S FALSE [x] y 1 20 9120761 -28.5 SIRGAS 2000 / UTM zone 25S FALSE [y] band 1 6 NA NA NA NA > # names(x) = "L7_ETM" > > # library(abind) > # x = adrop(x) > image(x) > (sfc = st_as_sfc(x, as_points = FALSE)) Geometry set for 400 features Geometry type: POLYGON Dimension: XY Bounding box: xmin: 288776.3 ymin: 9120191 xmax: 289346.3 ymax: 9120761 Projected CRS: SIRGAS 2000 / UTM zone 25S First 5 geometries: POLYGON ((288776.3 9120761, 288804.8 9120761, 2... POLYGON ((288804.8 9120761, 288833.3 9120761, 2... POLYGON ((288833.3 9120761, 288861.8 9120761, 2... POLYGON ((288861.8 9120761, 288890.3 9120761, 2... POLYGON ((288890.3 9120761, 288918.8 9120761, 2... > plot(sfc, add =TRUE) > (sfc = st_as_sfc(x, as_points = TRUE)) Geometry set for 400 features Geometry type: POINT Dimension: XY Bounding box: xmin: 288790.5 ymin: 9120205 xmax: 289332 ymax: 9120747 Projected CRS: SIRGAS 2000 / UTM zone 25S First 5 geometries: POINT (288790.5 9120747) POINT (288819 9120747) POINT (288847.5 9120747) POINT (288876 9120747) POINT (288904.5 9120747) > plot(sfc, add = TRUE) > > sf = st_as_sf(x, as_points = FALSE) > plot(sf, border = NA) > sf = st_as_sf(x[,,,1], as_points = FALSE, merge = TRUE) > plot(sf, border = NA) > > sfc1 <- st_as_sfc(x, as_points = TRUE, use_cpp = TRUE, na.rm = FALSE) > sfc2 <- st_as_sfc(x, as_points = TRUE, use_cpp = FALSE) > identical(sfc1, sfc2) [1] TRUE > > sfc1 <- st_as_sfc(x, as_points = FALSE, use_cpp = TRUE, na.rm = FALSE) > sfc2 <- st_as_sfc(x, as_points = FALSE, use_cpp = FALSE) > identical(sfc1, sfc2) [1] TRUE > > # sf -> stars -> sf > x = st_sfc(st_point(0:1), st_point(1:2), st_point(2:3)) > m = matrix(1:9,3) > colnames(m) = c("a", "b", "c") > foo = st_sf(m, geom = x) > x = st_as_stars(foo) > c(x) stars object with 1 dimensions and 3 attributes attribute(s): Min. 1st Qu. Median Mean 3rd Qu. Max. a 1 1.5 2 2 2.5 3 b 4 4.5 5 5 5.5 6 c 7 7.5 8 8 8.5 9 dimension(s): from to point values geom 1 3 TRUE POINT (0 1),...,POINT (2 3) > st_redimension(x) stars object with 2 dimensions and 1 attribute attribute(s): Min. 1st Qu. Median Mean 3rd Qu. Max. a.b.c 1 3 5 5 7 9 dimension(s): from to point values geom 1 3 TRUE POINT (0 1),...,POINT (2 3) new_dim 1 3 NA a, b, c > c(x, x) stars object with 1 dimensions and 6 attributes attribute(s): Min. 1st Qu. Median Mean 3rd Qu. Max. a 1 1.5 2 2 2.5 3 b 4 4.5 5 5 5.5 6 c 7 7.5 8 8 8.5 9 a.1 1 1.5 2 2 2.5 3 b.1 4 4.5 5 5 5.5 6 c.1 7 7.5 8 8 8.5 9 dimension(s): from to point values geom 1 3 TRUE POINT (0 1),...,POINT (2 3) > (y = st_redimension(x, along = list(time = as.Date("2017-11-27") + 0:2))) stars object with 2 dimensions and 1 attribute attribute(s): Min. 1st Qu. Median Mean 3rd Qu. Max. a.b.c 1 3 5 5 7 9 dimension(s): from to offset delta refsys point values geom 1 3 NA NA NA TRUE POINT (0 1),...,POINT (2 3) time 1 3 2017-11-27 1 days Date NA NULL > st_as_sf(y) Simple feature collection with 3 features and 3 fields Geometry type: POINT Dimension: XY Bounding box: xmin: 0 ymin: 1 xmax: 2 ymax: 3 CRS: NA 2017-11-27 2017-11-28 2017-11-29 geom 1 1 4 7 POINT (0 1) 2 2 5 8 POINT (1 2) 3 3 6 9 POINT (2 3) > st_as_sf(st_as_stars(foo)) Simple feature collection with 3 features and 3 fields Geometry type: POINT Dimension: XY Bounding box: xmin: 0 ymin: 1 xmax: 2 ymax: 3 CRS: NA a b c geom 1 1 4 7 POINT (0 1) 2 2 5 8 POINT (1 2) 3 3 6 9 POINT (2 3) > > c(x,x,along=1) # NEEDS FIXING! stars object with 1 dimensions and 3 attributes attribute(s): Min. 1st Qu. Median Mean 3rd Qu. Max. a 1 1.25 2 2 2.75 3 b 4 4.25 5 5 5.75 6 c 7 7.25 8 8 8.75 9 dimension(s): from to point values geom 1 6 TRUE POINT (0 1),...,POINT (2 3) > # st_as_stars.sfc > st_as_stars(st_geometry(st_as_sf(y)), nx = 10, ny = 10, as_points = FALSE) stars object with 2 dimensions and 1 attribute attribute(s): Min. 1st Qu. Median Mean 3rd Qu. Max. values 0 0 0 0.06 0 1 dimension(s): from to offset delta x/y x 1 10 0 0.2 [x] y 1 10 3 -0.2 [y] > > proc.time() user system elapsed 1.03 0.18 1.21 stars/tests/aggregate.Rout.save0000644000176200001440000002015014644736040016314 0ustar liggesusers R Under development (unstable) (2024-07-13 r86895 ucrt) -- "Unsuffered Consequences" Copyright (C) 2024 The R Foundation for Statistical Computing Platform: x86_64-w64-mingw32/x64 R is free software and comes with ABSOLUTELY NO WARRANTY. You are welcome to redistribute it under certain conditions. Type 'license()' or 'licence()' for distribution details. R is a collaborative project with many contributors. Type 'contributors()' for more information and 'citation()' on how to cite R or R packages in publications. Type 'demo()' for some demos, 'help()' for on-line help, or 'help.start()' for an HTML browser interface to help. Type 'q()' to quit R. > ## IGNORE_RDIFF_BEGIN > library(stars) Loading required package: abind Loading required package: sf Linking to GEOS 3.12.1, GDAL 3.8.4, PROJ 9.3.1; sf_use_s2() is TRUE > ## IGNORE_RDIFF_END > > if (suppressPackageStartupMessages(require(sp, quietly = TRUE)) && require(spacetime, quietly = TRUE)) { + data(air) # this loads several datasets in .GlobalEnv + dim(air) + d = st_dimensions(station = st_set_crs(st_as_sfc(stations), 4326), time = dates) + + blocks = st_make_grid(st_as_sfc("POLYGON ((5.871619 47.26986, 15.03811 47.26986, 15.03811 55.05653, 5.871619 55.05653, 5.871619 47.26986))", crs = 4326), + n = c(3,3)) + + print(aq <- st_as_stars(list(PM10 = air), dimensions = d)) + print(a <- aggregate(aq, blocks, mean, na.rm = TRUE)) + } stars object with 2 dimensions and 1 attribute attribute(s): Min. 1st Qu. Median Mean 3rd Qu. Max. NA's PM10 0 9.921 14.792 17.69728 21.992 274.333 157659 dimension(s): from to offset delta refsys point station 1 70 NA NA WGS 84 TRUE time 1 4383 1998-01-01 1 days Date FALSE values station POINT (9.585911 53.67057),...,POINT (9.446661 49.24068) time NULL stars object with 2 dimensions and 1 attribute attribute(s): Min. 1st Qu. Median Mean 3rd Qu. Max. NA's PM10 1 11 15.57425 18.1715 22.17197 183.271 8429 dimension(s): from to offset delta refsys point geometry 1 9 NA NA WGS 84 FALSE time 1 4383 1998-01-01 1 days Date FALSE values geometry POLYGON ((5.871619 47.269...,...,POLYGON ((11.98261 52.460... time NULL > > # adapted from ?read_stars: > m = array(1:720, dim = c(x = 10, y = 12, t = 6)) # named dim > st = st_as_stars(m) > attr(st, "dimensions")$y$delta = -1 > attr(st, "dimensions")$y$offset = 12 > tm = as.Date("2019-02-19") + 1:6 > st = st_set_crs(st_set_dimensions(st, 3, values = tm), 4326) > > tmp = tempfile(fileext = ".tif") > write_stars(st, tmp) > > (red <- setNames(read_stars(tmp, RasterIO = list(nXOff = 1, nYOff = 1, nXsize = 10, nYSize = 12, + nBufXSize = 2, nBufYSize = 2)), "foo")) stars object with 3 dimensions and 1 attribute attribute(s): Min. 1st Qu. Median Mean 3rd Qu. Max. foo 33 199.25 365.5 365.5 531.75 698 dimension(s): from to offset delta refsys point x/y x 1 2 0 5 WGS 84 FALSE [x] y 1 2 12 -6 WGS 84 FALSE [y] band 1 6 NA NA NA NA > > sfc = st_set_crs(st_as_sfc(red, as_points = FALSE), st_crs(st)) > (a = aggregate(st, st_sf(a = 1, geom = sfc), mean)) stars object with 2 dimensions and 1 attribute attribute(s): Min. 1st Qu. Median Mean 3rd Qu. Max. A1 28 194.25 360.5 360.5 526.75 693 dimension(s): from to offset delta refsys point geom 1 4 NA NA WGS 84 FALSE t 1 6 2019-02-20 1 days Date FALSE values geom POLYGON ((0 12, 5 12, 5 6...,...,POLYGON ((5 6, 10 6, 10 0... t NULL > (a = aggregate(st, sfc, mean)) stars object with 2 dimensions and 1 attribute attribute(s): Min. 1st Qu. Median Mean 3rd Qu. Max. A1 28 194.25 360.5 360.5 526.75 693 dimension(s): from to offset delta refsys point geometry 1 4 NA NA WGS 84 FALSE t 1 6 2019-02-20 1 days Date FALSE values geometry POLYGON ((0 12, 5 12, 5 6...,...,POLYGON ((5 6, 10 6, 10 0... t NULL > if (require(raster)) { + print(a <- aggregate(st, sfc, mean, exact = TRUE)) + print(a[[1]]) + print(sum(a[[1]])*30 == sum(1:720)) + } Loading required package: raster stars object with 2 dimensions and 1 attribute attribute(s): Min. 1st Qu. Median Mean 3rd Qu. Max. A1 28 194.25 360.5 360.5 526.75 693 dimension(s): from to offset delta refsys point geometry 1 4 NA NA WGS 84 FALSE t 1 6 2019-02-20 1 days Date FALSE values geometry POLYGON ((0 12, 5 12, 5 6...,...,POLYGON ((5 6, 10 6, 10 0... t NULL [,1] [,2] [,3] [,4] [,5] [,6] [1,] 28 148 268 388 508 628 [2,] 33 153 273 393 513 633 [3,] 88 208 328 448 568 688 [4,] 93 213 333 453 573 693 [1] TRUE > > tm0 = as.Date("2019-02-19") + -1:8 > (a = aggregate(st, tm0, mean, na.rm = TRUE)) stars object with 3 dimensions and 1 attribute attribute(s): Min. 1st Qu. Median Mean 3rd Qu. Max. NA's A1 1 180.75 360.5 360.5 540.25 720 480 dimension(s): from to offset delta refsys point x/y time 1 10 2019-02-18 1 days Date NA x 1 10 0 1 WGS 84 FALSE [x] y 1 12 12 -1 WGS 84 FALSE [y] > (a = aggregate(st, "days", mean, na.rm = TRUE)) stars object with 3 dimensions and 1 attribute attribute(s): Min. 1st Qu. Median Mean 3rd Qu. Max. A1 1 180.75 360.5 360.5 540.25 720 dimension(s): from to offset delta refsys point x/y time 1 6 2019-02-20 1 days Date NA x 1 10 0 1 WGS 84 FALSE [x] y 1 12 12 -1 WGS 84 FALSE [y] > yd = function(x) as.POSIXlt(x)$yday > (a = aggregate(st, yd, mean, na.rm = TRUE)) stars object with 3 dimensions and 1 attribute attribute(s): Min. 1st Qu. Median Mean 3rd Qu. Max. A1 1 180.75 360.5 360.5 540.25 720 dimension(s): from to offset delta refsys point values x/y geometry 1 6 NA NA NA NA 50,...,55 x 1 10 0 1 WGS 84 FALSE NULL [x] y 1 12 12 -1 WGS 84 FALSE NULL [y] > > # with "by" geometry not overlapping x > pt = st_point(c(-10,-10)) > (sfc = c(sfc, st_sfc(pt, crs = st_crs(sfc)))) Geometry set for 5 features Geometry type: GEOMETRY Dimension: XY Bounding box: xmin: -10 ymin: -10 xmax: 10 ymax: 12 Geodetic CRS: WGS 84 POLYGON ((0 12, 5 12, 5 6, 0 6, 0 12)) POLYGON ((5 12, 10 12, 10 6, 5 6, 5 12)) POLYGON ((0 6, 5 6, 5 0, 0 0, 0 6)) POLYGON ((5 6, 10 6, 10 0, 5 0, 5 6)) POINT (-10 -10) > (a = aggregate(st, sfc, mean)) stars object with 2 dimensions and 1 attribute attribute(s): Min. 1st Qu. Median Mean 3rd Qu. Max. NA's A1 28 194.25 360.5 360.5 526.75 693 6 dimension(s): from to offset delta refsys point geometry 1 5 NA NA WGS 84 FALSE t 1 6 2019-02-20 1 days Date FALSE values geometry POLYGON ((0 12, 5 12, 5 6...,...,POINT (-10 -10) t NULL > (a = aggregate(st, st, mean)) stars object with 2 dimensions and 1 attribute attribute(s): Min. 1st Qu. Median Mean 3rd Qu. Max. A1 1 180.75 360.5 360.5 540.25 720 dimension(s): from to offset delta refsys point geometry 1 120 NA NA WGS 84 FALSE t 1 6 2019-02-20 1 days Date FALSE values geometry POLYGON ((0 12, 1 12, 1 1...,...,POLYGON ((9 1, 10 1, 10 0... t NULL > > proc.time() user system elapsed 5.67 0.43 6.12 stars/tests/plot.Rout.save0000644000176200001440000000411714644736040015351 0ustar liggesusers R Under development (unstable) (2024-07-13 r86895 ucrt) -- "Unsuffered Consequences" Copyright (C) 2024 The R Foundation for Statistical Computing Platform: x86_64-w64-mingw32/x64 R is free software and comes with ABSOLUTELY NO WARRANTY. You are welcome to redistribute it under certain conditions. Type 'license()' or 'licence()' for distribution details. R is a collaborative project with many contributors. Type 'contributors()' for more information and 'citation()' on how to cite R or R packages in publications. Type 'demo()' for some demos, 'help()' for on-line help, or 'help.start()' for an HTML browser interface to help. Type 'q()' to quit R. > suppressPackageStartupMessages(library(stars)) > if (suppressPackageStartupMessages(require(sp, quietly = TRUE)) && require(spacetime, quietly = TRUE)) { + data(air) # this loads several datasets in .GlobalEnv + dim(air) + d = st_dimensions(station = st_as_sfc(stations), time = dates) + aq = st_as_stars(list(PM10 = air), dimensions = d) + image(aperm(log(aq), 2:1), main = "NA pattern (white) in PM10 station time series") + st_geometry(aq) + } Geometry set for 70 features Geometry type: POINT Dimension: XY Bounding box: xmin: 6.28107 ymin: 47.80847 xmax: 14.78617 ymax: 54.92497 Geodetic CRS: +proj=longlat +datum=WGS84 +no_defs First 5 geometries: POINT (9.585911 53.67057) POINT (9.68503 53.52418) POINT (9.791584 54.07312) POINT (13.64701 52.44775) POINT (13.29635 52.65315) > > csv = system.file("tif/ESACCI-LC-Legend.csv", package = "starsdata") > if (csv != "") { + rgb = read.csv(csv, header = TRUE, sep = ";") + luc = read_stars(system.file("tif/LUC3.tif", package = "starsdata")) + plot(luc, rgb = rgb, axes = TRUE, key.pos = NULL) + } > > # https://github.com/r-spatial/stars/issues/233 : > nc = read_sf(system.file("shape/nc.shp", package = "sf")) > output = st_as_stars(st_bbox(nc), nx = 1, ny = 1, values = 1) > plot(output) Warning message: In plot.stars(output) : breaks="quantile" leads to a single class; maybe try breaks="equal" instead? > > proc.time() user system elapsed 1.89 0.37 2.25 stars/tests/tidy.Rout.save0000644000176200001440000000526014644736040015344 0ustar liggesusers R Under development (unstable) (2024-07-13 r86895 ucrt) -- "Unsuffered Consequences" Copyright (C) 2024 The R Foundation for Statistical Computing Platform: x86_64-w64-mingw32/x64 R is free software and comes with ABSOLUTELY NO WARRANTY. You are welcome to redistribute it under certain conditions. Type 'license()' or 'licence()' for distribution details. R is a collaborative project with many contributors. Type 'contributors()' for more information and 'citation()' on how to cite R or R packages in publications. Type 'demo()' for some demos, 'help()' for on-line help, or 'help.start()' for an HTML browser interface to help. Type 'q()' to quit R. > suppressPackageStartupMessages(library(stars)) > > if (require(dplyr, quietly = TRUE)) { + tif = system.file("tif/L7_ETMs.tif", package = "stars") + x <- read_stars(tif) + (y <- x %>% select(L7_ETMs.tif)) + (y <- x %>% filter(band > 2)) + (z <- x %>% mutate(foo = 2* L7_ETMs.tif)) + x %>% pull(L7_ETMs.tif) %>% class() + x %>% filter(x > 289900) + x %>% slice("x", 50:100) + + suppressPackageStartupMessages(library(ggplot2)) + ggplot() + geom_stars(data = x) + coord_equal() + facet_wrap(~band) + ggplot() + geom_stars(data = x[,c(1:50,52:100),c(1:45, 50:60)]) + + coord_equal() + ggplot() + geom_stars(data = x[,1:10,1:10,1:2], sf = TRUE) + facet_wrap(~band) + + + # see https://github.com/r-spatial/stars/issues/539 : + x <- read_stars(system.file("tif/L7_ETMs.tif", package = "stars")) + y <- transmute(x, L7_ETMs.tif = units::set_units(L7_ETMs.tif, degree_C)) # same as x but with units + + x - slice(x, 'band', 1) # runs as expected, recycling the second object + try(y - slice(x, 'band', 1)) # fails as expected because x is unitless + try(x - slice(y, 'band', 1)) # fails as expected + y - slice(y, 'band', 1) # runs as expected + } 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 Error in Ops.units(X[[i]], ...) : both operands of the expression should be "units" objects Error in Ops.units(X[[i]], ...) : both operands of the expression should be "units" objects stars object with 3 dimensions and 1 attribute attribute(s): Min. 1st Qu. Median Mean 3rd Qu. Max. L7_ETMs.tif [°C] -184 -19 -8 -10.2353 0 131 dimension(s): from to offset delta refsys point x/y x 1 349 288776 28.5 SIRGAS 2000 / UTM zone 25S FALSE [x] y 1 352 9120761 -28.5 SIRGAS 2000 / UTM zone 25S FALSE [y] band 1 6 NA NA NA NA > > proc.time() user system elapsed 1.29 0.14 1.42 stars/tests/sf.R0000644000176200001440000000257614536637433013333 0ustar liggesuserssuppressPackageStartupMessages(library(stars)) library(sf) #jp2 = system.file("jp2/B01.jp2", package = "stars") #(x = read_stars(jp2, options = c("OVERVIEW_LEVEL=3"))) tif = system.file("tif/L7_ETMs.tif", package = "stars") #(x = read_stars(tif, options = c("OVERVIEW_LEVEL=3"))) # FIXME: breaks on windows (x = read_stars(tif)[,1:20,1:20]) # names(x) = "L7_ETM" # library(abind) # x = adrop(x) image(x) (sfc = st_as_sfc(x, as_points = FALSE)) plot(sfc, add =TRUE) (sfc = st_as_sfc(x, as_points = TRUE)) plot(sfc, add = TRUE) sf = st_as_sf(x, as_points = FALSE) plot(sf, border = NA) sf = st_as_sf(x[,,,1], as_points = FALSE, merge = TRUE) plot(sf, border = NA) sfc1 <- st_as_sfc(x, as_points = TRUE, use_cpp = TRUE, na.rm = FALSE) sfc2 <- st_as_sfc(x, as_points = TRUE, use_cpp = FALSE) identical(sfc1, sfc2) sfc1 <- st_as_sfc(x, as_points = FALSE, use_cpp = TRUE, na.rm = FALSE) sfc2 <- st_as_sfc(x, as_points = FALSE, use_cpp = FALSE) identical(sfc1, sfc2) # sf -> stars -> sf x = st_sfc(st_point(0:1), st_point(1:2), st_point(2:3)) m = matrix(1:9,3) colnames(m) = c("a", "b", "c") foo = st_sf(m, geom = x) x = st_as_stars(foo) c(x) st_redimension(x) c(x, x) (y = st_redimension(x, along = list(time = as.Date("2017-11-27") + 0:2))) st_as_sf(y) st_as_sf(st_as_stars(foo)) c(x,x,along=1) # NEEDS FIXING! # st_as_stars.sfc st_as_stars(st_geometry(st_as_sf(y)), nx = 10, ny = 10, as_points = FALSE) stars/tests/ee.Rout.save0000644000176200001440000000303314644736040014760 0ustar liggesusers R Under development (unstable) (2024-07-13 r86895 ucrt) -- "Unsuffered Consequences" Copyright (C) 2024 The R Foundation for Statistical Computing Platform: x86_64-w64-mingw32/x64 R is free software and comes with ABSOLUTELY NO WARRANTY. You are welcome to redistribute it under certain conditions. Type 'license()' or 'licence()' for distribution details. R is a collaborative project with many contributors. Type 'contributors()' for more information and 'citation()' on how to cite R or R packages in publications. Type 'demo()' for some demos, 'help()' for on-line help, or 'help.start()' for an HTML browser interface to help. Type 'q()' to quit R. > # show that extract with exact=TRUE equals st_interpolate_aw(), only much faster > options(digits=15) > suppressPackageStartupMessages(library(stars)) > L = st_as_stars(L7_ETMs)[,,,1] > set.seed(123432) > st_bbox(L) |> + st_as_sfc() |> + st_centroid() |> + st_buffer(50) |> + st_sample(10) |> + st_combine() |> + st_convex_hull() -> ch > if (interactive()) { + plot(L, reset = FALSE) + plot(ch, add = TRUE, border = 'red', col = NA) + } > if (FALSE) { + e = st_extract(L, ch)[[1]] + a = aggregate(L, ch, mean)[[1]] + identical(e, a) # same code path + ee = aggregate(L, ch, mean, exact = TRUE)[[1]] + all.equal(e, ee) + aw = suppressWarnings(st_interpolate_aw(L, ch, extensive = FALSE)$V1) + all.equal(as.vector(ee), aw) + ee0 = st_extract(L, ch, exact = TRUE)[[1]] + all.equal(ee0, ee) # ?? + } > > proc.time() user system elapsed 0.73 0.07 0.81 stars/tests/tidy.R0000644000176200001440000000210614271153074013647 0ustar liggesuserssuppressPackageStartupMessages(library(stars)) if (require(dplyr, quietly = TRUE)) { tif = system.file("tif/L7_ETMs.tif", package = "stars") x <- read_stars(tif) (y <- x %>% select(L7_ETMs.tif)) (y <- x %>% filter(band > 2)) (z <- x %>% mutate(foo = 2* L7_ETMs.tif)) x %>% pull(L7_ETMs.tif) %>% class() x %>% filter(x > 289900) x %>% slice("x", 50:100) suppressPackageStartupMessages(library(ggplot2)) ggplot() + geom_stars(data = x) + coord_equal() + facet_wrap(~band) ggplot() + geom_stars(data = x[,c(1:50,52:100),c(1:45, 50:60)]) + coord_equal() ggplot() + geom_stars(data = x[,1:10,1:10,1:2], sf = TRUE) + facet_wrap(~band) # see https://github.com/r-spatial/stars/issues/539 : x <- read_stars(system.file("tif/L7_ETMs.tif", package = "stars")) y <- transmute(x, L7_ETMs.tif = units::set_units(L7_ETMs.tif, degree_C)) # same as x but with units x - slice(x, 'band', 1) # runs as expected, recycling the second object try(y - slice(x, 'band', 1)) # fails as expected because x is unitless try(x - slice(y, 'band', 1)) # fails as expected y - slice(y, 'band', 1) # runs as expected } stars/tests/curvilinear.R0000644000176200001440000000341514353574021015225 0ustar liggesuserssuppressPackageStartupMessages(library(stars)) s5p = system.file("sentinel5p/S5P_NRTI_L2__NO2____20180717T120113_20180717T120613_03932_01_010002_20180717T125231.nc", package = "starsdata") if (s5p != "") { lat_ds = paste0("HDF5:\"", s5p, "\"://PRODUCT/latitude") lon_ds = paste0("HDF5:\"", s5p, "\"://PRODUCT/longitude") nit_ds = paste0("HDF5:\"", s5p, "\"://PRODUCT/SUPPORT_DATA/DETAILED_RESULTS/nitrogendioxide_summed_total_column") lat = read_stars(lat_ds) lon = read_stars(lon_ds) nit = read_stars(nit_ds) nit[[1]][nit[[1]] > 9e+36] = NA ll = setNames(c(lon, lat), c("x", "y")) nit.c0 = st_as_stars(nit, curvilinear = ll) # more direct method: nit.c = read_stars(s5p, sub = "//PRODUCT/SUPPORT_DATA/DETAILED_RESULTS/nitrogendioxide_summed_total_column", curvilinear = c("//PRODUCT/longitude", "//PRODUCT/latitude"), driver = NULL) if (inherits(nit.c[[1]], "units")) nit.c[[1]] = units::drop_units(nit.c[[1]]) nit.c[[1]][nit.c[[1]] > 9e+36] = NA all.equal(nit.c0, nit.c) st_crs(nit.c) = 4326 print(nit.c) if (FALSE && capabilities()["png"]) { png("nit1.png", 800, 800) plot(nit.c, breaks = "equal", reset = FALSE, axes = TRUE, as_points = TRUE, pch = 16) maps::map('world', add = TRUE, col = 'red') dev.off() png("nit2.png", 800, 800) plot(nit.c, breaks = "equal", reset = FALSE, axes = TRUE, as_points = FALSE, border = NA) maps::map('world', add = TRUE, col = 'red') dev.off() nit.c = stars:::st_downsample(nit.c, 8) print(nit.c) png("nit3.png", 800, 800) plot(nit.c, breaks = "equal", reset = FALSE, axes = TRUE, as_points = TRUE, pch = 16) maps::map('world', add = TRUE, col = 'red') dev.off() png("nit4.png", 800, 800) plot(nit.c, breaks = "equal", reset = FALSE, axes = TRUE, as_points = FALSE, border = NA) maps::map('world', add = TRUE, col = 'red') dev.off() } } stars/tests/datasets.R0000644000176200001440000000020414271153074014503 0ustar liggesuserssuppressPackageStartupMessages(library(stars)) bcsd_obs L7_ETMs if (require(starsdata, quietly = TRUE)) { print(stars_sentinel2) } stars/tests/spacetime.R0000644000176200001440000000121314452766315014657 0ustar liggesuserssuppressPackageStartupMessages(library(stars)) if (suppressPackageStartupMessages(require(sp, quietly = TRUE)) && require(spacetime, quietly = TRUE)) { set.seed(1331) Sys.setenv(TZ="UTC") example(STFDF) x = st_as_stars(stfdf) xx = as(x, "STFDF") # as.xts.stars: library(xts) xts = as.xts(x) st_as_stars(xts) st_as_stars(xts, dimensions = st_dimensions(x)) library(sp) pts = SpatialPoints(rbind(c(0,0), c(0,1), c(1,0), c(1,1))) gridded(pts) = TRUE tm = xts(1:10, as.Date("2010-01-01")+0:9) stfdf = STFDF(pts, tm, data.frame(foo = seq_len(length(pts) * length(tm)))) x = st_as_stars(stfdf) xx = as(x, "STFDF") x = st_as_stars(as(stfdf, "STSDF")) } stars/tests/warp.Rout.save0000644000176200001440000001337114644736040015346 0ustar liggesusers R Under development (unstable) (2024-07-13 r86895 ucrt) -- "Unsuffered Consequences" Copyright (C) 2024 The R Foundation for Statistical Computing Platform: x86_64-w64-mingw32/x64 R is free software and comes with ABSOLUTELY NO WARRANTY. You are welcome to redistribute it under certain conditions. Type 'license()' or 'licence()' for distribution details. R is a collaborative project with many contributors. Type 'contributors()' for more information and 'citation()' on how to cite R or R packages in publications. Type 'demo()' for some demos, 'help()' for on-line help, or 'help.start()' for an HTML browser interface to help. Type 'q()' to quit R. > suppressPackageStartupMessages(library(stars)) > geomatrix = system.file("tif/geomatrix.tif", package = "stars") > x = read_stars(geomatrix) > # can stars reproduce what gdal does, by default? > x2 = st_warp(x, use_gdal = TRUE, no_data_value = -9999) > y = st_warp(x, x2) > if (interactive()) { plot(x2, breaks = "equal", axes=TRUE) } > if (interactive()) { plot(y, breaks = "equal", axes=TRUE) } > names(x2) = names(y) > all.equal(x2, y) # yes? [1] TRUE > > # does gdal reproduce with stars template object? > (x2 = setNames(st_warp(x, y, use_gdal = TRUE, no_data_value=-1), "file.tif")) stars object with 2 dimensions and 1 attribute attribute(s): Min. 1st Qu. Median Mean 3rd Qu. Max. NA's file.tif 74 115 123 126.4289 132 255 224 dimension(s): from to offset delta refsys point x/y x 1 25 1840902 5.22 WGS 84 / UTM zone 11N FALSE [x] y 1 25 1144003 -5.22 WGS 84 / UTM zone 11N FALSE [y] > > # does gdal reproduce what stars does, default cell size? > (x2 = st_warp(x, crs = st_crs(x), use_gdal = FALSE)) stars object with 2 dimensions and 1 attribute attribute(s): Min. 1st Qu. Median Mean 3rd Qu. Max. NA's geomatrix.tif 74 115 123 126.4289 132 255 224 dimension(s): from to offset delta refsys x/y x 1 25 1840902 5.22 WGS 84 / UTM zone 11N [x] y 1 25 1144003 -5.22 WGS 84 / UTM zone 11N [y] > (y = setNames(st_warp(x, x2, use_gdal = TRUE, debug = FALSE, no_data_value=-1), "file.tif")) stars object with 2 dimensions and 1 attribute attribute(s): Min. 1st Qu. Median Mean 3rd Qu. Max. NA's file.tif 74 115 123 126.4289 132 255 224 dimension(s): from to offset delta refsys point x/y x 1 25 1840902 5.22 WGS 84 / UTM zone 11N FALSE [x] y 1 25 1144003 -5.22 WGS 84 / UTM zone 11N FALSE [y] > > # try with multiple bands: > tif = system.file("tif/L7_ETMs.tif", package = "stars") > (x1 = read_stars(tif)) stars object with 3 dimensions and 1 attribute attribute(s): Min. 1st Qu. Median Mean 3rd Qu. Max. L7_ETMs.tif 1 54 69 68.91242 86 255 dimension(s): from to offset delta refsys point x/y x 1 349 288776 28.5 SIRGAS 2000 / UTM zone 25S FALSE [x] y 1 352 9120761 -28.5 SIRGAS 2000 / UTM zone 25S FALSE [y] band 1 6 NA NA NA NA > tifcp = tempfile(fileext = ".tif") > file.copy(tif, tifcp) [1] TRUE > x1p = read_stars(tifcp, proxy = TRUE) > st_dimensions(x1p) from to offset delta refsys point x/y x 1 349 288776 28.5 SIRGAS 2000 / UTM zone 25S FALSE [x] y 1 352 9120761 -28.5 SIRGAS 2000 / UTM zone 25S FALSE [y] band 1 6 NA NA NA NA > (x1a = st_warp(x1, crs = st_crs(4326))) stars object with 3 dimensions and 1 attribute attribute(s): Min. 1st Qu. Median Mean 3rd Qu. Max. NA's L7_ETMs.tif 1 54 69 68.93072 86 255 8778 dimension(s): from to offset delta refsys x/y x 1 350 -34.92 0.0002592 WGS 84 [x] y 1 352 -7.95 -0.0002592 WGS 84 [y] band 1 6 NA NA NA > (x1b = setNames(st_warp(x1, x1p, use_gdal = TRUE, no_data_value=-1), "file.tif")) stars object with 3 dimensions and 1 attribute attribute(s): Min. 1st Qu. Median Mean 3rd Qu. Max. file.tif 1 54 69 68.91242 86 255 dimension(s): from to offset delta refsys point x/y x 1 349 288776 28.5 SIRGAS 2000 / UTM zone 25S FALSE [x] y 1 352 9120761 -28.5 SIRGAS 2000 / UTM zone 25S FALSE [y] band 1 6 NA NA NA NA > > # does gdal reproduce what stars does? Smaller grid: > x2 = st_warp(x, crs = st_crs(x), use_gdal = FALSE, cellsize = 3) > # x2 = x2[,2:43,2:43] > if (interactive()) { plot(x2, breaks = "equal", axes=TRUE, reset = FALSE) } > if (interactive()) { plot(st_as_sfc(st_bbox(x2)), add = TRUE, col = NA, border = 'red') } > ### doesn't work: FIXME: check with more recent GDAL: > (y = setNames(st_warp(x, x2, use_gdal = TRUE, debug = FALSE, no_data_value=-1), "file.tif")) stars object with 2 dimensions and 1 attribute attribute(s): Min. 1st Qu. Median Mean 3rd Qu. Max. NA's file.tif 74 107 123 126.6584 132 255 727 dimension(s): from to offset delta refsys point x/y x 1 44 1840902 3 WGS 84 / UTM zone 11N FALSE [x] y 1 44 1144003 -3 WGS 84 / UTM zone 11N FALSE [y] > if (interactive()) { plot(y, breaks = "equal") } > names(x2) = names(y) > # isTRUE(all.equal(x2, y, check.attributes=FALSE)) > m = mean(as.vector(x2[[1]]==y[[1]]), na.rm = TRUE) > u = unique(as.vector(x2[[1]]-y[[1]])) > > g = st_as_stars() > attr(g, "dimensions")$x$offset = 0 > g$values = as.vector(t(matrix(seq_len(prod(dim(g))), 180, 360))) > if (interactive()) { plot(g, axes=TRUE) } > a = st_warp(g, st_as_stars(), use_gdal=FALSE) > b = st_warp(g, st_as_stars(), use_gdal=TRUE, no_data_value = -1) > all.equal(a, b, check.attributes = FALSE) [1] TRUE > > proc.time() user system elapsed 1.54 0.21 1.76 stars/tests/mdim.Rout.save0000644000176200001440000001101414644736040015313 0ustar liggesusers R Under development (unstable) (2024-07-13 r86895 ucrt) -- "Unsuffered Consequences" Copyright (C) 2024 The R Foundation for Statistical Computing Platform: x86_64-w64-mingw32/x64 R is free software and comes with ABSOLUTELY NO WARRANTY. You are welcome to redistribute it under certain conditions. Type 'license()' or 'licence()' for distribution details. R is a collaborative project with many contributors. Type 'contributors()' for more information and 'citation()' on how to cite R or R packages in publications. Type 'demo()' for some demos, 'help()' for on-line help, or 'help.start()' for an HTML browser interface to help. Type 'q()' to quit R. > Sys.setenv(TZ="") > suppressPackageStartupMessages(library(stars)) > matrix(1:4,2) [,1] [,2] [1,] 1 3 [2,] 2 4 > m = matrix(1:4,2) > s = st_as_stars(m) > > s = st_set_dimensions(s, st_dimensions(s), xy = NULL) > sfc = st_sfc(st_point(0:1), st_point(3:2)) > s = st_set_dimensions(s, 1, sfc) |> st_set_crs(4326) > tm = as.POSIXct("2023-03-04 12:35") + c(0, 3600) > s = st_set_dimensions(s, 2, tm) > f = tempfile(fileext = ".nc") > write_mdim(s, f, as_float = FALSE) Warning messages: 1: In CPL_write_mdim(file, driver, dimx, cdl, wkt, xy, root_group_options, : GDAL Error 6: SetIndexingVariable() not implemented 2: In CPL_write_mdim(file, driver, dimx, cdl, wkt, xy, root_group_options, : GDAL Error 6: SetIndexingVariable() not implemented > s2 = read_mdim(f) > #print(s2) > print(all.equal(s, s2, check.attributes = FALSE)) [1] TRUE > print(all.equal(s, s2)) [1] "Attributes: < Component \"dimensions\": Component \"X1\": Component \"refsys\": Component \"input\": 1 string mismatch >" [2] "Attributes: < Component \"dimensions\": Component \"X1\": Component \"refsys\": Component \"wkt\": 1 string mismatch >" [3] "Attributes: < Component \"dimensions\": Component \"X1\": Component \"values\": Attributes: < Component \"crs\": Component \"input\": 1 string mismatch > >" [4] "Attributes: < Component \"dimensions\": Component \"X1\": Component \"values\": Attributes: < Component \"crs\": Component \"wkt\": 1 string mismatch > >" [5] "Attributes: < Component \"dimensions\": Component \"X2\": Component \"offset\": 'tzone' attributes are inconsistent ('' and 'UTC') >" [6] "Attributes: < Component \"dimensions\": Component \"X2\": Component \"point\": 'is.NA' value mismatch: 1 in current 0 in target >" > > st_crs(sfc) = st_crs(s2) > st_crs(s) = st_crs(s2) > tm = as.Date("2023-02-25") + 0:1 > sd = st_set_dimensions(s, 2, tm) > print(sd) stars object with 2 dimensions and 1 attribute attribute(s): Min. 1st Qu. Median Mean 3rd Qu. Max. A1 1 1.75 2.5 2.5 3.25 4 dimension(s): from to offset delta refsys point values X1 1 2 NA NA WGS 84 TRUE POINT (0 1), POINT (3 2) X2 1 2 2023-02-25 1 days Date FALSE NULL > sd = st_set_dimensions(sd, 1, sfc) > print(sd) stars object with 2 dimensions and 1 attribute attribute(s): Min. 1st Qu. Median Mean 3rd Qu. Max. A1 1 1.75 2.5 2.5 3.25 4 dimension(s): from to offset delta refsys point values X1 1 2 NA NA WGS 84 TRUE POINT (0 1), POINT (3 2) X2 1 2 2023-02-25 1 days Date FALSE NULL > f = tempfile(fileext = ".nc") > write_mdim(sd, f) Warning messages: 1: In CPL_write_mdim(file, driver, dimx, cdl, wkt, xy, root_group_options, : GDAL Error 6: SetIndexingVariable() not implemented 2: In CPL_write_mdim(file, driver, dimx, cdl, wkt, xy, root_group_options, : GDAL Error 6: SetIndexingVariable() not implemented > sd2 = read_mdim(f) > print(sd2) stars object with 2 dimensions and 1 attribute attribute(s): Min. 1st Qu. Median Mean 3rd Qu. Max. A1 1 1.75 2.5 2.5 3.25 4 dimension(s): from to offset delta refsys point values X1 1 2 NA NA WGS 84 TRUE POINT (0 1), POINT (3 2) X2 1 2 2023-02-25 1 days Date NA NULL > print(all.equal(sd, sd2, check.attributes = FALSE)) [1] TRUE > print(all.equal(sd, sd2)) [1] "Attributes: < Component \"dimensions\": Component \"X2\": Component \"point\": 'is.NA' value mismatch: 1 in current 0 in target >" > # if (.Platform$OS.type == "unix") { > # print(read_mdim(paste0("/vsizip/", (system.file("zarr/sic_daily_sample.zarr.zip", package = "stars")))) > # } > > proc.time() user system elapsed 0.51 0.23 0.73 stars/tests/write.Rout.save0000644000176200001440000000352614644736040015530 0ustar liggesusers R Under development (unstable) (2024-07-13 r86895 ucrt) -- "Unsuffered Consequences" Copyright (C) 2024 The R Foundation for Statistical Computing Platform: x86_64-w64-mingw32/x64 R is free software and comes with ABSOLUTELY NO WARRANTY. You are welcome to redistribute it under certain conditions. Type 'license()' or 'licence()' for distribution details. R is a collaborative project with many contributors. Type 'contributors()' for more information and 'citation()' on how to cite R or R packages in publications. Type 'demo()' for some demos, 'help()' for on-line help, or 'help.start()' for an HTML browser interface to help. Type 'q()' to quit R. > suppressPackageStartupMessages(library(stars)) > > tif = system.file("tif/L7_ETMs.tif", package = "stars") > (x = read_stars(tif)) stars object with 3 dimensions and 1 attribute attribute(s): Min. 1st Qu. Median Mean 3rd Qu. Max. L7_ETMs.tif 1 54 69 68.91242 86 255 dimension(s): from to offset delta refsys point x/y x 1 349 288776 28.5 SIRGAS 2000 / UTM zone 25S FALSE [x] y 1 352 9120761 -28.5 SIRGAS 2000 / UTM zone 25S FALSE [y] band 1 6 NA NA NA NA > > # write stars object: > write_stars(x, tempfile(), driver = "GTiff") > > # write stars_proxy object: > (x = read_stars(tif, proxy = TRUE)) stars_proxy object with 1 attribute in 1 file(s): $L7_ETMs.tif [1] "[...]/L7_ETMs.tif" dimension(s): from to offset delta refsys point x/y x 1 349 288776 28.5 SIRGAS 2000 / UTM zone 25S FALSE [x] y 1 352 9120761 -28.5 SIRGAS 2000 / UTM zone 25S FALSE [y] band 1 6 NA NA NA NA > write_stars(x, tempfile(), driver = "GTiff") > > proc.time() user system elapsed 0.76 0.15 0.92 stars/tests/crop.R0000644000176200001440000000220614453526025013644 0ustar liggesuserssuppressPackageStartupMessages(library(stars)) tif = system.file("tif/L7_ETMs.tif", package = "stars") x = read_stars(tif) xy = structure(list(x = c(293253.999046018, 296400.196497684), y = c(9113801.64775462, 9111328.49619133)), .Names = c("x", "y")) pts = st_as_sf(data.frame(do.call(cbind, xy)), coords = c("x", "y"), crs = st_crs(x)) image(x, axes = TRUE) plot(st_as_sfc(st_bbox(pts)), col = NA, add = TRUE) bb = st_bbox(pts) (xx = x[bb]) image(xx) plot(st_as_sfc(bb), add = TRUE, col = NA) st_contains(st_as_sfc(st_bbox(xx)), st_as_sfc(st_bbox(pts))) image(x) pt = st_point(c(x = 290462.103109179, y = 9114202.32594085)) buf = st_buffer(st_sfc(pt, crs = st_crs(x)), 1500) plot(buf, add = TRUE) buf = st_sfc(st_polygon(list(st_buffer(pt, 1500)[[1]], st_buffer(pt, 1000)[[1]])), crs = st_crs(x)) image(x[buf]) plot(buf, add = TRUE, col = NA) image(x[buf,crop=FALSE]) plot(buf, add = TRUE, col = NA) plot(x, rgb = 1:3) # #638: l = st_as_stars(L7_ETMs) st_bbox(l) |> st_as_sfc() -> bb (bb + 1e6) |> st_set_crs(st_crs(l)) -> b st_crop(l, b) st_intersects(b, st_as_sfc(st_bbox(l))) |> as.matrix() (bb - 1e6) |> st_set_crs(st_crs(l)) -> b st_crop(l, b) stars/tests/raster.Rout.save0000644000176200001440000000754614644736040015704 0ustar liggesusers R Under development (unstable) (2024-07-13 r86895 ucrt) -- "Unsuffered Consequences" Copyright (C) 2024 The R Foundation for Statistical Computing Platform: x86_64-w64-mingw32/x64 R is free software and comes with ABSOLUTELY NO WARRANTY. You are welcome to redistribute it under certain conditions. Type 'license()' or 'licence()' for distribution details. R is a collaborative project with many contributors. Type 'contributors()' for more information and 'citation()' on how to cite R or R packages in publications. Type 'demo()' for some demos, 'help()' for on-line help, or 'help.start()' for an HTML browser interface to help. Type 'q()' to quit R. > options(rgdal_show_exportToProj4_warnings = "none") > suppressPackageStartupMessages(library(stars)) > > if (suppressPackageStartupMessages(require(sp, quietly = TRUE)) && require("raster", quietly = TRUE) && + suppressPackageStartupMessages(require("terra", quietly = TRUE))) { + + tif = system.file("tif/L7_ETMs.tif", package = "stars") + (x = read_stars(tif)) + (r = as(x, "Raster")) + (y = st_as_stars(r)) + + # single band: + x = adrop(x[,,,1]) + r = as(x, "Raster") + (y = st_as_stars(r)) + + # proxy: + (x = read_stars(tif, proxy = TRUE)) + (r = as(x, "Raster")) + (y = st_as_stars(r)) + (y2 = st_as_stars(r, proxy = TRUE)) + } stars_proxy object with 1 attribute in 1 file(s): $L7_ETMs.tif [1] "[...]/L7_ETMs.tif" dimension(s): from to offset delta refsys point x/y x 1 349 288776 28.5 SIRGAS 2000 / UTM zone 25S FALSE [x] y 1 352 9120761 -28.5 SIRGAS 2000 / UTM zone 25S FALSE [y] band 1 6 NA NA NA NA > > if (require(terra, quietly = TRUE)) { + ## terra ------------- + stopifnot(require(terra)) + (x = read_stars(tif)) + (r = as(x, "SpatRaster")) + (y = st_as_stars(r)) + + # single band: + x = adrop(x[,,,1]) + r = as(x, "SpatRaster") + (y = st_as_stars(r)) + + # proxy: + (x = read_stars(tif, proxy = TRUE)) + (r = as(x, "SpatRaster")) + (y = st_as_stars(r)) + (y2 = st_as_stars(r, proxy = TRUE)) + + f = system.file("tif/lc.tif", package = "stars") + lc = read_stars(system.file("tif/lc.tif", package = "stars")) + levels(lc[[1]]) + r = rast(lc) + e = attr(lc[[1]], "exclude") + + if (packageVersion("terra") >= "1.5.48") { + + print(all.equal(terra::levels(r)[[1]][,2], terra::levels(rast(f))[[1]][!e,2])) + print(all.equal(terra::coltab(r)[[1]][!e,], terra::coltab(rast(f))[[1]][!e,], check.attributes = FALSE)) + + r<- rast(nrow=10, ncol=10) + set.seed(5) + values(r)<- sample(1:3, size = ncell(r), replace = TRUE) + levels(r) <- data.frame(ID=1:3, category = c("c1", "c2", "c3")) + + r2<- rast(nrow=10, ncol=10) + set.seed(6) + values(r2)<- sample(c(0,2,3), size = ncell(r2), replace = TRUE) + levels(r2) <- data.frame(ID=c(0,2,3), category = c("x1", "x2", "x3")) + + r3<- c(r,r2) + names(r3)<- c("lyr.1", "lyr.2") + + r3_stars<- st_as_stars(r3) + + st_redimension(r3_stars) |> print() + rast(r3_stars) |> levels() |> print() + + m = matrix(1:121, 11, 11) + bb = st_bbox(c(xmin=0, xmax=11, ymin=0, ymax=11)) + s = st_as_stars(bb, values = m) + r = rast(s) + aggregate(r, 2) |> st_as_stars() -> r.s + stopifnot(isTRUE(all.equal(r.s[,1:5,1:5], st_downsample(s, 1, FUN = mean)))) + } + } [1] TRUE [1] TRUE stars object with 3 dimensions and 1 attribute attribute(s): lyr.1.lyr.2 c1:34 c2:32 c3:34 x1:28 x2:37 x3:35 dimension(s): from to offset delta refsys values x/y x 1 10 -180 36 WGS 84 (CRS84) NULL [x] y 1 10 90 -18 WGS 84 (CRS84) NULL [y] new_dim 1 2 NA NA NA lyr.1, lyr.2 [[1]] IDs categories 1 1 c1 2 2 c2 3 3 c3 [[2]] IDs categories 1 0 x1 2 2 x2 3 3 x3 > > > proc.time() user system elapsed 6.21 0.35 6.51 stars/tests/testthat/0000755000176200001440000000000014645437762014432 5ustar liggesusersstars/tests/testthat/Rplots.pdf0000644000176200001440000024643614476142420016411 0ustar liggesusers%PDF-1.4 %ρ\r 1 0 obj << /CreationDate (D:20230906202918) /ModDate (D:20230906202918) /Title (R Graphics Output) /Producer (R 4.3.1) /Creator (R) >> endobj 2 0 obj << /Type /Catalog /Pages 3 0 R >> endobj 7 0 obj << /Type /Page /Parent 3 0 R /Contents 8 0 R /Resources 4 0 R >> endobj 8 0 obj << /Length 81095 /Filter /FlateDecode >> stream xK&<_QCNN'FRGbqju 5=/q9.$^Z;\v8~?~~3~i?o}?g>w_#ww*?#ݿ3?w~~T_g.?4|~~}?vʝ~W^&g~G3#4K&'<R/:krG]/W9@{~_'\+R5Q>ooxw#4krcFkrS)3'/krtW;{W Ot;_iI;7䌷twxP#^3?j'ߩ҈'@{Œ@P7w;ŷ,{ ‰H$a)sXvX?n͏[aDjIĢ:^2/+䌷tݔˬӾ7k`G/w퍰w\% ՒZvnH;Kw&F;Z(]>;ݹ_H&'< zVN?J_EG)7rB7 ]ƿ3=t2Uo~;XS}V;8{ϻ}ސ3{ߊ_oo^Z}xMn4 oohFkrvU`7䌷ᛳ7䌷M6^[ChO?^3ߺ/SooywMwpN=G;'a:K# ľsEb^qs>AoGָ9هHk5.N΃ *^qsym vXN֝YĉH$VsBl~8s;,8'kxf`ITG6I$v=kõQ\w >^lk|$ ݿ\#\lGL.pǓ}DG2^Ʒx^drmqbc H&X[5B-&g{{z=XxM> ؐpD 5e`һTK:Xg{>1FwXpnPhĦnHG%#\ eToGܗ8W'a^}#R-p(Xp׳J8<lH6]Xl0jQ'of>otsXv/OmYxMx;X7̜ 59{ +zyxC> {o 5HH87,Z|$ R(3^tuÇ>"䌷sÇRGL,& MW=7,& v~< 5LH'87O7eO%Ʃtxw(XpnX蕄841GXk/csS7#\|wwpog5wX Q,-R-;,8߫6seHsB`$:IXVIhT~͎ lӕyV~,Z^-)C燕G֦[q mތ-w~IJ-&g@#r 5T8?l|d g9Q5T8?|8%oGG`kra/\⪗ ^LuCqD885.xxCx;8peF^&^tJA?ސt}#^tL.}SxM>R^&c59`m|5HA8{hJ#]llyk.6ЈTxM>R*M@wv6R6xM>W?'8_0!#\lF`5L+'X분1xM> UZ`&?gs5 H `m >HtUzca@j$jz&FpG̨L 1hr%Z7_UKY]2ke'[ݝevIQǨ~xM>rW6 !g}4GrИ2xM>rHk[NpFƄ#^r5:45|ёӈ -XkkS 153R-d[;,8{)({%I`SfZ,8{ER-l~6i59C傀*{=o5 ;N5@-qr,z]Pʭv$ U7#Z|$ }Xcp& u\Ā\;8{#(e1|$ =T#BxM>G<%#\Mo5L0'XCoi(R|&Uz}-{xC> Л ^ &2c^3/^pF^sud]O^fEwXpk'nOj$ ըqR H$1Wxks`K㤀Hbzx7#\8왓A 1 0F&VqCk8D<.!v.A{vRDՎ,ss>}xM>;j&g=2knxxM>>2{|d uw<"#\܎$%yxC>;>xM>ӈ/3˜܎Mg3˜`xM> *ͳސ,suCAJ,ss;6 + ՒNY ^_QXR-`=~[R-wvXs0`Y ojI'_a"Z=-ZZ-j WRvZ +;4xWL:&w֛8;$"V9rl9fMlzgfGwvAS^3ayE^t3GG|7#-]`ܟ`.1| 9Rk.pH{/pxM>oGZcr;Nr q|OK9^k>+Thg^ϴtA4'ZO&Â!5ig]ܺ6*R-d;,XsUt9DbYI.&ɮ_du!@`CǼ5b[#C{fLrϸ=$Z4HQi'3=d_BflskA; 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0000084662 00000 n 0000084759 00000 n 0000084861 00000 n trailer << /Size 13 /Info 1 0 R /Root 2 0 R >> startxref 84939 %%EOF stars/tests/testthat/test-stars.R0000644000176200001440000000431114576033423016652 0ustar liggesuserscontext("st_as_stars tests") options(rgdal_show_exportToProj4_warnings = "none") test_that("basic st_as_stars", { skip_if_not_installed("ncdfgeom") skip_on_os("solaris") skip_if_not_installed("ncdfgeom") f <- system.file("nc/timeseries.nc", package = "stars") test_list <- ncdfgeom::read_timeseries_dsg(f) stars_obj <- st_as_stars(test_list) expect_true("XYZ" %in% class(st_dimensions(stars_obj)$points$values[[1]])) expect_s3_class(stars_obj, "stars") dim <- stars::st_dimensions(stars_obj) #expect_true(sf::st_crs(dim$points$refsys) == sf::st_crs(4326)) expect_equal(dim$time$refsys, "POSIXct") expect_s3_class(dim$points$values, "sfc_POINT") expect_true(dim$points$point) geom_point <- sf::st_as_sf(data.frame(id = names(test_list$data_frames[[1]]), lon = test_list$lons, lat = test_list$lats, stringsAsFactors = FALSE), coords = c("lon", "lat"), crs = 4326) geom_poly <- sf::st_buffer(st_transform(geom_point, 3857), dist = 100) stars_obj <- st_as_stars(test_list, sf_geometry = geom_poly) dim <- stars::st_dimensions(stars_obj) expect_equal(sf::st_crs(dim$geometry$refsys)$proj4string, sf::st_crs(geom_poly)$proj4string) expect_s3_class(dim$geometry$values, "sfc_POLYGON") expect_false(dim$geometry$point) stars_obj <- st_as_stars(test_list, sf_geometry = geom_point) dim <- stars::st_dimensions(stars_obj) expect_equal(sf::st_crs(dim$geometry$refsys)$proj4string, sf::st_crs(geom_point)$proj4string) expect_true(dim$geometry$point) expect_s3_class(dim$geometry$values, "sfc_POINT") test_list$alts <- numeric(0) stars_obj <- st_as_stars(test_list) expect_true("XY" %in% class(st_dimensions(stars_obj)$points$values[[1]])) }) test_that("st_as_stars.Raster", { skip_if_not_installed("raster") tif = system.file("tif/L7_ETMs.tif", package = "stars") x = raster::raster(tif) stars_obj <- st_as_stars(x) expect_s3_class(stars_obj, "stars") expect_equal(names(stars_obj), names(x)) # change the name of the attribute names(x) <- "newname" stars_obj2 <- st_as_stars(x) expect_equal(names(stars_obj2), names(x)) }) stars/tests/testthat/test-cubble.R0000644000176200001440000000245714642501067016761 0ustar liggesuserstest_that("cubble", { skip_if_not_installed("cubble") skip_if_not_installed("dplyr") library(dplyr) library(cubble) skip_if(packageVersion("cubble") == "0.3.1") # https://github.com/huizezhang-sherry/cubble/issues/30 library(units) tm = set_units(1:6, "days since 1985-01-01") # l7 = st_as_stars(L7_ETMs) |> st_set_dimensions(3, values = Sys.time() + 1:6) l7 = st_as_stars(L7_ETMs) |> st_set_dimensions(3, values = tm) l7sub = l7[,1:30,1:30] cu = as_cubble(l7sub) s2 = st_as_stars(cu) # expect_equal(l7sub, s2) expect_true(all.equal(l7sub, s2, check.attributes=FALSE)) prec_file = system.file("nc/test_stageiv_xyt.nc", package = "stars") #(prec = read_stars(gdal_subdatasets(prec_file)[[1]])) (prec = read_ncdf(prec_file)) sf::read_sf(system.file("gpkg/nc.gpkg", package = "sf"), "nc.gpkg") %>% st_transform(st_crs(prec)) -> nc # transform from NAD27 to WGS84 nc_outline = st_union(st_geometry(nc)) plot_hook = function() plot(nc_outline, border = 'red', add = TRUE) prec %>% slice(index = 1:12, along = "time") %>% plot(downsample = c(3, 3, 1), hook = plot_hook) a = aggregate(prec, by = nc, FUN = max) a.cb = as_cubble(a, key = id, index = time) a2 = st_as_stars(a.cb) # expect_equal(a, a2) expect_true(all.equal(drop_units(a),a2,check.attributes=FALSE)) }) stars/tests/testthat/test-gdal.R0000644000176200001440000000247414576033423016435 0ustar liggesuserslibrary(testthat) library(stars) context("gdal utils") test_that('gdal_utils work', { skip_on_appveyor() fname = system.file("nc/tos_O1_2001-2002.nc", package = "stars") if (fname != "") { info = gdal_utils("info", fname, quiet = TRUE) sd2 = gdal_subdatasets(fname)[[4]] info = gdal_utils("info", sd2, quiet = TRUE) tf = tempfile() tf2 = tempfile() tf3 = tempfile() #tf = "foo" #gdal_utils("rasterize", points, tif) -> need a good example expect_true(gdal_utils("warp", sd2, tf, c("-t_srs", "+proj=utm +zone=11 +datum=WGS84"))) # expect_true(gdal_utils("rasterize", sd2, tf)) # breaks on fedora-clang expect_true(gdal_utils("translate", sd2, tf)) expect_true(gdal_utils("vectortranslate", sd2, tf2)) expect_warning(gdal_utils("nearblack", sd2, tf)) # create point geom: expect_true(gdal_utils("buildvrt", sd2, tf3)) # expect_true(gdal_utils("demprocessing", sd2, tf, processing = "hillshade")) # breaks on fedora-clang } tf = tempfile() points = system.file("gpkg/nc.gpkg", package="sf") expect_true(gdal_utils("grid", points, tf)) }) # gdalwarp -t_srs '+proj=utm +zone=11 +datum=WGS84' -overwrite NETCDF:avhrr-only-v2.19810901.nc:anom utm11.tif # becomes: # gdal_utils("warp" "NETCDF:avhrr-only-v2.19810901.nc:anom", "utm11.tif", c("-t_srs", "+proj=utm +zone=11 +datum=WGS84")) stars/tests/testthat/test-rectilinear.R0000644000176200001440000000617014576033423020024 0ustar liggesuserscontext("rectilinear matrices") m <- matrix(1:12, nrow = 4, ncol = 3) x = c(0, 1, 2, 3) y = c(2, 1, 0) r_cell_edge = st_as_stars(list(m = m), dimensions = st_dimensions(x = x, y = y, .raster = c("x", "y"), cell_midpoints = FALSE)) sf::st_crs(r_cell_edge) <- sf::st_crs(5070) r_ce_dim <- st_dimensions(r_cell_edge) r_cell_mid = st_as_stars(list(m = m), dimensions = st_dimensions(x = x, y = y, .raster = c("x", "y"), cell_midpoints = TRUE)) sf::st_crs(r_cell_mid) <- sf::st_crs(5070) r_cm_dim <- st_dimensions(r_cell_mid) x = c(0, 1, 2, 3, 4) y = c(2, 1, 0, -1) r_cell_edges = st_as_stars(list(m = m), dimensions = st_dimensions(x = x, y = y, .raster = c("x", "y"), cell_midpoints = FALSE)) sf::st_crs(r_cell_edges) <- sf::st_crs(5070) r_ces_dim <- st_dimensions(r_cell_edges) r_cell_mids = st_as_stars(list(m = m), dimensions = st_dimensions(x = x, y = y, .raster = c("x", "y"), cell_midpoints = TRUE)) sf::st_crs(r_cell_mids) <- sf::st_crs(5070) r_cms_dim <- st_dimensions(r_cell_edges) test_that("same number of values as rows/cols", { expect_s3_class(r_cell_edge, "stars") expect_equal(names(dim(r_cell_edge)), names(r_ce_dim)) expect_equal(names(r_ce_dim), c("x", "y")) #expect_true(st_crs(r_ce_dim) == sf::st_crs(5070)) expect_equal(r_ce_dim$x$from, 1) expect_equal(r_ce_dim$x$to, 4) expect_equal(r_ce_dim$x$offset, 0) expect_equal(r_ce_dim$x$delta, 1) expect_equal(r_ce_dim$y$from, 1) expect_equal(r_ce_dim$y$to, 3) expect_equal(r_ce_dim$y$offset, 2) expect_equal(r_ce_dim$y$delta, -1) expect_s3_class(r_cell_mid, "stars") expect_equal(names(dim(r_cell_mid)), names(r_cm_dim)) expect_equal(names(r_cm_dim), c("x", "y")) #expect_true(st_crs(r_cm_dim) == sf::st_crs(5070)) expect_equal(r_cm_dim$x$from, 1) expect_equal(r_cm_dim$x$to, 4) expect_equal(r_cm_dim$x$offset, -0.5) expect_equal(r_cm_dim$x$delta, 1) expect_equal(r_cm_dim$y$from, 1) expect_equal(r_cm_dim$y$to, 3) expect_equal(r_cm_dim$y$offset, 2.5) expect_equal(r_cm_dim$y$delta, -1) }) test_that("one more cell coordinate than matrix cells", { expect_s3_class(r_cell_edges, "stars") expect_equal(r_ces_dim, r_ce_dim) # Make sure cell_midpoint argument is ignored here. expect_equal(r_cms_dim, r_ces_dim) }) test_that("st_as_sf works for these", { skip_on_cran() # too costly? sf_cell_edge <- sf::st_as_sf(r_cell_edge) expect_s3_class(sf_cell_edge, "sf") expect_s3_class(sf_cell_edge$geometry, "sfc_POLYGON") sf_cell_mid <- sf::st_as_sf(r_cell_mid) expect_s3_class(sf_cell_mid, "sf") expect_s3_class(sf_cell_mid$geometry, "sfc_POLYGON") sf_cell_edges <- sf::st_as_sf(r_cell_edges) expect_equal(sf_cell_edge, sf_cell_edges) sf_cell_mids <- sf::st_as_sf(r_cell_mids) expect_equal(sf_cell_mid, sf_cell_mids) }) stars/tests/testthat/test-ncdf.R0000644000176200001440000002500314612224636016430 0ustar liggesusersskip_on_cran() context("read ncdf") f <- system.file("nc/reduced.nc", package = "stars") test_that("basic reduced comes back as expected", { skip_if_not_installed("ncmeta") nc <- read_ncdf(f) expect_equal(names(nc), c("sst", "anom", "err", "ice")) st_dim <- st_dimensions(nc) expect_equal(names(st_dim), c("lon", "lat", "zlev", "time")) expect_equal(st_dim$lon$delta, 2) expect_equal(length(st_dim$zlev$values), 1) expect_equal(length(st_dim$time$values), 1) }) test_that("variable subsetting", { skip_if_not_installed("ncmeta") nc <- read_ncdf(f, var = c("anom")) expect_equal(names(nc), "anom") }) test_that("domain subsetting", { skip_if_not_installed("ncmeta") nc <- read_ncdf(f, ncsub = cbind(start = c(20, 1, 1, 1), count = c(10, 12, 1, 1))) st_dim <- st_dimensions(nc) expect_equal(st_dim$lon$to - st_dim$lon$from, 9) expect_equal(st_dim$lat$to - st_dim$lat$from, 11) expect_equal(st_dim$lon$offset, 37) expect_error(nc <- read_ncdf(f, ncsub = cbind(start = c(1, 1, 1, 1), count = c(200, 12, 1, 1))), "start or count out of bounds") expect_error(nc <- read_ncdf(f, ncsub = cbind(start = c(1, 1, 1), count = c(200, 12, 1))), "input ncsub doesn't match available dims") # Leaving this here -- NA or -1 counts should return all but this causes other errors. # nc <- read_ncdf(f, ncsub = cbind(start = c(1, 1, 1, 1), # count = c(NA, NA, 1, 1))) # st_dim <- st_dimensions(nc) # expect_equal(st_dim$lon$to - st_dim$lon$from, c("lon" = 179)) # expect_equal(st_dim$lat$to - st_dim$lat$from, c("lat" = 89)) }) test_that("normal bcsd", { skip_if_not_installed("ncmeta") f <- system.file("nc/bcsd_obs_1999.nc", package = "stars") nc <- read_ncdf(f, package = "stars") expect_equal(names(nc), c("pr", "tas")) st_dim <- st_dimensions(nc) expect_equal(names(st_dim), c("longitude", "latitude", "time")) expect_equal(st_dim$longitude$delta, 0.125) file.copy(f, (tf <- tempfile())) nc_tf <- RNetCDF::open.nc(tf, write = TRUE) RNetCDF::var.put.nc(nc_tf, "longitude", (360 + stars::st_get_dimension_values(nc, "longitude"))) RNetCDF::close.nc(nc_tf) nc <- read_ncdf(tf, package = "stars") expect_equal(stars::st_get_dimension_values(nc, "longitude")[1], -84.9375) }) test_that("non canonical axis order is handled right", { skip_if_not_installed("ncmeta") expect_warning(nc <- read_ncdf(system.file("nc/3B42_Daily.19991231.7.test.nc", package = "stars")), "Non-canonical axis order found, attempting to correct.") expect_equal(names(st_dimensions(nc)), c("lon", "lat")) expect_equal(st_dimensions(nc)[[1]]$to, 4L) expect_equal(st_dimensions(nc)[[2]]$to, 5L) }) test_that("euro cordex extra dimvars", { skip_if_not_installed("ncmeta") f <- unzip(zipfile = system.file("nc/EURO-CORDEX_81_DOMAIN000_mask_int.nc.zip", package = "stars"), exdir = tempdir()) expect_warning(out <- read_ncdf(f, var = "mask"), "Didn't find a longitude of prime meridian for datum, assuming 0.") expect_equal(names(out), c("mask")) #expect_true(sf::st_crs(out) == #sf::st_crs("+proj=lcc +lat_1=30 +lat_2=65 +lat_0=48 +lon_0=9.75 +x_0=-6000 +y_0=-6000 +a=6371229 +b=6371229 +units=m +no_defs")) expect_error(out <- suppressWarnings(read_ncdf(f, var = "mask", curvilinear = c("X", "Y"))), "Curvilinear variables not found in file.") out1 <- suppressWarnings(read_ncdf(f, var = "mask", curvilinear = c("xlat", "xlon"))) out2 <- suppressWarnings(read_ncdf(f, var = "mask", curvilinear = c("xlon", "xlat"))) expect_equal(sf::st_crs(out1), sf::st_crs(NULL)) expect_true(attr(st_dimensions(out1), "raster")$curvilinear) expect_true(attr(st_dimensions(out2), "raster")$curvilinear) expect_equal(st_dimensions(out1), st_dimensions(out2)) }) test_that("curvilinear", { skip_if_not_installed("ncmeta") f <- system.file("nc/test_stageiv_xyt.nc", package = "stars") out <-read_ncdf(f, curvilinear = c(X = "lon", Y = "lat")) # expect_match(warn[1], "bounds for time seem to be reversed; reverting them") st_dim <- st_dimensions(out) expect_true(all(st_dim$x$values < -74 & st_dim$x$values > -81)) expect_true(all(st_dim$y$values < 38 & st_dim$y$values > 32)) expect_equal(dim(st_dim$y$values), setNames(c(87, 118), c("x", "y"))) nc <- RNetCDF::open.nc(f) expect_equal(st_get_dimension_values(st_dim, "time"), RNetCDF::utcal.nc(RNetCDF::att.get.nc(nc, "time", "units"), RNetCDF::var.get.nc(nc, "time"), type = "c")) RNetCDF::close.nc(nc) # Should also find the curvilinear grid. suppressWarnings(out <- read_ncdf(f, var = "Total_precipitation_surface_1_Hour_Accumulation")) expect_true(attr(st_dimensions(out), "raster")$curvilinear) }) test_that("curvilinear broked", { skip_if_not_installed("ncmeta") f <- system.file("nc/test_stageiv_xyt_borked.nc", package = "stars") expect_error(suppressMessages(read_ncdf(f, curvilinear = c("lon", "lat", "time"))), "Curvilinear input must be a length two character vector.") expect_error(read_ncdf(f, curvilinear = c("time", "time_bounds")), "Specified curvilinear coordinates are not 2-dimensional.") expect_error(suppressMessages(read_ncdf(f, curvilinear = c("lon", "time_bounds"))), "Specified curvilinear coordinate variables not found as X/Y coordinate variables.") warn <- capture_warnings(out <-read_ncdf(f, curvilinear = c(X = "lon", Y = "lat"))) expect_match(warn[1], "Non-canonical axis order found, attempting to correct.") st_dim <- st_dimensions(out) expect_true(all(st_dim$y$values < -74 & st_dim$y$values > -81)) expect_true(all(st_dim$x$values < 38 & st_dim$x$values > 32)) expect_equal(dim(st_dim$y$values), setNames(c(118, 87), c("y", "x"))) expect_equal(as.character(st_dim$time$values), "2018-09-14 05:00:00") }) test_that("high-dim from rasterwise", { skip_if_not_installed("ncmeta") f <- system.file("nc/test-1.nc", package = "stars") out <- suppressWarnings(read_ncdf(f, var = "a")) expect_equal(names(st_dimensions(out)), c("x", "y", "c3", "c4", "c5")) }) test_that("timeseries.nc", { skip_if_not_installed("ncmeta") skip_if_not_installed("ncdfgeom") skip_on_os("solaris") f <- system.file("nc/timeseries.nc", package = "stars") nc <- read_ncdf(f) dims <- st_dimensions(nc) expect_s3_class(nc, "stars") expect_equal(names(nc), "pr") expect_equal(names(dims), c("time", "points")) poly <- sf::st_buffer(st_transform(st_sf(id = seq_along(dims$points$values), geom = dims$points$values, crs = 4326), 3857), dist = 100, nQuadSegs = 1) temp_f <- tempfile() file.copy(f, temp_f) suppressWarnings(temp_f <- ncdfgeom::write_geometry(temp_f, poly, "station", "pr")) nc <- read_ncdf(temp_f) dims <- st_dimensions(nc) expect_s3_class(nc, "stars") expect_equal(names(nc), "pr") expect_equal(names(dims), c("time", "points", "geometry")) }) test_that("curvilinear 2", { skip_if_not_installed("ncmeta") f <- system.file("nc/c201923412.out1_4.nc", package = "stars") nc <- read_ncdf(f) expect_equal(names(st_dimensions(nc)), c("nx", "ny", "time")) expect_true(attributes(st_dimensions(nc))$raster$curvilinear) expect_equal(attributes(st_dimensions(nc))$raster$dimensions, c("nx", "ny")) }) test_that("lon cross 360", { skip_if_not_installed("ncmeta") f <- system.file("nc/test_adaptor.cams_regional_fc.nc", package = "stars") suppressWarnings(nc <- read_ncdf(f)) expect_true(head(st_dimensions(nc)$longitude$values, 1) < 0 & tail(st_dimensions(nc)$longitude$values, 1) > 0) }) test_that("4d not 4d", { skip_if_not_installed("ncmeta") f <- system.file("nc/sub.nc", package = "stars") nc <- read_ncdf(f) dim <- st_dimensions(nc) expect_equal(dim$time$to, 10) expect_equal(dim$level$to, 2) }) test_that("units are right with lcc km", { skip_if_not_installed("ncmeta") f <- system.file("nc/lcc_km.nc", package = "stars") nc <- expect_warning(read_ncdf(f), "prime meridian") expect_equal(units(sf::st_crs(nc)$ud_unit)$numerator, "km") }) test_that("axis attribute order -- see #680", { # Example with two netCDFs tas <- array( data = rowSums( expand.grid(seq_len(9), 10 * (seq_len(2) - 1), 100 * (seq_len(3) - 1)) ), dim = c(9, 2, 3) ) # File1 has no "axis" attributes file1 <- tempfile("tas_example_", fileext = ".nc") nc <- RNetCDF::create.nc(file1) id_lat <- RNetCDF::dim.def.nc(nc, "lat", 3) iv_lat <- RNetCDF::var.def.nc(nc, "lat", "NC_FLOAT", id_lat) RNetCDF::var.put.nc(nc, "lat", c(40, 45, 50)) id_lon <- RNetCDF::dim.def.nc(nc, "lon", 2) iv_lon <- RNetCDF::var.def.nc(nc, "lon", "NC_FLOAT", id_lon) RNetCDF::var.put.nc(nc, "lon", c(-100, -95)) id_bnds <- RNetCDF::dim.def.nc(nc, "bnds", 2) id_time <- RNetCDF::dim.def.nc(nc, "time", 9) iv_time <- RNetCDF::var.def.nc(nc, "time", "NC_INT", id_time) RNetCDF::var.put.nc(nc, "time", 1:9) iv_tas <- RNetCDF::var.def.nc(nc, "temperature", "NC_FLOAT", c(id_time, id_lon, id_lat)) RNetCDF::var.put.nc(nc, "temperature", tas) RNetCDF::close.nc(nc) # File2 has X, Y, T axis attributes file2 <- tempfile("tas_example_", fileext = ".nc") file.copy(from = file1, to = file2) #> [1] TRUE nc <- RNetCDF::open.nc(file2, write = TRUE) RNetCDF::att.put.nc(nc, "lon", "axis", "NC_CHAR", "X") RNetCDF::att.put.nc(nc, "lat", "axis", "NC_CHAR", "Y") RNetCDF::att.put.nc(nc, "time", "axis", "NC_CHAR", "T") RNetCDF::close.nc(nc) file3 <- tempfile("tas_example_", fileext = ".nc") file.copy(from = file1, to = file3) #> [1] TRUE nc <- RNetCDF::open.nc(file3, write = TRUE) RNetCDF::att.put.nc(nc, "lon", "standard_name", "NC_CHAR", "longitude") RNetCDF::att.put.nc(nc, "lat", "standard_name", "NC_CHAR", "latitude") RNetCDF::att.put.nc(nc, "time", "standard_name", "NC_CHAR", "time") RNetCDF::att.put.nc(nc, "lon", "units", "NC_CHAR", "degrees") RNetCDF::att.put.nc(nc, "lat", "units", "NC_CHAR", "degrees") RNetCDF::att.put.nc(nc, "time", "units", "NC_CHAR", "days since 1900-01-01") RNetCDF::close.nc(nc) s1 <- suppressWarnings(stars::read_ncdf(file1)) expect_equal(names(stars::st_dimensions(s1)), c("time", "lon", "lat")) s2 <- suppressWarnings(stars::read_ncdf(file2)) expect_equal(names(stars::st_dimensions(s2)), c("lon", "lat", "time")) s3 <- suppressWarnings(stars::read_ncdf(file3)) expect_equal(names(stars::st_dimensions(s3)), c("lon", "lat", "time")) }) stars/tests/testthat/test-OpenStreetMap.R0000644000176200001440000000034214576033423020244 0ustar liggesuserstest_that("st_as_stars.OpenStreetMap works", { skip_on_cran() skip_if_not_installed("OpenStreetMap") m = OpenStreetMap::openmap(c(52,7.3),c(52.1,7.15), type = "osm") s = st_as_stars(m) expect_s3_class(s, "stars") }) stars/tests/testthat/test-tidyverse.R0000644000176200001440000000234114576033423017535 0ustar liggesuserstest_that("slice", { skip_if_not_installed("dplyr", "1.0.0") library(dplyr) f <- system.file("nc/reduced.nc", package = "stars") nc <- read_ncdf(f, proxy = TRUE) expect_s3_class(nc, "stars_proxy") # Slicing after other lazy operations should be lazy nc2 <- adrop(nc) nc3 <- slice(nc2, "lon", 90) expect_equal(length(attr(nc3, "call_list")), 2) # Slicing dimension that are part of the file should be lazy nc4 <- slice(nc, "lon", 90) expect_equal(length(attr(nc4, "call_list")), 1) # Trailing singleton dimensions in files nc5 <- slice(nc, "time", 1) expect_null(attr(nc5, "call_list")) expect_equal(st_dimensions(nc), st_dimensions(nc5)) # concatenate to simulate an extra dimension nc6 <- c(nc, nc, nc, nc, along = "dummy") expect_equal(dim(nc6)["dummy"], c(dummy = 4)) expect_s3_class(nc6, "stars_proxy") nc7 <- slice(nc6, "dummy", 1) expect_s3_class(nc7, "stars_proxy") expect_equal(dim(nc7)["dummy"], c(dummy = 1)) expect_null(attr(nc7, "call_list")) expect_equal(names(nc7), names(nc6)) nc8 <- slice(nc6, "dummy", 2:3) expect_s3_class(nc8, "stars_proxy") expect_equal(dim(nc8)["dummy"], c(dummy = 2)) expect_null(attr(nc8, "call_list")) expect_equal(names(nc8), names(nc6)) }) stars/tests/testthat/test-ncproxy.R0000644000176200001440000000645414576033423017232 0ustar liggesusersskip_on_cran() f <- system.file("nc/reduced.nc", package = "stars") test_that("proxy", { skip_if_not_installed("ncmeta") nc <- read_ncdf(f, proxy = TRUE) expect_equal(as.character(nc[[1]]), f) expect_s3_class(nc, "stars_proxy") output <- capture_output(print(nc)) expect_true(grepl("^netcdf source", output)) old_opts <- options("stars.n_proxy" = 100) nc <- read_ncdf(f) expect_equal(as.character(nc[[1]]), f) expect_warning(nc <- read_ncdf(f, proxy = FALSE)) expect_s3_class(nc, "stars") options(old_opts) }) test_that("st_as_stars.nc_proxy", { skip_if_not_installed("ncmeta") nc <- read_ncdf(f, proxy = TRUE) nc2 <- nc[1, 25:35, 25:35, 1, 1] nc2 <- st_as_stars(nc2) expect_s3_class(nc2, 'stars', exact = TRUE) expect_equal(names(nc2), "sst") dim <- st_dimensions(nc2) expect_equal(dim$lon$to, 11) expect_equal(dim$lon$offset, 47) nc2 <- st_as_stars(nc, var = "sst", proxy = TRUE) expect_s3_class(nc2, c("nc_proxy", "stars_proxy", "stars"), exact = TRUE) dim <- st_dimensions(nc2) expect_equal(dim$lon$to, 180) expect_equal(dim$lon$offset, -1) }) test_that("basics", { skip_if_not_installed("ncmeta") nc <- read_ncdf(f, proxy = TRUE) x <- nc["sst"] expect_s3_class(x, "nc_proxy") expect_equal(length(x), 1) expect_equal(names(x), "sst") nc[[5]] <- nc[[4]] expect_equal(length(nc), 5) expect_equal(names(nc), c("sst", "anom", "err", "ice", "")) xx <- c(nc["sst"], nc["ice"]) expect_equal(length(xx), 2) expect_equal(names(xx), c("sst", "ice")) expect_error(st_mosaic(nc)) expect_error(st_redimension(nc)) }) test_that("subset", { skip_if_not_installed("ncmeta") nc <- read_ncdf(f, proxy = TRUE) nc2 <- nc[1, 80:100, 45:55, , ] nc3 <- nc["sst", 80:100, 45:55, , ] expect_equal(nc2, nc3) expect_equal(dim(nc2), setNames(c(21, 11, 1, 1), c("lon", "lat", "zlev", "time"))) nc3 <- st_as_stars(nc2) expect_s3_class(nc3, "stars") expect_equal(as.numeric(dim(nc3)), dim(nc3$sst)) nc <- read_ncdf(system.file("nc/bcsd_obs_1999.nc", package = "stars"), proxy = TRUE) nc2 <- nc[ , , , 5] expect_equal(st_dimensions(nc2)$time$from, 5) expect_equal(st_dimensions(nc2)$time$values, structure(928108800, class = c("POSIXct", "POSIXt"), tzone = "UTC")) nc3 <- st_as_stars(nc2) expect_equal(dim(nc3$pr), as.numeric(dim(nc2))) nc_sf <- sf::st_transform( read_sf(system.file("gpkg/nc.gpkg", package="sf")), sf::st_crs(nc)) nc2 <- st_crop(nc, nc_sf[10, ]) nc2 <- nc2["pr", , , 1] nc2 <- st_as_stars(nc2) nc3 <- nc["pr", , , 1] nc3 <- st_crop(nc3, nc_sf[10, ]) nc3 <- st_as_stars(nc3) expect_equal(nc2, nc3) }) test_that("curvilinear", { skip_if_not_installed("ncmeta") f <- system.file("nc/test_stageiv_xyt.nc", package = "stars") out <-read_ncdf(f, curvilinear = c(X = "lon", Y = "lat"), proxy = TRUE) out <- st_as_stars(out) st_dim <- st_dimensions(out) expect_true(all(st_dim$x$values < -74 & st_dim$x$values > -81)) expect_true(all(st_dim$y$values < 38 & st_dim$y$values > 32)) expect_equal(dim(st_dim$y$values), setNames(c(87, 118), c("x", "y"))) # Should also find the curvilinear grid. suppressWarnings( out <- read_ncdf(f, var = "Total_precipitation_surface_1_Hour_Accumulation", proxy = TRUE)) expect_true(attr(st_dimensions(out), "raster")$curvilinear) }) stars/tests/testthat/test-tile.R0000644000176200001440000000205614576033423016457 0ustar liggesusersmat = matrix(0L, 10, 10) test1 = st_tile(nrow(mat), ncol(mat), 10, 10) test_that("one block", { expect_equal(nrow(test1), 1) expect_setequal(test1, c(1, 1, 10, 10)) # output matrix should be integer to best performance expect_type(test1, "integer") }) test2 = st_tile(nrow(mat), ncol(mat), 5, 5) test_that("equal blocks", { expect_equal(nrow(test2), 4) }) test3 = st_tile(nrow(mat), ncol(mat), 9, 5) test_that("different size blocks", { expect_equal(nrow(test3), 4) }) test4 = st_tile(nrow(mat), ncol(mat), 1, 1) test_that("many small blocks ", { expect_equal(nrow(test4), 100) expect_setequal(test4[, "nXOff"], rep(1:10, each = 10)) expect_setequal(test4[, "nYOff"], rep(1:10, times = 10)) expect_setequal(test4[, "nXSize"], rep(1, times = 100)) expect_setequal(test4[, "nYSize"], rep(1, times = 100)) }) test5 = st_tile(nrow(mat), ncol(mat), 5, 5, overlap = 5) test_that("blocks with overlap", { expect_equal(nrow(test5), 4) expect_setequal(test5[, "nXSize"], rep(c(10, 5), each = 2)) expect_setequal(test5[, "nYSize"], rep(c(10, 5), times = 2)) }) stars/tests/subset.R0000644000176200001440000000115313777653456014227 0ustar liggesuserssuppressPackageStartupMessages(library(stars)) tif = system.file("tif/L7_ETMs.tif", package = "stars") x = read_stars(tif) y = st_set_dimensions(x, "band", point = TRUE) y[,1:10, 1:10, c(1,4,3,2)] y = st_set_dimensions(x, "band", point = NA) y[,1:10, 1:10, c(1,4,3,2)] y = st_set_dimensions(x, "band", point = FALSE) y[,1:10, 1:10, c(1,4,3,2)] y = st_set_dimensions(x, "band", values = letters[1:6]) y[,1:10, 1:10, c(1,4,3,2)] y = st_set_dimensions(x, "band", values = letters[1:6], point = NA) y[,1:10, 1:10, c(1,4,3,2)] y = st_set_dimensions(x, "band", values = letters[1:6], point = TRUE) y[,1:10, 1:10, c(1,4,3,2)] stars/tests/redimension.Rout.save0000644000176200001440000001057014644736040016707 0ustar liggesusers R Under development (unstable) (2024-07-13 r86895 ucrt) -- "Unsuffered Consequences" Copyright (C) 2024 The R Foundation for Statistical Computing Platform: x86_64-w64-mingw32/x64 R is free software and comes with ABSOLUTELY NO WARRANTY. You are welcome to redistribute it under certain conditions. Type 'license()' or 'licence()' for distribution details. R is a collaborative project with many contributors. Type 'contributors()' for more information and 'citation()' on how to cite R or R packages in publications. Type 'demo()' for some demos, 'help()' for on-line help, or 'help.start()' for an HTML browser interface to help. Type 'q()' to quit R. > suppressPackageStartupMessages(library(stars)) > tif = system.file("tif/L7_ETMs.tif", package = "stars") > r = read_stars(list(a = c(tif,tif), b = c(tif, tif)), proxy = TRUE) > (xx = st_redimension(r, along = list(foo = 1:4))) stars_proxy object with 1 attribute in 4 file(s): $a.L7_ETMs.tif.a.L7_ETMs.tif.b.L7_ETMs.tif.b.L7_ETMs.tif [1] "[...]/L7_ETMs.tif" "[...]/L7_ETMs.tif" "[...]/L7_ETMs.tif" [4] "[...]/L7_ETMs.tif" dimension(s): from to offset delta refsys point x/y x 1 349 288776 28.5 SIRGAS 2000 / UTM zone 25S FALSE [x] y 1 352 9120761 -28.5 SIRGAS 2000 / UTM zone 25S FALSE [y] band 1 6 NA NA NA NA foo 1 4 1 1 NA NA > (rr = c(xx, xx)) stars_proxy object with 2 attributes in 8 file(s): $a.L7_ETMs.tif.a.L7_ETMs.tif.b.L7_ETMs.tif.b.L7_ETMs.tif [1] "[...]/L7_ETMs.tif" "[...]/L7_ETMs.tif" "[...]/L7_ETMs.tif" [4] "[...]/L7_ETMs.tif" $a.L7_ETMs.tif.a.L7_ETMs.tif.b.L7_ETMs.tif.b.L7_ETMs.tif [1] "[...]/L7_ETMs.tif" "[...]/L7_ETMs.tif" "[...]/L7_ETMs.tif" [4] "[...]/L7_ETMs.tif" dimension(s): from to offset delta refsys point x/y x 1 349 288776 28.5 SIRGAS 2000 / UTM zone 25S FALSE [x] y 1 352 9120761 -28.5 SIRGAS 2000 / UTM zone 25S FALSE [y] band 1 6 NA NA NA NA foo 1 4 1 1 NA NA > # st_as_stars(rr) > (rrr = st_redimension(rr, along = list(bar = as.Date(c("2001-01-01", "2002-01-01"))))) stars_proxy object with 1 attribute in 8 file(s): $a.L7_ETMs.tif.a.L7_ETMs.tif.b.L7_ETMs.tif.b.L7_ETMs.tif.a.L7_ETMs.tif.a.L7_ETMs.tif.b.L7_ETMs.tif.b.L7_ETMs.tif [1] "[...]/L7_ETMs.tif" "[...]/L7_ETMs.tif" "[...]/L7_ETMs.tif" [4] "[...]/L7_ETMs.tif" "[...]/L7_ETMs.tif" "[...]/L7_ETMs.tif" [7] "[...]/L7_ETMs.tif" "[...]/L7_ETMs.tif" dimension(s): from to offset delta refsys point x/y x 1 349 288776 28.5 SIRGAS 2000 / UTM zone 25S FALSE [x] y 1 352 9120761 -28.5 SIRGAS 2000 / UTM zone 25S FALSE [y] band 1 6 NA NA NA NA foo 1 4 1 1 NA NA bar 1 2 2001-01-01 365 days Date NA > (rr = c(a = xx, b = xx)) stars_proxy object with 2 attributes in 8 file(s): $a [1] "[...]/L7_ETMs.tif" "[...]/L7_ETMs.tif" "[...]/L7_ETMs.tif" [4] "[...]/L7_ETMs.tif" $b [1] "[...]/L7_ETMs.tif" "[...]/L7_ETMs.tif" "[...]/L7_ETMs.tif" [4] "[...]/L7_ETMs.tif" dimension(s): from to offset delta refsys point x/y x 1 349 288776 28.5 SIRGAS 2000 / UTM zone 25S FALSE [x] y 1 352 9120761 -28.5 SIRGAS 2000 / UTM zone 25S FALSE [y] band 1 6 NA NA NA NA foo 1 4 1 1 NA NA > # st_as_stars(rr) > (rrr = st_redimension(rr, along = list(bar = as.Date(c("2001-01-01", "2002-01-01"))))) stars_proxy object with 1 attribute in 8 file(s): $a.b [1] "[...]/L7_ETMs.tif" "[...]/L7_ETMs.tif" "[...]/L7_ETMs.tif" [4] "[...]/L7_ETMs.tif" "[...]/L7_ETMs.tif" "[...]/L7_ETMs.tif" [7] "[...]/L7_ETMs.tif" "[...]/L7_ETMs.tif" dimension(s): from to offset delta refsys point x/y x 1 349 288776 28.5 SIRGAS 2000 / UTM zone 25S FALSE [x] y 1 352 9120761 -28.5 SIRGAS 2000 / UTM zone 25S FALSE [y] band 1 6 NA NA NA NA foo 1 4 1 1 NA NA bar 1 2 2001-01-01 365 days Date NA > > proc.time() user system elapsed 0.92 0.20 1.12 stars/tests/subset.Rout.save0000644000176200001440000001025714644736040015702 0ustar liggesusers R Under development (unstable) (2024-07-13 r86895 ucrt) -- "Unsuffered Consequences" Copyright (C) 2024 The R Foundation for Statistical Computing Platform: x86_64-w64-mingw32/x64 R is free software and comes with ABSOLUTELY NO WARRANTY. You are welcome to redistribute it under certain conditions. Type 'license()' or 'licence()' for distribution details. R is a collaborative project with many contributors. Type 'contributors()' for more information and 'citation()' on how to cite R or R packages in publications. Type 'demo()' for some demos, 'help()' for on-line help, or 'help.start()' for an HTML browser interface to help. Type 'q()' to quit R. > suppressPackageStartupMessages(library(stars)) > tif = system.file("tif/L7_ETMs.tif", package = "stars") > x = read_stars(tif) > y = st_set_dimensions(x, "band", point = TRUE) > y[,1:10, 1:10, c(1,4,3,2)] stars object with 3 dimensions and 1 attribute attribute(s): Min. 1st Qu. Median Mean 3rd Qu. Max. L7_ETMs.tif 25 45 57 56.535 64 97 dimension(s): from to offset delta refsys point values x/y x 1 10 288776 28.5 SIRGAS 2000 / UTM zone 25S FALSE NULL [x] y 1 10 9120761 -28.5 SIRGAS 2000 / UTM zone 25S FALSE NULL [y] band 1 4 NA NA NA TRUE 1,...,2 > y = st_set_dimensions(x, "band", point = NA) > y[,1:10, 1:10, c(1,4,3,2)] stars object with 3 dimensions and 1 attribute attribute(s): Min. 1st Qu. Median Mean 3rd Qu. Max. L7_ETMs.tif 25 45 57 56.535 64 97 dimension(s): from to offset delta refsys point values x/y x 1 10 288776 28.5 SIRGAS 2000 / UTM zone 25S FALSE NULL [x] y 1 10 9120761 -28.5 SIRGAS 2000 / UTM zone 25S FALSE NULL [y] band 1 4 NA NA NA NA [1,2),...,[2,3) > y = st_set_dimensions(x, "band", point = FALSE) > y[,1:10, 1:10, c(1,4,3,2)] stars object with 3 dimensions and 1 attribute attribute(s): Min. 1st Qu. Median Mean 3rd Qu. Max. L7_ETMs.tif 25 45 57 56.535 64 97 dimension(s): from to offset delta refsys point values x/y x 1 10 288776 28.5 SIRGAS 2000 / UTM zone 25S FALSE NULL [x] y 1 10 9120761 -28.5 SIRGAS 2000 / UTM zone 25S FALSE NULL [y] band 1 4 NA NA NA FALSE [1,2),...,[2,3) > y = st_set_dimensions(x, "band", values = letters[1:6]) > y[,1:10, 1:10, c(1,4,3,2)] stars object with 3 dimensions and 1 attribute attribute(s): Min. 1st Qu. Median Mean 3rd Qu. Max. L7_ETMs.tif 25 45 57 56.535 64 97 dimension(s): from to offset delta refsys point values x/y x 1 10 288776 28.5 SIRGAS 2000 / UTM zone 25S FALSE NULL [x] y 1 10 9120761 -28.5 SIRGAS 2000 / UTM zone 25S FALSE NULL [y] band 1 4 NA NA NA NA a,...,b > y = st_set_dimensions(x, "band", values = letters[1:6], point = NA) > y[,1:10, 1:10, c(1,4,3,2)] stars object with 3 dimensions and 1 attribute attribute(s): Min. 1st Qu. Median Mean 3rd Qu. Max. L7_ETMs.tif 25 45 57 56.535 64 97 dimension(s): from to offset delta refsys point values x/y x 1 10 288776 28.5 SIRGAS 2000 / UTM zone 25S FALSE NULL [x] y 1 10 9120761 -28.5 SIRGAS 2000 / UTM zone 25S FALSE NULL [y] band 1 4 NA NA NA NA a,...,b > y = st_set_dimensions(x, "band", values = letters[1:6], point = TRUE) > y[,1:10, 1:10, c(1,4,3,2)] stars object with 3 dimensions and 1 attribute attribute(s): Min. 1st Qu. Median Mean 3rd Qu. Max. L7_ETMs.tif 25 45 57 56.535 64 97 dimension(s): from to offset delta refsys point values x/y x 1 10 288776 28.5 SIRGAS 2000 / UTM zone 25S FALSE NULL [x] y 1 10 9120761 -28.5 SIRGAS 2000 / UTM zone 25S FALSE NULL [y] band 1 4 NA NA NA TRUE a,...,b > > proc.time() user system elapsed 0.93 0.21 1.09 stars/tests/sp.Rout.save0000644000176200001440000000425214644736040015015 0ustar liggesusers R Under development (unstable) (2024-07-13 r86895 ucrt) -- "Unsuffered Consequences" Copyright (C) 2024 The R Foundation for Statistical Computing Platform: x86_64-w64-mingw32/x64 R is free software and comes with ABSOLUTELY NO WARRANTY. You are welcome to redistribute it under certain conditions. Type 'license()' or 'licence()' for distribution details. R is a collaborative project with many contributors. Type 'contributors()' for more information and 'citation()' on how to cite R or R packages in publications. Type 'demo()' for some demos, 'help()' for on-line help, or 'help.start()' for an HTML browser interface to help. Type 'q()' to quit R. > options(rgdal_show_exportToProj4_warnings = "none") > if (suppressPackageStartupMessages(require(sp, quietly = TRUE))) { + demo(meuse) + # remove +init=xxx + meuse@proj4string = CRS( + "+proj=sterea +lat_0=52.1561605555556 +lon_0=5.38763888888889 +k=0.9999079 +x_0=155000 +y_0=463000 +ellps=bessel +units=m +no_defs" + ) + meuse.grid@proj4string = CRS( + "+proj=sterea +lat_0=52.1561605555556 +lon_0=5.38763888888889 +k=0.9999079 +x_0=155000 +y_0=463000 +ellps=bessel +units=m +no_defs" + ) + + suppressPackageStartupMessages(library(stars)) + (m = st_as_stars(meuse)) + (gr = st_as_stars(meuse.grid)) + + m2 = as(m, "Spatial") + summary(m2) + gr2 = as(gr, "Spatial") + summary(gr2) + + all.equal(meuse, m2, check.attributes = FALSE) + fullgrid(meuse.grid) = TRUE + all.equal(meuse.grid, gr2, check.attributes = FALSE) + + #all.equal(meuse, m2) + #all.equal(meuse.grid, gr2) + } demo(meuse) ---- ~~~~~ > require(sp) > crs = CRS("EPSG:28992") > data("meuse") > coordinates(meuse) <- ~x+y > proj4string(meuse) <- crs > data("meuse.grid") > coordinates(meuse.grid) <- ~x+y > gridded(meuse.grid) <- TRUE > proj4string(meuse.grid) <- crs > data("meuse.riv") > meuse.riv <- SpatialPolygons(list(Polygons(list(Polygon(meuse.riv)),"meuse.riv"))) > proj4string(meuse.riv) <- crs > data("meuse.area") > meuse.area = SpatialPolygons(list(Polygons(list(Polygon(meuse.area)), "area"))) > proj4string(meuse.area) <- crs [1] TRUE > > proc.time() user system elapsed 0.85 0.25 1.10 stars/tests/testthat.R0000644000176200001440000000017313777653456014563 0ustar liggesusers## load dependencies library(testthat) suppressPackageStartupMessages(library(stars)) ## test package test_check("stars") stars/tests/transform.Rout.save0000644000176200001440000000553414644736040016412 0ustar liggesusers R Under development (unstable) (2024-07-13 r86895 ucrt) -- "Unsuffered Consequences" Copyright (C) 2024 The R Foundation for Statistical Computing Platform: x86_64-w64-mingw32/x64 R is free software and comes with ABSOLUTELY NO WARRANTY. You are welcome to redistribute it under certain conditions. Type 'license()' or 'licence()' for distribution details. R is a collaborative project with many contributors. Type 'contributors()' for more information and 'citation()' on how to cite R or R packages in publications. Type 'demo()' for some demos, 'help()' for on-line help, or 'help.start()' for an HTML browser interface to help. Type 'q()' to quit R. > suppressPackageStartupMessages(library(stars)) > #suppressPackageStartupMessages(library(lwgeom)) > geomatrix = system.file("tif/geomatrix.tif", package = "stars") > (x = read_stars(geomatrix)) stars object with 2 dimensions and 1 attribute attribute(s): Min. 1st Qu. Median Mean 3rd Qu. Max. geomatrix.tif 74 107 123 126.765 132 255 dimension(s): from to offset delta refsys point x/y x 1 20 1841002 1.5 WGS 84 / UTM zone 11N TRUE [x] y 1 20 1144003 -1.5 WGS 84 / UTM zone 11N TRUE [y] sheared raster with parameters: -5 -5 > new = st_crs(4326) > y = st_transform(x, new) > plot(st_transform(st_as_sfc(st_bbox(x)), new), col = NA, border = 'red') > plot(st_as_sfc(y, as_points=FALSE), col = NA, border = 'green', axes = TRUE, add = TRUE) > image(y, add = TRUE) > plot(st_as_sfc(y, as_points=TRUE), pch=3, cex=.5, col = 'blue', add = TRUE) > plot(st_transform(st_as_sfc(x, as_points=FALSE), new), add = TRUE) > > tif = system.file("tif/L7_ETMs.tif", package = "stars") > x = read_stars(tif)[,1:10,1:10,1:3] > x_ = st_transform(x, st_crs(4326)) > ## IGNORE_RDIFF_BEGIN > if (require(lwgeom, quietly = TRUE)) { + x__ = st_transform_proj(x, st_crs(4326)$proj4string) + st_crs(x_) = st_crs(x__) + print(all.equal(x_,x__)) + + # x__ = st_transform(x, x_) #now in st_warp + # all.equal(x_, x__) + + x = st_xy2sfc(x, as_points = FALSE) + (x_ = st_transform(x, st_crs(4326))) + (x__ = st_transform_proj(x, st_crs(4326))) + print(all.equal(x_,x__,check.attributes = FALSE)) + } Linking to liblwgeom 3.0.0beta1 r16016, GEOS 3.12.1, PROJ 9.3.1 Attaching package: 'lwgeom' The following object is masked from 'package:sf': st_perimeter [1] TRUE [1] TRUE > ## IGNORE_RDIFF_END > > # nothing to transform: now errs: > try(st_transform(st_as_stars(list(m = matrix(1,10,10))), st_crs(4326))) Error in st_transform.stars(st_as_stars(list(m = matrix(1, 10, 10))), : !is.na(st_crs(x)) is not TRUE > try(st_transform_proj(st_as_stars(list(m = matrix(1,10,10))), st_crs(4326))) Error in st_transform_proj.stars(st_as_stars(list(m = matrix(1, 10, 10))), : !is.na(st_crs(x)) is not TRUE > > proc.time() user system elapsed 0.76 0.21 0.95 stars/tests/gridtypes.Rout.save0000644000176200001440000002542214644736040016407 0ustar liggesusers R Under development (unstable) (2024-07-13 r86895 ucrt) -- "Unsuffered Consequences" Copyright (C) 2024 The R Foundation for Statistical Computing Platform: x86_64-w64-mingw32/x64 R is free software and comes with ABSOLUTELY NO WARRANTY. You are welcome to redistribute it under certain conditions. Type 'license()' or 'licence()' for distribution details. R is a collaborative project with many contributors. Type 'contributors()' for more information and 'citation()' on how to cite R or R packages in publications. Type 'demo()' for some demos, 'help()' for on-line help, or 'help.start()' for an HTML browser interface to help. Type 'q()' to quit R. > suppressPackageStartupMessages(library(stars)) > > # regular, but not spatial: > d = st_dimensions(a = 1:3, b = 1:3, band = c("foo", "bar")) > (st = st_as_stars(array(1:18, c(3,3,2)), dimension = d)) stars object with 3 dimensions and 1 attribute attribute(s): Min. 1st Qu. Median Mean 3rd Qu. Max. X 1 5.25 9.5 9.5 13.75 18 dimension(s): from to offset delta point values a 1 3 1 1 FALSE NULL b 1 3 1 1 FALSE NULL band 1 2 NA NA FALSE foo, bar > as.data.frame(st, add_max=FALSE)[1:4,] a b band X 1 1 1 foo 1 2 2 1 foo 2 3 3 1 foo 3 4 1 2 foo 4 > try(x <- st_bbox(st)) # error Error in st_bbox.dimensions(st_dimensions(obj), ...) : dimensions table does not have x & y, nor an sfc dimension > > # regular, geotransform: > d = st_dimensions(x = 1:3, y = 1:3, band = c("foo", "bar"), .raster = c("x", "y")) > (st = st_as_stars(array(1:18, c(3,3,2)), dimension = d)) stars object with 3 dimensions and 1 attribute attribute(s): Min. 1st Qu. Median Mean 3rd Qu. Max. X 1 5.25 9.5 9.5 13.75 18 dimension(s): from to offset delta point values x/y x 1 3 1 1 FALSE NULL [x] y 1 3 1 1 FALSE NULL [y] band 1 2 NA NA FALSE foo, bar > as.data.frame(st, add_max=FALSE)[1:4,] x y band X 1 1.5 1.5 foo 1 2 2.5 1.5 foo 2 3 3.5 1.5 foo 3 4 1.5 2.5 foo 4 > as.data.frame(st, add_max=TRUE)[1:4,] x y band x_max y_max X 1 1 1 foo 2 2 1 2 2 1 foo 3 2 2 3 3 1 foo 4 2 3 4 1 2 foo 2 3 4 > st_bbox(st) xmin ymin xmax ymax 1 1 4 4 > > # rectilinear with offset given: > xd = c(1, 2, 4) > d = st_dimensions(x = xd, y = 1:3, band = c("foo", "bar"), .raster = c("x", "y")) > (st = st_as_stars(array(1:18, c(3,3,2)), dimension = d)) stars object with 3 dimensions and 1 attribute attribute(s): Min. 1st Qu. Median Mean 3rd Qu. Max. X 1 5.25 9.5 9.5 13.75 18 dimension(s): from to offset delta point values x/y x 1 3 NA NA FALSE [1,2), [2,4), [4,6) [x] y 1 3 1 1 FALSE NULL [y] band 1 2 NA NA FALSE foo, bar > as.data.frame(st, add_max=FALSE)[1:4,] x y band X 1 1.5 1.5 foo 1 2 3.0 1.5 foo 2 3 5.0 1.5 foo 3 4 1.5 2.5 foo 4 > as.data.frame(st, add_max=TRUE)[1:4,] x y band x_max y_max X 1 1 1 foo 2 2 1 2 2 1 foo 4 2 2 3 4 1 foo 6 2 3 4 1 2 foo 2 3 4 > st_bbox(st) xmin ymin xmax ymax 1 1 6 4 > > # rectilinear with midpoints given: > xd = c(1, 2, 4) > d = st_dimensions(x = xd, y = 1:3, band = c("foo", "bar"), .raster = c("x", "y"), cell_midpoints = TRUE) > (st = st_as_stars(array(1:18, c(3,3,2)), dimension = d)) stars object with 3 dimensions and 1 attribute attribute(s): Min. 1st Qu. Median Mean 3rd Qu. Max. X 1 5.25 9.5 9.5 13.75 18 dimension(s): from to offset delta point values x/y x 1 3 NA NA FALSE [0.5,1.5), [1.5,3.0), [3.0,5.0) [x] y 1 3 0.5 1 FALSE NULL [y] band 1 2 NA NA FALSE foo, bar > as.data.frame(st, add_max=FALSE)[1:4,] x y band X 1 1.00 1 foo 1 2 2.25 1 foo 2 3 4.00 1 foo 3 4 1.00 2 foo 4 > as.data.frame(st, add_max=TRUE)[1:4,] x y band x_max y_max X 1 0.5 0.5 foo 1.5 1.5 1 2 1.5 0.5 foo 3.0 1.5 2 3 3.0 0.5 foo 5.0 1.5 3 4 0.5 1.5 foo 1.5 2.5 4 > st_bbox(st) xmin ymin xmax ymax 0.5 0.5 5.0 3.5 > > # rectilinear with midpoints given, point support > xd = c(1, 2, 4) > d = st_dimensions(x = xd, y = 1:3, band = c("foo", "bar"), .raster = c("x", "y"), cell_midpoints = TRUE, point = TRUE) > (st = st_as_stars(array(1:18, c(3,3,2)), dimension = d)) stars object with 3 dimensions and 1 attribute attribute(s): Min. 1st Qu. Median Mean 3rd Qu. Max. X 1 5.25 9.5 9.5 13.75 18 dimension(s): from to offset delta point values x/y x 1 3 NA NA TRUE 1, 2, 4 [x] y 1 3 0.5 1 TRUE NULL [y] band 1 2 NA NA TRUE foo, bar > as.data.frame(st, add_max=FALSE)[1:4,] x y band X 1 1 1 foo 1 2 2 1 foo 2 3 4 1 foo 3 4 1 2 foo 4 > as.data.frame(st, add_max=TRUE)[1:4,] x y band x_max y_max X 1 1 0.5 foo 1 1.5 1 2 2 0.5 foo 2 1.5 2 3 4 0.5 foo 4 1.5 3 4 1 1.5 foo 1 2.5 4 > st_bbox(st) xmin ymin xmax ymax 1.0 0.5 4.0 3.5 > > # rectilinear with start/end given: > #xd = stars:::make_intervals(start = c(1,2,4), end = c(2, 4, 8)) > xd = c(1, 2, 4, 8) # one more than dim of the data array > d = st_dimensions(x = xd, y = 1:3, band = c("foo", "bar"), .raster = c("x", "y")) > (st = st_as_stars(array(1:18, c(3,3,2)), dimension = d)) stars object with 3 dimensions and 1 attribute attribute(s): Min. 1st Qu. Median Mean 3rd Qu. Max. X 1 5.25 9.5 9.5 13.75 18 dimension(s): from to offset delta point values x/y x 1 3 NA NA FALSE [1,2), [2,4), [4,8) [x] y 1 3 1 1 FALSE NULL [y] band 1 2 NA NA FALSE foo, bar > as.data.frame(st, add_max=FALSE)[1:4,] x y band X 1 1.5 1.5 foo 1 2 3.0 1.5 foo 2 3 6.0 1.5 foo 3 4 1.5 2.5 foo 4 > as.data.frame(st, add_max=TRUE)[1:4,] x y band x_max y_max X 1 1 1 foo 2 2 1 2 2 1 foo 4 2 2 3 4 1 foo 8 2 3 4 1 2 foo 2 3 4 > st_bbox(st) xmin ymin xmax ymax 1 1 8 4 > > # with sfc: > sfc = st_sfc(st_point(0:1), st_point(2:1), st_point(4:3)) > d = st_dimensions(x = sfc, y = 1:3, band = c("foo", "bar")) > (st = st_as_stars(array(1:18, c(3,3,2)), dimension = d)) stars object with 3 dimensions and 1 attribute attribute(s): Min. 1st Qu. Median Mean 3rd Qu. Max. X 1 5.25 9.5 9.5 13.75 18 dimension(s): from to offset delta point values x 1 3 NA NA TRUE POINT (0 1),...,POINT (4 3) y 1 3 1 1 FALSE NULL band 1 2 NA NA FALSE foo, bar > as.data.frame(st, add_max=FALSE)[1:4,] x y band X 1 POINT (0 1) 1 foo 1 2 POINT (2 1) 1 foo 2 3 POINT (4 3) 1 foo 3 4 POINT (0 1) 2 foo 4 > st_as_sf(st, long = TRUE)[1:4,] Simple feature collection with 4 features and 3 fields Geometry type: POINT Dimension: XY Bounding box: xmin: 0 ymin: 1 xmax: 4 ymax: 3 CRS: NA y band X x 1 1 foo 1 POINT (0 1) 2 1 foo 2 POINT (2 1) 3 1 foo 3 POINT (4 3) 4 2 foo 4 POINT (0 1) > st_bbox(st) xmin ymin xmax ymax 0 1 4 3 > > # rotated/sheared: > d = st_dimensions(x = 1:3, y = 1:3, band = c("foo", "bar"), .raster = c("x", "y"), affine = c(0.2, -0.2)) > (st = st_as_stars(array(1:18, c(3,3,2)), dimension = d)) stars object with 3 dimensions and 1 attribute attribute(s): Min. 1st Qu. Median Mean 3rd Qu. Max. X 1 5.25 9.5 9.5 13.75 18 dimension(s): from to offset delta point values x/y x 1 3 1 1 FALSE NULL [x] y 1 3 1 1 FALSE NULL [y] band 1 2 NA NA FALSE foo, bar sheared raster with parameters: 0.2 -0.2 > as.data.frame(st, add_max=FALSE)[1:4,] x y X 1 1.6 1.4 1 2 2.6 1.2 2 3 3.6 1.0 3 4 1.8 2.4 4 > try(as.data.frame(st, add_max=TRUE)[1:4,]) # errors Error in st_coordinates.stars(x, add_max = add_max, center = center, ...) : add_max will not work for rotated/shared rasters > st_bbox(st) xmin ymin xmax ymax 1.0 0.4 4.6 4.0 > > # curvilinear: > set.seed(13531) > lon = st_as_stars(matrix(signif(runif(9), 2), 3, 3)) > lat = st_as_stars(matrix(signif(runif(9), 2), 3, 3)) > ll = setNames(c(lon, lat), c("lon", "lat")) > d = st_dimensions(lon = 1:3, lat = 1:3) > a = st_as_stars(list(X = array(1:9, c(3,3))), dimensions = d) > (st = st_as_stars(a, curvilinear = ll)) stars object with 2 dimensions and 1 attribute attribute(s): Min. 1st Qu. Median Mean 3rd Qu. Max. X 1 3 5 5 7 9 dimension(s): from to refsys point values x/y lon 1 3 WGS 84 (CRS84) FALSE [3x3] 0.03,...,0.89 [x] lat 1 3 WGS 84 (CRS84) FALSE [3x3] 0.021,...,0.89 [y] curvilinear grid > as.data.frame(st) lon lat X 1 0.89 0.250 1 2 0.64 0.510 2 3 0.30 0.670 3 4 0.69 0.890 4 5 0.47 0.890 5 6 0.26 0.190 6 7 0.57 0.021 7 8 0.27 0.220 8 9 0.03 0.340 9 > st_bbox(st) xmin ymin xmax ymax 0.030 0.021 0.890 0.890 > > # time regular: > t = as.Date("2019-01-20") + 0:3 > d = st_dimensions(t, sfc) > (st = st_as_stars(list(a = matrix(1:12, 4, 3)), dimensions = d)) stars object with 2 dimensions and 1 attribute attribute(s): Min. 1st Qu. Median Mean 3rd Qu. Max. a 1 3.75 6.5 6.5 9.25 12 dimension(s): from to offset delta refsys point values X1 1 4 2019-01-20 1 days Date FALSE NULL X2 1 3 NA NA NA TRUE POINT (0 1),...,POINT (4 3) > as.data.frame(st) X1 X2 a 1 2019-01-20 POINT (0 1) 1 2 2019-01-21 POINT (0 1) 2 3 2019-01-22 POINT (0 1) 3 4 2019-01-23 POINT (0 1) 4 5 2019-01-20 POINT (2 1) 5 6 2019-01-21 POINT (2 1) 6 7 2019-01-22 POINT (2 1) 7 8 2019-01-23 POINT (2 1) 8 9 2019-01-20 POINT (4 3) 9 10 2019-01-21 POINT (4 3) 10 11 2019-01-22 POINT (4 3) 11 12 2019-01-23 POINT (4 3) 12 > > # time, rectilinear > t = as.Date("2019-01-20") + c(0, 2, 3, 10) > d = st_dimensions(t, sfc) > (st = st_as_stars(list(a = matrix(1:12, 4, 3)), dimensions = d)) stars object with 2 dimensions and 1 attribute attribute(s): Min. 1st Qu. Median Mean 3rd Qu. Max. a 1 3.75 6.5 6.5 9.25 12 dimension(s): from to refsys point values X1 1 4 Date FALSE 2019-01-20,...,2019-01-30 X2 1 3 NA TRUE POINT (0 1),...,POINT (4 3) > as.data.frame(st) X1 X2 a 1 2019-01-20 POINT (0 1) 1 2 2019-01-22 POINT (0 1) 2 3 2019-01-23 POINT (0 1) 3 4 2019-01-30 POINT (0 1) 4 5 2019-01-20 POINT (2 1) 5 6 2019-01-22 POINT (2 1) 6 7 2019-01-23 POINT (2 1) 7 8 2019-01-30 POINT (2 1) 8 9 2019-01-20 POINT (4 3) 9 10 2019-01-22 POINT (4 3) 10 11 2019-01-23 POINT (4 3) 11 12 2019-01-30 POINT (4 3) 12 > > proc.time() user system elapsed 0.85 0.12 0.96 stars/tests/crop.Rout.save0000644000176200001440000000702514644736040015337 0ustar liggesusers R Under development (unstable) (2024-07-13 r86895 ucrt) -- "Unsuffered Consequences" Copyright (C) 2024 The R Foundation for Statistical Computing Platform: x86_64-w64-mingw32/x64 R is free software and comes with ABSOLUTELY NO WARRANTY. You are welcome to redistribute it under certain conditions. Type 'license()' or 'licence()' for distribution details. R is a collaborative project with many contributors. Type 'contributors()' for more information and 'citation()' on how to cite R or R packages in publications. Type 'demo()' for some demos, 'help()' for on-line help, or 'help.start()' for an HTML browser interface to help. Type 'q()' to quit R. > suppressPackageStartupMessages(library(stars)) > tif = system.file("tif/L7_ETMs.tif", package = "stars") > x = read_stars(tif) > xy = structure(list(x = c(293253.999046018, 296400.196497684), y = c(9113801.64775462, + 9111328.49619133)), .Names = c("x", "y")) > pts = st_as_sf(data.frame(do.call(cbind, xy)), coords = c("x", "y"), crs = st_crs(x)) > image(x, axes = TRUE) > plot(st_as_sfc(st_bbox(pts)), col = NA, add = TRUE) > > bb = st_bbox(pts) > (xx = x[bb]) stars object with 3 dimensions and 1 attribute attribute(s): Min. 1st Qu. Median Mean 3rd Qu. Max. L7_ETMs.tif 3 56 73 70.87303 89 255 dimension(s): from to offset delta refsys point x/y x 158 268 288776 28.5 SIRGAS 2000 / UTM zone 25S FALSE [x] y 245 331 9120761 -28.5 SIRGAS 2000 / UTM zone 25S FALSE [y] band 1 6 NA NA NA NA > image(xx) > plot(st_as_sfc(bb), add = TRUE, col = NA) > st_contains(st_as_sfc(st_bbox(xx)), st_as_sfc(st_bbox(pts))) Sparse geometry binary predicate list of length 1, where the predicate was `contains' 1: 1 > > image(x) > pt = st_point(c(x = 290462.103109179, y = 9114202.32594085)) > buf = st_buffer(st_sfc(pt, crs = st_crs(x)), 1500) > plot(buf, add = TRUE) > > buf = st_sfc(st_polygon(list(st_buffer(pt, 1500)[[1]], st_buffer(pt, 1000)[[1]])), + crs = st_crs(x)) > image(x[buf]) > plot(buf, add = TRUE, col = NA) > image(x[buf,crop=FALSE]) > plot(buf, add = TRUE, col = NA) > > plot(x, rgb = 1:3) > > # #638: > l = st_as_stars(L7_ETMs) > st_bbox(l) |> st_as_sfc() -> bb > (bb + 1e6) |> st_set_crs(st_crs(l)) -> b > st_crop(l, b) stars object with 3 dimensions and 1 attribute attribute(s): Min. 1st Qu. Median Mean 3rd Qu. Max. NA's L7_ETMs NA NA NA NaN NA NA 737088 dimension(s): from to offset delta refsys point x/y x 1 349 288776 28.5 SIRGAS 2000 / UTM zone 25S FALSE [x] y 1 352 9120761 -28.5 SIRGAS 2000 / UTM zone 25S FALSE [y] band 1 6 NA NA NA NA Warning message: In st_crop.stars(l, b) : st_crop: bounding boxes of x and y do not overlap > st_intersects(b, st_as_sfc(st_bbox(l))) |> as.matrix() [,1] [1,] FALSE > (bb - 1e6) |> st_set_crs(st_crs(l)) -> b > st_crop(l, b) stars object with 3 dimensions and 1 attribute attribute(s): Min. 1st Qu. Median Mean 3rd Qu. Max. NA's L7_ETMs NA NA NA NaN NA NA 737088 dimension(s): from to offset delta refsys point x/y x 1 349 288776 28.5 SIRGAS 2000 / UTM zone 25S FALSE [x] y 1 352 9120761 -28.5 SIRGAS 2000 / UTM zone 25S FALSE [y] band 1 6 NA NA NA NA Warning message: In st_crop.stars(l, b) : st_crop: bounding boxes of x and y do not overlap > > proc.time() user system elapsed 3.15 0.39 3.48 stars/tests/predict.R0000644000176200001440000000074013777653456014355 0ustar liggesuserssuppressPackageStartupMessages(library(stars)) # predict: tif = system.file("tif/L7_ETMs.tif", package = "stars") (x = read_stars(tif)) st_crs(x) = st_crs(x) # cleans trailing space model = lm(x~L7_ETMs.tif, head(as.data.frame(x), 50)) x = predict(x, model) (y = read_stars(tif, proxy = TRUE)) (yp = predict(y, model)) all.equal(x, st_as_stars(yp)) yy = st_set_dimensions(y, 3, values = paste0("band", 1:6)) (y_spl = split(yy, 3)) as.data.frame(st_as_stars(split(yy, 3)))[1:3,] stars/tests/stars.R0000644000176200001440000001114614603250422014030 0ustar liggesusersSys.setenv(TZ="UTC") suppressPackageStartupMessages(library(stars)) set.seed(13521) # runif tif = system.file("tif/L7_ETMs.tif", package = "stars") (x_ = read_stars(c(tif,tif))) # FIXME: not what you'd expect (x = read_stars(tif)) # image(x) gdal_crs(tif) plot(x) plot(x, join_zlim = FALSE) x %>% st_set_dimensions(names = c('a', 'b', 'c')) st_get_dimension_values(x, 3) (x1 = st_set_dimensions(x, "band", values = c(1,2,3,4,5,7), names = "band_number", point = TRUE)) rbind(c(0.45,0.515), c(0.525,0.605), c(0.63,0.69), c(0.775,0.90), c(1.55,1.75), c(2.08,2.35)) %>% units::set_units(um) -> bw # units::set_units(µm) -> bw # set bandwidth midpoint: (x2 = st_set_dimensions(x, "band", values = 0.5 * (bw[,1]+bw[,2]), names = "bandwidth_midpoint", point = TRUE)) # set bandwidth intervals: (x3 = st_set_dimensions(x, "band", values = make_intervals(bw), names = "bandwidth")) x + x x * x x[,,,1:3] x[,1:100,100:200,] sqrt(x) st_apply(x, 3, min) st_apply(x, 1:2, max) st_apply(x, 1:2, range) geomatrix = system.file("tif/geomatrix.tif", package = "stars") x = read_stars(geomatrix) y = st_transform(x, st_crs(4326)) st_coordinates(x)[1:2,] nc = system.file("nc/tos_O1_2001-2002.nc", package = "stars") if (nc != "" && require(PCICt, quietly = TRUE)) { print(x <- read_stars(nc)) print(st_bbox(x)) s = st_as_stars(st_bbox(x)) # inside = NA print(st_bbox(s)) s = st_as_stars(st_bbox(x), inside = TRUE) print(st_bbox(s)) s = st_as_stars(st_bbox(x), inside = FALSE) print(st_bbox(s)) (s = st_as_stars(st_bbox(x), dy = 1)) print(st_bbox(s)) print(identical(st_as_stars(st_bbox(x), dx = 1), st_as_stars(st_bbox(x), dy = 1))) s = st_as_stars(st_bbox(x), dx = 10) print(st_bbox(s)) s = st_as_stars(st_bbox(x), dx = 20) print(st_bbox(s)) x1 = x st_crs(x1) = "OGC:CRS84" print(identical(st_as_stars(st_bbox(x1), dx = 1), st_as_stars(st_bbox(x1), dx = units::set_units(1, degree)))) df = as.data.frame(x) if (require(units, quietly = TRUE)) print(units::drop_units(x)) print(dimnames(x)) dimnames(x) <- letters[1:3] print(dimnames(x)) } # PCICt st_as_stars() # multiple sub-datasets: nc_red = system.file("nc/reduced.nc", package = "stars") red = read_stars(nc_red) red plot(red) x = st_xy2sfc(read_stars(tif)[,1:10,1:10,], as_points = FALSE) st_bbox(x) x = read_stars(tif) merge(split(x, "band")) read_stars(c(tif,tif)) # merges as attributes read_stars(c(tif,tif), along = "sensor") read_stars(c(tif,tif), along = 4) read_stars(c(tif,tif), along = "band") read_stars(c(tif,tif), along = 3) # cut: tif = system.file("tif/L7_ETMs.tif", package = "stars") x = read_stars(tif) cut(x, c(0, 50, 100, 255)) cut(x[,,,1,drop=TRUE], c(0, 50, 100, 255)) plot(cut(x[,,,1,drop=TRUE], c(0, 50, 100, 255))) st_bbox(st_dimensions(x)) x[x < 0] = NA x[is.na(x)] = 0 # c: f = system.file("netcdf/avhrr-only-v2.19810902.nc", package = "starsdata") if (FALSE && f != "") { files = c("avhrr-only-v2.19810901.nc", "avhrr-only-v2.19810902.nc", "avhrr-only-v2.19810903.nc", "avhrr-only-v2.19810904.nc", "avhrr-only-v2.19810905.nc", "avhrr-only-v2.19810906.nc", "avhrr-only-v2.19810907.nc", "avhrr-only-v2.19810908.nc", "avhrr-only-v2.19810909.nc") l = list() for (f in files) { from = system.file(paste0("netcdf/", f), package = "starsdata") l[[f]] = read_stars(from, sub = c("sst", "anom")) } ret = do.call(c, l) print(ret) ret = adrop(c(l[[1]], l[[2]], l[[3]], along = list(times = as.Date("1981-09-01") + 0:2))) print(ret) #ret = adrop(adrop(c(l[[1]], l[[2]], l[[3]], along = "times"))) #print(ret) } st_dimensions(list(matrix(1, 4, 4))) # st_dimensions.default if (FALSE && require("starsdata", quietly = TRUE)) { # curvilinear: s5p = system.file( "sentinel5p/S5P_NRTI_L2__NO2____20180717T120113_20180717T120613_03932_01_010002_20180717T125231.nc", package = "starsdata") print(s5p) lat_ds = paste0("HDF5:\"", s5p, "\"://PRODUCT/latitude") lon_ds = paste0("HDF5:\"", s5p, "\"://PRODUCT/longitude") nit_ds = paste0("HDF5:\"", s5p, "\"://PRODUCT/SUPPORT_DATA/DETAILED_RESULTS/nitrogendioxide_summed_total_column") lat = read_stars(lat_ds) lon = read_stars(lon_ds) nit = read_stars(nit_ds) nit[[1]][nit[[1]] > 9e+36] = NA ll = setNames(c(lon, lat), c("x", "y")) nit.c = st_as_stars(nit, curvilinear = ll) print(nit.c) s5p = system.file( "sentinel5p/S5P_NRTI_L2__NO2____20180717T120113_20180717T120613_03932_01_010002_20180717T125231.nc", package = "starsdata") nit.c2 = read_stars(s5p, sub = "//PRODUCT/SUPPORT_DATA/DETAILED_RESULTS/nitrogendioxide_summed_total_column", curvilinear = c("//PRODUCT/latitude", "//PRODUCT/longitude")) print(all.equal(nit.c, nit.c2)) } stars/tests/ee.R0000644000176200001440000000137314576033423013277 0ustar liggesusers# show that extract with exact=TRUE equals st_interpolate_aw(), only much faster options(digits=15) suppressPackageStartupMessages(library(stars)) L = st_as_stars(L7_ETMs)[,,,1] set.seed(123432) st_bbox(L) |> st_as_sfc() |> st_centroid() |> st_buffer(50) |> st_sample(10) |> st_combine() |> st_convex_hull() -> ch if (interactive()) { plot(L, reset = FALSE) plot(ch, add = TRUE, border = 'red', col = NA) } if (FALSE) { e = st_extract(L, ch)[[1]] a = aggregate(L, ch, mean)[[1]] identical(e, a) # same code path ee = aggregate(L, ch, mean, exact = TRUE)[[1]] all.equal(e, ee) aw = suppressWarnings(st_interpolate_aw(L, ch, extensive = FALSE)$V1) all.equal(as.vector(ee), aw) ee0 = st_extract(L, ch, exact = TRUE)[[1]] all.equal(ee0, ee) # ?? } stars/tests/gridtypes.R0000644000176200001440000000576413777653456014750 0ustar liggesuserssuppressPackageStartupMessages(library(stars)) # regular, but not spatial: d = st_dimensions(a = 1:3, b = 1:3, band = c("foo", "bar")) (st = st_as_stars(array(1:18, c(3,3,2)), dimension = d)) as.data.frame(st, add_max=FALSE)[1:4,] try(x <- st_bbox(st)) # error # regular, geotransform: d = st_dimensions(x = 1:3, y = 1:3, band = c("foo", "bar"), .raster = c("x", "y")) (st = st_as_stars(array(1:18, c(3,3,2)), dimension = d)) as.data.frame(st, add_max=FALSE)[1:4,] as.data.frame(st, add_max=TRUE)[1:4,] st_bbox(st) # rectilinear with offset given: xd = c(1, 2, 4) d = st_dimensions(x = xd, y = 1:3, band = c("foo", "bar"), .raster = c("x", "y")) (st = st_as_stars(array(1:18, c(3,3,2)), dimension = d)) as.data.frame(st, add_max=FALSE)[1:4,] as.data.frame(st, add_max=TRUE)[1:4,] st_bbox(st) # rectilinear with midpoints given: xd = c(1, 2, 4) d = st_dimensions(x = xd, y = 1:3, band = c("foo", "bar"), .raster = c("x", "y"), cell_midpoints = TRUE) (st = st_as_stars(array(1:18, c(3,3,2)), dimension = d)) as.data.frame(st, add_max=FALSE)[1:4,] as.data.frame(st, add_max=TRUE)[1:4,] st_bbox(st) # rectilinear with midpoints given, point support xd = c(1, 2, 4) d = st_dimensions(x = xd, y = 1:3, band = c("foo", "bar"), .raster = c("x", "y"), cell_midpoints = TRUE, point = TRUE) (st = st_as_stars(array(1:18, c(3,3,2)), dimension = d)) as.data.frame(st, add_max=FALSE)[1:4,] as.data.frame(st, add_max=TRUE)[1:4,] st_bbox(st) # rectilinear with start/end given: #xd = stars:::make_intervals(start = c(1,2,4), end = c(2, 4, 8)) xd = c(1, 2, 4, 8) # one more than dim of the data array d = st_dimensions(x = xd, y = 1:3, band = c("foo", "bar"), .raster = c("x", "y")) (st = st_as_stars(array(1:18, c(3,3,2)), dimension = d)) as.data.frame(st, add_max=FALSE)[1:4,] as.data.frame(st, add_max=TRUE)[1:4,] st_bbox(st) # with sfc: sfc = st_sfc(st_point(0:1), st_point(2:1), st_point(4:3)) d = st_dimensions(x = sfc, y = 1:3, band = c("foo", "bar")) (st = st_as_stars(array(1:18, c(3,3,2)), dimension = d)) as.data.frame(st, add_max=FALSE)[1:4,] st_as_sf(st, long = TRUE)[1:4,] st_bbox(st) # rotated/sheared: d = st_dimensions(x = 1:3, y = 1:3, band = c("foo", "bar"), .raster = c("x", "y"), affine = c(0.2, -0.2)) (st = st_as_stars(array(1:18, c(3,3,2)), dimension = d)) as.data.frame(st, add_max=FALSE)[1:4,] try(as.data.frame(st, add_max=TRUE)[1:4,]) # errors st_bbox(st) # curvilinear: set.seed(13531) lon = st_as_stars(matrix(signif(runif(9), 2), 3, 3)) lat = st_as_stars(matrix(signif(runif(9), 2), 3, 3)) ll = setNames(c(lon, lat), c("lon", "lat")) d = st_dimensions(lon = 1:3, lat = 1:3) a = st_as_stars(list(X = array(1:9, c(3,3))), dimensions = d) (st = st_as_stars(a, curvilinear = ll)) as.data.frame(st) st_bbox(st) # time regular: t = as.Date("2019-01-20") + 0:3 d = st_dimensions(t, sfc) (st = st_as_stars(list(a = matrix(1:12, 4, 3)), dimensions = d)) as.data.frame(st) # time, rectilinear t = as.Date("2019-01-20") + c(0, 2, 3, 10) d = st_dimensions(t, sfc) (st = st_as_stars(list(a = matrix(1:12, 4, 3)), dimensions = d)) as.data.frame(st) stars/tests/align.R0000644000176200001440000000201114157612520013762 0ustar liggesuserssuppressPackageStartupMessages(library(stars)) set.seed(1331) st_sample(st_as_sfc(st_bbox(L7_ETMs)), 1) b = st_buffer(st_sample(st_as_sfc(st_bbox(L7_ETMs)), 1), 335) bba = stars:::st_align(st_bbox(b), st_dimensions(L7_ETMs)) plot(L7_ETMs[,,,1], extent = st_buffer(b, 20), reset = FALSE, key.pos = NULL) plot(b, add = TRUE, col = NA, border = 'red') plot(st_as_sfc(st_bbox(b)), col = NA, border = 'red', add = TRUE) plot(st_as_sfc(bba), col = NA, border = 'blue', add = TRUE) st_bbox(b) bba plot(L7_ETMs[,,,1], reset = FALSE, key.pos = NULL) plot(b, col = 'red', add = TRUE) plot(st_as_sfc(bba), col = NA, border = 'blue', add = TRUE) b0 = st_buffer(b, -100) L7 = st_as_stars(L7_ETMs)[b0][,,,1] |> st_normalize() bbb = stars:::st_align(st_bbox(b), st_dimensions(L7)) plot(L7, extent = st_buffer(b, 120), reset = FALSE, key.pos = NULL) plot(st_as_sfc(st_bbox(L7)), col = NA, border = 'black', add = TRUE) plot(st_as_sfc(bbb), col = NA, border = 'blue', add = TRUE) plot(b, col = NA, border = 'red', add = TRUE) all.equal(bbb, bba) stars/tests/dimensions.R0000644000176200001440000000113713777653456015074 0ustar liggesusers# https://github.com/r-spatial/stars/issues/295 suppressPackageStartupMessages(library(stars)) raw <- read_stars(system.file("nc/bcsd_obs_1999.nc", package = "stars")) foo <- function(x, idx) stats::lowess(idx, x)$y timeline <- st_get_dimension_values(raw, "time") smooth = st_apply(raw, MARGIN = c("x", "y"), FUN = foo, idx = st_get_dimension_values(raw, "time") ) st_set_dimensions(smooth, which = "foo", values = st_get_dimension_values(raw, "time"), names = "time" ) raw %>% st_apply(MARGIN = c("x", "y"), FUN = foo, idx = timeline) %>% st_set_dimensions("foo", st_dimensions(raw)["time"]) stars/tests/curvilinear.Rout.save0000644000176200001440000000513514644736040016717 0ustar liggesusers R Under development (unstable) (2024-07-13 r86895 ucrt) -- "Unsuffered Consequences" Copyright (C) 2024 The R Foundation for Statistical Computing Platform: x86_64-w64-mingw32/x64 R is free software and comes with ABSOLUTELY NO WARRANTY. You are welcome to redistribute it under certain conditions. Type 'license()' or 'licence()' for distribution details. R is a collaborative project with many contributors. Type 'contributors()' for more information and 'citation()' on how to cite R or R packages in publications. Type 'demo()' for some demos, 'help()' for on-line help, or 'help.start()' for an HTML browser interface to help. Type 'q()' to quit R. > suppressPackageStartupMessages(library(stars)) > > s5p = system.file("sentinel5p/S5P_NRTI_L2__NO2____20180717T120113_20180717T120613_03932_01_010002_20180717T125231.nc", package = "starsdata") > if (s5p != "") { + + lat_ds = paste0("HDF5:\"", s5p, "\"://PRODUCT/latitude") + lon_ds = paste0("HDF5:\"", s5p, "\"://PRODUCT/longitude") + nit_ds = paste0("HDF5:\"", s5p, "\"://PRODUCT/SUPPORT_DATA/DETAILED_RESULTS/nitrogendioxide_summed_total_column") + lat = read_stars(lat_ds) + lon = read_stars(lon_ds) + nit = read_stars(nit_ds) + nit[[1]][nit[[1]] > 9e+36] = NA + + ll = setNames(c(lon, lat), c("x", "y")) + nit.c0 = st_as_stars(nit, curvilinear = ll) + + # more direct method: + nit.c = read_stars(s5p, sub = "//PRODUCT/SUPPORT_DATA/DETAILED_RESULTS/nitrogendioxide_summed_total_column", + curvilinear = c("//PRODUCT/longitude", "//PRODUCT/latitude"), driver = NULL) + if (inherits(nit.c[[1]], "units")) + nit.c[[1]] = units::drop_units(nit.c[[1]]) + nit.c[[1]][nit.c[[1]] > 9e+36] = NA + all.equal(nit.c0, nit.c) + st_crs(nit.c) = 4326 + print(nit.c) + + if (FALSE && capabilities()["png"]) { + png("nit1.png", 800, 800) + plot(nit.c, breaks = "equal", reset = FALSE, axes = TRUE, as_points = TRUE, pch = 16) + maps::map('world', add = TRUE, col = 'red') + dev.off() + + png("nit2.png", 800, 800) + plot(nit.c, breaks = "equal", reset = FALSE, axes = TRUE, as_points = FALSE, border = NA) + maps::map('world', add = TRUE, col = 'red') + dev.off() + + nit.c = stars:::st_downsample(nit.c, 8) + print(nit.c) + + png("nit3.png", 800, 800) + plot(nit.c, breaks = "equal", reset = FALSE, axes = TRUE, as_points = TRUE, pch = 16) + maps::map('world', add = TRUE, col = 'red') + dev.off() + + png("nit4.png", 800, 800) + plot(nit.c, breaks = "equal", reset = FALSE, axes = TRUE, as_points = FALSE, border = NA) + maps::map('world', add = TRUE, col = 'red') + dev.off() + } + + } > > proc.time() user system elapsed 0.60 0.14 0.75 stars/tests/rasterize.Rout.save0000644000176200001440000001270314644736040016403 0ustar liggesusers R Under development (unstable) (2024-07-13 r86895 ucrt) -- "Unsuffered Consequences" Copyright (C) 2024 The R Foundation for Statistical Computing Platform: x86_64-w64-mingw32/x64 R is free software and comes with ABSOLUTELY NO WARRANTY. You are welcome to redistribute it under certain conditions. Type 'license()' or 'licence()' for distribution details. R is a collaborative project with many contributors. Type 'contributors()' for more information and 'citation()' on how to cite R or R packages in publications. Type 'demo()' for some demos, 'help()' for on-line help, or 'help.start()' for an HTML browser interface to help. Type 'q()' to quit R. > suppressPackageStartupMessages(library(stars)) > nc = read_sf(system.file("gpkg/nc.gpkg", package="sf")) > (x = st_rasterize(nc)) # default grid: stars object with 2 dimensions and 12 attributes attribute(s): Min. 1st Qu. Median Mean 3rd Qu. Max. NA's AREA 0.042 0.108 0.142 1.451932e-01 0.181 0.241 30904 PERIMETER 0.999 1.461 1.716 1.786110e+00 2.004 3.640 30904 CNTY_ 1825.000 1907.000 1989.000 1.998403e+03 2085.000 2241.000 30904 CNTY_ID 1825.000 1907.000 1989.000 1.998403e+03 2085.000 2241.000 30904 FIPSNO 37001.000 37049.000 37101.000 3.710042e+04 37153.000 37199.000 30904 CRESS_ID 1.000 25.000 51.000 5.071206e+01 77.000 100.000 30904 BIR74 248.000 1323.000 2648.000 3.791637e+03 4139.000 21588.000 30904 SID74 0.000 3.000 5.000 7.891985e+00 10.000 44.000 30904 NWBIR74 1.000 297.000 844.000 1.246210e+03 1396.000 8027.000 30904 BIR79 319.000 1606.000 3108.000 4.852046e+03 5400.000 30757.000 30904 SID79 0.000 3.000 6.000 9.584098e+00 13.000 57.000 30904 NWBIR79 3.000 360.000 1058.000 1.604642e+03 1524.000 11631.000 30904 dimension(s): from to offset delta refsys point x/y x 1 461 -84.32 0.01925 NAD27 FALSE [x] y 1 141 36.59 -0.01925 NAD27 FALSE [y] > plot(x, axes = TRUE) > # a bit more customized grid: > (x = st_rasterize(nc, st_as_stars(st_bbox(nc), nx = 100, ny = 50, values = NA_real_))) stars object with 2 dimensions and 12 attributes attribute(s): Min. 1st Qu. Median Mean 3rd Qu. Max. NA's AREA 0.042 0.108 0.142 0.145536 0.181 0.241 2373 PERIMETER 0.999 1.470 1.716 1.789032 2.004 3.640 2373 CNTY_ 1825.000 1908.000 1989.000 1999.761325 2088.000 2241.000 2373 CNTY_ID 1825.000 1908.000 1989.000 1999.761325 2088.000 2241.000 2373 FIPSNO 37001.000 37049.000 37101.000 37100.327750 37151.000 37199.000 2373 CRESS_ID 1.000 25.000 51.000 50.663875 76.000 100.000 2373 BIR74 248.000 1323.500 2648.000 3790.530263 4139.000 21588.000 2373 SID74 0.000 3.000 5.000 7.905976 10.000 44.000 2373 NWBIR74 1.000 289.000 844.000 1246.116483 1396.000 8027.000 2373 BIR79 319.000 1606.000 3108.000 4848.231062 5386.000 30757.000 2373 SID79 0.000 3.000 6.000 9.574419 13.000 57.000 2373 NWBIR79 3.000 360.000 1058.000 1602.740769 1524.000 11631.000 2373 dimension(s): from to offset delta refsys point x/y x 1 100 -84.32 0.08867 NAD27 FALSE [x] y 1 50 36.59 -0.05415 NAD27 FALSE [y] > plot(x, axes = TRUE) > (ls = st_sf(a = 1:2, st_sfc(st_linestring(rbind(c(0.1, 0), c(1.1, 1))), st_linestring(rbind(c(0, 0.05), c(1, 0.05)))))) Simple feature collection with 2 features and 1 field Geometry type: LINESTRING Dimension: XY Bounding box: xmin: 0 ymin: 0 xmax: 1.1 ymax: 1 CRS: NA a 1 1 2 2 c..st_sfc.st_linestring.rbind.c.0.1..0...c.1.1..1.....st_linestring.rbind.c.0..........0.05...c.1..0.05...... 1 LINESTRING (0.1 0, 1.1 1) 2 LINESTRING (0 0.05, 1 0.05) > (grd = st_as_stars(st_bbox(ls), nx = 10, ny = 10, xlim = c(0, 1.0), ylim = c(0, 1), values = NA_real_)) stars object with 2 dimensions and 1 attribute attribute(s): Min. 1st Qu. Median Mean 3rd Qu. Max. NA's values NA NA NA NaN NA NA 100 dimension(s): from to offset delta x/y x 1 10 0 0.1 [x] y 1 10 1 -0.1 [y] > # the following two plots confirm that (only) the > # upper-left corner is part of the grid cell (when dy is negative), leading > # to a seemingly half-gridcell-shift problem: > ## sf_extSoftVersion()["GDAL"] > plot(st_rasterize(ls, grd), axes = TRUE, reset = FALSE) # ALL_TOUCHED=FALSE; > plot(ls, add = TRUE, col = "red") > plot(st_rasterize(ls, grd, options = "ALL_TOUCHED=TRUE"), axes = TRUE, reset = FALSE) > plot(ls, add = TRUE, col = "red") > # add lines to existing 0 values, summing values in case of multiple lines: > (grd = st_as_stars(st_bbox(ls), nx = 10, ny = 10, xlim = c(0, 1.0), ylim = c(0, 1), values = 0)) stars object with 2 dimensions and 1 attribute attribute(s): Min. 1st Qu. Median Mean 3rd Qu. Max. values 0 0 0 0 0 0 dimension(s): from to offset delta x/y x 1 10 0 0.1 [x] y 1 10 1 -0.1 [y] > r = st_rasterize(ls, grd, options = c("MERGE_ALG=ADD", "ALL_TOUCHED=TRUE")) > plot(r, axes = TRUE, reset = FALSE) > plot(ls, add = TRUE, col = "red") > > proc.time() user system elapsed 1.42 0.28 1.70 stars/tests/sp.R0000644000176200001440000000147714452766244013344 0ustar liggesusersoptions(rgdal_show_exportToProj4_warnings = "none") if (suppressPackageStartupMessages(require(sp, quietly = TRUE))) { demo(meuse) # remove +init=xxx meuse@proj4string = CRS( "+proj=sterea +lat_0=52.1561605555556 +lon_0=5.38763888888889 +k=0.9999079 +x_0=155000 +y_0=463000 +ellps=bessel +units=m +no_defs" ) meuse.grid@proj4string = CRS( "+proj=sterea +lat_0=52.1561605555556 +lon_0=5.38763888888889 +k=0.9999079 +x_0=155000 +y_0=463000 +ellps=bessel +units=m +no_defs" ) suppressPackageStartupMessages(library(stars)) (m = st_as_stars(meuse)) (gr = st_as_stars(meuse.grid)) m2 = as(m, "Spatial") summary(m2) gr2 = as(gr, "Spatial") summary(gr2) all.equal(meuse, m2, check.attributes = FALSE) fullgrid(meuse.grid) = TRUE all.equal(meuse.grid, gr2, check.attributes = FALSE) #all.equal(meuse, m2) #all.equal(meuse.grid, gr2) } stars/tests/proxy.Rout.save0000644000176200001440000002760214644736040015560 0ustar liggesusers R Under development (unstable) (2024-07-13 r86895 ucrt) -- "Unsuffered Consequences" Copyright (C) 2024 The R Foundation for Statistical Computing Platform: x86_64-w64-mingw32/x64 R is free software and comes with ABSOLUTELY NO WARRANTY. You are welcome to redistribute it under certain conditions. Type 'license()' or 'licence()' for distribution details. R is a collaborative project with many contributors. Type 'contributors()' for more information and 'citation()' on how to cite R or R packages in publications. Type 'demo()' for some demos, 'help()' for on-line help, or 'help.start()' for an HTML browser interface to help. Type 'q()' to quit R. > Sys.setenv(TZ="UTC") > suppressPackageStartupMessages(library(sf)) > suppressPackageStartupMessages(library(stars)) > tif = system.file("tif/L7_ETMs.tif", package = "stars") > (r = read_stars(tif, proxy = TRUE)) stars_proxy object with 1 attribute in 1 file(s): $L7_ETMs.tif [1] "[...]/L7_ETMs.tif" dimension(s): from to offset delta refsys point x/y x 1 349 288776 28.5 SIRGAS 2000 / UTM zone 25S FALSE [x] y 1 352 9120761 -28.5 SIRGAS 2000 / UTM zone 25S FALSE [y] band 1 6 NA NA NA NA > if (interactive()) { + plot(r) + } > dim(r) x y band 349 352 6 > r[,,,2:3] stars_proxy object with 1 attribute in 1 file(s): $L7_ETMs.tif [1] "[...]/L7_ETMs.tif" dimension(s): from to offset delta refsys point x/y x 1 349 288776 28.5 SIRGAS 2000 / UTM zone 25S FALSE [x] y 1 352 9120761 -28.5 SIRGAS 2000 / UTM zone 25S FALSE [y] band 2 3 NA NA NA NA > r+r stars_proxy object with 1 attribute in 1 file(s): $L7_ETMs.tif [1] "[...]/L7_ETMs.tif" dimension(s): from to offset delta refsys point x/y x 1 349 288776 28.5 SIRGAS 2000 / UTM zone 25S FALSE [x] y 1 352 9120761 -28.5 SIRGAS 2000 / UTM zone 25S FALSE [y] band 1 6 NA NA NA NA call_list: [[1]] e1 + e2 attr(,".Environment") This object has pending lazy operations: dimensions as printed may not reflect this. > st_as_stars(sin(r), downsample = 2) stars object with 3 dimensions and 1 attribute attribute(s): Min. 1st Qu. Median Mean 3rd Qu. Max. L7_ETMs.tif -0.9999902 -0.676772 0.0353983 0.01655317 0.7210377 0.9999119 dimension(s): from to offset delta refsys point x/y x 1 117 288776 85.01 SIRGAS 2000 / UTM zone 25S FALSE [x] y 1 118 9120761 -85.02 SIRGAS 2000 / UTM zone 25S FALSE [y] band 1 6 NA NA NA NA > aperm(r, c(3,2,1)) stars_proxy object with 1 attribute in 1 file(s): $L7_ETMs.tif [1] "[...]/L7_ETMs.tif" dimension(s): from to offset delta refsys point x/y x 1 349 288776 28.5 SIRGAS 2000 / UTM zone 25S FALSE [x] y 1 352 9120761 -28.5 SIRGAS 2000 / UTM zone 25S FALSE [y] band 1 6 NA NA NA NA call_list: [[1]] aperm(a = a, perm = perm) attr(,".Environment") This object has pending lazy operations: dimensions as printed may not reflect this. > if (interactive()) { + plot(r) + } > (xx = c(r,r)) stars_proxy object with 2 attributes in 2 file(s): $L7_ETMs.tif [1] "[...]/L7_ETMs.tif" $L7_ETMs.tif [1] "[...]/L7_ETMs.tif" dimension(s): from to offset delta refsys point x/y x 1 349 288776 28.5 SIRGAS 2000 / UTM zone 25S FALSE [x] y 1 352 9120761 -28.5 SIRGAS 2000 / UTM zone 25S FALSE [y] band 1 6 NA NA NA NA > st_redimension(xx) stars_proxy object with 1 attribute in 2 file(s): $L7_ETMs.tif.L7_ETMs.tif [1] "[...]/L7_ETMs.tif" "[...]/L7_ETMs.tif" dimension(s): from to offset delta refsys point x 1 349 288776 28.5 SIRGAS 2000 / UTM zone 25S FALSE y 1 352 9120761 -28.5 SIRGAS 2000 / UTM zone 25S FALSE band 1 6 NA NA NA NA new_dim 1 2 NA NA NA NA values x/y x NULL [x] y NULL [y] band NULL new_dim L7_ETMs.tif, L7_ETMs.tif > st_as_stars(r) stars object with 3 dimensions and 1 attribute attribute(s): Min. 1st Qu. Median Mean 3rd Qu. Max. L7_ETMs.tif 1 54 69 68.91242 86 255 dimension(s): from to offset delta refsys point x/y x 1 349 288776 28.5 SIRGAS 2000 / UTM zone 25S FALSE [x] y 1 352 9120761 -28.5 SIRGAS 2000 / UTM zone 25S FALSE [y] band 1 6 NA NA NA NA > (y = st_apply(r, 1:2, max)) stars_proxy object with 1 attribute in 1 file(s): $L7_ETMs.tif [1] "[...]/L7_ETMs.tif" dimension(s): from to offset delta refsys point x/y x 1 349 288776 28.5 SIRGAS 2000 / UTM zone 25S FALSE [x] y 1 352 9120761 -28.5 SIRGAS 2000 / UTM zone 25S FALSE [y] band 1 6 NA NA NA NA call_list: [[1]] st_apply(X = X, MARGIN = MARGIN, FUN = FUN, CLUSTER = CLUSTER, PROGRESS = PROGRESS, FUTURE = FUTURE, rename = rename, .fname = .fname) attr(,".Environment") This object has pending lazy operations: dimensions as printed may not reflect this. > (yy = st_as_stars(y, downsample = 1)) stars object with 2 dimensions and 1 attribute attribute(s): Min. 1st Qu. Median Mean 3rd Qu. Max. max 55 85 96 99.38568 113 255 dimension(s): from to offset delta refsys point x/y x 1 175 288776 56.84 SIRGAS 2000 / UTM zone 25S FALSE [x] y 1 176 9120761 -57 SIRGAS 2000 / UTM zone 25S FALSE [y] > (y = adrop(st_apply(r, c("x", "y"), max))) stars_proxy object with 1 attribute in 1 file(s): $L7_ETMs.tif [1] "[...]/L7_ETMs.tif" dimension(s): from to offset delta refsys point x/y x 1 349 288776 28.5 SIRGAS 2000 / UTM zone 25S FALSE [x] y 1 352 9120761 -28.5 SIRGAS 2000 / UTM zone 25S FALSE [y] band 1 6 NA NA NA NA call_list: [[1]] st_apply(X = X, MARGIN = MARGIN, FUN = FUN, CLUSTER = CLUSTER, PROGRESS = PROGRESS, FUTURE = FUTURE, rename = rename, .fname = .fname) attr(,".Environment") [[2]] adrop(x = x, drop = drop) attr(,".Environment") This object has pending lazy operations: dimensions as printed may not reflect this. > (yy = st_as_stars(y, downsample = 1)) stars object with 2 dimensions and 1 attribute attribute(s): Min. 1st Qu. Median Mean 3rd Qu. Max. max 55 85 96 99.38568 113 255 dimension(s): from to offset delta refsys point x/y x 1 175 288776 56.84 SIRGAS 2000 / UTM zone 25S FALSE [x] y 1 176 9120761 -57 SIRGAS 2000 / UTM zone 25S FALSE [y] > plot(yy) > (y = adrop(st_apply(r, c("x", "band"), max))) stars_proxy object with 1 attribute in 1 file(s): $L7_ETMs.tif [1] "[...]/L7_ETMs.tif" dimension(s): from to offset delta refsys point x/y x 1 349 288776 28.5 SIRGAS 2000 / UTM zone 25S FALSE [x] y 1 352 9120761 -28.5 SIRGAS 2000 / UTM zone 25S FALSE [y] band 1 6 NA NA NA NA call_list: [[1]] st_apply(X = X, MARGIN = MARGIN, FUN = FUN, CLUSTER = CLUSTER, PROGRESS = PROGRESS, FUTURE = FUTURE, rename = rename, .fname = .fname) attr(,".Environment") [[2]] adrop(x = x, drop = drop) attr(,".Environment") This object has pending lazy operations: dimensions as printed may not reflect this. > (yy = st_as_stars(y, downsample = 1)) stars object with 2 dimensions and 1 attribute attribute(s): Min. 1st Qu. Median Mean 3rd Qu. Max. max 77 111 133 137.8505 157 255 dimension(s): from to offset delta refsys point x 1 175 288776 56.84 SIRGAS 2000 / UTM zone 25S FALSE band 1 6 NA NA NA NA Warning message: In FUN(X[[i]], ...) : st_apply on x/y dimensions applied to downsampled image(s) > rm(yy) > > (xx = c(r,r)) stars_proxy object with 2 attributes in 2 file(s): $L7_ETMs.tif [1] "[...]/L7_ETMs.tif" $L7_ETMs.tif [1] "[...]/L7_ETMs.tif" dimension(s): from to offset delta refsys point x/y x 1 349 288776 28.5 SIRGAS 2000 / UTM zone 25S FALSE [x] y 1 352 9120761 -28.5 SIRGAS 2000 / UTM zone 25S FALSE [y] band 1 6 NA NA NA NA > names(xx) = c("a", "b") > xx["a"] stars_proxy object with 1 attribute in 1 file(s): $a [1] "[...]/L7_ETMs.tif" dimension(s): from to offset delta refsys point x/y x 1 349 288776 28.5 SIRGAS 2000 / UTM zone 25S FALSE [x] y 1 352 9120761 -28.5 SIRGAS 2000 / UTM zone 25S FALSE [y] band 1 6 NA NA NA NA > pt = st_point(c(x = 290462.103109179, y = 9114202.32594085)) > buf = st_buffer(st_sfc(pt, crs = st_crs(r)), 1500) > buf = st_sfc(st_polygon(list(st_buffer(pt, 1500)[[1]], st_buffer(pt, 1000)[[1]])), + crs = st_crs(r)) > r = r[buf] # crops > r = r[buf, epsilon = 1e-5] # crops with shrinked bounding box > > # c: > f = system.file("netcdf/avhrr-only-v2.19810902.nc", package = "starsdata") > if (FALSE && f != "") { + files = c("avhrr-only-v2.19810901.nc", + "avhrr-only-v2.19810902.nc", + "avhrr-only-v2.19810903.nc", + "avhrr-only-v2.19810904.nc", + "avhrr-only-v2.19810905.nc", + "avhrr-only-v2.19810906.nc", + "avhrr-only-v2.19810907.nc", + "avhrr-only-v2.19810908.nc", + "avhrr-only-v2.19810909.nc") + l = list() + for (f in files) { + from = system.file(paste0("netcdf/", f), package = "starsdata") + l[[f]] = read_stars(from, sub = c("sst", "anom"), proxy = TRUE) + } + ret = do.call(c, l) + print(ret) + all = system.file(paste0("netcdf/", files), package = "starsdata") + ret = read_stars(all, sub = c("sst", "anom")) + print(ret) + print(st_redimension(ret)) # collapse the two attributes into new dimension + + try(ret <- c(l[[1]], l[[2]], l[[3]], along = list(times = as.Date("1981-09-01") + 0:2))) + #print(ret) + #ret = adrop(adrop(c(l[[1]], l[[2]], l[[3]], along = "times"))) + #print(ret) + ret <- st_redimension(l[[1]], along = list(times = as.Date("1981-09-01") + 0:1)) + print(ret) + } > > # demonstrate that environments work: > x = read_stars(tif, proxy = TRUE) > f = function(x) { + g = function(xx) { + ndvi = function(z1, z2, z3, z4, z5, z6) (z2-z1)/(z2+z1) + st_apply(xx, 1:2, ndvi) + } + g(x) + } > (x = f(x)) stars_proxy object with 1 attribute in 1 file(s): $L7_ETMs.tif [1] "[...]/L7_ETMs.tif" dimension(s): from to offset delta refsys point x/y x 1 349 288776 28.5 SIRGAS 2000 / UTM zone 25S FALSE [x] y 1 352 9120761 -28.5 SIRGAS 2000 / UTM zone 25S FALSE [y] band 1 6 NA NA NA NA call_list: [[1]] st_apply(X = X, MARGIN = MARGIN, FUN = FUN, CLUSTER = CLUSTER, PROGRESS = PROGRESS, FUTURE = FUTURE, rename = rename, .fname = .fname) attr(,".Environment") This object has pending lazy operations: dimensions as printed may not reflect this. > st_as_stars(x) stars object with 2 dimensions and 1 attribute attribute(s): Min. 1st Qu. Median Mean 3rd Qu. Max. ndvi -0.2828283 -0.1081081 -0.08287293 -0.08464615 -0.05882353 0.09259259 dimension(s): from to offset delta refsys point x/y x 1 349 288776 28.5 SIRGAS 2000 / UTM zone 25S FALSE [x] y 1 352 9120761 -28.5 SIRGAS 2000 / UTM zone 25S FALSE [y] > > proc.time() user system elapsed 2.12 0.14 2.26 stars/tests/redimension.R0000644000176200001440000000073114353574021015214 0ustar liggesuserssuppressPackageStartupMessages(library(stars)) tif = system.file("tif/L7_ETMs.tif", package = "stars") r = read_stars(list(a = c(tif,tif), b = c(tif, tif)), proxy = TRUE) (xx = st_redimension(r, along = list(foo = 1:4))) (rr = c(xx, xx)) # st_as_stars(rr) (rrr = st_redimension(rr, along = list(bar = as.Date(c("2001-01-01", "2002-01-01"))))) (rr = c(a = xx, b = xx)) # st_as_stars(rr) (rrr = st_redimension(rr, along = list(bar = as.Date(c("2001-01-01", "2002-01-01"))))) stars/MD50000644000176200001440000003140514645437762011743 0ustar liggesusers51df8acd6d880ca7d79e2ea90bde418c *DESCRIPTION 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(is.character(x[[1]]) || is.factor(x[[1]]))) { strings = if (is.factor(x[[1]])) levels(x[[1]]) else x[[1]] lcm(cm(max(strwidth(strings, "inches"))) * 1.3 + font_scale) # cm #lcm(cm(max(strwidth(strings, "inches"))) * 1.3) # cm } else lcm(1.8 * font_scale) } #' plot stars object, with subplots for each level of first non-spatial dimension #' #' plot stars object, with subplots for each level of first non-spatial dimension, and customization of legend key #' #' @name plot #' @param x object of class \code{stars} #' @param y ignored #' @param join_zlim logical; if \code{TRUE}, compute a single, joint zlim (color scale) for all subplots from \code{x} #' @param main character; subplot title prefix; use \code{""} to get only time, use \code{NULL} to suppress subplot titles #' @param axes logical; should axes and box be added to the plot? #' @param downsample logical or numeric; if \code{TRUE} will try to plot not many more pixels than actually are visible, if \code{FALSE}, no downsampling takes place, if numeric, the number of pixels/lines/bands etc that will be skipped; see Details. #' @param nbreaks number of color breaks; should be one more than number of colors. If missing and \code{col} is specified, it is derived from that. #' @param breaks numeric vector with actual color breaks, or a \code{style} name used in \link[classInt]{classIntervals}. #' @param col colors to use for grid cells, or color palette function #' @param ... further arguments: for \code{plot}, passed on to \code{image.stars}; for \code{image}, passed on to \code{image.default} or \code{rasterImage}. #' @param key.pos numeric; side to plot a color key: 1 bottom, 2 left, 3 top, 4 right; set to \code{NULL} to omit key. Ignored if multiple columns are plotted in a single function call. Default depends on plot size, map aspect, and, if set, parameter \code{asp}. If it has length 2, the second value, ranging from 0 to 1, determines where the key is placed in the available space (default: 0.5, center). #' @param key.width amount of space reserved for width of the key (labels); relative or absolute (using lcm) #' @param key.length amount of space reserved for length of the key (labels); relative or absolute (using lcm) #' @param key.lab character; label for color key in case of multiple subplots, use \code{""} to suppress #' @param reset logical; if \code{FALSE}, keep the plot in a mode that allows adding further map elements; if \code{TRUE} restore original mode after plotting #' @param box_col color for box around sub-plots; use \code{NA} to suppress plotting of boxes around sub-plots. #' @param center_time logical; if \code{TRUE}, sub-plot titles will show the center of time intervals, otherwise their start #' @param hook NULL or function; hook function that will be called on every sub-plot; see examples. #' @param mfrow length-2 integer vector with nrows, ncolumns of a composite plot, to override the default layout #' @param compact logical; place facets compactly (TRUE), or spread over the plotting device area? #' @details #' Downsampling: a value for \code{downsample} of 0: no downsampling, 1: after every dimension value (pixel/line/band), one value is skipped (half of the original resolution), 2: after every dimension value, 2 values are skipped (one third of the original resolution), etc. If \code{downsample} is \code{TRUE} or a length 1 numeric vector, downsampling is only applied to the raster [x] and [y] dimensions. #' #' To remove unused classes in a categorical raster, use the \link[base]{droplevels} function. #' #' When bitmaps show visual artefacts (Moiré effects), make sure that device \link{png} is used rather than \code{ragg::agg_png} as the latter uses antialiasing for filled polygons which causes this; see also https://github.com/r-spatial/stars/issues/573 . #' @export #' @examples #' st_bbox(L7_ETMs) |> st_as_sfc() |> st_centroid() |> st_coordinates() -> pt #' hook1 = function() { #' text(pt[,"X"], pt[,"Y"], "foo", col = 'orange', cex = 2) #' } #' plot(L7_ETMs, hook = hook1) #' x = st_set_dimensions(L7_ETMs, 3, paste0("B_", 1:6)) #' hook2 = function(..., row, col, nr, nrow, ncol, value, bbox) { #' str = paste0("row ", row, "/", nrow, ", col ", col, "/", ncol, "\nnr: ", nr, " value: ", value) #' bbox |> st_as_sfc() |> st_centroid() |> st_coordinates() -> pt #' text(pt[,"X"], pt[,"Y"], str, col = 'red', cex = 2) #' } #' plot(x, hook = hook2, col = grey(c(.2,.25,.3,.35))) #' if (isTRUE(dev.capabilities()$rasterImage == "yes")) { #' lc = read_stars(system.file("tif/lc.tif", package = "stars")) #' levels(lc[[1]]) = abbreviate(levels(lc[[1]]), 6) # so it's not only legend #' plot(lc, key.pos=4) #' } plot.stars = function(x, y, ..., join_zlim = TRUE, main = make_label(x, 1), axes = FALSE, downsample = TRUE, nbreaks = 11, breaks = "quantile", col = grey(1:(nbreaks-1)/nbreaks), key.pos = get_key_pos(x, ...), key.width = kw_dflt(x, key.pos), key.length = 0.618, key.lab = main, reset = TRUE, box_col = NA, center_time = FALSE, hook = NULL, mfrow = NULL, compact = TRUE) { if (!missing(y)) stop("y argument should be missing") flatten = function(x, i) { # collapse all non-x/y dims into one, and select "layer" i d = st_dimensions(x) dxy = attr(d, "raster")$dimensions dims = dim(x) nms = names(x) x = x[[1]] aux = setdiff(names(dims), dxy) newdims = c(dims[dxy], prod(dims[aux])) dim(x) = newdims st_as_stars(setNames(list(x[,,i]), nms[1]), dimensions = d[dxy]) } x = st_normalize(x) if (is.character(x[[1]])) # rgb values key.pos = NULL if (!missing(col) && is.function(col)) col = col(nbreaks - 1) if (is.factor(x[[1]])) { if (missing(col)) col = attr(x[[1]], "colors") %||% sf.colors(length(levels(x[[1]])), categorical = TRUE) else attr(x[[1]], "colors") = col } key.pos.missing = missing(key.pos) breaks.missing = missing(breaks) if (missing(nbreaks) && !missing(col)) nbreaks = length(col) + 1 opar = par(no.readonly = TRUE) dots = list(...) need_to_reset = TRUE #if (any(dim(x) == 1)) # x = adrop(x) if (is.factor(x[[1]]) && any(is.na(levels(x[[1]])))) x = droplevels(x) # https://github.com/r-spatial/stars/issues/339 zlim = if (join_zlim && is.null(dots$rgb)) range(unclass(x[[1]]), na.rm = TRUE) else rep(NA_real_, 2) # downsample if raster: if (has_raster(x) && !identical(downsample, FALSE)) { dxy = attr(st_dimensions(x), "raster")$dimensions loop = setdiff(names(dim(x)), dxy) # dimension (name) over which we loop, if any x = aperm(x, c(dxy, loop)) dims = dim(x) if (isTRUE(downsample)) { n = dims * 0 # keep names n[dxy] = get_downsample(dims, rgb = is.numeric(dots$rgb)) x = st_downsample(x, n) } else if (is.numeric(downsample)) { x = st_downsample(x, downsample) } } # find breaks: if (join_zlim && !is.character(x[[1]]) && is.null(dots$rgb)) { breaks = if (is.factor(x[[1]])) seq(.5, length.out = length(levels(x[[1]])) + 1) else get_breaks(x, breaks, nbreaks, dots$logz) if (!inherits(breaks, c("POSIXt", "Date"))) breaks = as.numeric(breaks) if (length(breaks) > 2) breaks = unique(breaks) nbreaks = length(breaks) # might be shorter than originally intended! if (breaks.missing && nbreaks <= 2) # unlucky default! warning('breaks="quantile" leads to a single class; maybe try breaks="equal" instead?') } if (isTRUE(dots$logz) && !((has_raster(x) && (is_curvilinear(x) || has_rotate_or_shear(x))) || has_sfc(x))) x = log10(x) # otherwise, defer log-transforming to sf::plot.sf if (has_raster(x)) { dims = dim(x) if (length(dims) == 2 || dims[3] == 1 || (!is.null(dots$rgb) && is.numeric(dots$rgb))) { ## ONE IMAGE: # set up key region values = structure(x[[1]], dim = NULL) # array -> vector if (! isTRUE(dots$add) && ! is.null(key.pos) && !all(is.na(values)) && is.null(dots$rgb) && (is.factor(values) || length(unique(na.omit(values))) > 1) && length(col) > 1 && !is_curvilinear(x)) { # plot key? switch(key.pos[1], layout(matrix(c(2,1), nrow = 2, ncol = 1), widths = 1, heights = c(1, key.width)), # 1 bottom layout(matrix(c(1,2), nrow = 1, ncol = 2), widths = c(key.width, 1), heights = 1), # 2 left layout(matrix(c(1,2), nrow = 2, ncol = 1), widths = 1, heights = c(key.width, 1)), # 3 top layout(matrix(c(2,1), nrow = 1, ncol = 2), widths = c(1, key.width), heights = 1) # 4 right ) if (missing(key.lab)) key.lab = "" if (is.factor(values)) .image_scale_factor(levels(values), col, key.pos = key.pos, key.width = key.width, key.length = key.length, axes = axes, ...) else .image_scale(values, col, breaks = breaks, key.pos = key.pos, key.width = key.width, key.length = key.length, axes = axes, ..., lab = key.lab) } else need_to_reset = FALSE # map panel: mar = c(axes * 2.1, axes * 2.1, 1.2 * !is.null(main), 0) if (!is.null(key.pos) && key.pos[1] %in% 1:4) mar[key.pos[1]] = mar[key.pos[1]] + .5 par(mar = mar) # plot the map: image(x, ..., axes = axes, breaks = breaks, col = col, key.pos = key.pos, key.width = key.width, key.length = key.length, main = NULL) if (!is.null(main)) title(main) } else { ## MULTIPLE IMAGES -- now loop over dimensions 3: draw.key = !is.null(key.pos) && join_zlim if (! draw.key) key.pos = NULL lt = sf::.get_layout(st_bbox(x), dims[3], par("din"), if (join_zlim && key.pos.missing) -1 else key.pos[1], key.width, mfrow = mfrow, main = main) if (key.pos.missing) key.pos = lt$key.pos title_size = if (is.null(main)) 0 else 1.2 layout(lt$m, widths = lt$widths, heights = lt$heights, respect = compact) par(mar = c(axes * 2.1, axes * 2.1, title_size, 0)) labels = st_get_dimension_values(x, 3, center = center_time) for (i in seq_len(dims[3])) { im = flatten(x, i) if (! join_zlim) { zlim = range(im[[1]], na.rm = TRUE) br = get_breaks(im, breaks, nbreaks, dots$logz) } else br = breaks image(im, xlab = "", ylab = "", axes = axes, zlim = zlim, breaks = br, col = col, key.pos = NULL, main = NULL, ...) if (!is.null(main)) { if (length(main) == dims[3]) title(main[i]) else if (identical(main, names(x)[1])) # default value: omit on multi title(paste(format(labels[i]))) else # user-defined title(paste(main, format(labels[i]))) } if (!is.null(hook) && is.function(hook)) { if (is.null(formals(hook))) hook() else { nc = lt$mfrow[[2]] # nr of columns in the plot hook(row = ((i - 1) %/% nc) + 1, col = ((i - 1) %% nc) + 1, nrow = lt$mfrow[[1]], ncol = nc, nr = i, value = labels[i], bbox = st_bbox(x)) } } if (!is.na(box_col)) box(col = box_col) } for (i in seq_len(prod(lt$mfrow) - dims[3])) # empty panels: plot.new() if (draw.key) { if (missing(key.lab) && inherits(x[[1]], "units")) key.lab = units::make_unit_label(names(x)[1], x[[1]]) values = structure(x[[1]], dim = NULL) if (is.factor(values)) .image_scale_factor(levels(values), col, key.pos = key.pos, key.width = key.width, key.length = key.length, axes = axes,...) else .image_scale(values, col, breaks = breaks, key.pos = key.pos, key.width = key.width, key.length = key.length, axes = axes,..., lab = key.lab) } } } else if (has_sfc(x)) { # if (key.pos.missing) # key.pos = -1 if (! join_zlim) key.pos = NULL # omit key plot(st_as_sf(x[1]), ..., breaks = breaks, key.pos = key.pos, key.length = key.length, key.width = key.width, reset = reset, axes = axes, main = main) } else stop("no raster, no features geometries: no default plot method set up yet!") if (reset && need_to_reset) { layout(matrix(1)) # reset par(opar) } invisible(NULL) } get_breaks = function(x, breaks, nbreaks, logz = NULL) { if (is.character(breaks)) { # compute breaks from values in x: pdx = prod(dim(x[[1]])) ## take a regular sample from x[[1]]: # values = structure(x[[1]], dim = NULL)[seq(1, pdx, length.out = min(pdx, 10000))] values = structure(x[[1]], dim = NULL) if (isTRUE(logz)) values = log10(values) if (is.factor(values) || is.logical(values)) values = as.numeric(values) n.unq = length(unique(na.omit(values))) if (! all(is.na(values)) && n.unq > 1) classInt::classIntervals(na.omit(values), min(nbreaks-1, n.unq), breaks, warnSmallN = FALSE)$brks else range(values, na.rm = TRUE) # lowest and highest! } else # breaks was given: breaks } #' @name plot #' @param band integer; which band (dimension) to plot #' @param attr integer; which attribute to plot #' @param asp numeric; aspect ratio of image #' @param rgb integer; specify three bands to form an rgb composite. Experimental: rgb color table; see Details. #' @param maxColorValue numeric; passed on to \link{rgb} #' @param xlab character; x axis label #' @param ylab character; y axis label #' @param xlim x axis limits #' @param ylim y axis limits #' @param text_values logical; print values as text on image? #' @param text_color character; color for printed text values #' @param interpolate logical; when using \link{rasterImage} (rgb), should pixels be interpolated? #' @param as_points logical; for curvilinear or sheared grids: parameter passed on to \link{st_as_sf}, determining whether raster cells will be plotted as symbols (fast, approximate) or small polygons (slow, exact) #' @param logz logical; if \code{TRUE}, use log10-scale for the attribute variable. In that case, \code{breaks} and \code{at} need to be given as log10-values; see examples. #' @param add.geom object of class \code{sfc}, or list with arguments to \code{plot}, that will be added to an image or sub-image #' @param border color used for cell borders (only in case \code{x} is a curvilinear or rotated/sheared grid) #' @param useRaster logical; use the rasterImage capabilities of the graphics device? #' @param extent object which has a \code{st_bbox} method; sets the plotting extent #' @details use of an rgb color table is experimental; see https://github.com/r-spatial/mapview/issues/208 #' @export #' @examples #' tif = system.file("tif/L7_ETMs.tif", package = "stars") #' x = read_stars(tif) #' image(x, col = grey((3:9)/10)) #' if (isTRUE(dev.capabilities()$rasterImage == "yes")) { #' image(x, rgb = c(1,3,5)) # false color composite #' } image.stars = function(x, ..., band = 1, attr = 1, asp = NULL, rgb = NULL, maxColorValue = ifelse(inherits(rgb, "data.frame"), 255, max(x[[attr]], na.rm = TRUE)), xlab = if (!axes) "" else names(d)[1], ylab = if (!axes) "" else names(d)[2], xlim = st_bbox(extent)$xlim, ylim = st_bbox(extent)$ylim, text_values = FALSE, text_color = 'black', axes = FALSE, interpolate = FALSE, as_points = FALSE, key.pos = NULL, logz = FALSE, key.width = kw_dflt(x, key.pos), key.length = 0.618, add.geom = NULL, border = NA, useRaster = isTRUE(dev.capabilities()$rasterImage == "yes"), extent = x) { dots = list(...) #stopifnot(!has_rotate_or_shear(x)) # FIXME: use rasterImage() with rotate, if only rotate & no shear if (any(dim(x) == 1)) x = adrop(x, drop_xy = TRUE) d = st_dimensions(x) if (has_raster(x) || has_sfc(x)) { if (missing(xlim) || missing(ylim)) { force(xlim) force(ylim) } dimxy = attr(d, "raster")$dimensions dimx = dimxy[1] dimy = dimxy[2] dimxn = match(dimx, names(d)) dimyn = match(dimy, names(d)) is_rectilinear = is_rectilinear(x) } else { e = expand_dimensions(x) if (missing(xlim)) xlim = range(e[[1]]) if (missing(ylim)) ylim = range(e[[2]]) dimx = names(dim(x))[1] dimy = names(dim(x))[2] dimxn = 1 dimyn = 2 is_rectilinear = (is.na(d[[1]]$delta) || is.na(d[[2]]$delta)) && (!regular_intervals(d[[1]]$values) || !regular_intervals(d[[2]]$values)) if (missing(axes)) axes = TRUE } if (has_sfc(x) && length(dim(x)) == 2) { # time series of features: ed = lapply(expand_dimensions(x), function(x) if (inherits(x, "sfc")) seq_along(x) else x) names(ed) = c("x", "y") ed$z = x[[attr]] axes = TRUE return(image(ed, ..., xlab = xlab, ylab = ylab)) } if (! is_curvilinear(x)) { dims = expand_dimensions(x, center = FALSE, max = FALSE) d_max = expand_dimensions(x, center = FALSE, max = TRUE) if (tail(dims[[dimx]], 1) != tail(d_max[[dimx]], 1)) dims[[ dimx ]] = c(dims[[dimx]], tail(d_max[[dimx]], 1)) if (tail(dims[[dimy]], 1) != tail(d_max[[dimy]], 1)) dims[[ dimy ]] = c(dims[[dimy]], tail(d_max[[dimy]], 1)) y_is_neg = all(diff(dims[[ dimy ]]) < 0) if (y_is_neg) dims[[ dimy ]] = rev(dims[[ dimy ]]) } if (is.null(asp)) asp = if (isTRUE(st_is_longlat(x))) 1 / cos((mean(st_bbox(x)[c(2,4)]) * pi)/180) else if (!has_raster(x) && !has_sfc(x)) NA_real_ else 1.0 ar = unclass(x[[ attr ]]) # raw data matrix/array # handle color table, if present: co = attr(ar, "colors") if (! is.null(co)) { if (is.null(rgb)) rgb = TRUE interpretation = attr(co, "interpretation") if (!is.null(interpretation) && interpretation != 1) warning("color interpretation is not RGB, but rgb is used nevertheless: colors will probably be wrong") } # rearrange ar: others = setdiff(seq_along(dim(ar)), c(dimxn, dimyn)) ar = aperm(ar, c(dimxn, dimyn, others)) if (text_values) ar_text = ar # keep original order for cell text labels if (is.null(rgb) && is.character(ar)) rgb = TRUE if (! is.null(rgb)) { if (is_curvilinear(x)) { x.sf = st_as_sf(x, as_points = as_points) plot_sf = function(x, col, ...) plot(x, ...) # absorb col if (!is.null(co)) # #456: plot_sf(x.sf, pal = co, border = FALSE, ...) else plot(x.sf, border = FALSE, ...) return() } xy = dim(ar)[1:2] if (! y_is_neg) { # need to flip y? ar = if (length(dim(ar)) == 3) ar[ , rev(seq_len(dim(ar)[2])), ] else ar[ , rev(seq_len(dim(ar)[2]))] } if (!useRaster) stop("rgb plotting not supported on this device") if (! isTRUE(dots$add)) { plot.new() plot.window(xlim = xlim, ylim = ylim, asp = asp) } if (is.numeric(rgb) && length(rgb) == 3) { ar = structure(ar[ , , rgb], dim = c(prod(xy), 3)) # flattens x/y #nas = apply(ar, 1, function(x) any(is.na(x))) #503 nas = !complete.cases(ar) ar = grDevices::rgb(ar[!nas,], maxColorValue = maxColorValue) mat = rep(NA_character_, prod(xy)) mat[!nas] = ar dim(mat) = xy } else { stopifnot(isTRUE(rgb) || inherits(rgb, "data.frame")) # rgb has col 1: index, col 2: label, col 3-5: R, G, B # ar = as.vector(ar[ , , 1]) # flattens x/y to 1-D index vector mat = if (isTRUE(rgb)) { if (!is.null(co)) structure(co[as.vector(ar)], dim = dim(ar)) else ar } else { ar = as.vector(ar) # flattens x/y to 1-D index vector rgb = grDevices::rgb(rgb[match(ar, rgb[[1]]), 3:5], maxColorValue = maxColorValue) structure(rgb, dim = xy) } } myRasterImage = function(x, xmin, ymin, xmax, ymax, interpolate, ..., breaks, add, zlim) # absorbs breaks, add & zlim rasterImage(x, xmin, ymin, xmax, ymax, interpolate = interpolate, ...) myRasterImage(t(mat), xlim[1], ylim[1], xlim[2], ylim[2], interpolate = interpolate, ...) } else if (is_curvilinear(x) || has_rotate_or_shear(x)) { x = st_as_sf(x[1], as_points = as_points) mplot = function(x, col, ..., zlim) { if (missing(col)) plot(x, ...) else plot(x, pal = col, ...) # need to swap arg names: FIXME:? } mplot(x, xlab = xlab, ylab = ylab, xlim = xlim, ylim = ylim, axes = axes, reset = FALSE, key.pos = key.pos, key.width = key.width, key.length = key.length, logz = logz, border = border, ...) } else { # regular & rectilinear grid, no RGB: if (y_is_neg) { # need to flip y? ar = if (length(dim(ar)) == 2) ar[ , rev(seq_len(dim(ar)[2])), drop = FALSE] else ar[ , rev(seq_len(dim(ar)[2])), band] # FIXME: breaks if more than 3? } image.default(dims[[ dimx ]], dims[[ dimy ]], ar, asp = asp, xlab = xlab, ylab = ylab, xlim = xlim, ylim = ylim, axes = FALSE, useRaster = useRaster && !is_rectilinear, ...) } if (text_values) { dims = expand_dimensions.stars(x, center = TRUE) text(do.call(expand.grid, dims[1:2]), labels = as.character(as.vector(ar_text)), col = text_color) } if (axes) { # FIXME: see sf::plot.sf for refinements to be ported here? if (!is.null(dots$cex.axis)) par(cex.axis = dots$cex.axis) if (isTRUE(st_is_longlat(x))) { if (isTRUE(all.equal(st_bbox(x), st_bbox(), tolerance = .1, check.attributes = FALSE))) { .degAxis(1, at = seq(-180, 180, 60)) .degAxis(2, at = seq(-90, 90, 30)) } else { .degAxis(1) .degAxis(2) } } else { axis(1) axis(2) } } if (!is.null(add.geom)) { if (inherits(add.geom, c("sf", "sfc"))) add.geom = list(add.geom) do.call(plot, c(add.geom, add = TRUE)) } } # compute the degree of downsampling allowed to still have more than # one cell per screen/device pixel: get_downsample = function(dims, px = dev.size("px"), rgb = FALSE) { if (rgb) dims = dims[1:2] if (n <- max(0L, floor(sqrt(prod(dims) / prod(px))) - 1.0)) message(paste0("downsample set to ", n)) n } #' plot contours of a stars object #' #' plot contours of a stars object #' @param x object of class \code{stars} #' @param ... other parameters passed on to \link[graphics]{contour} #' @details this uses the R internal contour algorithm, which (by default) plots contours; \link{st_contour} uses the GDAL contour algorithm that returns contours as simple features. #' @export #' @examples #' d = st_dimensions(x = 1:ncol(volcano), y = 1:nrow(volcano)) #' r = st_as_stars(t(volcano)) #' r = st_set_dimensions(r, 1, offset = 0, delta = 1) #' r = st_set_dimensions(r, 2, offset = 0, delta = -1) #' plot(r, reset = FALSE) #' contour(r, add = TRUE) contour.stars = function(x, ...) { if (!(is_regular_grid(x) || is_rectilinear(x))) stop("contour only works for regular or rectilinear grids") if (inherits(x[[1]], "units")) x[[1]] = units::drop_units(x[[1]]) x = st_upfront(adrop(x)) # drop singular dimensions, put x/y first dx = dim(x) if (length(dx) != 2) stop("contour only supported for 2-D arrays") # nocov e = expand_dimensions(x) contour(z = x[[1]][,rev(seq_len(dx[2]))], x = e[[1]], y = rev(e[[2]]), ...) } #' reduce dimension to rgb (alpha) hex values #' #' @export #' @param x object of class \code{stars} #' @param dimension dimension name or number to reduce #' @param use_alpha logical; if TRUE, the fourth band will be used as alpha values #' @param maxColorValue integer; maximum value for colors #' @param stretch logical or character; if \code{TRUE} or \code{"percent"}, #' each band is stretched to 0 ... maxColorValue by "percent clip" method using #' probs values. If \code{"histogram"}, a "histogram equalization" is performed #' (\code{probs} values are ignored). If stretch is \code{NULL} or \code{FALSE}, no stretching #' is performed. Other character values are interpreted as "percent" and a message #' will be printed. #' @param probs probability values for quantiles used for stretching by "percent". #' @seealso \link{st_apply}, \link[grDevices]{rgb} #' @details the dimension's bands are mapped to red, green, blue, alpha; if a different #' ordering is wanted, use \link{[.stars} to reorder a dimension, see examples. #' Alternatively, you can use \link{plot.stars} with the \code{rgb} argument to create a three-band composition. #' @examples #' tif = system.file("tif/L7_ETMs.tif", package = "stars") #' x = read_stars(tif) #' st_rgb(x[,,,3:1]) #' r = st_rgb(x[,,,c(6,5,4,3)], 3, use_alpha=TRUE) # now R=6,G=5,B=4,alpha=3 #' if (require(ggplot2)) { #' ggplot() + geom_stars(data = r) + scale_fill_identity() #' } #' r = st_rgb(x[,,,3:1], #' probs = c(0.01, 0.99), #' stretch = "percent") #' plot(r) #' r = st_rgb(x[,,,3:1], #' probs = c(0.01, 0.99), #' stretch = "histogram") #' plot(r) st_rgb = function (x, dimension = 3, use_alpha = dim(x)[dimension] == 4, maxColorValue = 255L, probs = c(0, 1), stretch = NULL) { if (inherits(x, "stars_proxy")) return(collect(x, match.call(), "st_rgb", c("x", "dimension", "use_alpha", "maxColorValue", "probs", "stretch"), env = environment())) # RETURNS!! # if not stars_proxy: if (is.character(dimension)) dimension = match(dimension, names(dim(x))) stopifnot(is.numeric(dimension), length(dimension) == 1) if (!dim(x)[dimension] %in% c(3, 4)) stop(paste("number of bands along dimension", dimension, "should be 3 or 4")) dims = setdiff(seq_along(dim(x)), dimension) cutoff = function(x, probs, stretch.method = "percent") { if (stretch.method == "percent"){ qs = if (all(probs == c(0, 1))) range(x) else quantile(x, probs, na.rm = TRUE) x = (x - qs[1])/(qs[2] - qs[1]) x[x > 1] = 1 x[x < 0] = 0 x * maxColorValue } else if(stretch.method == "histogram"){ x = stats::ecdf(x)(x) x * maxColorValue } else { qs = range(x) (x - qs[1])/(qs[2] - qs[1]) * maxColorValue } } if (is.null(stretch)) { stretch.method = "none" stretch = FALSE } if (is.logical(stretch)) { if(stretch) stretch.method = "percent" else maxColorValue = max(maxColorValue, max(x[[1]], na.rm = TRUE)) } if (is.character(stretch)) { if (!stretch %in% c("percent", "histogram")) stretch.method = "percent" else stretch.method = stretch stretch = TRUE } if (stretch) x = st_apply(x, dimension, cutoff, probs = probs, stretch.method = stretch.method) if (anyNA(x[[1]])) { rgb4 = function(r, g, b, a) { r = cbind(as.vector(r), as.vector(g), as.vector(b), as.vector(a)) sel = !apply(r, 1, anyNA) ret = rep(NA_character_, nrow(r)) ret[sel] = rgb(r[sel, 1:3], alpha = a[sel], maxColorValue = maxColorValue) structure(ret, dim = dim(g)) } rgb3 = function(r, g, b) { r = cbind(as.vector(r), as.vector(g), as.vector(b)) sel = !apply(r, 1, anyNA) ret = rep(NA_character_, nrow(r)) ret[sel] = rgb(r[sel, 1:3], maxColorValue = maxColorValue) structure(ret, dim = dim(g)) } } else { rgb4 = function(r, g, b, a) structure(rgb(r, g, b, a,maxColorValue = maxColorValue), dim = dim(r)) rgb3 = function(r, g, b) structure(rgb(r, g, b, maxColorValue = maxColorValue), dim = dim(r)) } st_apply(x, dims, if (use_alpha) rgb4 else rgb3) } #' @export hist.stars = function(x, ..., main = names(x)[1]) { hist(x[[1]], ..., main = main) } #' @export hist.stars_proxy = function(x, ..., downsample = 0) { x = st_as_stars(x, downsample = downsample) NextMethod() } stars/R/rasterize.R0000644000176200001440000002254214576033423013756 0ustar liggesusers# align bb with the first two dimensions of d st_align = function(bb, d) { up1 = function(x) ifelse(sign(x) < 0, -ceiling(abs(x)), floor(x)) up2 = function(x) ifelse(sign(x) > 0, ceiling(abs(x)), -floor(abs(x))) dx = d[[1]]$delta dy = d[[2]]$delta ox = d[[1]]$offset oy = d[[2]]$offset dbbx = diff(bb[c("xmin", "xmax")]) dbby = diff(bb[c("ymin", "ymax")]) bb["xmin"] = ox - up2((ox - bb["xmin"])/dx) * dx bb["ymin"] = oy - up1((oy - bb["ymin"])/dy) * dy bb["xmax"] = ox - up1((ox - bb["xmax"])/dx) * dx bb["ymax"] = oy - up2((oy - bb["ymax"])/dy) * dy bb } #' rasterize simple feature geometries #' #' rasterize simple feature geometries #' @export #' @param sf object of class \code{sf} #' @param template optional; stars object with desired target geometry, or target geometry alignment if \code{align=TRUE}; see details #' @param file temporary file name #' @param driver driver for temporary file #' @param options character; options vector for \code{GDALRasterize} #' @param align logical; if \code{TRUE}, \code{template} is only used for the geometry _alignment_, #' informing target resolution and offset #' @param proxy logical; should a proxy object be returned? #' @param ... arguments passed on to \link{st_as_stars} #' @details if `template` is a `stars` object, non-NA cells that are not covered by `sf` receive the value in `template`; see also argument `align`. #' @examples #' demo(nc, echo = FALSE, ask = FALSE) #' (x = st_rasterize(nc)) # default grid: #' plot(x, axes = TRUE) #' # a bit more customized grid: #' (x = st_rasterize(nc, st_as_stars(st_bbox(nc), nx = 100, ny = 50, values = NA_real_))) #' plot(x, axes = TRUE) #' (ls = st_sf(a = 1:2, st_sfc(st_linestring(rbind(c(0.1, 0), c(1.1, 1))), #' st_linestring(rbind(c(0, 0.05), c(1, 0.05)))))) #' (grd = st_as_stars(st_bbox(ls), nx = 10, ny = 10, xlim = c(0, 1.0), ylim = c(0, 1), #' values = NA_real_)) #' # Only the left-top corner is part of the grid cell: #' sf_extSoftVersion()["GDAL"] #' plot(st_rasterize(ls, grd), axes = TRUE, reset = FALSE) # ALL_TOUCHED=FALSE; #' plot(ls, add = TRUE, col = "red") #' plot(st_rasterize(ls, grd, options = "ALL_TOUCHED=TRUE"), axes = TRUE, reset = FALSE) #' plot(ls, add = TRUE, col = "red") #' # add lines to existing 0 values, summing values in case of multiple lines: #' (grd = st_as_stars(st_bbox(ls), nx = 10, ny = 10, xlim = c(0, 1.0), ylim = c(0, 1), values = 0)) #' r = st_rasterize(ls, grd, options = c("MERGE_ALG=ADD", "ALL_TOUCHED=TRUE")) #' plot(r, axes = TRUE, reset = FALSE) #' plot(ls, add = TRUE, col = "red") st_rasterize = function(sf, template = guess_raster(sf, ...) %||% st_as_stars(st_bbox(sf), values = NA_real_, ...), file = tempfile(), driver = "GTiff", options = character(0), align = FALSE, proxy = FALSE, ...) { if (align) { if (missing(template)) stop("align=TRUE requires template to be specified") stopifnot(is_regular_grid(template)) bb = st_align(st_bbox(sf), d <- st_dimensions(st_normalize(template))) dx = d[[1]]$delta dy = d[[2]]$delta template = st_as_stars(bb, values = NA, dx = dx, dy = dy, nx = round(diff(bb[c("xmin", "xmax")])/abs(dx)), ny = round(diff(bb[c("ymin", "ymax")])/abs(dy)), proxy = proxy) } else template = st_normalize(template) is_numeric = function(x) is.numeric(x) || is.factor(x) isn = sapply(sf, is_numeric) sf = if (!any(isn)) { sf$ID = seq_len(nrow(sf)) sf["ID"] } else sf[isn] n_attr = length(which(isn)) if (length(dim(template)) == 2 && n_attr > 1) template = merge(do.call(c, lapply(seq_len(n_attr), function(x) template))) geoms = which(sapply(sf, inherits, "sfc")) attrs = as.data.frame(sf)[-geoms] for (i in which(sapply(sf, inherits, "factor"))) # factors: sf[[i]] = as.numeric(sf[[i]]) sf::gdal_rasterize(sf, template, st_geotransform(template), file, driver, options) ret = read_stars(file, driver = driver, proxy = proxy) if (!proxy) { if (length(dim(ret)) > 2) ret = split(ret) for (i in seq_along(ret)) { ret[[i]][is.nan(ret[[i]])] = NA_real_ if (inherits(sf[[i]], "units")) units(ret[[i]]) = units(sf[[i]]) if (is.factor(attrs[[i]])) ret[[i]] = structure(ret[[i]], class = class(attrs[[i]]), levels = levels(attrs[[i]])) } } setNames(ret, names(attrs)) } guess_raster = function(x, ...) { if (length(list(...))) # ... hints at other arguments meant at not guessing the raster return(NULL) get_cell_size = function(x) { du = unique(diff(x)) if (length(du) > 1) { if (var(du)/mean(du) < 1e-8) # some fuzz: du = mean(du) else if (all(du %% min(du) == 0)) # missing cols/rows: du = min(du) else numeric(0) # no regular step size } else du } if (all(st_dimension(x) == 0)) { # POINT cc = st_coordinates(x) ux = sort(unique(cc[,1])) if (length(dux <- get_cell_size(ux)) == 0) return(NULL); uy = sort(unique(cc[,2])) if (length(duy <- get_cell_size(uy)) == 0) return(NULL); bb = st_bbox(x) bb = st_bbox(setNames(c(bb["xmin"] - 0.5 * dux, bb["ymin"] - 0.5 * duy, bb["xmax"] + 0.5 * dux, bb["ymax"] + 0.5 * duy), c("xmin", "ymin", "xmax", "ymax")), crs = st_crs(x)) return(st_as_stars(bb, dx = dux, dy = duy)) } NULL } #' @export #' @param dims the column names or indices that form the cube dimensions #' @param coords same as dims, for symmetry with \link[sf]{st_as_sf} #' @param xy the x and y raster dimension names or indices; only takes effect after \code{dims} has been specified, see details #' @param y_decreasing logical; if TRUE, (numeric) y values get a negative delta (decrease with increasing index) #' @name st_as_stars #' @details #' If \code{xy} is not specified and the first two dimensions in \code{dims} are both numeric, #' then it is set to these two dimensions. #' @examples #' if (require(plm, quietly = TRUE)) { #' data(Produc, package = "plm") #' st_as_stars(Produc) #' } #' if (require(dplyr, quietly = TRUE)) { #' # https://stackoverflow.com/questions/77368957/ #' spatial_dim <- st_sf( #' ID = 1:3, #' geometry = list( #' st_polygon(list( #' cbind(c(0, 1, 1, 0, 0), c(0, 0, 1, 1, 0)) #' )), #' st_polygon(list( #' cbind(c(1, 2, 2, 1, 1), c(0, 0, 1, 1, 0)) #' )), #' st_polygon(list( #' cbind(c(2, 3, 3, 2, 2), c(0, 0, 1, 1, 0)) #' )) #' ) #' ) #' weekdays_dim <- data.frame(weekdays = c("Monday", "Tuesday", "Wednesday", #' "Thursday", "Friday", "Saturday", "Sunday")) #' hours_dim <- data.frame(hours = c("8am", "11am", "4pm", "11pm")) #' sf_dta <- spatial_dim |> #' cross_join(weekdays_dim)|> #' cross_join(hours_dim) |> #' mutate(population = rnorm(n(), mean = 1000, sd = 200)) |> #' select(everything(), geometry) #' #' st_as_stars(sf_dta, dims = c("weekdays", "hours", "geometry")) #' } #' demo(nc, echo=FALSE,ask=FALSE) #' st_as_stars(nc) #' st_as_stars(st_drop_geometry(nc), dims = "NAME") #' data.frame(expand.grid(x=1:5, y = 1:5), z = rnorm(25)) |> st_as_stars() st_as_stars.data.frame = function(.x, ..., dims = coords, xy, y_decreasing = TRUE, coords = 1:2) { if (missing(dims) && !missing(xy)) stop("parameter xy only takes effect when the cube dimensions are set with dims") if (is.character(dims)) dims = match(dims, names(.x)) if (missing(xy)) { if (length(dims) > 1 && all(sapply(.x[dims[1:2]], is.numeric))) xy = dims[1:2] else xy = c(-1, -1) # cancels y_decreasing } if (is.character(xy)) { xy = match(xy, names(.x)) if (any(is.na(xy))) stop("xy coordinates not found in data") } stopifnot(length(dims) >= 1, all(dims >= 1), !any(is.na(dims))) index = NULL dimensions = list() this_dim = 1 for (i in dims) { v = .x[[i]] if (inherits(v, "sfc")) { if (!requireNamespace("digest", quietly = TRUE)) stop("package digest required, please install it first") # nocov dig = sapply(st_equals(v), digest::digest) uv = unique(dig) # no need to sort ix = match(dig, uv) # but look up "hash collision" } else { suv = if (is.factor(v)) levels(v) else if (is.character(v)) unique(v) else # numeric: sort(unique(v), decreasing = y_decreasing && i == xy[2]) ix = match(v, suv) } index = cbind(index, ix) dimensions[[this_dim]] = if (inherits(v, "sfc")) create_dimension(values = v[match(uv, dig)]) else create_dimension(values = suv, is_raster = i %in% xy) this_dim = this_dim + 1 } names(dimensions) = names(.x)[dims] raster_xy = if (length(xy) == 2 && all(xy > 0)) names(.x)[xy] else c(NA_character_, NA_character_) d = create_dimensions(dimensions, raster = get_raster(dimensions = raster_xy)) l = lapply(.x[-dims], function(x) { m = if (is.factor(x)) structure(factor(rep(NA_character_, prod(dim(d))), levels = levels(x)), dim = dim(d)) else array(NA, dim = dim(d)) m[index] = x # match order m } ) st_stars(l, d) } #' replace POINT simple feature geometry list with an x y raster #' @param x object of class \code{stars}, or of class \code{sf} #' @param ... passed on to \link{as.data.frame.stars} #' @return object of class \code{stars} with a POINT list replaced by x and y raster dimensions. This only works when the points are distributed over a regular or rectilinear grid. #' @export st_sfc2xy = function(x, ...) { if (inherits(x, "sf")) x = st_as_stars(x) i = which_sfc(x) d = st_dimensions(x) if (!inherits(d[[i]]$values, "sfc_POINT")) stop("point geometries expected") cc = st_coordinates(d[[i]]$values) df = as.data.frame(x) df$geometry = NULL s = st_as_stars(cbind(cc, df)) st_set_crs(s, st_crs(d)) } stars/R/spatstat.R0000644000176200001440000000263214576033423013607 0ustar liggesuserscheck_spatstat <- function(pkg, X = NULL){ if(!requireNamespace(pkg, quietly = TRUE)){ stop("package ", pkg, " required, please install it (or the full spatstat package) first") } else{ spst_ver <- try(packageVersion("spatstat"), silent = TRUE) if (!inherits(spst_ver, "try-error") && spst_ver < "2.0-0") { stop("You have an old version of spatstat installed which is incompatible with ", pkg, ". Please update spatstat (or uninstall it).") } } if (!is.null(X) && isTRUE(st_is_longlat(X))) stop("Only projected coordinates may be converted to spatstat class objects", call. = FALSE) } as.owin.stars = function(W, ..., fatal) { check_spatstat("spatstat.geom", W) if (length(dim(W)) != 2) stop("as.owin.stars requires a 2-dimensional object (single raster layer)") m = t(W[[1]]) bb = st_bbox(W) spatstat.geom::owin(bb[c("xmin", "xmax")], bb[c("ymin", "ymax")], mask = m[nrow(m):1,]) } #' @export st_as_stars.im = function(.x, ...) { # see https://github.com/r-spatial/stars/issues/648 setNames(st_as_stars(as.data.frame(.x, ...)), "v") } as.im.stars = function(X, ...) { check_spatstat("spatstat.geom", X) if (!has_raster(X) || !is_regular_grid(X)) stop("as.im.stars only works for stars objects with regular raster data") if (length(dim(X)) > 2) stop("as.im.stars only works for two-dimensional rasters") X = as.data.frame(X) spatstat.geom::as.im.data.frame(X, ...) } stars/R/intervals.R0000644000176200001440000000544714576033423013762 0ustar liggesusers#' create an intervals object #' #' create an intervals object, assuming left-closed and right-open intervals #' @param start vector with start values, or 2-column matrix with start and end values in column 1 and 2, respectively #' @param end vector with end values #' @export make_intervals = function(start, end) { if (missing(end) && is.matrix(start) && ncol(start) == 2) { end = start[,2] start = start[,1] } stopifnot(length(start) > 0, length(start) == length(end)) structure(list(start = start, end = end), class = "intervals") } as_intervals = function(x, add_last = FALSE) { stopifnot(is.atomic(x)) if (add_last) x = c(x, tail(x, 1) + diff(tail(x, 2))) make_intervals(start = head(x, -1), end = tail(x, -1)) } #' @export length.intervals = function(x) length(x$start) #' @export head.intervals = function(x, n, ...) make_intervals(head(x$start, n, ...), head(x$end, n, ...)) #' @export tail.intervals = function(x, n, ...) make_intervals(tail(x$start, n, ...), tail(x$end, n, ...)) #' @export c.intervals = function(...) { dots = list(...) start = do.call(c, lapply(dots, function(x) x$start)) end = do.call(c, lapply(dots, function(x) x$end)) make_intervals(start, end) } #' @export `[.intervals` = function(x, i, ...) { make_intervals(x$start[i], x$end[i]) } #' @export format.intervals = function(x, ...) { mformat = function(x, ..., digits = getOption("digits")) { if (inherits(x, "PCICt")) format(x, ...) else format(x, digits = digits, ...) } if (inherits(x$start, "units") && inherits(x$end, "units")) { stopifnot(units(x$start) == units(x$end)) paste0("[", format(as.numeric(x$start), ...), ",", format(as.numeric(x$end), ...), ") ", "[", as.character(units(x$start)), "]") } else paste0("[", mformat(x$start, ...), ",", mformat(x$end, ...), ")") } find_interval = function(x, intervals) { if (inherits(intervals$start, "Date") && inherits(x, "POSIXct")) x = as.Date(x) if (inherits(x, "Date") && inherits(intervals$start, "POSIXct")) x = as.POSIXct(x) if (all(intervals$start > intervals$end)) { # decreasing intervals start = intervals$end intervals$end = intervals$start intervals$start = start } if (getRversion() < "4.1.0") stop("R >= 4.1.0 required for handling intervals") w = apply(outer(x, intervals$start, ">=") & outer(x, intervals$end, "<"), 1, which, simplify = FALSE) w[lengths(w) == 0] = NA_integer_ unlist(w) } #' @export as.list.intervals = function(x, ...) { structure(mapply(make_intervals, x$start, x$end, SIMPLIFY = FALSE), class = "intervals_list") } #' @export format.intervals_list = function(x, ...) { sapply(x, format, ...) } #' @export `[.intervals_list` = function(x, i, ...) { ret = NextMethod() is_null = sapply(ret, is.null) ret[is_null] = list(make_intervals(NA_real_, NA_real_)) structure(ret, class = "intervals_list") } stars/R/ncproxy.R0000644000176200001440000001214614451263410013440 0ustar liggesusers.update_dims <- function(dims, proxy_dimensions, coords, tdim) { uc <- coords for(coord in names(coords)) { pv <- st_get_dimension_values(proxy_dimensions, coord) between <- function(x, ymin, ymax, z = x) { z[x >= ymin & x <= ymax, drop = FALSE] } if(inherits(pv, "POSIXt")) { # Need to deal with the case where tdim isn't values but is from to. uc[[coord]] <- between(tdim$values, min(pv), max(pv), uc[[coord]]) } else { if(attr(proxy_dimensions, "raster")$curvilinear) { uc[[coord]] <- seq(proxy_dimensions[[coord]]$from, proxy_dimensions[[coord]]$to) } else { uc[[coord]] <- between(uc[[coord]], min(pv), max(pv)) } } dims$start[dims$name == coord] <- which(coords[[coord]] == uc[[coord]][1])[1] dims$count[dims$name == coord] <- dims$length[dims$name == coord] <- length(uc[[coord]]) } dims } #' @export print.nc_proxy = function(x, ..., n = 1e5, nfiles = 10, simplify = TRUE) { cat("netcdf source stars proxy object from:\n") if(simplify) print(shorten_names(x[[1]], nfiles)) cat("\nAvailable nc variables:\n") cat(paste(names(x), collapse = "\n")) cat("\n\ndimension(s):\n") print(st_dimensions(x), ...) cat("\n") if (!is.null(attr(x, "call_list"))) { cat("call_list:\n") print(unlist(attr(x, "call_list"))) cat("This object has pending lazy operations: dimensions as printed may not reflect this.\n") } } # as.data.frame.stars_proxy works for nc_proxy. #' @export st_as_stars.nc_proxy <- function(.x, ..., downsample = 0, envir = parent.frame()) { process_call_list(read_ncdf(.x, downsample = downsample, ...), attr(.x, "call_list"), envir = envir, downsample = downsample) } #' @name plot #' @param max_times integer; maximum number of time steps to attempt to plot. #' @export #' @details when plotting a subsetted \code{stars_proxy} object, the default value for argument \code{downsample} will not be computed correctly, and has to be set manually. plot.nc_proxy = function(x, y, ..., downsample = get_downsample(dim(x)), max_times = 16) { if(length(x) > 1) { message("Plotting first variable only.") x <- x[1] } tdim <- which(sapply(st_dimensions(x), function(x) any(grepl("^POSIX|^PCIC", x$refsys)))) if(length(tdim)) { if(length(st_get_dimension_values(x, tdim)) > max_times) { stop("Time dimension of nc_proxy is longer than max_times in plot.nc_proxy.") } } plot(st_as_stars(x, downsample = downsample, ...), ..., downsample = 0) } #' @export "[.nc_proxy" <- function(x, i = TRUE, ...) { # stars_proxy works but need to match class. structure(NextMethod(), class = class(x)) } #' @export st_crop.nc_proxy <- function(x, y, ..., crop = TRUE, epsilon = sqrt(.Machine$double.eps), collect = TRUE) { # will this work? Couild be slick if it does. structure(NextMethod(), class = class(x)) } # "[[<-.nc_proxy" not needed. #' @export c.nc_proxy = function(..., along = NA_integer_, along_crs = FALSE, try_hard = FALSE, nms = names(list(...)), tolerance = sqrt(.Machine$double.eps)) { dots = list(...) if (!all(sapply(dots, function(x) inherits(x, "nc_proxy")))) stop("all arguments to c() should be nc_proxy objects") x <- dots[[1]] structure(NextMethod(), class = class(x)) } #' @export st_redimension.nc_proxy <- function(x, new_dims, along, ...) stop("st_redimension not support for nc_proxy") #' @export st_mosaic.nc_proxy = function(.x, ...) stop("st_mosaic not supported for nc_proxy") #' @param .x stars object to add curvilinear coordinates too. #' @param curvilinear only for creating curvilinear grids: named length #' 2 list holding longitude and latitude matrices; the names of this #' list should correspond to raster dimensions referred to #' @param crs object of class \code{crs} with the coordinate reference #' system of the values in \code{curvilinear}; see details #' @details if \code{curvilinear} is a \code{stars} object with longitude #' and latitude values, its coordinate reference system is typically not #' that of the latitude and longitude values. #' @noRd add_curvilinear <- function(.x, curvilinear = NULL, crs = st_crs('OGC:CRS84')) { # so we can just call add_curvilinear regardless if (is.null(curvilinear)) .x else { stopifnot(is.list(curvilinear), names(curvilinear) %in% names(dim(.x))) if (inherits(curvilinear[[1]], "stars")) curvilinear[[1]] = curvilinear[[1]][[1]] if (inherits(curvilinear[[2]], "stars")) curvilinear[[2]] = curvilinear[[2]][[1]] dimensions = st_dimensions(.x) xy = names(curvilinear) dimensions[[ xy[1] ]]$values = structure(curvilinear[[1]], dim = setNames(dim(curvilinear[[1]]), xy)) dimensions[[ xy[2] ]]$values = structure(curvilinear[[2]], dim = setNames(dim(curvilinear[[1]]), xy)) # erase regular grid coefficients $offset and $delta: dimensions[[ xy[1] ]]$offset = dimensions[[ xy[1] ]]$delta = NA_real_ dimensions[[ xy[2] ]]$offset = dimensions[[ xy[2] ]]$delta = NA_real_ raster = get_raster(dimensions = names(curvilinear), curvilinear = TRUE) st_set_crs(st_stars(.x, create_dimensions(dimensions, raster), class = class(.x)), crs) } } stars/R/prcomp.R0000644000176200001440000000224114600276514013236 0ustar liggesusers#' @name prcomp #' @param downsample see \link{st_as_stars} #' @export prcomp.stars_proxy = function(x, ..., downsample = 0) { prcomp(st_as_stars(x, downsample = downsample), ...) } #' Principle components of stars object #' #' Compute principle components of stars object #' @param x object of class `stars` or `stars_proxy` #' @param quiet logical; if `TRUE`, suppress message that PCs will be computed on last dimension; see details #' @param ... see \link[stats]{prcomp} #' @details if `x` has only one attribute, principle components will be computed in the space of the last dimension of `x` #' to predict PC scores into a `stars` object, use \link{predict.stars}; see example below #' @name prcomp #' @returns object of class `prcomp`, see \link[stats]{prcomp} #' @export #' @examples #' l7 = split(st_as_stars(L7_ETMs), 3) # use bands as features #' l7 |> prcomp() |> plot() #' l7 |> prcomp() |> predict(l7, model = _) |> merge() |> plot() prcomp.stars = function(x, ..., quiet = FALSE) { if (length(x) == 1) { if (!quiet) message(paste0("computing PCs over dimension `", tail(names(dim(x)), 1), "'")) x = split(x, length(dim(x))) } prcomp(as.data.frame(x), ...) } stars/R/warp.R0000644000176200001440000002707114576033423012721 0ustar liggesusers## https://mastodon.social/@EvenRouault/111738407524055989 has_global_longitude = function(x) { st_is_longlat(x) && isTRUE(all.equal(as.numeric(st_bbox(x)$xlim), c(-180.,180.))) } cut_latitude_to_3857 = function(bb) { # Pseudomercator does not have global coverage: bb["ymin"] = max(bb["ymin"], -85.06) bb["ymax"] = min(bb["ymax"], 85.06) bb } # create a target grid from x and crs, determining cellsize if unknown # return a dimensions object default_target_grid = function(x, crs, cellsize = NA_real_, segments = NA) { bb_x = st_bbox(x) if (st_is_longlat(x) && crs == st_crs(3857)) # common case: bb_x = cut_latitude_to_3857(bb_x) envelope = st_as_sfc(bb_x) # global adjustment: needed to have st_segmentize span global extent # https://github.com/r-spatial/mapview/issues/256 envelope = if (!is.na(segments) && !has_global_longitude(x)) # FIXME: should this branch be retained? st_segmentize(envelope, st_length(st_cast(envelope, "LINESTRING"))/segments) else { # https://github.com/r-tmap/tmap/issues/526 : old_crs = st_crs(envelope) st_crs(envelope) = NA_crs_ st_set_crs(st_segmentize(envelope, st_length(st_cast(envelope, "LINESTRING"))/segments), old_crs) } envelope_new = st_transform(envelope, crs) bb = st_bbox(envelope_new) # in new crs if (any(is.na(cellsize))) { area = if (st_is_longlat(crs)) # we need a cell size in degree lon lat diff(bb[c("xmin", "xmax")]) * diff(bb[c("ymin", "ymax")]) else st_area(envelope_new) ratio = if (has_rotate_or_shear(x)) { d = st_dimensions(x) xy = xy_from_colrow(rbind(c(0,0), c(1,1)), st_geotransform(d)) cellarea = sum((xy[1,] - xy[2,])^2) / 2 # lenght is cell diagonal xy_dims = attr(d, "raster")$dimensions unclass(st_area(envelope)) / (prod(dim(x)[xy_dims]) * cellarea) # > 1 } else 1.0 dxy = attr(st_dimensions(x), "raster")$dimensions cellsize = sqrt(unclass(area)/prod(dim(x)[dxy])/ratio) # TODO: divide by st_area(evelope_new)/st_area(envelope) ? } cellsize = rep(abs(cellsize), length.out = 2) nx = ceiling(diff(bb[c("xmin", "xmax")])/cellsize[1]) ny = ceiling(diff(bb[c("ymin", "ymax")])/cellsize[2]) if (has_global_longitude(x)) { # if global coverage, don't cross the boundaries: cellsize[1] = diff(bb[c("xmin", "xmax")])/nx cellsize[2] = diff(bb[c("ymin", "ymax")])/ny } x = create_dimension(from = 1, to = nx, offset = bb["xmin"], delta = cellsize[1], refsys = crs) y = create_dimension(from = 1, to = ny, offset = bb["ymax"], delta = -cellsize[2], refsys = crs) create_dimensions(list(x = x, y = y), get_raster()) } rename_xy_dimensions = function(x, dims) { #stopifnot(inherits(x, "stars"), inherits(dims, "dimensions")) dx = st_dimensions(x) xxy = attr(dx, "raster")$dimensions dimsxy = attr(dims, "raster")$dimensions if (all(xxy == dimsxy)) x else { na = names(dx) names(dx)[match(xxy, na)] = dimsxy attr(dx, "raster")$dimensions = dimsxy structure(x, dimensions = dx) } } # transform grid x to dimensions target # x is a stars object, target is a dimensions object transform_grid_grid = function(x, target, threshold) { stopifnot(inherits(x, "stars"), inherits(target, "dimensions")) x = rename_xy_dimensions(x, target) # so we can match by name xy_names = attr(target, "raster")$dimensions new_pts = st_coordinates(target[xy_names]) dxy = attr(target, "raster")$dimensions from = st_crs(target) pts = sf::sf_project(from = from, to = st_crs(x), pts = new_pts) # at xy (target) locations, get values from x, or put NA # to array: d = st_dimensions(x) # get col/row from x/y: xy = if (is_curvilinear(x)) { if (!requireNamespace("FNN", quietly = TRUE)) stop("package FNN required, please install it first") #nocov if (st_is_longlat(x)) warning("using Euclidean distance measures on geodetic coordinates") fnn = FNN::get.knnx(cc_x <- st_coordinates(d[xy_names]), pts, 1) if (is.na(threshold)) { p12 = st_as_sf(as.data.frame(cc_x[1:2,]), coords = 1:2) threshold = signif(st_distance(p12)[1,2]) message(paste("threshold set to", format(threshold), ": set a larger value if you see missing values where there shouldn't be")) } i = fnn$nn.index - 1 i[fnn$nn.dist > threshold] = NA ny = dim(x)[1] cbind(i %% ny, i %/% ny) + 1 } else colrow_from_xy(pts, x, NA_outside = TRUE) dims = dim(x) index = matrix(seq_len(prod(dims[dxy])), dims[ dxy[1] ], dims[ dxy[2] ])[xy] x = unclass(x) # avoid using [[<-.stars: if (length(dims) > 2) { remaining_dims = dims[setdiff(names(dims), dxy)] newdim = c(prod(dims[dxy]), prod(remaining_dims)) for (i in seq_along(x)) { dim(x[[i]]) = newdim x[[i]] = x[[i]][index,] dim(x[[i]]) = c(dim(target)[dxy], remaining_dims) } } else { for (i in seq_along(x)) { x[[i]] = x[[i]][index] dim(x[[i]]) = dim(target) } } d[dxy] = target[1:2] st_stars(x, dimensions = create_dimensions(d, attr(target, "raster"))) } #' Warp (resample) grids in stars objects to a new grid, possibly in an new coordinate reference system #' #' @param src object of class \code{stars} with source raster #' @param dest object of class \code{stars} with target raster geometry #' @param crs coordinate reference system for destination grid, only used when \code{dest} is missing #' @param cellsize length 1 or 2 numeric; cellsize in target coordinate reference system units #' @param segments (total) number of segments for segmentizing the bounding box before transforming to the new crs #' @param use_gdal logical; if \code{TRUE}, use gdal's warp or warper, through \link[sf]{gdal_utils} #' @param options character vector with options, passed on to gdalwarp #' @param no_data_value value used by gdalwarp for no_data (NA) when writing to temporary file; #' not setting this when \code{use_gdal} is \code{TRUE} leads to a warning #' @param debug logical; if \code{TRUE}, do not remove the temporary gdalwarp destination file, and print its name #' @param method character; see details for options; methods other than \code{near} only work when \code{use_gdal=TRUE} #' @param threshold numeric; distance threshold for warping curvilinear grids: new cells at distances larger than threshold are assigned NA values. #' @param ... ignored #' @details \code{method} should be one of \code{near}, \code{bilinear}, \code{cubic}, \code{cubicspline}, \code{lanczos}, \code{average}, \code{mode}, \code{max}, \code{min}, \code{med}, \code{q1} or \code{q3}; see https://github.com/r-spatial/stars/issues/109 #' @examples #' geomatrix = system.file("tif/geomatrix.tif", package = "stars") #' (x = read_stars(geomatrix)) #' new_crs = st_crs('OGC:CRS84') #' y = st_warp(x, crs = new_crs) #' plot(st_transform(st_as_sfc(st_bbox(x)), new_crs), col = NA, border = 'red') #' plot(st_as_sfc(y, as_points=FALSE), col = NA, border = 'green', axes = TRUE, add = TRUE) #' image(y, add = TRUE, nbreaks = 6) #' plot(st_as_sfc(y, as_points=TRUE), pch=3, cex=.5, col = 'blue', add = TRUE) #' plot(st_transform(st_as_sfc(x, as_points=FALSE), new_crs), add = TRUE) #' # warp 0-360 raster to -180-180 raster: #' r = read_stars(system.file("nc/reduced.nc", package = "stars")) #' r %>% st_set_crs('OGC:CRS84') %>% st_warp(st_as_stars(st_bbox(), dx = 2)) -> s #' plot(r, axes = TRUE) # no CRS set, so no degree symbols in labels #' plot(s, axes = TRUE) #' # downsample raster (90 to 270 m) #' r = read_stars(system.file("tif/olinda_dem_utm25s.tif", package = "stars")) #' r270 = st_as_stars(st_bbox(r), dx = 270) #' r270 = st_warp(r, r270) #' @details For gridded spatial data (dimensions \code{x} and \code{y}), see figure; the existing grid is transformed into a regular grid defined by \code{dest}, possibly in a new coordinate reference system. If \code{dest} is not specified, but \code{crs} is, the procedure used to choose a target grid is similar to that of \link[raster]{projectRaster}. This entails: (i) the envelope (bounding box polygon) is transformed into the new crs, possibly after segmentation (red box); (ii) a grid is formed in this new crs, touching the transformed envelope on its East and North side, with (if cellsize is not given) a cellsize similar to the cell size of \code{src}, with an extent that at least covers \code{x}; (iii) for each cell center of this new grid, the matching grid cell of \code{x} is used; if there is no match, an \code{NA} value is used. #' @export st_warp = function(src, dest, ..., crs = NA_crs_, cellsize = NA_real_, segments = 100, use_gdal = FALSE, options = character(0), no_data_value = NA_real_, debug = FALSE, method = "near", threshold = NA_real_) { if (!inherits(src, "stars_proxy")) src = st_normalize(src) if (!is.na(crs)) crs = st_crs(crs) if (!missing(dest)) { if (inherits(dest, "crs")) stop("target crs should be specified with crs = ..., not as argument dest") } else if (is.na(crs) && !use_gdal) stop("when use_gdal is FALSE, either dest or crs need to be specified") ret = if (use_gdal) { if (is_curvilinear(src)) stop("for warping curvilinear grids using GDAL, use gdal_utils(\"warp\",...) directly on the source dataset of this object") if (!is.na(no_data_value)) options = c(options, "-dstnodata", no_data_value) else warning("no_data_value not set: missing values will appear as zero values") options = c(options, "-r", method) if (all(!is.na(cellsize))) { cellsize = rep(abs(cellsize), length.out = 2) options = c(options, "-tr", cellsize[1], cellsize[2]) } if (! inherits(src, "stars_proxy")) { src = st_as_stars_proxy(src, NA_value = no_data_value) if (debug) cat("Writing input to: ", src[[1]], "\n") else on.exit(unlink(src[[1]])) # temp file } # collapse a multi-file proxy objects into single-file/multi-band: if (length(src[[1]]) > 1 || length(src) > 1) { tmp = tempfile(fileext = ".vrt") # multi-band destination sf::gdal_utils("buildvrt", unlist(src), tmp, options = "-separate") src[[1]] = tmp } if (missing(dest) && is.na(crs)) { delete = TRUE dest = tempfile(fileext = ".tif") sf::gdal_utils("warp", src[[1]], dest, options = options) } else { # dest exists, and should be used: should use warper rather than warp # https://github.com/r-spatial/stars/issues/407 if (missing(dest)) { dest = default_target_grid(src, crs = crs, cellsize = cellsize, segments = segments) # dimensions dest = st_stars(list(values = array(NA_real_, dim(dest))), dest) } if (length(dim(src)) == 3 && length(dim(dest)) == 2) dest = merge(do.call(c, lapply(seq_len(dim(src)[3]), function(x) dest))) dest = if (! inherits(dest, "stars_proxy")) { dest[[1]] = NA_real_ * dest[[1]] # blank out values delete = !debug st_as_stars_proxy(dest, NA_value = no_data_value)[[1]] } else { delete = FALSE dest[[1]] # the file name of the stars_proxy, not to be deleted } sf::gdal_utils("warper", src[[1]], dest, options = method) # not "warp"! } if (debug) cat("Writing result to: ", dest, "\n") else if (delete) on.exit(unlink(dest)) # a temp file read_stars(dest) } else { if (method != "near") stop("methods other than \"near\" are only supported if use_gdal=TRUE") if (missing(dest)) dest = default_target_grid(src, crs = crs, cellsize = cellsize, segments = segments) if (!inherits(dest, "stars") && !inherits(dest, "dimensions")) stop("dest should be a stars object, or a dimensions object") transform_grid_grid(st_as_stars(src), st_dimensions(dest), threshold) } # restore attributes? if (method %in% c("near", "mode")) { a = lapply(src, attributes) for (i in seq_along(ret)) ret[[i]] = structure(ret[[i]], levels = attr(ret[[i]], "levels") %||% a[[i]]$levels, colors = attr(ret[[i]], "colors") %||% a[[i]]$colors, class = attr(ret[[i]], "class") %||% a[[i]]$class) } ret } stars/R/tidyverse.R0000644000176200001440000003720414627273120013762 0ustar liggesusers# convert arrays to data.frame, in long form to_df = function(x) { dplyr::as_tibble(lapply(x, function(y) structure(y, dim = NULL))) } set_dim = function(x, d) { lapply(x, function(y, dims) structure(y, dim = dims), dims = d) } get_dims = function(d_cube, d_stars) { xy = attr(d_stars, "raster")$dimensions d_stars = d_stars[names(d_cube)] for (i in seq_along(d_cube)) { d_stars[[i]]$values = if (inherits(d_stars[[i]]$values, "intervals")) { v = d_stars[[i]]$values d_stars[[i]]$values = v[ na.omit(find_interval(d_cube[[i]], v)) ] } else if (is.list(d_stars[[i]]$values)) { d_stars[[i]]$values[ d_cube[[i]] ] } else d_cube[[i]] d_stars[[i]] = create_dimension(values = d_stars[[i]]$values, point = d_stars[[i]]$point, refsys = d_stars[[i]]$refsys, is_raster = names(d_stars)[i] %in% xy) } d_stars } #' dplyr verbs for stars objects #' #' dplyr verbs for stars objects; package dplyr needs to be loaded before these methods can be used for stars objects. #' @param .data object of class \code{stars} #' @param ... see \link[dplyr]{filter} #' @name dplyr filter.stars <- function(.data, ...) { if (!requireNamespace("dplyr", quietly = TRUE)) stop("package dplyr required, please install it first") # nocov if (!requireNamespace("cubelyr", quietly = TRUE)) stop("package cubelyr required, please install it first") # nocov cb = cubelyr::as.tbl_cube(.data) cb = dplyr::filter(cb, ...) st_as_stars(cb$mets, dimensions = get_dims(cb$dims, st_dimensions(.data))) } #' @name dplyr filter.stars_proxy = function(.data, ...) { collect(.data, match.call(), "filter", ".data", env = environment()) } #' @name dplyr mutate.stars <- function(.data, ...) { ret = dplyr::mutate(to_df(.data), ...) st_as_stars(set_dim(ret, dim(.data)), dimensions = st_dimensions(.data)) } #' @name dplyr mutate.stars_proxy = function(.data, ...) { collect(.data, match.call(), "mutate", ".data", env = parent.frame()) } #' @name dplyr transmute.stars <- function(.data, ...) { ret = dplyr::transmute(to_df(.data), ...) st_as_stars(set_dim(ret, dim(.data)), dimensions = st_dimensions(.data)) } #' @name dplyr transmute.stars_proxy = function(.data, ...) { collect(.data, match.call(), "transmute", ".data", env = environment()) } #' @name dplyr select.stars <- function(.data, ...) { if (!requireNamespace("dplyr", quietly = TRUE)) stop("package dplyr required, please install it first") # nocov ret <- dplyr::select(to_df(.data), ...) st_as_stars(set_dim(ret, dim(.data)), dimensions = st_dimensions(.data)) } #' @name dplyr select.stars_proxy = function(.data, ...) { collect(.data, match.call(), "select", ".data", env = environment()) } #' @name dplyr rename.stars <- function(.data, ...) { if (!requireNamespace("dplyr", quietly = TRUE)) stop("package dplyr required, please install it first") # nocov ret <- dplyr::rename(to_df(.data), ...) st_as_stars(set_dim(ret, dim(.data)), dimensions = st_dimensions(.data)) } #' @name dplyr rename.stars_proxy = function(.data, ...) { collect(.data, match.call(), "rename", ".data", env = environment()) } #' @param var see \link[dplyr]{pull} #' @name dplyr pull.stars = function (.data, var = -1) { if (!requireNamespace("dplyr", quietly = TRUE)) stop("package dplyr required, please install it first") # nocov if (!requireNamespace("rlang", quietly = TRUE)) stop("package rlang required, please install it first") # nocov var = rlang::enquo(var) structure(dplyr::pull(to_df(.data), !!var), dim = dim(.data)) } #' @name dplyr pull.stars_proxy = function(.data, ...) { collect(.data, match.call(), "pull", ".data", env = environment()) } #' @name dplyr #' @param x object of class \code{stars} #' @export as.tbl_cube.stars = function(x, ...) { if (!requireNamespace("cubelyr", quietly = TRUE)) stop("package cubelyr required, please install it first") # nocov cleanup = function(y) { if (is.list(y)) seq_along(y) else y } dims = lapply(expand_dimensions(x), cleanup) cubelyr::tbl_cube(dims, c(unclass(x))) } #' @name dplyr #' @param along name or index of dimension to which the slice should be applied #' @param index integer value(s) for this index #' @param drop logical; drop dimensions that only have a single index? #' @examples #' tif = system.file("tif/L7_ETMs.tif", package = "stars") #' x1 = read_stars(tif) #' if (require(dplyr, quietly = TRUE)) { #' x1 %>% slice("band", 2:3) #' x1 %>% slice("x", 50:100) #' } slice.stars <- function(.data, along, index, ..., drop = length(index) == 1) { #stopifnot(length(index) == 1) if (!requireNamespace("rlang", quietly = TRUE)) stop("package rlang required, please install it first") # nocov nd <- length(dim(.data)) indices <- rep(list(rlang::missing_arg()), nd + 1) along = rlang::expr_text(rlang::ensym(along)) ix = which(along == names(st_dimensions(.data)))[1] indices[[ix + 1]] <- index indices[["drop"]] <- drop eval(rlang::expr(.data[!!!indices])) } #' @name dplyr slice.stars_proxy <- function(.data, along, index, ...) { # TODO: add adrop argument, this requires an eager implementation of # adrop.stars_proxy # If there are already operations queued, just add to the queue if (!is.null(attr(.data, "call_list"))) return(collect(.data, match.call(), "slice", ".data", env = parent.frame(), ...)) # figure out which dimensions are part of the files vecsize <- rev(cumprod(rev(dim(.data)))) # NOTE: The first set of dimensions corresponds to the dimensions in the # files. The second set of dimensions corresponds to the list of files. It may # be undecided where exactly the break is (at least without reading in the # files) if there are a singleton dimensions, I am not sure if this matters, # for now just assume the maximum index, this should be the safe choice. # Singleton dimensions that are part of files probably need some logic # somewhere else and cannot just be ignored. # Can we assume, that all elements of .data are the same? first_concat_dim <- max(which(vecsize == length(.data[[1]]))) stopifnot(first_concat_dim > 0) all_dims <- st_dimensions(.data) file_dims <- all_dims[seq_len(first_concat_dim - 1)] concat_dims <- all_dims[first_concat_dim:length(dim(.data))] d_concat_dims <- dim(concat_dims) l_concat_vec <- prod(d_concat_dims) # what is the dimension we have to subset ix <- which(names(all_dims) == along) - length(file_dims) stopifnot(length(ix) == 1) # if the slice is on file dimensions we have to queue the operation if (ix <= 0) return(collect(.data, match.call(), "slice", ".data", env = parent.frame(), ...)) # subset indices for the files, it may be faster to calculate these and not # take them from an array. d <- array(seq_len(l_concat_vec), d_concat_dims) idx <- rep(list(quote(expr = )), length(d_concat_dims)) idx[[ix]] <- index vidx <- as.vector(do.call(`[`, c(list(d), idx))) # The actual subsetting of files and dimensions file_list_new <- lapply(.data, function(x) x[vidx]) all_dims[[along]] <- all_dims[[along]][index] # construct stars_proxy st_stars_proxy(as.list(file_list_new), all_dims, NA_value = attr(.data, "NA_value"), resolutions = attr(.data, "resolutions"), RasterIO = attr(.data, "RasterIO")) } #' @name st_coordinates #' @param .x object to be converted to a tibble as_tibble.stars = function(.x, ..., add_max = FALSE, center = NA) { if (!requireNamespace("tibble", quietly = TRUE)) stop("package tibble required, please install it first") # nocov tibble::as_tibble(append( st_coordinates(.x, add_max = add_max, center = center), lapply(.x, function(y) structure(y, dim = NULL)) ) ) } #' @name dplyr #' @param data data set to work on #' @param replace see \link[tidyr]{replace_na}: list with variable=value pairs, where value is the replacement value for NA's replace_na.stars = function(data, replace, ...) { if (!requireNamespace("tidyr", quietly = TRUE)) stop("package tidyr required, please install it first") # nocov if (!requireNamespace("cubelyr", quietly = TRUE)) stop("package cubelyr required, please install it first") # nocov cb = cubelyr::as.tbl_cube(data) d = dim(cb$mets[[1]]) cb$mets = as.data.frame(lapply(cb$mets, as.vector)) cb$mets = unclass(tidyr::replace_na(cb$mets, replace, ...)) for (i in seq_along(cb$mets)) cb$mets[[i]] = structure(cb$mets[[i]], dim = d) st_as_stars(cb$mets, dimensions = get_dims(cb$dims, st_dimensions(data))) } #' @name dplyr replace_na.stars_proxy = function(data, ...) { collect(data, match.call(), "replace_na", "data", env = environment()) } #' ggplot geom for stars objects #' #' ggplot geom for stars objects #' @name geom_stars #' @param mapping see \link[ggplot2:geom_tile]{geom_raster} #' @param data see \link[ggplot2:geom_tile]{geom_raster} #' @param ... see \link[ggplot2:geom_tile]{geom_raster} #' @param downsample downsampling rate: e.g. 3 keeps rows and cols 1, 4, 7, 10 etc.; a value of 0 does not downsample; can be specified for each dimension, e.g. \code{c(5,5,0)} to downsample the first two dimensions but not the third. #' @param sf logical; if \code{TRUE} rasters will be converted to polygons and plotted using \link[ggplot2:ggsf]{geom_sf}. #' @param na.action function; if \code{NA} values need to be removed before plotting use the value \code{na.omit} here (only applies to objects with raster dimensions) #' @details \code{geom_stars} returns (a call to) either \link[ggplot2:geom_tile]{geom_raster}, \link[ggplot2]{geom_tile}, or \link[ggplot2:ggsf]{geom_sf}, depending on the raster or vector geometry; for the first to, an \link[ggplot2]{aes} call is constructed with the raster dimension names and the first array as fill variable. Further calls to \link[ggplot2:coord_fixed]{coord_equal} and \link[ggplot2]{facet_wrap} are needed to control aspect ratio and the layers to be plotted; see examples. If a \code{stars} array contains hex color values, and no \code{fill} parameter is given, the color values are used as fill color; see the example below. #' #' If visual artefacts occur (Moiré-Effekt), then see the details section of \link{plot.stars} #' @export #' @examples #' system.file("tif/L7_ETMs.tif", package = "stars") %>% read_stars() -> x #' if (require(ggplot2, quietly = TRUE)) { #' ggplot() + geom_stars(data = x) + #' coord_equal() + #' facet_wrap(~band) + #' theme_void() + #' scale_x_discrete(expand=c(0,0))+ #' scale_y_discrete(expand=c(0,0)) #' # plot rgb composite: #' st_as_stars(L7_ETMs)[,,,1:3] |> st_rgb() -> x # x contains colors as pixel values #' ggplot() + geom_stars(data = x) #' } geom_stars = function(mapping = NULL, data = NULL, ..., downsample = 0, sf = FALSE, na.action = na.pass) { if (!requireNamespace("ggplot2", quietly = TRUE)) stop("package ggplot2 required, please install it first") # nocov if (!requireNamespace("tibble", quietly = TRUE)) stop("package tibble required, please install it first") # nocov if (is.null(data)) stop("argument data should be a stars or stars_proxy object") for (i in seq_along(data)) { if (inherits(data[[i]], "units")) data[[i]] = units::drop_units(data[[i]]) } if (inherits(data, "stars_proxy")) data = st_as_stars(data, downsample = downsample) # fetches data else if (any(downsample > 0)) data = st_downsample(data, downsample) all_colors = function (x) { is.character(x) && all(nchar(x) %in% c(7, 9) & substr(x, 1, 1) == "#", na.rm = TRUE) } if (is.null(list(...)$fill) && all_colors(fill <- as.vector(data[[1]]))) return(geom_stars(mapping = mapping, data = data, sf = sf, na.action = na.action, ..., fill = fill)) # RETURNS/recurses if (is_curvilinear(data) || sf) data = st_xy2sfc(data, as_points = FALSE) # removes NA's by default d = st_dimensions(data) if (has_raster(d) && (is_regular_grid(d) || is_rectilinear(d))) { xy = attr(d, "raster")$dimensions if (is_regular_grid(d)) { mapping = if (is.null(mapping)) ggplot2::aes(x = !!rlang::sym(xy[1]), y = !!rlang::sym(xy[2]), fill = !!rlang::sym(names(data)[1])) else modifyList( ggplot2::aes(x = !!rlang::sym(xy[1]), y = !!rlang::sym(xy[2]), fill = !!rlang::sym(names(data)[1])), mapping) data = na.action(tibble::as_tibble(data)) ggplot2::geom_raster(mapping = mapping, data = data, ...) } else { # rectilinear: use geom_rect, passing on cell boundaries xy_max = paste0(xy, "_max") mapping = if (is.null(mapping)) ggplot2::aes(xmin = !!rlang::sym(xy[1]), ymin = !!rlang::sym(xy[2]), xmax = !!rlang::sym(xy_max[1]), ymax = !!rlang::sym(xy_max[2]), fill = !!rlang::sym(names(data)[1])) else modifyList(ggplot2::aes(xmin = !!rlang::sym(xy[1]), ymin = !!rlang::sym(xy[2]), xmax = !!rlang::sym(xy_max[1]), ymax = !!rlang::sym(xy_max[2]), fill = !!rlang::sym(names(data)[1])), mapping) data = na.action(tibble::as_tibble(data, add_max = TRUE)) ggplot2::geom_rect(mapping = mapping, data = data, ...) } } else if (has_sfc(d)) { if (is.null(mapping)) { mapping = ggplot2::aes(fill = !!rlang::sym(names(data)[1])) } else { mapping = modifyList( ggplot2::aes(fill = !!rlang::sym(names(data)[1])), mapping) } ggplot2::geom_sf(data = st_as_sf(data, long = TRUE), color = NA, mapping = mapping, ...) } else stop("geom_stars only works for objects with raster or vector geometries") } #' @name geom_stars theme_stars = function(...) { if (!requireNamespace("ggplot2", quietly = TRUE)) stop("package ggplot2 required, please install it first") # nocov # coord_equal() + # scale_fill_viridis() + # scale_x_discrete(expand=c(0,0)) + # scale_y_discrete(expand=c(0,0)) + ggplot2::theme_void() } register_all_s3_methods = function() { register_s3_method("cubelyr", "as.tbl_cube", "stars") # nocov start register_s3_method("dplyr", "filter", "stars") register_s3_method("dplyr", "filter", "stars_proxy") register_s3_method("dplyr", "select", "stars") register_s3_method("dplyr", "select", "stars_proxy") register_s3_method("dplyr", "mutate", "stars") register_s3_method("dplyr", "mutate", "stars_proxy") register_s3_method("dplyr", "pull", "stars") register_s3_method("dplyr", "pull", "stars_proxy") register_s3_method("dplyr", "rename", "stars") register_s3_method("dplyr", "rename", "stars_proxy") register_s3_method("dplyr", "slice", "stars") register_s3_method("dplyr", "slice", "stars_proxy") register_s3_method("dplyr", "transmute", "stars") register_s3_method("dplyr", "transmute", "stars_proxy") register_s3_method("tidyr", "replace_na", "stars") register_s3_method("tidyr", "replace_na", "stars_proxy") register_s3_method("lwgeom", "st_transform_proj", "stars") register_s3_method("sf", "st_join", "stars") register_s3_method("spatstat.geom", "as.owin", "stars") register_s3_method("spatstat.geom", "as.im", "stars") register_s3_method("tibble", "as_tibble", "stars") register_s3_method("xts", "as.xts", "stars") # nocov end } # from: https://github.com/tidyverse/hms/blob/master/R/zzz.R # Thu Apr 19 10:53:24 CEST 2018 #nocov start register_s3_method <- function(pkg, generic, class, fun = NULL) { stopifnot(is.character(pkg), length(pkg) == 1) stopifnot(is.character(generic), length(generic) == 1) stopifnot(is.character(class), length(class) == 1) if (is.null(fun)) { fun <- get(paste0(generic, ".", class), envir = parent.frame()) } else { stopifnot(is.function(fun)) } if (pkg %in% loadedNamespaces()) { registerS3method(generic, class, fun, envir = asNamespace(pkg)) } # Always register hook in case package is later unloaded & reloaded setHook( packageEvent(pkg, "onLoad"), function(...) { registerS3method(generic, class, fun, envir = asNamespace(pkg)) } ) } #nocov end stars/R/tile.R0000644000176200001440000000377014166576214012712 0ustar liggesusers#' Specify parameters to load raster in blocks #' #' Helper function for specifying the block parameters (\code{nXOff}, #' \code{nYOff}, \code{nXsize}, and \code{nYSize}) required by \code{RasterIO} #' argument in \link{read_stars} #' #' @param img_rows number of input raster rows (integer) #' @param img_cols number of input raster columns (integer) #' @param x_window number of rows in block (integer) #' @param y_window number of columns in block (integer) #' @param overlap number of overlapping pixels (integer) #' #' @return matrix with specified \code{nXOff}, \code{nYOff}, \code{nXsize}, #' and \code{nYSize} parameters for every block #' #' @examples #' \dontrun{ #' tif = system.file("tif/L7_ETMs.tif", package = "stars") #' r = read_stars(tif, proxy = TRUE) #' tiles = st_tile(nrow(r), ncol(r), 256, 256) #' for (i in seq_len(nrow(tiles))) { #' tile = read_stars(tif, proxy = FALSE, RasterIO = tiles[i, ]) #' # write tiles to separate files #' write_stars(tile, dsn = paste0(i, ".tif")) #' } #' } #' #' @export st_tile = function(img_rows, img_cols, x_window, y_window, overlap = 0) { # make sure input values are integers img_rows = as.integer(img_rows) img_cols = as.integer(img_cols) x_window = as.integer(x_window) y_window = as.integer(y_window) overlap = as.integer(overlap) n = ceiling((img_rows / x_window)) * ceiling((img_cols / y_window)) x_vec = y_vec = integer(n) nXSize_vec = nYSize_vec = integer(n) i = 1L for (x in seq.int(1L, img_rows, y_window)) { if (x + y_window + overlap <= img_rows) { nXSize = y_window + overlap } else { nXSize = img_rows - x + 1L } for (y in seq.int(1L, img_cols, x_window)) { if (y + x_window + overlap <= img_cols) { nYSize = x_window + overlap } else { nYSize = img_cols - y + 1L } x_vec[i] = x y_vec[i] = y nXSize_vec[i] = nXSize nYSize_vec[i] = nYSize i = i + 1L } } mat = matrix(c(x_vec, y_vec, nXSize_vec, nYSize_vec), ncol = 4) colnames(mat) = c("nXOff", "nYOff", "nXSize", "nYSize") return(mat) } stars/R/mdim.R0000644000176200001440000004001214612223663012662 0ustar liggesusers# close rings by repeating the first point close_mat = function(m) { if (any(m[1,] != m[nrow(m),])) m = rbind(m, m[1,]) if (nrow(m) < 4) stop("polygons require at least 4 points") unclass(m) } regrp = function(grp, i) { # work on single MULTIPOLYGON, group outer rings or outer rings+holes if (all(i == 0)) # only outer rings lapply(grp, function(x) list(close_mat(x))) # add one list level else { for (j in seq_along(grp)) grp[[j]] = close_mat(grp[[j]]) starts = which(i == 0) lst = vector("list", length(starts)) for (j in seq_along(lst)) { end = start = starts[j] while (end < length(i) && i[end + 1] == 1) end = end + 1 # consume holes from start to end lst[j] = list(grp[start:end]) } lst } } regroup = function(grps, ir) { mapply(regrp, grps, ir, SIMPLIFY = FALSE) } recreate_geometry = function(l) { if (length(geom <- l$geometry)) { cc = cbind(geom$x[[1]], geom$y[[1]]) if (geom$geometry_type == "point") { sfc = do.call(st_sfc, lapply(seq_len(nrow(cc)), function(i) st_point(cc[i,]))) dimension = attr(geom$x[[1]], "d_names") } else { nc = geom$node_count[[1]] pnc = c(0, cumsum(geom$part_node_count[[1]])) parts = vector("list", length(geom$part_node_count[[1]])) for (i in seq_along(parts)) parts[[i]] = cc[(pnc[i]+1):pnc[i+1],] i = findInterval(pnc[-1], cumsum(c(0, nc)), left.open = TRUE) grps = lapply(split(seq_along(parts), i), function(x) parts[x]) dimension = attr(nc, "d_names") if (geom$geometry_type == "line") sfc = do.call(st_sfc, lapply(grps, st_multilinestring)) else if (geom$geometry_type == "polygon") { ir = geom$interior_ring[[1]] grps = regroup(grps, split(ir, i)) sfc = do.call(st_sfc, lapply(grps, st_multipolygon)) # if all rings are exterior rings: add one list level to each # do.call(st_sfc, lapply(lapply(grps, function(x) lapply(x, list)), st_multipolygon)) } else stop(paste("unsupported geometry type:", geom$geometry_type)) } l$dimensions[[dimension]] = sfc } l } get_values_from_bounds = function(x, bnd, center) { a = gdal_read_mdim(x, bnd)$array_list[[1]] dim(a) = rev(dim(a)) a = t(a) if (length(dim(a)) == 2) { if (!dim(a)[2] %in% 1:2) warning(paste("bounds variable", bnd, "has", dim(a)[2], "vertices and may be treated incorrectly")) if (center) { m = apply(a, 1, mean) if (regular_intervals(m)) m else make_intervals(a[,1], a[,2]) } else # start of bound: a[,1] } else a } mdim_use_bounds = function(dims, x, bnds, center = TRUE) { if (isTRUE(bnds)) { bnds = character() for (d in names(dims)) if (d != "time" && !is.null(b <- attr(dims[[d]]$values[[1]], "attributes")["bounds"])) bnds = c(bnds, setNames(b, d)) } if (length(bnds) && is.null(names(bnds))) stop("bounds must be a named vector, names indicating the non-bounds dimension variables") for (b in names(bnds)) if (!is.na(bnds[b])) { v <- try(get_values_from_bounds(x, bnds[b], center = TRUE), silent = TRUE) if (!inherits(v, "try-error")) dims[[b]]$values[[1]] = v } dims } match_raster_dims = function(nms) { x = tolower(nms) %in% c("lon", "long", "longitude") y = tolower(nms) %in% c("lat", "latitude") if (sum(xor(x, y)) == 2) c(which(x), which(y)) else 1:2 } #' Read or write data using GDAL's multidimensional array API #' #' Read or write data using GDAL's multidimensional array API #' @name mdim #' @param filename name of the source or destination file or data source #' @param variable name of the array to be read; if `"?"`, a list of array names is returned, with group name as list element names. #' @param options character; driver specific options regarding the opening (read_mdim) or creation (write_mdim) of the dataset #' @param raster names of the raster variables (default: first two dimensions) #' @param offset integer; offset for each dimension (pixels) of sub-array to read, defaults to 0 for each dimension(requires sf >= 1.0-9) #' @param count integer; size for each dimension (pixels) of sub-array to read (default: read all); a value of NA will read the corresponding dimension entirely; counts are relative to the step size (requires sf >= 1.0-9) #' @param step integer; step size for each dimension (pixels) of sub-array to read; defaults to 1 for each dimension (requires sf >= 1.0-9) #' @param proxy logical; return proxy object? (not functional yet) #' @param debug logical; print debug info? #' @param bounds logical or character: if \code{TRUE} tries to infer from "bounds" attribute; if character, #' named vector of the form \code{c(longitude="lon_bnds", latitude="lat_bnds")} with names dimension names #' @param curvilinear control reading curvilinear (geolocation) coordinate arrays; if \code{NA} try reading the x/y dimension names; if character, defines the arrays to read; if \code{FALSE} do not try; see also \link{read_stars} #' @details it is assumed that the first two dimensions are easting and northing #' @param ... ignored #' @seealso \link[sf]{gdal_utils}, in particular util \code{mdiminfo} to query properties of a file or data source containing arrays #' @export read_mdim = function(filename, variable = character(0), ..., options = character(0), raster = NULL, offset = integer(0), count = integer(0), step = integer(0), proxy = FALSE, debug = FALSE, bounds = TRUE, curvilinear = NA) { if (proxy) stop("proxy not yet implemented in read_mdim()") stopifnot(is.character(filename), is.character(variable), is.character(options)) ret = gdal_read_mdim(filename, variable, options, rev(offset), rev(count), rev(step), proxy, debug) if (length(ret$dimensions) == 1 && length(ret$array_list) == 1 && is.data.frame(ret$array_list[[1]])) return(ret$array_list[[1]]) ## composite data: RETURNS ret = recreate_geometry(ret) if (isTRUE(bounds) || is.character(bounds)) ret$dimensions = mdim_use_bounds(ret$dimensions, filename, bounds) create_units = function(x) { u = attr(x, "units") x = structure(x, units = NULL) # remove attribute if (is.null(u) || u %in% c("", "none")) x else { if (!is.null(a <- attr(x, "attributes")) && !is.na(cal <- a["calendar"]) && cal %in% c("360_day", "365_day", "noleap")) get_pcict(x, u, cal) else { days_since = grepl("days since", u) u = try_as_units(u) if (!inherits(u, "units")) # FAIL: x else { units(x) = u if (days_since && inherits(d <- try(as.Date(x), silent = TRUE), "Date")) d else if (inherits(p <- try(as.POSIXct(x), silent = TRUE), "POSIXct")) p else x } } } } l = rev(lapply(ret$dimensions, function(x) { if (inherits(x, "sfc")) x else create_units(x$values[[1]]) })) if (length(offset) != 0 || length(step) != 0 || length(count) != 0) { if (length(offset) == 0) offset = rep(0, length(l)) if (length(step) == 0) step = rep(1, length(l)) ll = lengths(lapply(l, as.list)) # take care of dimensions of class intervals if (length(count) == 0) count = floor((ll - offset)/step) else if (any(a <- is.na(count))) count[a] = floor((ll[a] - offset[a])/step[a]) for (i in seq_along(l)) { l[[i]] = l[[i]][seq(from = offset[i]+1, length.out = count[i], by = step[i])] } } # create dimensions table: sf = any(sapply(l, function(x) inherits(x, "sfc"))) # FIXME: i %in% 1:2 always the case? raster_dims = match_raster_dims(names(l)) d = mapply(function(x, i) create_dimension(values = x, is_raster = !sf && i %in% raster_dims, point = ifelse(length(x) == 1, TRUE, NA)), l, seq_along(l), SIMPLIFY = FALSE) if (is.null(raster)) { raster = if (sf) get_raster(dimensions = rep(NA_character_,2)) else get_raster(dimensions = names(d)[raster_dims]) } else raster = get_raster(dimensions = raster) dimensions = create_dimensions(d, raster = raster) # handle array units: for (i in seq_along(ret$array_list)) if (nchar(u <- attr(ret$array_list[[i]], "units")) && inherits(u <- try_as_units(u), "units")) units(ret$array_list[[i]]) = u clean_units = function(x) { if (identical(attr(x, "units"), "")) structure(x, units = NULL) else x } lst = lapply(ret$array_list, function(x) structure(clean_units(x), dim = rev(dim(x)))) # create return object: st = st_stars(lst, dimensions) if (is.null(ret$srs) || is.na(ret$srs)) { if (missing(curvilinear) || is.character(curvilinear)) { # try curvilinear: xy = raster$dimensions ll = curvilinear if (is.character(curvilinear)) { if (is.null(names(curvilinear))) names(curvilinear) = xy[1:2] ret = try(st_as_stars(st, curvilinear = curvilinear), silent = TRUE) if (inherits(ret, "stars")) st = ret } if (!is_curvilinear(st) && inherits(x <- try(read_mdim(filename, ll[1], curvilinear = FALSE), silent = TRUE), "stars") && inherits(y <- try(read_mdim(filename, ll[2], curvilinear = FALSE), silent = TRUE), "stars") && identical(dim(x)[xy], dim(st)[xy]) && identical(dim(y)[xy], dim(st)[xy])) st = st_as_stars(st, curvilinear = setNames(list(x, y), xy)) } st } else st_set_crs(st, ret$srs) } # WRITE helper functions: add_attr = function(x, at) { # append at to attribute "attrs" structure(x, attrs = c(attr(x, "attrs"), at)) } add_units_attr = function(l) { f = function(x) { if (inherits(x, "units")) add_attr(x, c(units = as.character(units(x)))) else if (inherits(x, c("POSIXct", "PCICt"))) { cal = if (!is.null(cal <- attr(x, "cal"))) c(calendar = paste0(cal, "_day")) # else NULL, intended x = as.numeric(x) if (all(x %% 86400 == 0)) add_attr(x/86400, c(units = "days since 1970-01-01", cal)) else if (all(x %% 3600 == 0)) add_attr(x / 3600, c(units = "hours since 1970-01-01 00:00:00", cal)) else add_attr(x, c(units = "seconds since 1970-01-01 00:00:00", cal)) } else if (inherits(x, "Date")) add_attr(as.numeric(x), c(units = "days since 1970-01-01")) else x } lapply(l, f) } cdl_add_geometry = function(e, i, sfc) { stopifnot(inherits(sfc, c("sfc_POINT", "sfc_POLYGON", "sfc_MULTIPOLYGON", "sfc_LINESTRING", "sfc_MULTILINESTRING"))) if (inherits(sfc, "sfc_POLYGON")) sfc = st_cast(sfc, "MULTIPOLYGON") if (inherits(sfc, "sfc_LINESTRING")) sfc = st_cast(sfc, "MULTILINESTRING") cc = st_coordinates(sfc) if (inherits(sfc, c("sfc_MULTIPOLYGON"))) { e$node = structure(seq_len(nrow(cc)), dim = c(node = nrow(cc))) e$x = structure(cc[, 1], dim = c(node = nrow(cc))) e$y = structure(cc[, 2], dim = c(node = nrow(cc))) e$node_count = structure(rle(cc[,"L3"])$lengths, dim = setNames(length(sfc), names(e)[i])) part = rle(cc[,"L3"] * 2 * max(cc[,"L2"]) + 2 * (cc[,"L2"] - 1) + cc[,"L1"])$lengths e$part_node_count = structure(part, dim = c(part = length(part))) e$interior_ring = structure(as.numeric(cc[cumsum(part), "L1"] > 1), dim = c(part = length(part))) attr(e, "dims") = c(node = nrow(cc), part = length(part)) e$geometry = add_attr(structure(numeric(0), dim = c("somethingNonEx%isting" = 0)), c(geometry_type = "polygon", node_count = "node_count", node_coordinates = "x y", part_node_count = "part_node_count", interior_ring = "interior_ring", grid_mapping = if (!is.na(st_crs(sfc))) "crs" else NULL)) } else if (inherits(sfc, "sfc_MULTILINESTRING")) { # LINE: e$node = structure(seq_len(nrow(cc)), dim = c(node = nrow(cc))) e$x = structure(cc[, 1], dim = c(node = nrow(cc))) e$y = structure(cc[, 2], dim = c(node = nrow(cc))) e$node_count = structure(rle(cc[,"L2"])$lengths, dim = setNames(length(sfc), names(e)[i])) part = rle((cc[,"L2"] - 1) * max(cc[,"L1"]) + cc[,"L1"])$lengths e$part_node_count = structure(part, dim = c(part = length(part))) attr(e, "dims") = c(node = nrow(cc), part = length(part)) e$geometry = add_attr(structure(numeric(0), dim = c("somethingNonEx%isting" = 0)), c(geometry_type = "line", node_count = "node_count", node_coordinates = "x y", part_node_count = "part_node_count", grid_mapping = if (!is.na(st_crs(sfc))) "crs" else NULL)) } else { # POINT: if (ll <- isTRUE(st_is_longlat(sfc))) { e$lon = add_attr(structure(cc[, 1], dim = setNames(nrow(cc), names(e)[i])), c(units = "degrees_north", standard_name = "longitude")) e$lat = add_attr(structure(cc[, 2], dim = setNames(nrow(cc), names(e)[i])), c(units = "degrees_east", standard_name = "latitude")) } else { e$x = structure(cc[, 1], dim = setNames(nrow(cc), names(e)[i])) e$y = structure(cc[, 2], dim = setNames(nrow(cc), names(e)[i])) } e$geometry = add_attr(structure(numeric(0), dim = c("somethingNonEx%isting" = 0)), c(geometry_type = "point", node_coordinates = ifelse(ll, "lon lat", "x y"), grid_mapping = if (!is.na(st_crs(sfc))) "crs" else NULL)) } e } # convert stars object into a list of CDL-like variables with named dimensions st_as_cdl = function(x) { e = add_units_attr(expand_dimensions(x)) # TODO: handle sfc dimensions d = st_dimensions(x) dimx = dim(x) xy = attr(st_dimensions(x), "raster")$dimensions co = NULL for (i in seq_along(e)) { if (is.null(dim(e[[i]])) && !is.list(e[[i]])) e[[i]] = structure(e[[i]], dim = setNames(length(e[[i]]), names(e)[i])) else if (is_curvilinear(x)) { # curvilinear coordinate matrices: if (names(e)[i] == xy[1]) names(e)[i] = "lon" if (names(e)[i] == xy[2]) names(e)[i] = "lat" } else if (inherits(e[[i]], "sfc")) { # vector data cube: sfc = e[[i]] e[[i]] = structure(seq_along(sfc), dim = setNames(length(sfc), names(e)[i])) e = cdl_add_geometry(e, i, sfc) if (inherits(sfc, "sfc_POINT")) { if (isTRUE(st_is_longlat(x))) { co = c(coordinates = "lat lon") xy = c("lon", "lat") # hack } else { co = c(coordinates = "x y") xy = c("x", "y") # hack } } else { dimx = c(dimx, attr(e, "dims")) co = c(coordinates = "x y") xy = c("x", "y") # hack } co = c(co, geometry = "geometry") } } if (is_curvilinear(x)) { e[["lat"]] = add_attr(e[["lat"]], c(units = "degrees_north", "_CoordinateAxisType" = "Lat", axis = "Y")) e[["lon"]] = add_attr(e[["lon"]], c(units = "degrees_east", "_CoordinateAxisType" = "Lon", axis = "X")) co = paste(rev(setdiff(names(d), c("x", "y", "lat", "lon"))), "lat lon") } else { if (!any(is.na(xy))) { e[[ xy[1] ]] = add_attr(e[[ xy[1] ]], c(axis = "X")) e[[ xy[2] ]] = add_attr(e[[ xy[2] ]], c(axis = "Y")) } } for (i in seq_along(x)) x[[i]] = add_attr(x[[i]], co) x = add_units_attr(x) # unclasses for (i in seq_along(x)) if (is.null(names(dim(x[[i]])))) # FIXME: read_ncdf() doesn't name dim names(dim(x[[i]])) = names(d) for (i in names(e)) x[[i]] = e[[i]] which_dims = function(a, dimx) { m = match(names(dim(a)), names(dimx), nomatch = numeric(0)) - 1 if (all(is.na(m))) integer(0) else m } ret = lapply(x, function(a) structure(a, which_dims = which_dims(a, dimx))) structure(ret, which_crs = !(names(ret) %in% names(e)), is_numeric = sapply(ret, is.numeric), dims = attr(e, "dims")) } #' @name mdim #' @param x stars object #' @param driver character; driver name #' @param root_group_options character; driver specific options regarding the creation of the root group #' @param as_float logical; if \code{TRUE} write 4-byte floating point numbers, if \code{FALSE} write 8-byte doubles #' @param ... ignored #' @export #' @examples #' set.seed(135) #' m = matrix(runif(10), 2, 5) #' names(dim(m)) = c("stations", "time") #' times = as.Date("2022-05-01") + 1:5 #' pts = st_as_sfc(c("POINT(0 1)", "POINT(3 5)")) #' s = st_as_stars(list(Precipitation = m)) |> #' st_set_dimensions(1, values = pts) |> #' st_set_dimensions(2, values = times) #' nc = tempfile(fileext=".nc") #' if (compareVersion(sf_extSoftVersion()["GDAL"], "3.4.0") > -1) { #' write_mdim(s, nc) #' # try ncdump on the generated file #' print(read_mdim(nc)) #' } write_mdim = function(x, filename, driver = detect.driver(filename), ..., root_group_options = character(0), options = character(0), as_float = TRUE) { cdl = st_as_cdl(x) wkt = if (is.na(st_crs(x))) character(0) else st_crs(x)$wkt xy = attr(st_dimensions(x), "raster")$dimensions gdal_write_mdim(filename, driver, c(dim(x), attr(cdl, "dims")), cdl, wkt, xy, root_group_options = root_group_options, options = options, as_float = as_float) invisible(x) } stars/R/transform.R0000644000176200001440000001073214642501334013752 0ustar liggesusersto_curvilinear = function(x) { if (! is_curvilinear(x)) { xy = attr(st_dimensions(x), "raster")$dimensions nx = dim(x)[ xy[1] ] ny = dim(x)[ xy[2] ] cl = if (has_rotate_or_shear(x)) { pts = xy_from_colrow(as.matrix(expand.grid(seq_len(nx), seq_len(ny))) - 0.5, st_geotransform(x)) list(matrix(pts[,1], nx, ny), matrix(pts[,2], nx, ny)) } else { ed = expand_dimensions(x) list(matrix(ed[[ xy[1] ]], nx, ny), matrix(ed[[ xy[2] ]], nx, ny, byrow = TRUE)) } st_as_stars(x, curvilinear = setNames(cl, xy), crs = st_crs(x)) } else x } transform_curvilinear = function(x, crs, ...) { if (is.numeric(crs)) crs = st_crs(crs) # nocov to = crs from = if (sf_extSoftVersion()["proj.4"] < "5.0.0") { if (inherits(crs, "crs")) to = crs$proj4string else stopifnot(is.character(crs)) st_crs(x)$proj4string } else st_crs(x) get_yx = function(x) isTRUE(sf::st_crs(sf::st_sfc(sf::st_point(), crs = x), parameters=TRUE)$yx) d = st_dimensions(x) xy = attr(d, "raster")$dimensions cc = cbind(as.vector(d[[ xy[1] ]]$values), as.vector(d[[ xy[2] ]]$values)) pts = sf::sf_project(from, to, cc, ...) d[[ xy[1] ]]$refsys = d[[ xy[2] ]]$refsys = st_crs(crs) d[[ xy[1] ]]$values = matrix(pts[,1], dim(x)[xy]) d[[ xy[2] ]]$values = matrix(pts[,2], dim(x)[xy]) if (sf::st_axis_order() && get_yx(st_crs(from)) != get_yx(st_crs(crs))) { message("swapping [x] and [y] roles") attr(d, "raster")$dimensions = rev(attr(d, "raster")$dimensions) d[[ xy[1] ]]$values = t(d[[ xy[1] ]]$values) d[[ xy[2] ]]$values = t(d[[ xy[2] ]]$values) } st_stars(x, d) } #' transform geometries in stars objects to a new coordinate reference system, without warping #' #' @name st_transform #' @param x object of class \code{stars}, with either raster or simple feature geometries #' @param crs object of class \code{crs} with target crs #' @param ... ignored #' @examples #' geomatrix = system.file("tif/geomatrix.tif", package = "stars") #' (x = read_stars(geomatrix)) #' new = st_crs('OGC:CRS84') #' y = st_transform(x, new) #' plot(st_transform(st_as_sfc(st_bbox(x)), new), col = NA, border = 'red') #' plot(st_as_sfc(y, as_points=FALSE), col = NA, border = 'green', axes = TRUE, add = TRUE) #' image(y, col = heat.colors(12), add = TRUE) #' plot(st_as_sfc(y, as_points=TRUE), pch=3, cex=.5, col = 'blue', add = TRUE) #' plot(st_transform(st_as_sfc(x, as_points=FALSE), new), add = TRUE) #' @seealso \link{st_warp} #' @details For simple feature dimensions, \link[sf]{st_transform} is called, leading to lossless transformation. For gridded spatial data, a curvilinear grid with transformed grid cell (centers) is returned, which is also lossless. To convert this to a regular grid in the new \code{CRS}, use \link{st_warp} (which is in general lossy). #' #' If array values contain geometries and an array as a whole is of class `sfc` and #' has a non-missing CRS, array geometries are also transformed. #' @export st_transform.stars = function(x, crs, ...) { stopifnot(!is.na(crs), !is.na(st_crs(x))) for (i in seq_along(x)) { if (inherits(x[[i]], "sfc") && !is.na(st_crs(x[[i]]))) # array cells are geometries x[[i]] = structure(st_transform(x[[i]], crs), dim = dim(x[[i]])) } if (has_sfc(x)) { if (!inherits(crs, "crs") && !inherits(crs, "stars")) crs = st_crs(crs) # needed for GDAL's transform of features d = st_dimensions(x) ix = which_sfc(x) for (j in ix) { d[[j]]$values = st_transform(d[[j]]$values, crs, ...) d[[j]]$refsys = crs } structure(x, dimensions = d) } else { if (! has_raster(x)) { warning("no spatial coordinates present: st_transform does nothing") x } else transform_curvilinear(to_curvilinear(x), crs, ...) } } #' @name st_transform st_transform_proj.stars = function(x, crs, ...) { stopifnot(!is.na(crs), !is.na(st_crs(x))) if (has_sfc(x)) { try_proj = function(x, crs) { ret = try(st_transform(x, crs), silent = TRUE) if (inherits(ret, "try-error")) { if (!requireNamespace("lwgeom", quietly = TRUE)) stop("package lwgeom required, please install it first") # nocov if (inherits(crs, "crs")) crs = crs$proj4string ret = lwgeom::st_transform_proj(x, crs) } ret } d = st_dimensions(x) ix = which_sfc(x) for (j in ix) { d[[j]]$values = try_proj(d[[j]]$values, crs) d[[j]]$refsys = crs } structure(x, dimensions = d) } else { if (! has_raster(x)) { warning("no spatial coordinates present: st_transform_proj does nothing") x } else transform_curvilinear(to_curvilinear(x), crs) } } stars/R/write.R0000644000176200001440000001640614576033423013102 0ustar liggesusers# reset the offset when x is a sub-raster (i.e, starts at index larger than 1) reset_sub = function(x) { d = st_dimensions(x) xy = attr(d, "raster")$dimensions if (all(is.na(xy))) return(x) for (i in xy) { if (d[[ i ]]$from > 1) { if (!is_regular_grid(x)) stop("can only write sub-rasters for regular grids") ioff = d[[ i ]]$from - 1 d[[ i ]]$offset = d[[ i ]]$offset + ioff * d[[ i ]]$delta d[[ i ]]$to = d[[ i ]]$to - ioff d[[ i ]]$from = 1 } } structure(x, dimensions = d) } #' @export st_write.stars = function(obj, dsn, layer, ...) { .Deprecated("write_stars") # nocov } #' @name write_stars #' @export write_stars = function(obj, dsn, layer, ...) UseMethod("write_stars") #' write stars object to gdal dataset (typically: to file) #' #' @param obj object of class \code{stars} #' @param dsn gdal dataset (file) name #' @param layer attribute name; if missing, the first attribute is written #' @param ... passed on to \link[sf:gdal]{gdal_write} #' @param driver driver driver name; see \link[sf]{st_drivers} #' @param options character vector with dataset creation options, passed on to GDAL #' @param type character; output binary type, one of: \code{Byte} for eight bit unsigned integer, \code{UInt16} for sixteen bit unsigned integer, \code{Int16} for sixteen bit signed integer, \code{UInt32} for thirty two bit unsigned integer, \code{Int32} for thirty two bit signed integer, \code{Float32} for thirty two bit floating point, \code{Float64} for sixty four bit floating point. #' @param NA_value non-NA value that should represent R's \code{NA} value in the target raster file; if set to \code{NA}, it will be ignored. #' @param update logical; if \code{TRUE}, an existing file is being updated #' @param normalize_path logical; see \link{read_stars} #' @param scale_offset length 2 numeric vector with scale, offset values: raw values computed by raw = (value - offset) / scale are written to dsn; scale and offset values are written to dsn or else a warning is raised #' @name write_stars #' @export write_stars.stars = function(obj, dsn, layer = 1, ..., driver = detect.driver(dsn), options = character(0), type = if (is.factor(obj[[1]]) && length(levels(obj[[1]])) < 256) "Byte" else "Float32", NA_value = NA_real_, update = FALSE, normalize_path = TRUE, scale_offset = c(1.0, 0.0)) { if (missing(layer) && length(obj) > 1) warning("all but first attribute are ignored") if (length(layer) > 1) stop("layer should have length 1; for writing multi-band images use merge() to merge layers into a dimension") obj = st_upfront(obj[layer]) if (! update) # new file: should not be a sub-array obj = reset_sub(obj) if (!update && !is.null(attr(obj[[1]], "colors"))) # add r g b alpha table from colors: obj[[1]] = structure(obj[[1]], rgba = t(col2rgb(attr(obj[[1]], "colors"), alpha = TRUE))) if (normalize_path) dsn = enc2utf8(maybe_normalizePath(dsn, TRUE)) sf::gdal_write(obj, ..., file = dsn, driver = driver, options = options, type = type, NA_value = NA_value, geotransform = st_geotransform(obj), update = update, scale_offset = scale_offset) invisible(obj) } #' @name write_stars #' @param chunk_size length two integer vector with the number of pixels (x, y) used in the read/write loop; see details. #' @param progress logical; if \code{TRUE}, a progress bar is shown #' @details \code{write_stars} first creates the target file, then updates it sequentially by writing blocks of \code{chunk_size}. #' @details in case \code{obj} is a multi-file \code{stars_proxy} object, all files are written as layers into the output file \code{dsn} #' @export write_stars.stars_proxy = function(obj, dsn, layer = 1, ..., driver = detect.driver(dsn), options = character(0), scale_offset = c(1.0, 0.0), type = "Float32", NA_value = NA_real_, chunk_size = c(dim(obj)[1], floor(25e6 / dim(obj)[1])), progress = TRUE) { if (!missing(layer)) obj = obj[layer] cl = attr(obj, "call_list") if (is.null(cl) && (length(obj[[1]]) > 1 || length(obj) > 1)) { # collapse bands: out_file = tempfile(fileext = ".vrt") gdal_utils("buildvrt", unlist(obj), out_file, options = "-separate") obj[[1]] = out_file } if (progress) { pb = txtProgressBar() setTxtProgressBar(pb, 0) } dim_obj = dim(obj) if (prod(chunk_size) > prod(dim_obj[1:2])) write_stars(st_as_stars(obj), dsn, layer, ..., driver = driver, options = options, scale_offset = scale_offset, type = type, NA_value = NA_value) else { # write chunked: https://github.com/r-spatial/stars/pull/291/files di_write = di_read = st_dimensions(obj) di_from = c(di_read[[1]]$from, di_read[[2]]$from) created = FALSE ncol = ceiling(dim_obj[1] / chunk_size[1]) nrow = ceiling(dim_obj[2] / chunk_size[2]) for (col in 1:ncol) { di_write[[1]]$from = 1 + (col - 1) * chunk_size[1] di_write[[1]]$to = min(col * chunk_size[1], dim_obj[1]) di_read[[1]]$from = di_write[[1]]$from + di_from[1] - 1 di_read[[1]]$to = di_write[[1]]$to + di_from[1] - 1 di_write[[1]]$offset = with(di_read[[1]], offset + delta * (from - 1)) for (row in 1:nrow) { di_write[[2]]$from = 1 + (row - 1) * chunk_size[2] di_write[[2]]$to = min(row * chunk_size[2], dim_obj[2]) di_read[[2]]$from = di_write[[2]]$from + di_from[2] - 1 di_read[[2]]$to = di_write[[2]]$to + di_from[2] -1 di_write[[2]]$offset = with(di_read[[2]], offset + delta * (from - 1)) chunk = st_as_stars(structure(obj, dimensions = di_read)) attr(chunk, "dimensions")[[1]] <- di_write[[1]] # x attr(chunk, "dimensions")[[2]] <- di_write[[2]] # y if (! created) { # create: d = st_dimensions(chunk) d_obj = st_dimensions(obj) d[[1]]$from = d[[2]]$from = 1 d[[1]]$to = d[[1]]$from + dim_obj[1] - 1 d[[2]]$to = d[[2]]$from + dim_obj[2] - 1 gt = st_geotransform(structure(obj, dimensions = d)) sf::gdal_write(structure(obj, dimensions = d), ..., file = dsn, driver = driver, options = options, type = type, scale_offset = scale_offset, NA_value = NA_value, geotransform = gt) # branches on stars_proxy } created = TRUE write_stars(chunk, dsn = dsn, layer = layer, driver = driver, options = options, type = type, scale_offset = scale_offset, update = TRUE, NA_value = NA_value) if (progress) setTxtProgressBar(pb, ((col-1) * nrow + row) / (ncol * nrow)) } } } if (progress) close(pb) invisible(obj) } #' @name write_stars #' @export #' @param filename character; used for guessing driver short name based on file #' extension; see examples #' @examples #' detect.driver("L7_ETMs.tif") detect.driver = function(filename) { #nocov start # from raster::.getFormat: ext <- tolower(tools::file_ext(filename)) if (nchar(ext) < 2) { warning("file without extension, using driver GTiff", call. = FALSE) 'GTiff' } else { switch(ext, tif = , tiff = 'GTiff', grd = 'rraster', nc = , cdf = , ncdf = 'NetCDF', zarr = 'ZARR', kml = , kmz = 'KML', big = 'big.matrix', sgrd = , sdat = 'SAGA', bil = 'BIL', bsq = 'BSQ', bip = 'BIP', bmp = 'BMP', gen = 'ADRG', bt = 'BT', envi = 'ENVI', ers = 'ERS', img = 'HFA', rst = 'RST', mpr = 'ILWIS', rsw = 'RMF', flt = 'EHdr', gpkg = 'GPKG', { warning("extension ", ext, " is unknown. Using default driver GTiff.") 'GTiff' } ) } } #nocov end stars/R/raster.R0000644000176200001440000003250014576033423013241 0ustar liggesusers#' @name st_as_stars #' @param att see \link[raster:factor]{factorValues}; column in the RasterLayer's attribute table #' @param ignore_file logical; if \code{TRUE}, ignore the Raster object file name #' @export st_as_stars.Raster = function(.x, ..., att = 1, ignore_file = FALSE) { if (!requireNamespace("raster", quietly = TRUE)) stop("package raster required, please install it first") # nocov #0 360 -90 90 e = as.vector(raster::extent(.x)) # xmin xmax ymin ymax RasterIO = if ("data" %in% slotNames(.x) && inherits(.x@data, ".SingleLayerData")) list(bands = .x@data@band) else list() if (!ignore_file) { file = if ("file" %in% slotNames(.x)) .x@file@name else if ("filename" %in% slotNames(.x)) .x@filename else "" if (file != "") { r = try(read_stars(file, RasterIO = RasterIO, ...), silent = TRUE) if (!inherits(r, "try-error")) { if (is.na(st_crs(r))) r = st_set_crs(r, st_crs(raster::crs(.x))) r = fix_dims(r, e) if (inherits(.x, "RasterLayer")) names(r) <- names(.x) return(r) } } } if (!requireNamespace("sp", quietly = TRUE)) stop("package sp required, please install it first") # nocov v = raster::values(.x) dim(v) = dim(.x)[c(2,1,3)] if (all(raster::is.factor(.x))) { l = raster::levels(.x)[[1]]$levels if (length(l) == 0) # get the layer's RAT, column att: l = raster::factorValues(.x, seq_len(max(v, na.rm = TRUE)), att = att)[[1]] colors = try(.x@legend@colortable, silent = TRUE) if (inherits(colors, "try-error") || length(colors) == 0) colors = NULL else colors = colors[-1] v = structure(v, class = "factor", levels = as.character(l), colors = colors) # FIXME: should we handle levels for all layers here, or break on multiple different ones? } dimensions = list( x = create_dimension(from = 1, to = dim(v)[1], offset = e[1], delta = (e[2]-e[1])/dim(v)[1], refsys = st_crs(raster::crs(.x))), y = create_dimension(from = 1, to = dim(v)[2], offset = e[4], delta = (e[3]-e[4])/dim(v)[2], refsys = st_crs(raster::crs(.x)))) z = raster::getZ(.x) dimensions$band = if (is.null(z)) create_dimension(values = names(.x)) else create_dimension(values = z) l = if (length(names) > 1) setNames(list(v), deparse(substitute(.x), 50)) else setNames(list(v), names(.x)[1]) ret = st_as_stars(l, dimensions = create_dimensions(dimensions, get_raster())) if (dim(ret)[3] == 1) adrop(ret, 3) else ret } #' Coerce stars object into a Raster raster or brick #' #' Coerce stars object into a Raster raster or brick #' @param from object to coerce #' @name as #' @rdname coerce-methods #' @aliases coerce,stars,Raster-method #' @aliases coerce,stars_proxy,Raster-method #' @returns RasterLayer or RasterBrick #' @details If the stars object has more than three dimensions, all dimensions higher than the third will be collapsed into the third dimensions. If the stars object has only an x/y raster but multiple attributes, these are merged first, then put in a raster brick. setAs("stars", "Raster", function(from) { if (!requireNamespace("sp", quietly = TRUE)) stop("package sp required, please install it first") # nocov if (!requireNamespace("raster", quietly = TRUE)) stop("package raster required, please install it first") # nocov if (!is_regular_grid(from)) stop("only regular rasters can be converted to Raster* objects") st_as_raster(from, class = "Raster") }) setAs("stars_proxy", "Raster", function(from) { if (!requireNamespace("raster", quietly = TRUE)) stop("package raster required, please install it first") # nocov if (!is_regular_grid(from)) stop("only regular rasters can be converted to Raster* objects") if (length(attr(from, "call_list"))) { fname = paste0(tempfile(), ".tif") write_stars(from, fname) from = fname } raster::brick(unlist(from)) }) get_terra_levels = function(x, colors) { # create factor levels, as used by stars, from SpatRaster levels in a data.frame # see https://github.com/r-spatial/stars/pull/484 x = x[order(x[[1]]), ] # sort table on level missing_labels = x[[2]] == "" levels = x[[1]] if (any(levels < 0)) stop("negative IDs in SpatRaster levels not supported") ex = setdiff(0:max(levels), levels) exclude = rep(FALSE, max(levels) + 1) exclude[ex + 1] = TRUE # 0-based vector list(levels = levels[!missing_labels], labels = x[[2]][!missing_labels], exclude = exclude, colors = colors[!missing_labels]) } #' @name st_as_stars #' @param ignore_file logical; if \code{TRUE}, ignore the SpatRaster object file name #' @param as_attributes logical; if \code{TRUE} and \code{.x} has more than one layer, load these as separate attributes rather than as a band or time dimension (only implemented for the case where \code{ignore_file} is \code{TRUE}) #' @export st_as_stars.SpatRaster = function(.x, ..., ignore_file = FALSE, as_attributes = all(terra::is.factor(.x))) { if (!requireNamespace("terra", quietly = TRUE)) stop("package terra required, please install it first") # nocov #0 360 -90 90 e = as.vector(terra::ext(.x)) # xmin xmax ymin ymax src = terra::sources(.x, bands=TRUE) attr_name = basename(src$source[1]) if (!ignore_file && all(src$source != "")) { # there can be multiple files, but only the first one is used here. # perhaps a warning should be given; better would be to iterate over "sid" # but you might have a situation where some sources are filenames and others are not if (attr_name == "") attr_name = "values" lst = vector("list", length(unique(src$sid))) for (i in unique(src$sid)) { file = unique(src$source[src$sid == i]) if (length(file) > 1) stop("more than one file per sid: giving up; try ignore_file=FALSE") # RasterIO = if (dim(.x)[3] == 1) # > 1 would be more sensible? # But this can only be ignored if the _file_ has 1 band RasterIO = list(bands = src$bands[src$sid == i]) r = try(read_stars(file, RasterIO = RasterIO, ...), silent = TRUE) if (! inherits(r, "try-error")) { if (is.na(st_crs(r)) && terra::crs(.x) != "") r = st_set_crs(r, st_crs(terra::crs(.x))) r = fix_dims(r, e) if (length(unique(src$sid)) > 1 && length(dim(r)) > 2) r = split(r) #transfer the layer/band names as well? # ... = names(.x)[1:(dim(r)[3])] # perhaps check whether they represent a time dimension (all(!is.na(time(.x)))) } else stop(paste("error reading", file, "bands", paste0(RasterIO$bands, collapse = " "))) lst[[i]] = r } ret = if (length(lst) > 1) merge(setNames(do.call(c, lst), names(.x))) else r if (!all(is.na(terra::time(.x)))) ret = st_set_dimensions(ret, 3, values = terra::time(.x), names = "time") } else { # ignore_file TRUE: if (terra::nlyr(.x) > 1 && as_attributes) { ret = do.call(c, lapply(seq_len(terra::nlyr(.x)), function(i) st_as_stars(.x[[i]], ignore_file = TRUE))) if (!is.null(names(.x))) names(ret) = names(.x) return(ret) # RETURNS } if (attr_name == "") { if (all(names(.x) == "")) attr_name = "values" else attr_name = paste(names(.x)[1], collapse = ".") } v = terra::values(.x, mat = FALSE) dimv = dim(v) = setNames(dim(.x)[c(2,1,3)], c("x", "y", "band")) if (all(terra::is.factor(.x))) { if (length(terra::levels(.x)) > 1) warning("ignoring categories/levels for all but first layer") colors = try(rgb(terra::coltab(.x)[[1]][-1], maxColorValue = 255), silent = TRUE) if (inherits(colors, "try-error") || length(colors) == 0) colors = NULL if (inherits(l <- terra::levels(.x)[[1]], "data.frame")) l = get_terra_levels(l, colors) else stop("terra levels should return a list of data.frame's; pls update terra") v = structure(factor(as.vector(v), levels = l$levels, labels = l$labels), dim = dimv, colors = l$colors, exclude = l$exclude) } crs = if (terra::crs(.x) == "") NA_crs_ else st_crs(terra::crs(.x)) dimensions = list( x = create_dimension(from = 1, to = dim(v)[1], offset = e[1], delta = (e[2]-e[1])/dim(v)[1], refsys = crs), y = create_dimension(from = 1, to = dim(v)[2], offset = e[4], delta = (e[3]-e[4])/dim(v)[2], refsys = crs)) dimensions$band = create_dimension(values = names(.x)) ret = st_as_stars(setNames(list(v), attr_name), dimensions = create_dimensions(dimensions, get_raster())) if (dim(ret)[3] == 1) ret = adrop(ret, 3) else if (!all(is.na(terra::time(.x)))) ret = st_set_dimensions(ret, 3, values = terra::time(.x), names = "time") } ret } #' Coerce stars object into a terra SpatRaster #' #' Coerce stars object into a terra SpatRaster #' @param from object to coerce #' @name as #' @rdname coerce-methods #' @aliases coerce,stars,Terra-method #' @aliases coerce,stars_proxy,Terra-method #' @returns SpatRaster #' @details If the stars object has more than three dimensions, all dimensions higher than the third will be collapsed into the third dimensions. If the stars object has only an x/y raster but multiple attributes, these are merged first, then put in a SpatRaster. setAs("stars", "SpatRaster", function(from) { if (!requireNamespace("terra", quietly = TRUE)) stop("package terra required, please install it first") # nocov if (!is_regular_grid(from)) stop("only regular rasters can be converted to SpatRaster objects") st_as_raster(from, class = "SpatRaster") }) setAs("stars_proxy", "SpatRaster", function(from) { if (!requireNamespace("terra", quietly = TRUE)) stop("package terra required, please install it first") # nocov if (!is_regular_grid(from)) stop("only regular rasters can be converted to SpatRaster objects") if (length(attr(from, "call_list"))) { fname = paste0(tempfile(), ".tif") write_stars(from, fname) from = fname } terra::rast(unlist(from)) }) fix_dims = function(r, e){ e = unname(e) rdims = attr(r, "dimensions") dxy = attr(rdims, "raster")$dimensions dimx = rdims[[dxy[1]]] dimy = rdims[[dxy[2]]] xrev = dimx$delta < 0 yrev = dimy$delta < 0 if (xrev) { dimx$offset = e[2] dimx$delta = (e[1] - e[2]) / (dimx$to - dimx$from + 1) } else { dimx$offset = e[1] dimx$delta = (e[2] - e[1]) / (dimx$to - dimx$from + 1) } if (yrev) { dimy$offset = e[4] dimy$delta = (e[3] - e[4]) / (dimy$to - dimy$from + 1) } else { dimy$offset = e[3] dimy$delta = (e[4] - e[3]) / (dimy$to - dimy$from + 1) } attr(r, "dimensions")[[dxy[1]]] = dimx attr(r, "dimensions")[[dxy[2]]] = dimy r } st_as_raster = function(x, class, ...) { stopifnot(inherits(x, "stars")) if (is.null(names(x))) names(x) = make.names(seq_along(x)) # FIXME: why? sdsr_exercises x_crs = st_crs(x) x = st_upfront(x) # x/y dimensions first if (length(dim(x)) > 3) { warning("folding all higher dimensions into the third dimension") # nocov x = st_apply(x, 1:2, as.vector) # fortunes::fortune("side effect") # nocov } if (length(dim(x)) == 2 && length(x) > 1) x = merge(x) d = st_dimensions(x) if (d[[2]]$delta > 0) { # swap: ny = dim(x)[2] d[[2]]$offset = d[[2]]$offset + ny * d[[2]]$delta # top d[[2]]$delta = -d[[2]]$delta # going down x[[1]] = if (length(dim(x)) == 2) x[[1]][,ny:1] else x[[1]][,ny:1,] } dxy = attr(d, "raster")$dimensions stopifnot(all(dxy %in% names(d))) bb = st_bbox(x) levels = NULL coltab = vector("list", length(x)) values = if (all(sapply(x, is.factor))) { ex = attr(x[[1]], "exclude") if (is.null(ex) || class != "SpatRaster") structure(merge(x)[[1]], dim = NULL) # return the factor else { v = vector("list", length(x)) levels = vector("list", length(x)) for (i in seq_along(v)) { ex = attr(x[[i]], "exclude") ix = which(!ex) - 1 # 0-based index n = as.numeric(structure(x[[i]], dim = NULL)) # factor -> numeric v[[i]] = ix[n] levels[[i]] = data.frame(IDs = ix, categories = levels(x[[i]])) if (!is.null(ct <- attr(x[[i]], "colors"))) { coltab[[i]] = t(col2rgb(rep("#000000", length(ex)), alpha = TRUE)) coltab[[i]][which(!ex),] = t(col2rgb(ct, alpha = TRUE)) } } do.call(c, v) } } else { if (any(sapply(x, is.factor))) warning("mix of factor and non-factor attributes: all factor levels are ignored") as.vector(merge(x)[[1]]) } if (class == "SpatRaster") { third = setdiff(names(d), dxy) b = terra::rast(nrows = dim(x)[ dxy[2] ], ncols=dim(x)[ dxy[1] ], xmin = bb[1], xmax = bb[3], ymin = bb[2], ymax = bb[4], nlyrs = ifelse(length(dim(x)) == 2, 1, dim(x)[third]), crs = x_crs$wkt) terra::values(b) = values if (!is.null(levels)) { levels(b) = levels if (!all(sapply(coltab, is.null))) for (i in seq_len(terra::nlyr(b))) { m = coltab[[i]] terra::coltab(b, layer = i) = cbind(value = seq_len(nrow(m)) - 1, m) } } if (length(dim(x)) != 2) { z = seq(d[[third]]) if (!any(is.na(z))) { if (is.character(z)) { names(b) = z } else { names(b) = paste0(third, z) } } } else if (length(x) == terra::nlyr(b)) names(b) = names(x) } else { if (length(dim(x)) == 2) { b = raster::raster(nrows=dim(x)[ dxy[2] ], ncols=dim(x)[ dxy[1] ], xmn = bb[1], xmx = bb[3], ymn = bb[2], ymx = bb[4], crs = as(x_crs, "CRS"), vals = values) } else { third = setdiff(names(d), dxy) b = raster::brick(nrows=dim(x)[ dxy[2] ], ncols=dim(x)[ dxy[1] ], xmn = bb[1], xmx = bb[3], ymn = bb[2], ymx = bb[4], nl = dim(x)[third], crs = as(x_crs, "CRS")) raster::values(b) = values z = seq(d[[third]]) if (!any(is.na(z))) { if (is.character(z)) names(b) = z else b = raster::setZ(b, z) } } } return(b) } stars/R/values.R0000644000176200001440000000727014576033423013246 0ustar liggesusers## $values interface # if the offset and delta fields are not set, dimension values need # to be in the $values field of a dimension. # this can be in two forms: # 1. curvilinear grid (raster, 2d space only): here, a matrix keeps the values for all x (y) # coordinates for the full set of coordinates # 2. rectilinear grid (space, but also time or sth else): here, a data.frame with values # that specify the start, and that specify the end of the dimensions values values_get_start = function(x) { stopifnot(inherits(x, "dimension")) v = x$values stopifnot(inherits(v, "intervals") && !is.null(v$start)) v$start } values_get_end = function(x) { stopifnot(inherits(x, "dimension")) v = x$values stopifnot(inherits(v, "intervals") && !is.null(v$end)) v$end } values_get_where = function(x, where) { if (where == 0.0) values_get_start(x) else if (where == 1.0) values_get_end(x) else { stopifnot(inherits(x, "dimension")) v = x$values stopifnot(inherits(v, "intervals")) v$start + where * (v$end - v$start) } } # x is an object of class dimension # where specifies, in case of linear mapping, where in [0,1] the value is to be returned: # 0 begin, 1 end, 0.5 center # geotransform: the geotransform (for rotated or sheared raster) # what: "x" or "y" if geotransform is given get_dimension_values = function(x, where = 0.0, geotransform, what = NA_character_) { stopifnot(inherits(x, "dimension")) stopifnot(where >= 0.0 && where <= 1.0) if (!is.null(x$values)) { if (is.atomic(x$values) || inherits(x$values, "sfc")) x$values else values_get_where(x, where) } else { if (! any(is.na(geotransform))) switch(what, x = xy_from_colrow(cbind(seq(x$from, x$to) - 1 + where, 0), geotransform)[,1], y = xy_from_colrow(cbind(0, seq(x$from, x$to) - 1 + where), geotransform)[,2], stop("argument what needs to be x or y") ) else if (!any(c(is.na(x$offset), is.na(x$delta)))) { if (inherits(x$offset, "PCICt") && inherits(x$delta, "difftime")) # FIXME: why does this generate a warning, where POSIXct & difftime doesn't? suppressWarnings(seq(from = x$offset + (x$from - 1 + where) * x$delta, by = x$delta, length.out = x$to - x$from + 1)) else seq(from = x$offset + (x$from - 1 + where) * x$delta, by = x$delta, length.out = x$to - x$from + 1) } else if (!is.na(x$offset) && x$from == 1 && x$to == 1) x$offset else seq(x$from, x$to) } } #values_range = function(x) { # stopifnot(inherits(x, "dimension")) # range(as.vector(as.matrix(x$values))) #} # sets the values (start, end) field of a dimension, from centers, from start, or from start & end. # if end is not given, the last two intervals are assumed to be equal sized # if centers is given, the first two, and the last two intervals are assumed to be equal sized. set_dimension_values = function(..., start = NULL, end = NULL, centers = NULL) { if (!is.null(centers) && (!is.null(start) || !is.null(end))) stop("if centers is given, start and end should not be given") if (!is.null(centers)) { l = length(centers) if (l <= 1) stop("cannot derive cell boundaries from a single center: specify start and end") d = diff(centers) start = c(centers - 0.5 * c(d[1], d)) end = c(start[-1], centers[l] + 0.5 * d[l-1]) } else if (missing(end)) { l = length(start) end = if (l == 1) NA_real_ #stop("cannot infer end values from a single start value") else c(start[-1], start[l] + diff(c(start[l-1], start[l]))) } make_intervals(start = start, end = end) } #' @export range.dimension = function(..., na.rm = FALSE) { dots = list(...) stopifnot(length(dots) == 1) x = dots[[1]] range(get_dimension_values(x, 0.0, NA, NA_character_), get_dimension_values(x, 1.0, NA, NA_character_)) } stars/R/aggregate.R0000644000176200001440000002555014644747037013707 0ustar liggesusers#' spatially or temporally aggregate stars object #' #' spatially or temporally aggregate stars object, returning a data cube with lower spatial or temporal resolution #' #' @param x object of class \code{stars} with information to be aggregated #' @param by object of class \code{sf} or \code{sfc} for spatial aggregation, for temporal aggregation a vector with time values (\code{Date}, \code{POSIXct}, or \code{PCICt}) that is interpreted as a sequence of left-closed, right-open time intervals or a string like "months", "5 days" or the like (see \link{cut.POSIXt}), or a function that cuts time into intervals; if by is an object of class \code{stars}, it is converted to sfc by \code{st_as_sfc(by, as_points = FALSE)} thus ignoring its time component. Note: each pixel is assigned to only a single group (in the order the groups occur) so non-overlapping spatial features and temporal windows are recommended. #' @param FUN aggregation function, such as \code{mean} #' @param ... arguments passed on to \code{FUN}, such as \code{na.rm=TRUE} #' @param drop logical; ignored #' @param join function; function used to find matches of \code{x} to \code{by} #' @param rightmost.closed see \link{findInterval} #' @param left.open logical; used for time intervals, see \link{findInterval} and \link{cut.POSIXt} #' @param as_points see \link[stars]{st_as_sf}: shall raster pixels be taken as points, or small square polygons? #' @param exact logical; if \code{TRUE}, use \link[exactextractr]{coverage_fraction} to compute exact overlap fractions of polygons with raster cells #' @seealso \link[sf]{aggregate}, \link[sf]{st_interpolate_aw}, \link{st_extract}, https://github.com/r-spatial/stars/issues/317 #' @export #' @aliases aggregate #' @examples #' # aggregate time dimension in format Date #' tif = system.file("tif/L7_ETMs.tif", package = "stars") #' t1 = as.Date("2018-07-31") #' x = read_stars(c(tif, tif, tif, tif), along = list(time = c(t1, t1+1, t1+2, t1+3)))[,1:30,1:30] #' st_get_dimension_values(x, "time") #' x_agg_time = aggregate(x, by = t1 + c(0, 2, 4), FUN = max) #' #' # aggregate time dimension in format Date - interval #' by_t = "2 days" #' x_agg_time2 = aggregate(x, by = by_t, FUN = max) #' st_get_dimension_values(x_agg_time2, "time") #' #TBD: #' #x_agg_time - x_agg_time2 #' #' # aggregate time dimension in format POSIXct #' x = st_set_dimensions(x, 4, values = as.POSIXct(c("2018-07-31", #' "2018-08-01", #' "2018-08-02", #' "2018-08-03")), #' names = "time") #' by_t = as.POSIXct(c("2018-07-31", "2018-08-02")) #' x_agg_posix = aggregate(x, by = by_t, FUN = max) #' st_get_dimension_values(x_agg_posix, "time") #' #TBD: #' # x_agg_time - x_agg_posix #' aggregate(x, "2 days", mean) #' if (require(ncmeta, quietly = TRUE)) { #' # Spatial aggregation, see https://github.com/r-spatial/stars/issues/299 #' prec_file = system.file("nc/test_stageiv_xyt.nc", package = "stars") #' prec = read_ncdf(prec_file, curvilinear = c("lon", "lat")) #' prec_slice = dplyr::slice(prec, index = 17, along = "time") #' nc = sf::read_sf(system.file("gpkg/nc.gpkg", package = "sf"), "nc.gpkg") #' nc = st_transform(nc, st_crs(prec_slice)) #' agg = aggregate(prec_slice, st_geometry(nc), mean) #' plot(agg) #' } #' #' # example of using a function for "by": aggregate by month-of-year #' d = c(10, 10, 150) #' a = array(rnorm(prod(d)), d) # pure noise #' times = Sys.Date() + seq(1, 2000, length.out = d[3]) #' m = as.numeric(format(times, "%m")) #' signal = rep(sin(m / 12 * pi), each = prod(d[1:2])) # yearly period #' s = (st_as_stars(a) + signal) %>% #' st_set_dimensions(3, values = times) #' f = function(x, format = "%B") { #' months = format(as.Date(paste0("01-", 1:12, "-1970")), format) #' factor(format(x, format), levels = months) #' } #' agg = aggregate(s, f, mean) #' plot(agg) aggregate.stars = function(x, by, FUN, ..., drop = FALSE, join = st_intersects, as_points = any(st_dimension(by) == 2, na.rm = TRUE), rightmost.closed = FALSE, left.open = FALSE, exact = FALSE) { fn_name = substr(deparse1(substitute(FUN)), 1, 20) classes = c("sf", "sfc", "POSIXct", "Date", "PCICt", "character", "function", "stars") if (!is.function(by) && !inherits(by, classes)) stop(paste("currently, only `by' arguments of class", paste(classes, collapse= ", "), "supported")) if (inherits(by, "stars")) by = st_as_sfc(by, as_points = FALSE) # and if not, then use st_normalize(by) if (inherits(by, "sf")) { geom = attr(by, "sf_column") by = st_geometry(by) } else geom = "geometry" stopifnot(!missing(FUN), is.function(FUN)) if (exact && inherits(by, c("sfc_POLYGON", "sfc_MULTIPOLYGON")) && has_raster(x)) { if (!requireNamespace("raster", quietly = TRUE)) stop("package raster required, please install it first") # nocov if (!requireNamespace("exactextractr", quietly = TRUE)) stop("package exactextractr required, please install it first") # nocov x = st_upfront(x) d = st_dimensions(x)[1:2] r = st_as_stars(list(a = array(1, dim = dim(d))), dimensions = d) e = exactextractr::coverage_fraction(as(r, "Raster"), by) st = do.call(raster::stack, e) m = raster::getValues(st) if (!identical(FUN, sum)) { # see https://github.com/r-spatial/stars/issues/289 if (isTRUE(as.character(as.list(FUN)[[3]])[2] == "mean")) m = sweep(m, 2, colSums(m), "/") # mean: divide weights by the sum of weights else stop("for exact=TRUE, FUN should either be mean or sum") } new_dim = c(prod(dim(x)[1:2]), prod(dim(x)[-(1:2)])) out_dim = c(ncol(m), dim(x)[-(1:2)]) if (isTRUE(list(...)$na.rm)) x = lapply(x, function(y) { y[is.na(y)] = 0.0; y }) agg = lapply(x, function(a) array(t(m) %*% array(a, dim = new_dim), dim = out_dim)) # %*% dropped units, so to propagate units, if present we need to copy (mean/sum): d = create_dimensions(append(setNames(list(create_dimension(values = by)), geom), st_dimensions(x)[-(1:2)])) for (i in seq_along(x)) { if (inherits(x[[i]], "units")) agg[[i]] = units::set_units(agg[[i]], units(x[[i]]), mode = "standard") names(dim(agg[[i]])) = names(d) } return(st_as_stars(agg, dimensions = d)) } drop_y = FALSE grps = if (inherits(by, c("sf", "sfc"))) { x = if (has_raster(x)) { ndims = 2 drop_y = TRUE st_upfront(x) } else if (has_sfc(x)) { ndims = 1 st_upfront(x, which_sfc(x)) } # find groups: # don't use unlist(join(x_geoms, by)) as this would miss the empty groups, # and may have multiple if geometries in by overlap, hence: if (identical(join, st_intersects) && has_raster(x)) sapply(join(x, by, as_points = as_points), function(x) if (length(x)) x[1] else NA) else { x_geoms = if (has_raster(x)) st_as_sfc(x, as_points = as_points) else st_dimensions(x)[[ which_sfc(x) ]]$values sapply(join(x_geoms, by), function(x) if (length(x)) x[1] else NA) } } else { # time: by is POSIXct/Date or character ndims = 1 x = st_upfront(x, which_time(x)) values = expand_dimensions(x)[[1]] if (is.function(by)) { i = by(values) if (!is.factor(i)) i = as.factor(i) by = levels(i) } else if (inherits(by, "character")) { i = cut(values, by, right = left.open) by = if (inherits(values, "Date")) as.Date(levels(i)) else as.POSIXct(levels(i)) } else { if (!inherits(values, class(by))) warning(paste0('argument "by" is of a different class (', class(by)[1], ') than the time values (', class(values)[1], ')')) i = findInterval(values, by, left.open = left.open, rightmost.closed = rightmost.closed) i[ i == 0 | i == length(by) ] = NA } as.integer(i) } d = st_dimensions(x) dims = dim(d) agr_grps = function(x, grps, uq, FUN, bind, ...) { do.call(bind, lapply(uq, function(i) { sel <- which(grps == i) if (!isTRUE(any(sel))) NA_real_ else apply(x[sel, , drop = FALSE], 2, FUN, ...) } )) } bind = if (length(FUN(1:10, ...)) > 1) cbind else rbind # rearrange: x = structure(x, dimensions = NULL, class = NULL) # unclass newdims = c(prod(dims[1:ndims]), prod(dims[-(1:ndims)])) for (i in seq_along(x)) { a = array(x[[i]], newdims) u = if (inherits(x[[i]], "units") && dim(a)[2] > 0) { a = units::set_units(a, units(x[[i]]), mode = "standard") try(out <- FUN(a[,1], ...)) if (inherits(out, "units")) units(out) else NULL } else NULL x[[i]] = agr_grps(a, grps, seq_along(by), FUN, bind, ...) if (is.numeric(x[[i]]) && !is.null(u)) x[[i]] = units::set_units(x[[i]], u, mode = "standard") } # reconstruct dimensions table: d[[1]] = create_dimension(values = by) names(d)[1] = if (is.function(by) || inherits(by, c("POSIXct", "Date", "PCICt", "function"))) "time" else geom if (drop_y) d = d[-2] # y # suppose FUN resulted in more than one value? if ((r <- prod(dim(x[[1]])) / prod(dim(d))) > 1) { if (r %% 1 != 0) stop("unexpected array size: does FUN return a consistent number of values?") a = attributes(d) values = if (is.null(rn <- rownames(x[[1]]))) seq_len(r) else rn[1:r] d = append(d, list(create_dimension(values = values))) n = length(d) names(d)[n] = fn_name d = d[c(n, 1:(n-1))] attr(d, "raster") = a$raster attr(d, "class") = a$class newdim = setNames(c(r, length(by), dims[-(1:ndims)]), names(d)) } else newdim = setNames(c(length(by), dims[-(1:ndims)]), names(d)) st_stars(lapply(x, structure, dim = newdim), dimensions = d) } # aggregate is done over one or more dimensions # say we have dimensions 1,...,k and we want to aggregate over i,...,j # with 1 <= i <= j <= k; # let |n| = j-1+1 be the number of dimensions to aggregate over, n # let |m| = k - n be the number of remaining dimensions, m # permute the cube such that the n dimensions are followed by the m # rearrange the cube to a 2D matrix with |i| x ... x |j| rows, and remaining cols # find the grouping of the rows # (rearrange such that groups are together) # for each sub matrix, belonging to a group, do # apply FUN to every column # assing the resulting row to the group # now we have |g| rows, with |g| the number of groups # assign each group to the target group of "by" # redimension the matrix such that unaffected dimensions match again #' @export aggregate.stars_proxy = function(x, by, FUN, ...) { if (!inherits(by, c("sf", "sfc", "sfg", "stars"))) collect(x, match.call(), "aggregate", c("x", "by", "FUN"), env = environment(), ...) else { if (inherits(by, "stars")) by = st_as_sfc(by, as_points = FALSE) by = st_geometry(by) # this assumes each result of a [ selection is small enough to hold in memory l = lapply(seq_along(by), function(i) { sel_i = st_normalize(st_as_stars(x[by[i]])) aggregate(sel_i, by[i], FUN, ...) } ) do.call(c, c(l, along = list(which_sfc(l[[1]])))) } } stars/R/proxy.R0000644000176200001440000005720014576033423013126 0ustar liggesusersshorten_names = function(x, n) { x = head(x ,n) bn = basename(x) here = paste0(normalizePath("."), .Platform$file.sep, bn) clean = gsub("\"", "", bn) if (any(x != here)) paste0("[...]", .Platform$file.sep, clean) else clean } #' @export print.stars_proxy = function(x, ..., n = 1e5, nfiles = 10, simplify = TRUE) { if (!is.null(attr(x, "resolutions"))) cat("multi-resolution ") cat("stars_proxy object with", length(x), if (length(x) > 1) "attributes" else "attribute", "in", sum(lengths(x)), "file(s)") if (length(x[[1]]) > nfiles) cat("; showing the first", min(length(x[[1]]), nfiles), "filenames\n") else cat(":\n") names = lapply(x, function(nm) if (is.function(nm)) nm() else nm) if (simplify) print(lapply(names, shorten_names, n = nfiles)) else print(lapply(names, head, n = nfiles)) if (!is.null(attr(x, "NA_value")) && !is.na(attr(x, "NA_value"))) cat("NA_value: ", attr(x, "NA_value"), "\n") cat("dimension(s):\n") print(st_dimensions(x), ...) if (!is.null(attr(x, "call_list"))) { cat("call_list:\n") print(unlist(attr(x, "call_list"))) cat("This object has pending lazy operations: dimensions as printed may not reflect this.\n") } } #' @export dim.stars_proxy = function(x) { dim(st_dimensions(x)) } #' @export as.data.frame.stars_proxy = function(x, ...) { as.data.frame(st_as_stars(x), ...) } #' @name plot #' @export #' @details when plotting a subsetted \code{stars_proxy} object, the default value for argument \code{downsample} will not be computed correctly, and has to be set manually. plot.stars_proxy = function(x, y, ..., downsample = get_downsample(dim(x))) { plot(st_as_stars(x, downsample = downsample, ...), ..., downsample = 0) } st_stars_proxy = function(x, dimensions, ..., NA_value, resolutions, RasterIO = list(), file_dim = NULL) { stopifnot(!missing(NA_value)) stopifnot(!missing(resolutions)) stopifnot(length(list(...)) == 0) stopifnot(is.list(x)) stopifnot(inherits(dimensions, "dimensions")) if (length(RasterIO) == 0) RasterIO = NULL structure(x, dimensions = dimensions, NA_value = NA_value, resolutions = resolutions, RasterIO = RasterIO, file_dim = file_dim, class = c("stars_proxy", "stars")) } add_resolution = function(lst) { n = length(lst) resolutions = data.frame(x = numeric(n), y = numeric(n)) for (i in seq_along(lst)) { d = st_dimensions(lst[[i]]) xy = attr(d, "raster")$dimensions resolutions[i,] = c(d[[ xy[1] ]]$delta, d[[ xy[2] ]]$delta) } rownames(resolutions) = sapply(lst, names) structure(lst, resolutions = resolutions) } #' @export #' @param along_crs logical; if \code{TRUE}, combine arrays along a CRS dimension #' @name c.stars c.stars_proxy = function(..., along = NA_integer_, along_crs = FALSE, try_hard = FALSE, nms = names(list(...)), tolerance = sqrt(.Machine$double.eps)) { dots = list(...) get_file_dim = function(dots) { do.call(rbind, lapply(dots, attr, "file_dim")) } if (!all(sapply(dots, function(x) inherits(x, "stars_proxy")))) stop("all arguments to c() should be stars_proxy objects") rio = attr(dots[[1]], "RasterIO") # Case 1: merge attributes of several objects by simply putting them together in a single stars object; # dim does not change: if (length(dots) == 1 && length(along) == 1 && is.na(along)) # do nothing dots[[1]] else if (along_crs) combine_along_crs_proxy(dots) else if (identical(along, NA_integer_)) { if (identical_dimensions(dots)) st_stars_proxy(setNamesIfnn(do.call(c, lapply(dots, unclass)), nms), dimensions = st_dimensions(dots[[1]]), NA_value = attr(dots[[1]], "NA_value"), resolutions = NULL, file_dim = get_file_dim(dots), RasterIO = rio) else if (identical_dimensions(dots, ignore_resolution = TRUE, tolerance = tolerance)) { dots = add_resolution(dots) st_stars_proxy(setNamesIfnn(do.call(c, lapply(dots, unclass)), nms), dimensions = st_dimensions(dots[[1]]), resolutions = attr(dots, "resolutions"), NA_value = attr(dots[[1]], "NA_value"), file_dim = get_file_dim(dots), RasterIO = rio) } else { # currently catches only the special case of ... being a broken up time series: along = sort_out_along(dots) if (!is.na(along)) do.call(c, c(dots, along = along)) else if (!try_hard) stop("don't know how to merge arrays: please specify parameter along") else { d = lapply(dots, st_dimensions) ident = c(TRUE, sapply(d[-1], identical, d[[1]])) if (!all(ident)) warning(paste( "ignored subdataset(s) with dimensions different from first subdataset:", paste(which(!ident), collapse = ", "), "\nuse gdal_subdatasets() to find all subdataset names")) if (!is.null(nms)) nms = nms[ident] st_stars_proxy(setNamesIfnn(do.call(c, lapply(dots[ident], unclass)), nms), dimensions = st_dimensions(dots[[1]]), NA_value = attr(dots[[1]], "NA_value"), resolutions = NULL, file_dim = get_file_dim(dots), RasterIO = rio) } } } else { # arrange along "along" dimension: if (is.list(along)) { # custom ordering of ... over dimension(s) with values specified stop("for proxy objects, along argument as list is not implemented") } else { # loop over attributes, abind them: # along_dim: the number of the dimension along which we merge arrays d = st_dimensions(dots[[1]]) along_dim = if (is.character(along)) { along_dim = which(along == names(d)) if (length(along_dim) == 0) length(d) + 1 else along_dim } else along # ret = propagate_units(mapply(abind, ..., along = along_dim, SIMPLIFY = FALSE), dots[[1]]) ret = if (length(dots[[1]]) == 1) list(attr = unlist(do.call(c, lapply(dots, unclass)))) else { m = mapply(c, lapply(dots, unclass)) # simplifies to list matrix setNames(lapply(seq_len(nrow(m)), function(i) unlist(m[i,])), names(dots[[1]])) } dims = combine_dimensions(dots, along_dim) if (along_dim == length(d) + 1) names(dims)[along_dim] = if (is.character(along)) along else "new_dim" st_stars_proxy(ret, dimensions = dims, NA_value = attr(dots[[1]], "NA_value"), resolutions = NULL, file_dim = get_file_dim(dots), RasterIO = rio) } } } combine_along_crs_proxy = function(dots) { crs = lapply(l, st_crs) l = lapply(dots, st_set_crs, value = NA) ret = do.call(c, c(l, along = "crs")) st_set_dimensions(ret, "crs", values = crs, point = TRUE) } #' @export #' @name redimension st_redimension.stars_proxy = function(x, new_dims = st_dimensions(x), along = list(new_dim = names(x)), ...) { d = st_dimensions(x) new_dim = create_dimension(values = along[[1]]) dims = create_dimensions(c(d, new_dim = list(new_dim)), attr(d, "raster")) names(dims)[names(dims) == "new_dim"] = names(along) ret = list(unlist(do.call(c, lapply(x, unclass)))) st_stars_proxy(setNames(ret, paste(names(x), collapse = ".")), dimensions = dims, NA_value = attr(x, "NA_value"), resolutions = attr(x, "resolutions")) } # fetch a stars object from a stars_proxy object, using downsampling fetch = function(x, downsample = 0, ...) { stopifnot(inherits(x, "stars_proxy")) d = st_dimensions(x) xy = attr(d, "raster")$dimensions dx = d[[ xy[1] ]] dy = d[[ xy[2] ]] nBufXSize = nXSize = dx$to - dx$from + 1 nBufYSize = nYSize = dy$to - dy$from + 1 downsample = rep(downsample, length.out = 2) if (downsample[1] > 0) nBufXSize = ceiling(nBufXSize / (downsample[1] + 1)) if (downsample[2] > 0) nBufYSize = ceiling(nBufYSize / (downsample[2] + 1)) # issue #438: if (any(downsample > 0) && !is.null(attr(x, "RasterIO"))) warning("with RasterIO defined, argument downsample is ignored") rasterio = attr(x, "RasterIO") %||% list(nXOff = dx$from, nYOff = dy$from, nXSize = nXSize, nYSize = nYSize, nBufXSize = nBufXSize, nBufYSize = nBufYSize) # select bands? bands <- d[["band"]] if (!is.null(bands)) { if (!is.null(bands$values) && is.numeric(bands$values)) rasterio$bands = bands$values else if (!is.na(bands$from) && !is.na(bands$to) # && (bands$to - bands$from + 1) < length(x[[1]]) ) rasterio$bands = seq(bands$from, bands$to) if (!is.null(rasterio$bands) && length(rasterio$bands) > 1 && length(rasterio$bands) == length(x[[1]])) # one band in each file rasterio$bands = NULL # https://github.com/r-spatial/stars/issues/608 } # do it: ret = vector("list", length(x)) res <- attr(x, "resolutions") for (i in seq_along(ret)) { if (!is.null(res) && any(res[1,] != res[i,])) { mult = c(res[i,1] / res[1,1], res[i,2] / res[1,2]) rasterio$nXOff = floor((dx$from - 1) / mult[1]) + 1 rasterio$nYOff = floor((dy$from - 1) / mult[2]) + 1 rasterio$nXSize = ceiling(dx$to / mult[1]) - floor((dx$from - 1) / mult[1]) rasterio$nYSize = ceiling(dy$to / mult[2]) - floor((dy$from - 1) / mult[2]) rasterio$nBufXSize = ceiling(rasterio$nXSize * mult[1] / (downsample[1] + 1)) rasterio$nBufYSize = ceiling(rasterio$nXSize * mult[2] / (downsample[2] + 1)) mod = function(a, n) { a - n * floor(a/n) } offset = round(c(mod(dx$from - 1, mult[1]), mod(dy$from - 1, mult[2]))) } else offset = c(0,0) file_name = unclass(x)[[i]] if (is.function(file_name)) # realise/evaluate: file_name = file_name() ret[[i]] = read_stars(file_name, RasterIO = rasterio, NA_value = attr(x, "NA_value") %||% NA_real_, normalize_path = FALSE, proxy = FALSE, ...) if (i == 1) dm1 = dim(ret[[1]]) else { xrange = seq_len(dm1[1]) + offset[1] yrange = seq_len(dm1[2]) + offset[2] ret[[i]] = ret[[i]] [ , xrange, yrange ] st_dimensions(ret[[i]]) = st_dimensions(ret[[1]]) } } along = if (length(dim(x)) > 3) setNames(list(st_get_dimension_values(x, 4)), tail(names(st_dimensions(x)), 1)) else list(new_dim = names(x)) ret = if (length(ret) == 1) st_redimension(ret[[1]], name = along) else do.call(c, lapply(ret, st_redimension, along = along)) new_dim = st_dimensions(ret) # for (dm in setdiff(names(d), xy)) # copy over non x/y dimension values, if present: # if (dm %in% names(new_dim)) # new_dim[[dm]] = d[[dm]] if (length(d) > 2) for (dm in 3:length(d)) { new_dim[[dm]] = d[[dm]] # copy all fields - what if this was downsampled? names(new_dim)[dm] = names(d)[dm] } ret = unclass(ret) for (i in seq_along(ret)) { file_dim = attr(x, "file_dim") if (is.null(bands) && !is.null(file_dim) && ncol(file_dim) == length(dim(new_dim)) && ncol(file_dim) == 3) { # https://github.com/r-spatial/stars/issues/596 r = new_dim[[3]]$from:new_dim[[3]]$to # FIXME: or else use $values? ret[[i]] = ret[[i]][,,r] } dim(ret[[i]]) = dim(new_dim) } adrop(st_set_crs(st_stars(setNames(ret, names(x)), new_dim), st_crs(x))) } check_xy_warn = function(call, dimensions) { if (as.character(as.list(call)[[1]]) == "st_apply") { # check dims MARGIN = as.list(call)$MARGIN if (inherits(MARGIN, "call")) MARGIN = eval(MARGIN, environment(call)) if (inherits(MARGIN, "name")) MARGIN = get("MARGIN", environment(call)) xy = attr(dimensions, "raster")$dimensions ok = if (is.numeric(MARGIN)) all(which(names(dimensions) %in% xy) %in% MARGIN) else all(xy %in% MARGIN) if (!ok) warning("st_apply on x/y dimensions applied to downsampled image(s)") } } #' @name st_as_stars #' @param downsample integer: if larger than 0, downsample with this rate (number of pixels to skip in every row/column); if length 2, specifies downsampling rate in x and y. #' @param url character; URL of the stars endpoint where the data reside #' @param envir environment to resolve objects in #' @export st_as_stars.stars_proxy = function(.x, ..., downsample = 0, url = attr(.x, "url"), envir = parent.frame()) { if (! is.null(url)) { # execute/get remotely: # nocov start # if existing, convert call_list to character: attr(.x, "call_list") = lapply(attr(.x, "call_list"), deparse) # push the object to url, then st_as_stars() it there: tempnam = substr(tempfile(pattern = "Z", tmpdir = "", fileext = ""), 2, 15) put_data_url(url, tempnam, .x) expr = paste0("st_as_stars(", tempnam, ", url = NULL, downsample=", downsample, ", envir = data)") # evaluate in "data" first ret = get_data_url(url, expr) put_data_url(url, tempnam, NULL) # remove the temporary object ret # nocov end } else { cl = attr(.x, "call_list") # FIXME: this means we ALWAYS process after (possibly partial) reading; # there are cases where this is not right. Hence: # TODO: only warn when there is a reason to warn. if (!all(downsample == 0)) lapply(attr(.x, "call_list"), check_xy_warn, dimensions = st_dimensions(.x)) process_call_list(fetch(.x, ..., downsample = downsample), cl, envir = envir, downsample = downsample) } } st_as_stars_proxy = function(x, fname = tempfile(fileext = rep_len(".tif", length(x))), quiet = TRUE, NA_value = NA_real_) { stopifnot(inherits(x, "stars")) if (inherits(x, "stars_proxy")) return(x) for (i in seq_along(x)) write_stars(x[i], fname[i], NA_value = NA_value) if (!quiet) cat(paste("writing to", fname, "\n")) st_stars_proxy(setNames(as.list(fname), names(x)), st_dimensions(x), NA_value = NA_value, resolutions = NULL) } # execute the call list on a stars object process_call_list = function(x, cl, envir = new.env(), downsample = 0) { for (i in seq_along(cl)) { if (is.character(cl[[i]])) cl[[i]] = parse(text = cl[[i]])[[1]] stopifnot(is.call(cl[[i]])) env = environment(cl[[i]]) env [[ names(cl[[i]])[2] ]] = x # here, a stars_proxy may be replaced with the fetched stars object old_downsample = env$downsample # might be NULL if (!is.null(env$downsample) && any(env$downsample != downsample)) { cat(paste0("overriding downsample of (sub)expression to c(", paste(downsample, collapse = ","), ")\n")) env$downsample = downsample } # so we need to do that for other args too: https://github.com/r-spatial/stars/issues/390 : if ("e2" %in% names(env) && inherits(env$e2, "stars_proxy")) # binary ops: also fetch the second arg env$e2 = st_as_stars(env$e2, downsample = downsample) x = eval(cl[[i]], env, parent.frame()) env$downsample = old_downsample } x } # add a call to the call list, possibly replacing function name (fn) and first arg name collect = function(x, call, fn, args = "x", env, ...) { call_list = attr(x, "call_list") %||% list() dots = list(...) nd = names(dots) # I would say now to do # env = as.environment(append(as.list(env), dots)) -> but that didn't work. # so we iterate over ... : for (i in seq_along(dots)) env[[ nd[i] ]] = dots[[i]] args = c(args, nd) # set function to call: lst = as.list(call) if (!missing(fn)) lst[[1]] = as.name(fn) # set argument names: if (!missing(fn) && fn == "[") { lst[[2]] = as.name(args[1]) lst[[3]] = as.name(args[2]) for (i in seq_along(args)[-(1:2)]) { if (!args[i] %in% names(lst)) lst[[ args[i] ]] = as.name(args[i]) # appends } } else { for (i in seq_along(args)) { lst[[i+1]] = as.name(args[i]) names(lst)[[i+1]] = args[i] } } call = as.call(lst) environment(call) = env structure(x, call_list = c(call_list, call)) } #' @export adrop.stars_proxy = function(x, drop = which(dim(x) == 1), ...) { collect(x, match.call(), "adrop", c("x", "drop"), env = environment(), ...) } #' @export aperm.stars_proxy = function(a, perm = NULL, ...) { collect(a, match.call(), "aperm", c("a", "perm"), env = environment(), ...) } #' @export is.na.stars_proxy = function(x) { collect(x, match.call(), "is.na", "x", env = environment()) } #' @name stars_subset #' @export "[<-.stars_proxy" = function(x, i, downsample = 0, value) { # https://stackoverflow.com/questions/9965577/copy-move-one-environment-to-another # copy the environment, to avoid side effect later on: # FIXME: to investigate - should this be done to env in every call to collect()? env = as.environment(as.list(environment(), all.names = TRUE)) # copies parent.env(env) = parent.env(environment()) collect(x, match.call(), "[<-", c("x", "i", "value", "downsample"), env) } #' @export split.stars_proxy = function(x, ...) { collect(x, match.call(), "split", env = environment()) } #' @export merge.stars_proxy = function(x, y, ..., name = "attributes") { if (!missing(y)) stop("argument y needs to be missing: merging attributes of x") if (!is.null(attr(x, "call_list")) || !is.null(attr(x, "resolutions"))) # postpone: collect(x, match.call(), "merge", c("x", "y", "name"), env = environment(), ...) else { if (length(x) > 1) { cl = class(x) x = unclass(x) x[[1]] = unlist(x) x[2:length(x)] = NULL class(x) = cl } st_stars_proxy(x, dimensions = create_dimensions(append(st_dimensions(x), list(band = create_dimension(values = names(x[[1]])))), raster = attr(st_dimensions(x), "raster")), NA_value = attr(x, "NA_value"), resolutions = attr(x, "resolutions")) } } #' @export "[.stars_proxy" = function(x, i = TRUE, ..., drop = FALSE, crop = TRUE) { get_range = function(expr) { v = try(eval(expr, parent.frame(2)), silent = TRUE) if (is.numeric(v) && all(diff(v) == 1)) range(v) else NULL } rio = attr(x, "RasterIO") dim_orig = dim(x) mc = match.call() lst = as.list(mc) cl = attr(x, "call_list") if (length(lst) < 3) return(x) # if (missing(i) | !"i" %in% names(lst)) # insert: lst = c(lst[1:2], i = TRUE, lst[-(1:2)]) if (inherits(i, c("character", "logical", "numeric")) && is.null(cl)) { if (!is.null(unclass(x)[[i]])) { # can/should be selected now: if (!is.null(resolutions <- attr(x, "resolutions"))) resolutions = resolutions[i, ] x = st_stars_proxy(unclass(x)[i], st_dimensions(x), NA_value = attr(x, "NA_value"), resolutions = resolutions, file_dim = attr(x, "file_dim"), RasterIO = rio) lst[["i"]] = TRUE # this one has been handled now } ix = 1 while (length(lst) >= 4) { # https://github.com/r-spatial/stars/issues/496 if (!is.null(r <- get_range(lst[[4]]))) { attr(x, "dimensions")[[ix]]$from = r[1] attr(x, "dimensions")[[ix]]$to = r[2] if(!is.null(attr(x, "dimensions")[[ix]]$values)) { attr(x, "dimensions")[[ix]]$values <- attr(x, "dimensions")[[ix]]$values[r[1]:r[2]] } } ix = ix + 1 lst[[4]] = NULL # eat/remove } } else if (crop && inherits(i, c("sf", "sfc", "stars", "bbox"))) { x = st_crop(x, i, ..., collect = FALSE) # does bounding box cropping only if (inherits(i, c("stars", "bbox"))) lst[["i"]] = TRUE # this one has been handled now } # return or collect? file_dim = attr(x, "file_dim") %||% matrix(dim(x)[1:2], 1) n_file_dim = ncol(file_dim) if (length(lst) == 3 && isTRUE(lst[["i"]]) && is.null(cl) && # drop a number of files in the lists of files? length(x) && length(dim(x)) > n_file_dim && length(x[[1]]) == prod(dim_orig[-(seq_len(ncol(file_dim)))])) { # https://github.com/r-spatial/stars/issues/561 # select from the vectors of proxy object names: get_ix = function(d) { stopifnot(inherits(d, "dimension")) if (!is.na(d$from)) seq(d$from, d$to) else d$values } d = st_dimensions(x)[-seq_len(n_file_dim)] # dimensions not in the file(s) e = do.call(expand.grid, lapply(dim_orig[-seq_len(n_file_dim)], seq_len)) # all combinations e$rn = seq_len(nrow(e)) # their index f = do.call(expand.grid, lapply(d, get_ix)) # the ones we want if (!requireNamespace("dplyr", quietly = TRUE)) stop("package dplyr required, please install it first") # nocov sel = dplyr::inner_join(e, f, by = colnames(f))$rn for (i in seq_along(x)) # select: x[[i]] = x[[i]][sel] x } else { # still processing the geometries inside the bbox: if (length(lst) == 3 && isTRUE(lst[["i"]]) && is.null(cl)) x else collect(x, as.call(lst), "[", c("x", "i", "drop", "crop"), env = environment()) # postpone every arguments > 3 to after reading cells } } # shrink bbox with e * width in each direction bb_shrink = function(bb, e) { dx = diff(bb[c("xmin", "xmax")]) dy = diff(bb[c("ymin", "ymax")]) st_bbox(setNames(c(bb["xmin"] + e * dx, bb["ymin"] + e * dy, bb["xmax"] - e * dx, bb["ymax"] - e * dy), c("xmin", "ymin", "xmax", "ymax"))) } #' @name st_crop #' @param collect logical; if \code{TRUE}, repeat cropping on \code{stars} object, i.e. after data has been read #' @export st_crop.stars_proxy = function(x, y, ..., crop = TRUE, epsilon = sqrt(.Machine$double.eps), collect = TRUE) { dm = st_dimensions(x) d_max = dim(x) + sapply(dm, function(x) x$from) - 1 if (st_crs(x) != st_crs(y)) stop("for cropping, the CRS of both objects has to be identical") if (crop && has_raster(x)) { rast = attr(dm, "raster")$dimensions xd = rast[1] yd = rast[2] bb = if (!inherits(y, "bbox")) st_bbox(y) else y if (epsilon != 0) bb = bb_shrink(bb, epsilon) # FIXME: document how EXACTLY cropping works; https://github.com/hypertidy/tidync/issues/73 cr = colrow_from_xy(matrix(bb, 2, byrow=TRUE), dm) # crop x: dm[[ xd ]]$from = max(1, cr[1, 1], na.rm = TRUE) dm[[ xd ]]$to = min(d_max[xd], cr[2, 1], na.rm = TRUE) if(!is.null(dm[[ xd ]]$values)) dm[[ xd ]]$values = dm[[ xd ]]$values[dm[[ xd ]]$from:dm[[ xd ]]$to] # crop y: if (!is.na(dm[[ yd ]]$delta) && dm[[ yd ]]$delta < 0) # FIXME: just subtract values to avoid NA miss? cr[1:2, 2] = cr[2:1, 2] dm[[ yd ]]$from = max(1, cr[1, 2], na.rm = TRUE) dm[[ yd ]]$to = min(d_max[yd], cr[2, 2], na.rm = TRUE) if(!is.null(dm[[ yd ]]$values)) dm[[ yd ]]$values = dm[[ yd ]]$values[dm[[ yd ]]$from:dm[[ yd ]]$to] } x = st_stars_proxy(x, dm, NA_value = attr(x, "NA_value"), resolutions = attr(x, "resolutions"), file_dim = attr(x, "file_dim")) # crop to bb if (collect) collect(x, match.call(), "st_crop", c("x", "y", "crop", "epsilon"), env = environment(), ...) # crops further when realised else x } #' @export st_apply.stars_proxy = function(X, MARGIN, FUN, ..., CLUSTER = NULL, PROGRESS = FALSE, FUTURE = FALSE, rename = TRUE, .fname) { mc = match.call() if (missing(.fname)) .fname = as.character(mc[["FUN"]])[1] collect(X, mc, "st_apply", c("X", "MARGIN", "FUN", "CLUSTER", "PROGRESS", "FUTURE", "rename", ".fname"), env = environment(), ...) } #' @export #' @name predict.stars predict.stars_proxy = function(object, model, ...) { collect(object, match.call(), "predict", c("object", "model"), env = environment(), ...) } #' @export "[[<-.stars_proxy" = function(x, i, value) { y = unclass(x) y[[i]] = value structure(y, class = class(x)) } #' @export st_normalize.stars_proxy = function(x, domain = c(0, 0, 1, 1), ...) { stopifnot(all(domain == c(0,0,1,1))) d = st_dimensions(x) stopifnot(d[[1]]$from == 1, d[[2]]$from == 1) x } #' @export image.stars_proxy <- function(x, ..., downsample = get_downsample(dim(x))) { image(st_as_stars(x, downsample = downsample), ...) } #nocov start get_data_url = function(url, expr = NULL) { if (!requireNamespace("httr", quietly = TRUE)) # GET, POST, PUT stop("package httr required, please install it first") # nocov if (!requireNamespace("jsonlite", quietly = TRUE)) # base64_dec, base64_enc, toJSON, fromJSON stop("package jsonlite required, please install it first") # nocov if (is.null(expr)) jsonlite::fromJSON( httr::content(httr::GET(url), "text", encoding = "UTF-8")) else { js = jsonlite::fromJSON( httr::content(httr::POST(url, body = list(expr = expr), encode = "json"), "text", encoding = "UTF-8")) if (is.list(js) && !is.null(js$error)) stop(paste(js$error, ":", js$message)) unserialize(jsonlite::base64_dec(js)) } } put_data_url = function(url, name, value) { if (!requireNamespace("httr", quietly = TRUE)) # GET, POST, PUT stop("package httr required, please install it first") # nocov if (!requireNamespace("jsonlite", quietly = TRUE)) # base64_dec, base64_enc, toJSON, fromJSON stop("package jsonlite required, please install it first") # nocov value = jsonlite::toJSON(jsonlite::base64_enc(serialize(value, NULL))) httr::PUT(url, body = list(name = name, value = value), encode = "json") } #nocov end stars/R/extract.R0000644000176200001440000001615114600562123013407 0ustar liggesusers#' Extract cell values at point locations #' #' Extract cell values at point locations #' @name st_extract #' @export st_extract = function(x, ...) UseMethod("st_extract") #' @name 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 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 #' @returns if \code{at} is of class \code{matrix}, a matrix with extracted values is returned; #' otherwise: if \code{x} has more dimensions than only x and y (raster), an #' object of class \code{stars} with POINT geometries replacing x and y raster #' dimensions, if this is not the case, an object of \code{sf} with extracted values. #' @details points outside the raster are returned as \code{NA} values. For #' large sets of points for which extraction is needed, passing a matrix as #' to \code{at} may be much faster than passing an \code{sf} or \code{sfc} object. #' @export #' @examples #' tif = system.file("tif/L7_ETMs.tif", package = "stars") #' r = read_stars(tif) #' pnt = st_sample(st_as_sfc(st_bbox(r)), 10) #' st_extract(r, pnt) #' st_extract(r, pnt) %>% st_as_sf() #' st_extract(r[,,,1], pnt) #' st_extract(r, st_coordinates(pnt)) # "at" is a matrix: return a matrix st_extract.stars = function(x, at, ..., bilinear = FALSE, time_column = attr(at, "time_column") %||% attr(at, "time_col"), interpolate_time = bilinear, FUN = mean) { stopifnot(inherits(at, c("sf", "sfc", "matrix"))) if (inherits(at, "matrix")) pts = at else { stopifnot(st_crs(at) == st_crs(x)) if (! all(st_dimension(at) == 0)) { # should check & branch here in case of MULTIPOINT? stopifnot(is.null(time_column), !bilinear, !interpolate_time) # from aggregate.stars_proxy: by = st_geometry(at) # this assumes the result is small, no need to proxy l = lapply(seq_along(by), function(i) aggregate(st_normalize(st_as_stars(x[by[i]])), by[i], FUN, ...)) return(do.call(c, c(l, along = list(which_sfc(l[[1]]))))) # RETURNS! } sf_column = attr(at, "sf_column") %||% "geometry" tm_pts = if (!is.null(time_column)) at[[time_column]] # else NULL at = st_geometry(at) pts = st_coordinates(at) } if (bilinear && !inherits(x, "stars_proxy")) x = st_as_stars_proxy(x) min_dist = NULL m = if (inherits(x, "stars_proxy")) { try_result = try(x0 <- st_as_stars(x, downsample = dim(x)/2), silent = TRUE) lapply(x, function(y) do.call(abind, lapply(get_names(y), gdal_extract, pts = pts, bilinear = bilinear))) } else { x = st_normalize(st_upfront(x)) if (is_curvilinear(x)) { # https://github.com/r-spatial/stars/issues/632 d = st_distance(at, st_as_sfc(x, as_points = TRUE)) ix = apply(d, 1, which.min) # FIXME: handle points outside the raster min_dist = d[cbind(seq_along(at), ix)] } else { cr = colrow_from_xy(pts, x, NA_outside = TRUE) ix = (cr[,2] - 1) * dim(x)[1] + cr[,1] } lapply(x, function(y) array(y, dim = c(prod(dim(x)[1:2]), prod(dim(x)[-(1:2)])))[ix, , drop = FALSE]) } # reset factors & units attributes: for (i in seq_along(m)) { if (inherits(x[[i]], "factor")) { if (is.character(m[[i]])) m[[i]] = match(as.vector(m[[i]]), levels(x[[i]])) m[[i]] = structure(m[[i]], levels = levels(x[[i]]), colors = attr(x[[i]], "colors"), class = class(x[[i]])) } else if (inherits(x[[i]], "units")) units(m[[i]]) = units(x[[i]]) else if (inherits(x, "stars_proxy") && !inherits(try_result, "try-error") && inherits(x0[[i]], "units")) units(m[[i]]) = units(x0[[i]]) } # match times: if (!is.null(time_column)) { 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$refsys %in% refsys_time))))[1] if (is.na(tm)) stop("cannot match times: x does not have a temporal dimension") tm_cube = st_dimensions(x)[[tm]]$values %||% st_get_dimension_values(x, tm) tm_ix = match_time(tm_pts, tm_cube, intervals = !st_dimensions(x)[[tm]]$point, interpolate_time) if (!interpolate_time) m = lapply(m, function(p) p[cbind(seq_along(at), tm_ix)]) else { interpolate = function(x, ix) { i = floor(ix) di = ix - i if (is.na(ix) || di == 0) x[i] else (1 - di) * x[i] + di * x[i+1] } # each time series is reduced to a _single_ time step, interpolated: m = lapply(m, function(n) mapply(interpolate, asplit(n, 1), tm_ix)) } } if (NCOL(m[[1]]) > 1) { # multi-band: if (inherits(at, "matrix")) do.call(cbind, m) else { # return stars: for (i in seq_along(x)) dim(m[[i]]) = c(length(at), dim(x)[-(1:2)]) d = structure(st_dimensions(x), raster = get_raster(dimensions = rep(NA_character_,2))) d[[1]] = create_dimension(values = at) d[[2]] = NULL names(d)[1] = sf_column st = setNames(st_as_stars(m, dimensions = d), names(x)) if (!is.null(min_dist)) st$min_dist = min_dist st } } else { df = setNames(as.data.frame(lapply(m, function(i) structure(i, dim = NULL))), names(x)) if (inherits(at, "matrix")) df else { # return sf: df[[sf_column]] = st_geometry(at) 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[[tm]] = tm_cube[tm_ix] df[[time_column]] = tm_pts } sf = st_as_sf(df) if (!is.null(min_dist)) sf$min_dist = min_dist sf } } } # match the times in a to those of b, or interpolate: # if interpolate = FALSE, returns an integer in 1...length(b) or NA if outside # if interpolate = TRUE, returns a continuous index in 1...length(b) or NA if outside match_time = function(a, b, intervals = FALSE, interpolate = FALSE) { if (inherits(a, "POSIXct") && inherits(b, "Date")) a = as.Date(a) if (inherits(b, "POSIXct") && inherits(a, "Date")) b = as.Date(b) m = if (inherits(b, "intervals")) find_interval(a, b) else if (isTRUE(intervals) || interpolate) { m = findInterval(a, b, rightmost.closed = TRUE) m[ m == 0 | m == length(b) ] = NA m } else match(a, b) if (interpolate && !isTRUE(intervals) && !inherits(b, "intervals")) { b = as.numeric(b) a = as.numeric(a) b = c(b, tail(b, 1)) m + (a - b[m])/(diff(b)[m]) } else m } stars/R/OpenStreetMap.R0000644000176200001440000000227114342440632014464 0ustar liggesusers#' @name st_as_stars #' @param as_col logical; return rgb numbers (FALSE) or (character) color values (TRUE)? #' @export st_as_stars.OpenStreetMap = function(.x, ..., as_col = FALSE) { ncols = .x$tiles[[1]]$yres # not sure why OpenStreetMap has swapped y and x... nrows = .x$tiles[[1]]$xres a = if (as_col) array(.x$tiles[[1]]$colorData, dim = c(x = ncols, y = nrows)) else { rgb_values = grDevices::col2rgb(.x$tiles[[1]]$colorData) array(t(rgb_values), dim = c(x = ncols, y = nrows, band = 3)) } bbx <- unname(unlist(.x$bbox))[c(1,3,4,2)] names(bbx) <- c("xmin", "xmax", "ymin", "ymax") crs = st_crs(.x$tiles[[1]]$projection) dimensions = list( x = create_dimension(from = 1, to = ncols, offset = unname(bbx["xmin"]), delta = unname((bbx["xmax"] - bbx["xmin"]) / ncols), refsys = crs), y = create_dimension(from = 1, to = nrows, offset = unname(bbx["ymax"]), delta = unname((bbx["ymin"] - bbx["ymax"]) / nrows), refsys = crs), band = create_dimension(values = c("red", "green", "blue"))) if (as_col) st_as_stars(a, dimensions = create_dimensions(dimensions[1:2], get_raster())) else st_as_stars(a, dimensions = create_dimensions(dimensions, get_raster())) } stars/R/read.R0000644000176200001440000004115514607453662012670 0ustar liggesusersmaybe_normalizePath = function(.x, np = FALSE) { prefixes = c("NETCDF:", "HDF5:", "HDF4:", "HDF4_EOS:", "SENTINEL2_L1", "SENTINEL2_L2", "GPKG:", "/vsi", "http://", "https://") has_prefix = function(pf, x) substr(x, 1, nchar(pf)) == pf if (is.function(.x) || !np || any(sapply(prefixes, has_prefix, x = .x))) .x else setNames(normalizePath(.x, mustWork = FALSE), names(.x)) } enc2utf8char = function(x) { if (is.character(x)) enc2utf8(x) else x } get_names = function(x) { if (is.function(x)) x() else x } is_functions = function(x) { is.function(x) || all(sapply(x, is.function)) } is_big = function(x, ..., sub = sub, n_proxy) { prod(dim(read_stars(x, ..., sub = sub, proxy = TRUE, quiet = TRUE))) > n_proxy } # strip common start & end parts of x, if remainder != "" unique_part = function(x, prefix) { stopifnot(is.character(x), length(x) > 1) original = x while (nchar(x[1]) && length(unique(substr(x, 1, 1))) == 1) x = sapply(x, substring, 2) while ((n <- nchar(x[1])) && length(unique(substr(x, n, n))) == 1) x = sapply(x, substring, 1, n - 1) if (any(x == "")) original else if (is.character(prefix)) paste0(prefix, x) else x } geoloc_is_2D = function(geolocation, driver) { # the thing has 2-D x and y arrays: # read coordinate array dimensions only: gxy = c(geolocation$X_DATASET, geolocation$Y_DATASET) lon = adrop(read_stars(gxy[1], driver = driver, quiet = TRUE, proxy = TRUE, curvilinear = character(0))) lat = adrop(read_stars(gxy[2], driver = driver, quiet = TRUE, proxy = TRUE, curvilinear = character(0))) dim(lon)[2] > 1 || dim(lat)[2] > 1 } #' read raster/array dataset from file or connection #' #' read raster/array dataset from file or connection #' @param .x character vector with name(s) of file(s) or data source(s) to be read, or a function that returns such a vector #' @param options character; opening options #' @param driver character; driver to use for opening file. To override fixing for subdatasets and autodetect them as well, use \code{NULL}. #' @param sub character, integer or logical; name, index or indicator of sub-dataset(s) to be read #' @param quiet logical; print progress output? #' @param NA_value numeric value to be used for conversion into NA values; by default this is read from the input file #' @param along length-one character or integer, or list; determines how several arrays are combined, see Details. #' @param RasterIO list with named parameters for GDAL's RasterIO, to further control the extent, resolution and bands to be read from the data source; see details. #' @param proxy logical; if \code{TRUE}, an object of class \code{stars_proxy} is read which contains array #' metadata only; if \code{FALSE} the full array data is read in memory. Always \code{FALSE} for curvilinear girds. #' If set to a number, defaults to \code{TRUE} when the number of cells to be read is larger than that number. #' @param curvilinear length two character vector with names of subdatasets holding longitude and latitude values for all raster cells, or named length 2 list holding longitude and latitude matrices; the names of this list should correspond to raster dimensions referred to #' @param normalize_path logical; if \code{FALSE}, suppress a call to \link{normalizePath} on \code{.x} #' @param RAT character; raster attribute table column name to use as factor levels #' @param tolerance numeric; passed on to \link{all.equal} for comparing dimension parameters. #' @param ... passed on to \link{st_as_stars} if \code{curvilinear} was set #' @param exclude character; vector with category value(s) to exclude #' @param shorten logical or character; if \code{TRUE} and \code{length(.x) > 1}, remove common start and end parts of array names; if character a new prefix #' @return object of class \code{stars} #' @details In case \code{.x} contains multiple files, they will all be read and combined with \link{c.stars}. Along which dimension, or how should objects be merged? If \code{along} is set to \code{NA} it will merge arrays as new attributes if all objects have identical dimensions, or else try to merge along time if a dimension called \code{time} indicates different time stamps. A single name (or positive value) for \code{along} will merge along that dimension, or create a new one if it does not already exist. If the arrays should be arranged along one of more dimensions with values (e.g. time stamps), a named list can passed to \code{along} to specify them; see example. #' #' \code{RasterIO} is a list with zero or more of the following named arguments: #' \code{nXOff}, \code{nYOff} (both 1-based: the first row/col has offset value 1), #' \code{nXSize}, \code{nYSize}, \code{nBufXSize}, \code{nBufYSize}, \code{bands}, \code{resample}. #' See \url{https://gdal.org/doxygen/classGDALDataset.html} for their meaning; #' \code{bands} is an integer vector containing the band numbers to be read (1-based: first band is 1). #' Note that if \code{nBufXSize} or \code{nBufYSize} are specified for downsampling an image, #' resulting in an adjusted geotransform. \code{resample} reflects the resampling method and #' has to be one of: "nearest_neighbour" (the default), #' "bilinear", "cubic", "cubic_spline", "lanczos", "average", "mode", or "Gauss". #' #' Data that are read into memory (\code{proxy=FALSE}) are read into a numeric (double) array, except for categorical variables which are read into an numeric (integer) array of class \code{factor}. #' @export #' @examples #' tif = system.file("tif/L7_ETMs.tif", package = "stars") #' (x1 = read_stars(tif)) #' (x2 = read_stars(c(tif, tif))) #' (x3 = read_stars(c(tif, tif), along = "band")) #' (x4 = read_stars(c(tif, tif), along = "new_dimensions")) # create 4-dimensional array #' x1o = read_stars(tif, options = "OVERVIEW_LEVEL=1") #' t1 = as.Date("2018-07-31") #' # along is a named list indicating two dimensions: #' read_stars(c(tif, tif, tif, tif), along = list(foo = c("bar1", "bar2"), time = c(t1, t1+2))) #' #' m = matrix(1:120, nrow = 12, ncol = 10) #' dim(m) = c(x = 10, y = 12) # named dim #' st = st_as_stars(m) #' attr(st, "dimensions")$y$delta = -1 #' attr(st, "dimensions")$y$offset = 12 #' st #' tmp = tempfile(fileext = ".tif") #' write_stars(st, tmp) #' (red <- read_stars(tmp)) #' read_stars(tmp, RasterIO = list(nXOff = 1, nYOff = 1, nXSize = 10, nYSize = 12, #' nBufXSize = 2, nBufYSize = 2))[[1]] #' (red <- read_stars(tmp, RasterIO = list(nXOff = 1, nYOff = 1, nXSize = 10, nYSize = 12, #' nBufXSize = 2, nBufYSize = 2))) #' red[[1]] # cell values of subsample grid: #' \dontrun{ #' plot(st, reset = FALSE, axes = TRUE, ylim = c(-.1,12.1), xlim = c(-.1,10.1), #' main = "nBufXSize & nBufYSize demo", text_values = TRUE) #' plot(st_as_sfc(red, as_points = TRUE), add = TRUE, col = 'red', pch = 16) #' plot(st_as_sfc(st_as_stars(st), as_points = FALSE), add = TRUE, border = 'grey') #' plot(st_as_sfc(red, as_points = FALSE), add = TRUE, border = 'green', lwd = 2) #' } #' file.remove(tmp) read_stars = function(.x, sub = TRUE, ..., options = character(0), driver = character(0), quiet = FALSE, NA_value = NA_real_, along = NA_integer_, RasterIO = list(), proxy = getOption("stars.n_proxy") %||% 1.e8, curvilinear = character(0), normalize_path = TRUE, RAT = character(0), tolerance = 1e-10, exclude = "", shorten = TRUE) { is_false = function(x) { length(x) == 1 && is.logical(x) && !x } if (!is.list(.x) && length(.x) > 1 && is.null(names(.x)) && !is_false(shorten) && any((short_names <- unique_part(.x, shorten)) != .x)) names(.x) = short_names if (is.numeric(proxy)) { if (is_functions(.x)) proxy = TRUE else if (length(curvilinear)) proxy = FALSE } x = if (is.list(.x)) { f = function(y, np) enc2utf8char(maybe_normalizePath(y, np)) rapply(.x, f, classes = c("character", "function"), how = "replace", np = normalize_path) } else enc2utf8char(maybe_normalizePath(.x, np = normalize_path)) if (missing(curvilinear)) { # see if we can get it from metadata item "GEOLOCATION": geolocation <- try(gdal_metadata(.x, "GEOLOCATION"), silent = TRUE) if (!inherits(geolocation, "try-error") && geoloc_is_2D(geolocation, driver)) { # the thing has 2-D x and y arrays: # read coordinate arrays: gxy = c(geolocation$X_DATASET, geolocation$Y_DATASET) lon = adrop(read_stars(gxy[1], driver = driver, quiet = quiet, NA_value = NA_value, RasterIO = RasterIO, proxy = FALSE, curvilinear = character(0), ...)) lat = adrop(read_stars(gxy[2], driver = driver, quiet = quiet, NA_value = NA_value, RasterIO = RasterIO, proxy = FALSE, curvilinear = character(0), ...)) if (all(dim(lon) > 1) && all(dim(lat) > 1)) # both are 2-D matrices: curvilinear = setNames(c(st_set_dimensions(lon, names = c("x", "y")), st_set_dimensions(lat, names = c("x", "y"))), c("x", "y")) else { ulon = unique(diff(as.vector(lon[[1]]))) ulat = unique(diff(as.vector(lat[[1]]))) if (length(ulon) > 1 || length(ulat) > 1) warning("ignoring irregular (rectilinear) x and/or y coordinates!") } } } else if (length(curvilinear) == 2 && is.character(curvilinear)) { # user-set, read them lon = adrop(read_stars(.x, sub = curvilinear[1], driver = driver, quiet = quiet, NA_value = NA_value, RasterIO = RasterIO, proxy = FALSE, ..., sub_required = TRUE)) lat = adrop(read_stars(.x, sub = curvilinear[2], driver = driver, quiet = quiet, NA_value = NA_value, RasterIO = RasterIO, proxy = FALSE, ..., sub_required = TRUE)) curvilinear = setNames(c(st_set_dimensions(lon, names = c("x", "y")), st_set_dimensions(lat, names = c("x", "y"))), c("x", "y")) } if (length(curvilinear) && isTRUE(proxy)) warning("proxy = TRUE may not work for curvilinear rasters") if (length(x) > 1) { # loop over data sources, read, c() all, and RETURN: ret = lapply(x, read_stars, options = options, driver = driver, sub = sub, quiet = quiet, NA_value = NA_value, RasterIO = as.list(RasterIO), proxy = proxy, curvilinear = curvilinear, along = if (length(along) > 1) along[-1] else NA_integer_, normalize_path = normalize_path) return(do.call(c, append(ret, list(along = along, tolerance = tolerance)))) } # else: do it -- data = sf::gdal_read(get_names(x), options = options, driver = driver, read_data = ifelse(is.logical(proxy), !proxy, proxy), NA_value = NA_value, RasterIO_parameters = as.list(RasterIO)) if (!is.null(data$default_geotransform) && data$default_geotransform == 1) { ## we have the 0 1 0 0 0 1 transform indicated ## so stars policy is flip-y and shift to be in 0, ncol, 0, nrow data$geotransform <- c(0, 1, 0, data$rows[2L], 0, -1) } if (length(data$bands) == 0) { # read sub-datasets: different attributes sub_names = split_strings(data$sub) # get named list sub_datasets = sub_names[seq(1, length(sub_names), by = 2)] # sub_datasets = gdal_subdatasets(x, options)[sub] # -> would open x twice # FIXME: only tested for NetCDF: nms = sapply(strsplit(unlist(sub_datasets), ":"), tail, 1) names(sub_datasets) = nms if (any(sapply(sub_datasets[sub], is.null))) sub = sub("^//", "/", sub) # GDAL2->3, HDF5, double to single slash? sub_datasets = sub_datasets[sub] nms = names(sub_datasets) .read_stars = function(x, options, driver, quiet, proxy, curvilinear) { if (! quiet) cat(paste0(tail(strsplit(x, ":")[[1]], 1), ", ")) read_stars(x, options = options, driver = driver, NA_value = NA_value, RasterIO = as.list(RasterIO), proxy = proxy, curvilinear = curvilinear) } driver = if (is.null(driver) || data$driver[1] == "HDF5") # to override auto-detection: character(0) else data$driver[1] ret = lapply(sub_datasets, .read_stars, options = options, driver = driver, quiet = quiet, proxy = proxy, curvilinear = curvilinear) if (! quiet) cat("\n") # return: if (length(ret) == 1) ret[[1]] else { ret = do.call(c, append(ret, list(try_hard = TRUE, nms = nms))) if (length(nms) == length(ret)) # lost my ability to solve this here... setNames(ret, nms) else ret } } else { # we have one single array: read it if (!isTRUE(sub)) { sub_required = if (is.null(list(...)$sub_required)) FALSE else list(...)$sub_required if (sub_required) stop(paste("only one array present in", .x, ": cannot resolve subdataset", sub)) else warning("only one array present: argument 'sub' will be ignored") } meta_data = structure(data, data = NULL) # take meta_data only if (is.numeric(proxy) && !is.null(data$proxy)) proxy = data$proxy data = if (proxy) .x else get_data_units(attr(data, "data")) # extract data array; sets units if present if (meta_data$driver[1] == "netCDF") meta_data = parse_netcdf_meta(meta_data, get_names(x)) # sets all kind of units if (! proxy && !is.null(meta_data$units) && !is.na(meta_data$units) && !inherits(data, "units")) # set units units(data) = try_as_units(meta_data$units) meta_data = parse_gdal_meta(meta_data) newdims = lengths(meta_data$dim_extra) if (length(newdims) && !proxy) dim(data) = c(dim(data)[1:2], newdims) # handle color table and/or attribute table ct = meta_data$color_tables at = meta_data$attribute_tables if (meta_data$driver[1] == "AIG" && identical(names(at[[1]]), c("VALUE", "COUNT"))) at = list() # skip it: https://github.com/r-spatial/stars/issues/435 # FIXME: how to handle multiple color, category or attribute tables? if (!proxy && (any(lengths(ct) > 0) || any(lengths(at) > 0))) { if (any(meta_data$ranges[1, c(2,4)] == 1)) { # adjust data to min/max values from image metadata r = range(data, na.rm = TRUE) min_value = if (meta_data$ranges[1,2] == 1) meta_data$ranges[1,1] else r[1] max_value = if (meta_data$ranges[1,4] == 1) meta_data$ranges[1,3] else r[2] data[data < min_value | data > max_value] = NA } # convert color table ct to a vector of R colors: co = if (any(lengths(ct) > 0)) { ct = ct[[ which(length(ct) > 0)[1] ]] apply(ct, 1, function(x) rgb(x[1], x[2], x[3], x[4], maxColorValue = 255)) } else NULL if (any(lengths(at) > 0)) { # select attribute table: which.at = which(lengths(at) > 0)[1] which.column = if (length(RAT)) RAT[1] else which(sapply(at[[which.at]], class) == "character")[1] if (length(which.column) == 0) which.column = 1 } else which.column = NA if (!(all(is.na(which.column)))) { labels = at = at[[ which.at ]][[ which.column ]] levels = 0:(length(at) - 1) if (length(exclude)) { ex = at %in% exclude labels = labels[!ex] levels = levels[!ex] if (!is.null(co)) co = co[!ex] } else ex = rep(FALSE, length(levels)) } else { levels = sort(unique(as.vector(data))) labels = as.character(levels) if (!is.null(co)) co = co[levels + 1] ex = rep(FALSE, length(levels)) } # f = factor(as.vector(data), levels = levels, labels = labels) # is too costly; # see https://github.com/r-spatial/stars/issues/565: # construct factor array manually: data = structure(match(as.integer(as.vector(data)), levels), levels = labels, dim = dim(data), colors = co, exclude = ex, class = "factor") } dims = if (proxy) { if (length(meta_data$bands) > 1) c(x = meta_data$cols[2], y = meta_data$rows[2], band = length(meta_data$bands), lengths(meta_data$dim_extra)) else c(x = meta_data$cols[2], y = meta_data$rows[2], lengths(meta_data$dim_extra)) } else NULL # return: name_x = if (!is.null(meta_data$long_name) && !is.na(meta_data$long_name)) meta_data$long_name else if (is.function(.x)) names(.x()) %||% .x() else x name_x = tail(strsplit(name_x, '[\\\\/:]+')[[1]], 1) ret = if (proxy) { # no data present, subclass of "stars": dims = create_dimensions_from_gdal_meta(dims, meta_data) st_stars_proxy(setNames(list(x), names(.x) %||% name_x), dims, NA_value = NA_value, resolutions = NULL, RasterIO = RasterIO, file_dim = matrix(dim(dims), 1, dimnames = list(NULL, names(dims)))) } else st_stars(setNames(list(data), names(.x) %||% name_x), create_dimensions_from_gdal_meta(dim(data), meta_data)) if (is.list(curvilinear)) st_as_stars(ret, curvilinear = curvilinear, ...) else ret } } get_data_units = function(data) { units = unique(attr(data, "units")) # will fail parsing in as_units() when more than one if (length(units) > 1) { warning(paste("more than one unit available for array: ignoring all")) # nocov structure(data, units = NULL) } else { if (!is.null(units) && nzchar(units)) { if (units == "arc-second") # datum grids may have this units = "arcsecond" units = try_as_units(units) } if (inherits(units, "units")) units::set_units(structure(data, units = NULL), units, mode = "standard") else structure(data, units = NULL) } } stars/R/rotate.R0000644000176200001440000000653214576033423013245 0ustar liggesuserstoRad = function(x) x * pi / 180 toDeg = function(x) x * 180 / pi rotate = function(lonlat, lon0, lat0, north = TRUE) { # https://gis.stackexchange.com/questions/10808/manually-transforming-rotated-lat-lon-to-regular-lat-lon/14445 , by Miha! lon0 = toRad(lon0) lat0 = toRad(lat0) if (north) { lat0 = -lat0 lon0 = pi + lon0 } lonlat = toRad(lonlat) lon = lonlat[,1] lat = lonlat[,2] vartheta = -(pi/2 + lat0) varphi = -lon0 toDeg(cbind( atan2(sin(lon), tan(lat) * sin(vartheta) + cos(lon) * cos(vartheta)) - varphi, asin(cos(vartheta) * sin(lat) - cos(lon) * sin(vartheta) * cos(lat)) )) } #' @export st_rotate = function(.x, lon0, lat0, north, ...) UseMethod("st_rotate") #' Transform rotated pole long/lat regular grid to unrotated curvilinear grid #' #' Transform rotated long/lat regular grid to unrotated curvilinear grid #' @param .x object of class \code{stars} #' @param lon0 longitude of the rotated pole in degrees #' @param lat0 latitude of the rotated pole in degrees #' @param north logical; if \code{TRUE} the pole refers to the North pole, otherwise the South pole #' @param ... ignored #' @returns curvilinear stars object with coordinates in regular long/lat (North pole at lat=90) #' @export #' @name st_rotate #' @examples #' if (require("starsdata") && require("maps")) { #' # data downloaded from https://esgf-data.dkrz.de/search/cosmo-rea/ #' nc = "netcdf/ts_EUR-6km_ECMWF-ERAINT_REA6_r1i1p1f1_COSMO_v1_mon_201801-201812.nc" #' f = system.file(nc, package = "starsdata") #' m = read_mdim(f, "ts") #' print(m) #' # NOTE this function is obsolete when reading m as #' # m = read_mdim(f, "ts", curvilinear = c("longitude", "latitude")) #' if (require(RNetCDF)) { #' x = open.nc(f) #' lon = att.get.nc(x, "rotated_latitude_longitude", "grid_north_pole_longitude") #' lat = att.get.nc(x, "rotated_latitude_longitude", "grid_north_pole_latitude") #' close.nc(x) #' print(c(lon = lon, lat = lat)) #' } else { #' lon = -162 #' lat = 39.25 #' } #' m1 = st_rotate(m, lon, lat) #' print(m1) #' h = function() maps::map(add = TRUE) #' plot(m1, downsample = c(10, 10, 5), axes = TRUE, hook = h, mfrow = c(1, 2)) #' # curvilinear grid: downsample for plotting speed #' m2 = st_warp(m1, crs = st_crs("OGC:CRS84"), threshold = .1) #' plot(m2, hook = h, mfrow = c(3, 4)) # regular grid: plots fast #' } st_rotate.stars = function(.x, lon0, lat0, north = TRUE, ...) { stopifnot(is.na(st_crs(.x)) || st_is_longlat(.x), !is_curvilinear(.x), is.logical(north), !is.na(north), lat0 <= 90, lat0 >= -90, length(list(...)) == 0) if (has_sfc(.x)) st_set_dimensions(.x, which_sfc(.x), values = rotate(st_geometry(.x), lon0, lat0, north)) else { .x = st_upfront(.x) d = dim(.x) ccr = rotate(st_coordinates(st_dimensions(.x)[1:2]), lon0, lat0, north) st_as_stars(.x, curvilinear = setNames(list(matrix(ccr[,1], d[1], d[2]), matrix(ccr[,2], d[1], d[2])), names(d)[1:2])) } } #' @name st_rotate #' @export st_rotate.sfc = function(.x, lon0, lat0, north = TRUE, ...) { r = rapply(.x, rotate, how = "replace", lon0 = lon0, lat0 = lat0, north = north) st_set_crs(st_sfc(r, recompute_bbox = TRUE), NA_crs_) } #' @name st_rotate #' @export st_rotate.sf = function(.x, lon0, lat0, north = TRUE, ...) { st_set_geometry(.x, st_rotate(st_geometry(.x), lon0, lat0, north)) } stars/R/cubble.R0000644000176200001440000000310214451263410013162 0ustar liggesusers#' @name st_as_stars #' @export #' @param check_times logical; should we check that the time stamps of all time series are identical? st_as_stars.cubble_df = function(.x, ..., check_times = FALSE) { if (!requireNamespace("tsibble", quietly = TRUE)) stop("package cubble required, please install it first") #nocov if (!requireNamespace("cubble", quietly = TRUE)) stop("package cubble required, please install it first") #nocov if (!requireNamespace("dplyr", quietly = TRUE)) stop("package dplyr required, please install it first") #nocov # time_column = cubble::index(.x) nr = sapply(.x$ts, nrow) stopifnot(length(unique(nr)) == 1) ts1 = .x$ts[[1]] dt = which(sapply(ts1, inherits, c("Date", "POSIXct", "units"))) if (length(dt) > 1) { message("using only first time column for time index") dt = dt[1] } if (length(dt) == 0) stop("no time column found") times = ts1[[dt]] if (check_times) stopifnot(all(sapply(.x$ts, function(ts) identical(ts[[dt]], times)))) else message("assuming times are identical for all elements in in the ts list column") m = do.call(rbind, lapply(.x$ts, function(df) df[-dt][[1]])) if (inherits(.x, "sf")) { # vector data cube sfc = st_geometry(.x) d = create_dimensions(list( geometry = create_dimension(values = sfc), time = create_dimension(values = times)) ) st_stars(list(values = m), d) } else { # raster: kv = tsibble::key_vars(.x) .x = dplyr::as_tibble(.x) for (k in kv) .x[[k]] = NULL .x$ts = NULL # remove time series payload .x = cbind(.x, m) merge(st_as_stars(.x)) } } stars/R/xts.R0000644000176200001440000000336714576033423012570 0ustar liggesusersas.xts.stars <- function(x,...) { if (!requireNamespace("xts", quietly = TRUE)) stop("xts required: install that first") # nocov if (length(x) > 1) message("only the first attribute will used; maybe use merge() first?") ed = expand_dimensions.stars(x) time = which(sapply(ed, inherits, c("Date", "POSIXt"))) if (length(time) < 1) stop("no time dimension present in object") if (length(time) > 1) { message("more than one time dimension present in object; taking the first") time = time[1] } x = adrop(st_upfront(x[1], time), drop_xy = TRUE) if (length(dim(x)) > 2) { dims = setNames(c(dim(x)[1], prod(dim(x)[-1])), c(names(ed)[time], "other")) x = st_redimension(x, dims) } else { # set colnames d = st_dimensions(x) if (length(dim(x)) == 2 && length(d[[2]]$values) == dim(x)[2] && is.character(d[[2]]$values)) colnames(x[[1]]) = d[[2]]$values } xts::xts(x[[1]], ed[[time]]) } #' @name st_as_stars #' @param name character; attribute name for array from an \code{xts} object #' @details for the \code{xts} methods, if \code{dimensions} are provided, time has to be the first dimension. #' @export st_as_stars.xts = function(.x, ..., dimensions, name = "attr") { if (!requireNamespace("xts", quietly = TRUE)) stop("xts required: install that first") # nocov if (!requireNamespace("zoo", quietly = TRUE)) stop("zoo required: install that first") # nocov time = zoo::index(.x) cn = colnames(.x) if (!missing(dimensions)) { .x = as.matrix(.x) dim(.x) = dim(dimensions) st_stars(setNames(list(.x), name), dimensions) } else { .x = st_as_stars(setNames(list(as.matrix(.x)), name)) .x = st_set_dimensions(.x, 1, time) if (!is.null(cn)) .x = st_set_dimensions(.x, 2, cn) st_set_dimensions(.x, names = c("time", "others")) } } stars/R/sf.R0000644000176200001440000002424114576033423012354 0ustar liggesusers# sf conversion things #' @export #' @name st_as_sf st_as_sfc.stars = function(x, ..., as_points, which = seq_len(prod(dim(x)[1:2]))) { r = attr(st_dimensions(x), "raster") gt = st_geotransform(x) d = st_dimensions(x)[r$dimensions] if (xor(is.null(d[[1]]$values), is.null(d[[2]]$values))) { # mixed regular/rectilinear dimensions: make rectilinear; https://github.com/r-spatial/stars/issues/458 e = expand_dimensions(d) if (is.null(d[[1]]$values)) { d[[1]]$from = d[[1]]$to = NA d[[1]]$values = e[[1]] } if (is.null(d[[2]]$values)) { d[[2]]$from = d[[2]]$to = NA d[[2]]$values = e[[2]] } } sfc = st_as_sfc(d, ..., as_points = as_points, which = which, geotransform = gt) # swap axes? if (st_axis_order() && isTRUE(st_crs(x, parameters = TRUE)$yx)) st_transform(sfc, pipeline = "+proj=pipeline +step +proj=axisswap +order=2,1") else sfc } #' replace x y raster dimensions with simple feature geometry list (points, or polygons = rasterize) #' @param x object of class \code{stars} #' @param as_points logical; if \code{TRUE}, generate points at cell centers, else generate polygons #' @param ... arguments passed on to \code{st_as_sfc} #' @param na.rm logical; omit (remove) cells which are entirely missing valued (across other dimensions)? #' @return object of class \code{stars} with x and y raster dimensions replaced by a single sfc geometry list column containing either points, or polygons. Adjacent cells with identical values are not merged; see \code{st_rasterize} for this. #' @export st_xy2sfc = function(x, as_points, ..., na.rm = TRUE) { if (inherits(x, "stars_proxy")) x = st_as_stars(x) d = st_dimensions(x) olddim = dim(x) if (! has_raster(x)) stop("x and/or y not among dimensions") x = st_upfront(x) dxy = attr(d, "raster")$dimensions xy_pos = match(dxy, names(d)) stopifnot(all(xy_pos == 1:2)) if (missing(as_points) && isTRUE(d[[dxy[1]]]$point) && isTRUE(d[[dxy[2]]]$point)) as_points = TRUE # find which records are NA for all attributes: a = abind(x, along = length(dim(x)) + 1) keep = if (na.rm) as.vector(apply(a, c(1,2), function(x) !all(is.na(x)))) else rep(TRUE, prod(dim(x)[1:2])) # flatten two dims x,y to one dim sfc (replacing "x") sfc = st_as_sfc(x, as_points = as_points, ..., which = which(keep)) # overwrite raster-x with sfc: d[[ dxy[1] ]] = create_dimension(from = 1, to = length(sfc), values = sfc) # rename raster-x to sfc: names(d)[names(d) == dxy[1] ] = "geometry" # remove y: d[[ dxy[2] ]] = NULL attr(d, "raster") = get_raster(dimensions = rep(NA_character_, 2)) # flatten arrays: x = unclass(x) # to omit using [[<-.stars method: for (i in seq_along(x)) dim(x[[i]]) = c(geometry = length(keep), olddim[-xy_pos]) # reduce arrays to non-NA cells: if (na.rm) { args = rep(list(rlang::missing_arg()), length(dim(x[[1]]))) args[[1]] = which(keep) args[["drop"]] = FALSE for (i in seq_along(x)) x[[i]] = structure(eval(rlang::expr(x[[i]][ !!!args ])), levels = attr(x[[i]], "levels")) } st_stars(x, dimensions = d) } #' @export st_as_sf.dimensions = function(x, ...) { ix = which_sfc(x)[1] st_sf(setNames(list(x[[ ix ]]$values), names(x)[ix]), crs = st_crs(x), ...) } #' Convert stars object into an sf object #' #' Convert stars object into an sf object #' @name st_as_sf #' @param x object of class \code{stars} #' @param as_points logical; should cells be converted to points or to polygons? See details. #' @param which linear index of cells to keep (this argument is not recommended to be used) #' @param na.rm logical; should missing valued cells be removed, or also be converted to features? #' @param merge logical; if \code{TRUE}, cells with identical values are merged (using \code{GDAL_Polygonize} or \code{GDAL_FPolygonize}); if \code{FALSE}, a polygon for each raster cell is returned; see details #' @param use_integer (relevant only if \code{merge} is \code{TRUE}): if \code{TRUE}, before polygonizing values are rounded to 32-bits signed integer values (GDALPolygonize), otherwise they are converted to 32-bit floating point values (GDALFPolygonize). #' @param long logical; if \code{TRUE}, return a long table form \code{sf}, with geometries and other dimensions recycled #' @param connect8 logical; if \code{TRUE}, use 8 connectedness. Otherwise the 4 connectedness algorithm will be applied. #' @param ... ignored #' @details If \code{merge} is \code{TRUE}, only the first attribute is converted into an \code{sf} object. If \code{na.rm} is \code{FALSE}, areas with \code{NA} values are also written out as polygons. Note that the resulting polygons are typically invalid, and use \link[sf:valid]{st_make_valid} to create valid polygons out of them. #' @export #' @examples #' tif = system.file("tif/L7_ETMs.tif", package = "stars") #' x = read_stars(tif) #' x = x[,1:100,1:100,6] # subset of a band with lower values in it #' x[[1]][x[[1]] < 30] = NA # set lower values to NA #' x[[1]] = x[[1]] < 100 # make the rest binary #' x #' (p = st_as_sf(x)) # removes NA areas #' (p = st_as_sf(x[,,,1], merge = TRUE)) # glues polygons together #' all(st_is_valid(p)) # not all valid, see details #' plot(p, axes = TRUE) #' (p = st_as_sf(x, na.rm = FALSE, merge = TRUE)) # includes polygons with NA values #' plot(p, axes = TRUE) st_as_sf.stars = function(x, ..., as_points = FALSE, merge = FALSE, na.rm = TRUE, use_integer = is.logical(x[[1]]) || is.integer(x[[1]]), long = FALSE, connect8 = FALSE) { if (length(x) == 0) return(st_as_sf.dimensions(st_dimensions(st_xy2sfc(x, as_points = as_points, na.rm = FALSE)))) crs = st_crs(x) d = st_dimensions(x) if (merge && !as_points && has_raster(x) && !any(is.na(st_geotransform(x)))) { # uses GDAL polygonize path: x = st_normalize(x) mask = if (na.rm) { mask = x[1] mask[[1]] = !is.na(mask[[1]]) mask } else NULL ret = gdal_polygonize(x, mask, use_integer = use_integer, geotransform = st_geotransform(x), use_contours = FALSE, connect8 = connect8, ...) # factor levels? if (!is.null(lev <- attr(x[[1]], "levels"))) { ex = attr(x[[1]], "exclude") if(any(ex)){ id = (seq_along(ex) - 1)[!ex] # index-values matching to levels ret[[1]] = structure(match(ret[[1]], id), class = "factor", levels = lev) # ordered? } else { ret[[1]] = structure(ret[[1]], class = "factor", levels = lev) # ordered? } } st_set_crs(ret, crs) } else { if (merge) stop("merge=TRUE and as_points=TRUE unsupported; consider using st_contour for generating contour lines") if (has_raster(x)) x = st_xy2sfc(st_upfront(x), as_points = as_points, ..., na.rm = na.rm) if (! has_sfc(x)) stop("no feature geometry column found") if (long) { st_as_sf(as.data.frame(x), crs = crs) } else { ix = which_sfc(x) if (length(ix) > 1) warning("working on the first sfc dimension only") # FIXME: this probably only works for 2D arrays, now other_dim = setdiff(seq_along(dim(x)), ix[1]) sfc = st_dimensions(x)[[ ix[1] ]]$values # other_values = st_dimensions(x)[[ other_dim[1] ]]$values other_values = lapply(st_dimensions(x)[other_dim], function(x) x$values) varnames = apply(do.call(expand.grid, other_values), 1, paste, collapse = ".") un_dim = function(x) { # remove a dim attribute from data.frame columns for (i in seq_along(x)) x[[i]] = structure(x[[i]], dim = NULL) x } dfs = lapply(x, function(y) un_dim(as.data.frame(y))) nc = sapply(dfs, ncol) df = do.call(cbind, dfs) if (length(dim(x)) == 1) # one-dimensional cube... names(df) = names(x) else if (length(varnames) == ncol(df)) names(df) = varnames else if (length(unique(names(df))) < ncol(df) && length(names(dfs)) == ncol(df)) # I hate this names(df) = names(dfs) else { # another exception... time as second dimension e = expand_dimensions(x) if (length(e[-ix]) == 1 && inherits(e[-ix][[1]], c("Date", "POSIXt", "PCICt"))) names(df) = as.character(e[-ix][[1]]) } df[[ names(st_dimensions(x))[ ix[1] ] ]] = sfc # keep dimension name st_sf(df, crs = crs) } } } #' @export #' @name st_as_sf #' @param downsample see \link{st_as_stars} st_as_sf.stars_proxy = function(x, ..., downsample = 0) { st_as_sf(st_as_stars(x, downsample = downsample), ...) } #' Compute or plot contour lines or sets #' #' Compute contour lines or sets #' @param x object of class \code{stars} #' @param na.rm logical; should missing valued cells be removed, or also be converted to features? #' @param contour_lines logical; if \code{FALSE}, polygons are returned (contour sets), otherwise contour lines #' @param breaks numerical; values at which to "draw" contour levels #' @details this function requires GDAL >= 2.4.0 #' @seealso for polygonizing rasters following grid boundaries, see \link{st_as_sf} with arguments \code{as_points=FALSE} and \code{merge=TRUE}; \link{contour} plots contour lines using R's native algorithm (which also plots contour levels) #' @export st_contour = function(x, na.rm = TRUE, contour_lines = FALSE, breaks = classInt::classIntervals(na.omit(as.vector(x[[1]])))$brks) { #nocov start mask = if (na.rm) { mask = x[1] mask[[1]] = !is.na(mask[[1]]) mask } else NULL ret = gdal_polygonize(x, mask, use_integer = FALSE, geotransform = st_geotransform(x), use_contours = TRUE, contour_lines = contour_lines, breaks = breaks) # factor levels? if (!is.null(lev <- attr(x[[1]], "levels"))) ret[[1]] = structure(ret[[1]], class = "factor", levels = lev) st_set_crs(ret, st_crs(x)) #nocov end } #' @export st_as_stars.sfc = function(.x, ..., FUN = length, as_points = TRUE) { st = st_as_stars(st_bbox(.x), ...) sfc = st_as_sfc(st, as_points = as_points) i = st_intersects(sfc, .x) vals = sapply(i, FUN) st[[1]] = array(vals, dim(st[[1]])) st } #' @name st_as_stars #' @details The \code{st_as_stars} method for \code{sf} objects without any additional #' arguments returns a one-dimensional data cube with a dimension for the simple features #' geometries, and all remaining attributes as data cube attributes. When used with #' further arguments, the method for \code{data.frame}s is called. #' @examples #' nc = st_read(system.file("gpkg/nc.gpkg", package="sf")) #' st_as_stars(nc) #' @export st_as_stars.sf = function(.x, ..., dims = attr(.x, "sf_column")) { .x = as.data.frame(.x) NextMethod(dims = dims, ...) } stars/R/ncdf.R0000644000176200001440000010260214611433374012653 0ustar liggesusers #' Read NetCDF into stars object #' #' Read data from a file (or source) using the NetCDF library directly. #' #' The following logic is applied to coordinates. If any coordinate axes have #' regularly spaced coordinate variables they are reduced to the #' offset/delta form with 'affine = c(0, 0)', otherwise the values of the coordinates #' are stored and used to define a rectilinear grid. #' #' If the data has two or more dimensions and the first two are regular #' they are nominated as the 'raster' for plotting. #' #' If the \code{curvilinear} argument is used it specifies the 2D arrays #' containing coordinate values for the first two dimensions of the data read. It is currently #' assumed that the coordinates are 2D and that they relate to the first two dimensions in #' that order. #' @examples #' f <- system.file("nc/reduced.nc", package = "stars") #' if (require(ncmeta, quietly = TRUE)) { #' read_ncdf(f) #' read_ncdf(f, var = c("anom")) #' read_ncdf(f, ncsub = cbind(start = c(1, 1, 1, 1), count = c(10, 12, 1, 1))) #' } #' #' @param .x NetCDF file or source as a character vector or an nc_proxy object. #' @param ... ignored #' @param var variable name or names (they must be on matching grids) #' @param ncsub matrix of start, count columns (see Details) #' @param curvilinear length two character named vector with names of variables holding #' longitude and latitude values for all raster cells. `stars` attempts to figure out appropriate #' curvilinear coordinates if they are not supplied. #' @param eps numeric; dimension value increases are considered identical when they differ less than \code{eps} #' @param ignore_bounds logical; should bounds values for dimensions, if present, be ignored? #' @param make_time if \code{TRUE} (the default), an attempt is made to provide a date-time class from the "time" variable #' @param make_units if \code{TRUE} (the default), an attempt is made to set the units property of each variable #' @param proxy logical; if \code{TRUE}, an object of class \code{stars_proxy} is read which contains array #' metadata only; if \code{FALSE} the full array data is read in memory. If not set, defaults to \code{TRUE} #' when the number of cells to be read is larger than \code{options(stars.n_proxy)}, or to 1e8 if that option was not set. #' @param downsample integer; number of cells to omit between samples along each dimension. #' e.g. \code{c(1,1,2)} would return every other cell in x and y and every third cell #' in the third dimension (z or t). If 0, no downsampling is applied. Note that this transformation #' is applied AFTER NetCDF data are read using st_downsample. As such, if proxy=TRUE, this #' option is ignored. #' @details #' If \code{var} is not set the first set of variables on a shared grid is used. #' #' \code{start} and \code{count} columns of ncsub must correspond to the variable dimension (nrows) #' and be valid index using \code{\link[RNetCDF]{var.get.nc}} convention (start is 1-based). If the count value #' is \code{NA} then all steps are included. Axis order must match that of the variable/s being read. #' @export #' @examples #' if (require(ncmeta, quietly = TRUE)) { #' #' precipitation data in a curvilinear NetCDF #' prec_file = system.file("nc/test_stageiv_xyt.nc", package = "stars") #' prec = read_ncdf(prec_file, curvilinear = c("lon", "lat"), ignore_bounds = TRUE) #' } #' #' ##plot(prec) ## gives error about unique breaks #' ## remove NAs, zeros, and give a large number #' ## of breaks (used for validating in detail) #' qu_0_omit = function(x, ..., n = 22) { #' x = units::drop_units(na.omit(x)) #' c(0, quantile(x[x > 0], seq(0, 1, length.out = n))) #' } #' if (require(dplyr, quietly = TRUE)) { #' prec_slice = slice(prec, index = 17, along = "time") #' plot(prec_slice, border = NA, breaks = qu_0_omit(prec_slice[[1]]), reset = FALSE) #' nc = sf::read_sf(system.file("gpkg/nc.gpkg", package = "sf"), "nc.gpkg") #' plot(st_geometry(nc), add = TRUE, reset = FALSE, col = NA) #' } read_ncdf = function(.x, ..., var = NULL, ncsub = NULL, curvilinear = character(0), eps = sqrt(.Machine$double.eps), ignore_bounds = FALSE, make_time = TRUE, make_units = TRUE, proxy = NULL, downsample = 0) { if (!requireNamespace("ncmeta", quietly = TRUE)) stop("package ncmeta required, please install it first") # nocov if (!requireNamespace("RNetCDF", quietly = TRUE)) stop("package RNetCDF required, please install it first") # nocov if(inherits(.x, "nc_proxy")) { # grab the source file and all variables needed. x <- as.character(.x[[1]]) # FIXME: only supports one data source. var <- names(.x) proxy_dimensions <- st_dimensions(.x) nc_prj <- sf::st_crs(.x) if(!is.null(ncsub)) { warning("ncsub ignored when .x is class nc_proxy") ncsub <- NULL } } else { x <- .x proxy_dimensions <- NULL } # Get all the nc metadata meta <- .fix_meta(ncmeta::nc_meta(x)) if(.is_netcdf_cf_dsg(meta)) { if (!requireNamespace("ncdfgeom", quietly = TRUE)) stop("package ncdfgeom required, please install it first") # nocov if(!is.null(proxy) && proxy) warning("proxy behavior not supported for timeseries netcdf") geom <- .get_geom_name(meta) return(st_as_stars(ncdfgeom::read_timeseries_dsg(x), sf_geometry = geom)) } # Get relevant variables var <- .get_vars(var, meta) rep_var <- var[1L] # Get coordinate variable info all_coord_var <- ncmeta::nc_coord_var(x) if(ncol(all_coord_var) == 0) all_coord_var <- data.frame(variable = NA, X = NA, Y = NA, Z = NA, T = NA, bounds = NA) coord_var <- .clean_coord_var(all_coord_var, rep_var, meta, curvilinear) canon_order <- c("X", "Y", "Z", "T")[!is.na(coord_var[c("X", "Y", "Z", "T")])] curvilinear <- if(coord_var$curvilinear) { c(X = coord_var$X, Y = coord_var$Y) } else character(0) # Get dimensions for representative var in correct axis order. dims <- .get_dims(meta, rep_var, coord_var, meta$axis, canon_order) # Validate that ncsub matches dims dims <- .add_ncsub(dims, ncsub) nc <- RNetCDF::open.nc(x) on.exit(RNetCDF::close.nc(nc), add = TRUE) # Get coordinates from netcdf or create them coords <- .get_coords(nc, dims) coords <- .clean_coords(coords, coord_var, meta$attribute, eps) # Figure out if we have a raster or not raster <- .get_nc_raster(coords) # Get matcher for netcdf requests dimid_matcher <- .get_dimid_matcher(nc, coord_var, var) if(!is.null(proxy_dimensions)) { tdim <- if("T" %in% dims$axis) { tmeta <- .get_time_meta(coord_var, rep_var, meta) tvals <- coords[[which(dims$axis == "T")]] tdim <- create_dimension(from = 1, to = length(tvals)) tdim$values <- tvals make_cal_time2(tdim, time_unit = tmeta$tunit, cal = tmeta$calendar) } else NULL dims <- .update_dims(dims, proxy_dimensions, coords, tdim) coords <- .get_coords(nc, dims) coords <- .clean_coords(coords, coord_var, meta$attributes, eps) } pull <- .should_pull(proxy, array_size = prod(dims[, "count", drop = TRUE]), num_vars = length(var)) out_data <- if(pull) { # Get all the data from the nc file .set_nc_units(.get_data(nc, var, dims, dimid_matcher, pull = pull), meta$attribute, make_units) } else { # Just return the source file for each variable. setNames(as.list(rep(x, length(var))), var) } # Create stars dimensions object if(is.null(nc_dim <- dim(out_data[[1]]))) nc_dim <- dims$length dimensions <- create_dimensions(setNames(nc_dim, dims$name), raster) dimensions <- .get_nc_dimensions(dimensions, coord_var = all_coord_var, coords = coords, nc = nc, dims = dims, var_names = meta$variable$name, curvilinear, eps = eps, ignore_bounds = ignore_bounds, atts = meta$attribute) dimensions <- .get_nc_time(dimensions, make_time, coord_var, rep_var, meta) if (is.character(out_data[[1]])) { # this is a proxy out_data <- st_stars_proxy(out_data, dimensions, NA_value = NA_real_, resolutions = NULL) class(out_data) <- c("nc_proxy", "stars_proxy", "stars") } else { # Make initial response data out_data <- st_stars(out_data, dimensions) } if(is.null(proxy_dimensions)) { if(length(curvilinear) == 2) { cv <- curvilinear[1] } else if (length(attr(coords, "cv")) > 0) { cv <- attr(coords, "cv")[1] } else { cv <- all_coord_var[all_coord_var$variable == rep_var, ]$X } nc_prj <- .get_nc_projection(meta$attribute, rep_var, cv) } st_crs(out_data) <- nc_prj # Add curvilinear and return if (length(curvilinear) == 2) { curvi_coords = .get_curvilinear_coords(curvilinear, dimensions, nc, dims) out_data <- add_curvilinear(out_data, curvilinear = curvi_coords, nc_prj) } if(!all(downsample == 0)) { out_data <- st_downsample(out_data, downsample) } out_data } .fix_meta <- function(meta) { nas <- is.na(meta$axis$dimension) if (any(nas)) meta$axis$dimension[nas] <- -1 # nocov if(!is.null(names(meta$attribute))) { names(meta$attribute)[names(meta$attribute) == "attribute"] <- "name" # future proofing. } meta } .is_regular <- function(coords_list, eps) { sapply(coords_list, function(x) regular_intervals(x, epsilon = eps)) } .unique_fuzz <- function(x, eps) { u = unique(x) if (all(diff(sort(u)) < eps)) mean(x) # rather than mean(u) else u } .as_rectilinear = function(d) { ed = expand_dimensions(d, center = FALSE) for (i in attr(d, "raster")$dimensions) { if (!is.na(d[[ i ]]$offset)) { d[[i]]$values = as_intervals(ed[[i]], add_last = TRUE) d[[i]]$offset = d[[i]]$delta = NA } } d } .should_pull <- function(proxy, array_size, num_vars, n_proxy = options("stars.n_proxy")[[1]] %||% 1.e8) { if(is.null(proxy)) { if(array_size > n_proxy) { pull <- FALSE message("Large netcdf source found, returning proxy object.") } else { pull <- TRUE message(paste("Will return stars object with", array_size, "cells.")) } } else { pull <- !proxy } if(pull & array_size > n_proxy) warning("Large netcdf source will be requested. Consider using stars proxy.") pull } .get_vars <- function(var, meta) { if (is.null(var)) { ix <- 1 if (meta$grid$grid[ix] == "S") { ix <- which(!meta$grid$grid == "S")[1L] # nocov if (length(ix) < 1) stop("only scalar variables found, not yet supported") # nocov } grd = meta$grid$grid[which.max(nchar(meta$grid$grid))] var = meta$grid$variables[[match(grd, meta$grid$grid)]]$variable message(sprintf("no 'var' specified, using %s", paste(var, collapse = ", "))) other_vars <- setdiff(meta$variable$name, var) if (length(other_vars) > 0) message(sprintf("other available variables:\n %s", paste(other_vars, collapse = ", "))) } return(var) } .get_grid_mapping <- function(atts, rep_var) { if(!is.null(atts)) { suppressWarnings(ncmeta::nc_grid_mapping_atts(atts, rep_var)) } else { list() } } .get_nc_projection <- function(atts, rep_var, rep_coord_var) { nc_grid_mapping <- .get_grid_mapping(atts, rep_var) if(length(nc_grid_mapping) == 0) { if(.is_degrees(atts, rep_coord_var)) { message(paste("No projection information found in nc file. \n", "Coordinate variable units found to be degrees, \n", "assuming WGS84 Lat/Lon.")) st_crs('OGC:CRS84') } else { warning("No projection information found in nc file.") st_crs(NULL) } } else { tryCatch({ cv_units <- .get_gm_units(atts, rep_coord_var) base_gm <- ncmeta::nc_gm_to_prj(nc_grid_mapping) base_gm <- paste0(gsub("\\+units=m ", "", base_gm), " +units=", cv_units) st_crs(base_gm) }, error = function(e) { warning(paste0("failed to create crs based on grid mapping\n", "and coordinate variable units. Will return NULL crs.\n", "Original error: \n", e)) st_crs(NULL) }) } } .is_degrees <- function(atts, var) { units <- .get_attributes(atts, "units", var)$value[[1]] !is.null(units) && grepl("degrees", units, ignore.case = TRUE) } .get_gm_units <- function(atts, var) { if(.is_degrees(atts, var)) { "degrees" } else { .get_attributes(atts, "units", var)$value[[1]] } } .clean_coord_var <- function(c_v, var, meta, curvilinear) { c_v <- c_v[c_v$variable == var, ] if(nrow(c_v) == 0) c_v[1, ] <- NA check_curvi <- .check_curvilinear(c_v, var, meta$variable, curvilinear) if(length(curvilinear)) { c_v$X <- rep(check_curvi[1], nrow(c_v)) c_v$Y <- rep(check_curvi[2], nrow(c_v)) } not_na <- apply(c_v, 1, function(x) sum(!is.na(x))) c_v <- c_v[which(not_na == max(not_na)), ][1, ] has_prj <- length(.get_grid_mapping(meta$attribute, var)) != 0 if(length(curvilinear) > 0) { if(length(check_curvi) == 0) stop("Curvilinear coordinate variables provided by not found in file.") c_v <- c_v[c_v$variable == var & c_v$X == check_curvi[1], ] c_v$curvilinear <- TRUE } else if(nrow(c_v) > 1 | length(check_curvi) == 2) { # Should use convenience function? sn_atts <- meta$attribute[meta$attribute$name == "standard_name", ] auxiliary_x <- sn_atts[grepl("projection_x_coordinate", sn_atts$value), ]$variable auxiliary_y <- sn_atts[grepl("projection_y_coordinate", sn_atts$value), ]$variable if(length(auxiliary_x) > 0 && length(auxiliary_y) > 0 && auxiliary_x %in% c_v$X && auxiliary_y %in% c_v$Y & has_prj) { c_v <- c_v[c_v$variable == var & c_v$X == auxiliary_x, ] c_v$curvilinear <- FALSE } else if (length(check_curvi) == 2) { c_v <- c_v[c_v$variable == var & c_v$X == check_curvi[1], ] c_v$curvilinear <- TRUE } else{ c_v <- c_v[1, ] warning(paste("Found two coordinate variable pairs. Chosing:", paste(as.character(c_v)[2:5], collapse = " "), "for", as.character(c_v)[1])) #nocov } } else { c_v$curvilinear <- FALSE } return(c_v) } .check_curvilinear <- function(coord_var, var, variables, curvilinear) { if(length(curvilinear) > 0) { if(!all(curvilinear %in% variables$name)) stop("Curvilinear variables not found in file.") if(!length(curvilinear) == 2 | all(!is.character(curvilinear))) stop("Curvilinear input must be a length two character vector.") if(all(variables$ndims[match(curvilinear, variables$name)] == 2)) { xy_coords <- coord_var[c("X", "Y")] if(!curvilinear[1] %in% c(xy_coords$X, xy_coords$Y) | !curvilinear[2] %in% c(xy_coords$X, xy_coords$Y)) { stop("Specified curvilinear coordinate variables not found as X/Y coordinate variables.") } else { if(!curvilinear[1] %in% xy_coords$X) { curvilinear <- curvilinear[2:1] } return(stats::setNames(curvilinear, c("X", "Y"))) } } else { stop("Specified curvilinear coordinates are not 2-dimensional.") } } # If we have X and Y coordvars to look at. if (all(c("X", "Y") %in% names(coord_var))) { # Actually look at this one. XY_curvi = unlist(coord_var[coord_var$variable == var, ][c("X", "Y")]) if (all(!is.na(XY_curvi)) && # If all coordinate variables have 2 dims. all(variables$ndims[match(XY_curvi, variables$name)] == 2)) { return(XY_curvi) } else { return(character(0)) } } else { return(character(0)) } } #' Gets dimension info ensuring they are in an order suited to coordinate #' variables that need to be subset appropriately. #' @noRd .get_dims <- function(meta, var, c_v, axis, canon_order) { # This matches the axes of a given variable to the correct dimension dims <-meta$dimension[match(meta$axis$dimension[meta$axis$variable == var], meta$dimension$id), ] dims$coord_var <- "" dims$axis <- "" rep_var_coordinates <- .get_attributes(meta$attribute, "coordinates", var) if(!is.null(rep_var_coordinates)) { rep_var_coordinates <- strsplit(rep_var_coordinates$value[[1]], " ")[[1]] } x_cv <- axis[axis$variable == c_v$X, ] y_cv <- axis[axis$variable == c_v$Y, ] if(nrow(x_cv) == 1 & nrow(y_cv) == 1) { dims[dims$id == x_cv$dimension, ][c("coord_var", "axis")] <- list(x_cv$variable, "X") dims[dims$id == y_cv$dimension, ][c("coord_var", "axis")] <- list(y_cv$variable, "Y") } dim_matcher <- axis[axis$variable == c_v$X, ]$dimension if(length(dim_matcher) < 2) { dim_matcher <- c(dim_matcher, axis[axis$variable == c_v$Y, ]$dimension) } z_axis <- integer(0) if(!is.na(c_v$Z)) { z_cv <- axis[axis$variable == c_v$Z, ] z_axis <- z_cv$dimension dims[dims$id == z_cv$dimension, ][c("coord_var", "axis")] <- list(z_cv$variable, "Z") dim_matcher <- c(dim_matcher, z_axis) } else if(nrow(dims) == 4 & !c_v$curvilinear) { z_axis <- unique(axis[!axis$variable %in% c(c_v$X, c_v$Y, c_v$T) & !axis$dimension %in% dim_matcher, ]$dimension) } if(!is.na(c_v$T)) { t_cv <- axis[axis$variable == c_v$T, ] t_axis <- t_cv$dimension dims[dims$id == t_cv$dimension, ][c("coord_var", "axis")] <- list(t_cv$variable, "T") if(length(z_axis) > 1) { z_axis <- z_axis[z_axis != t_axis] } dim_matcher <- c(dim_matcher, z_axis, t_axis) } if(all(!is.na(dim_matcher))) { if(length(dim_matcher) != length(dims$id)) { dim_matcher <- c(dim_matcher, dims$id[!dims$id %in% dim_matcher]) dim_matcher <- unique(dim_matcher) } dims <- dims[match(dim_matcher, dims$id, nomatch = 0L), ] } return(dims) } #' Gets a list of dimension id matching indexes for use in #' requesting data from a given NetCDF file. #' @noRd .get_dimid_matcher <- function(nc, coord_var, var) { setNames(lapply(var, FUN = function(.v, c_v, nc) { matcher <- NULL if(all(!is.na(c_v[2:3]))) { coordvar_dimids <- RNetCDF::var.inq.nc(nc, c_v$X)$dimids if(length(coordvar_dimids) == 1) { coordvar_dimids <- c(coordvar_dimids, RNetCDF::var.inq.nc(nc, c_v$Y)$dimids) } if(!is.na(c_v$Z)) { coordvar_dimids <- c(coordvar_dimids, RNetCDF::var.inq.nc(nc, c_v$Z)$dimids) } if(!is.na(c_v$T)) { coordvar_dimids <- c(coordvar_dimids, RNetCDF::var.inq.nc(nc, c_v$T)$dimids) } var_dimids <- RNetCDF::var.inq.nc(nc, .v)$dimids matcher <- match(coordvar_dimids, var_dimids) if(!all(diff(matcher[1:2]) == 1)) { warning("Non-canonical axis order found, attempting to correct.") } } matcher }, c_v = coord_var, nc = nc), var) } .get_nc_var <- function(nc, .v, start, count) { RNetCDF::var.get.nc(nc, variable = .v, start = start, count = count, collapse = FALSE, ## keep 1-dims unpack = TRUE, ## offset and scale applied internally rawchar = TRUE) ## needed for NC_CHAR, as per } .get_data <- function(nc, var, dims, dimid_matcher, pull = pull) { out_data <- lapply(var, pull = pull, FUN = function(.v, pull) { dm <- match(RNetCDF::var.inq.nc(nc, .v)$dimids, dims[, "id", drop = TRUE]) if(is.null(dm)) dm <- c(1:nrow(dims)) request <- list(start = dims[, "start", drop = TRUE][dm], count = dims[, "count", drop = TRUE][dm]) request$size <- prod(request$count) request$dimid_match <- dm request$axis <- dims$axis[dm] if(pull) { ret <- .get_nc_var(nc, .v, request$start, request$count) if(length(dm) > 1 && !all(diff(dm[1:2]) == 1)) { ret <- aperm(ret, dm) } } return(ret) }) ## "../rasterwise/extdata/R13352.nc" ## https://github.com/hypertidy/tidync/issues/75 ## check for NC_CHAR case setNames(lapply(out_data, function(.v) { if (mode(.v) == "raw") { array(unlist(lapply(.v, rawToChar)), dims$length) } else .v }), var) } .get_attributes <- function(attribute, att, var = NULL) { if(!is.null(var)) attribute <- attribute[attribute$variable == var, ] if(att %in% attribute$name) attribute[attribute$name == att, ] else { NULL } } .add_ncsub <- function(dims, ncsub, proxy_dimensions) { if (is.null(ncsub)) { dims$start <- 1 dims$count <- dims$length } else { if (nrow(dims) != nrow(ncsub)) stop("input ncsub doesn't match available dims") ix <- is.na(ncsub[, "count"]) if (any(ix)) ncsub[ix, "count"] <- dims$length[ix] - ncsub[ix, "start"] + 1 if (any(ncsub[, "start"] < 1) || any((ncsub[, "count"] - ncsub[, "start"] + 1) > dims$length)) stop("start or count out of bounds") dims$start <- ncsub[, "start"] dims$count <- ncsub[, "count"] } return(dims) } .set_nc_units <- function(data_list, nc_atts, make_units) { # Get all the units nc_units <- .get_attributes(nc_atts, "units") if (!is.null(nc_units) && nrow(nc_units) > 0 && make_units) { for (i in names(data_list)) { if (i %in% nc_units$variable) { uval <- unlist(nc_units$value[nc_units$variable == i]) if(is.numeric(data_list[[i]])) { units(data_list[[i]]) <- try_as_units(uval[1L]) } else { data_list[[i]]$units <- try_as_units(uval[1L]) } } } } return(data_list) } .get_coords <- function(nc, dims) { ## cannot assume we have COORDS style coordinate varibales ## - so create them as 1:length if needed coords = setNames(vector("list", length(dims$name)), dims$name) attr(coords, "cv") <- c() for (ic in seq_along(coords)) { subidx <- seq(dims$start[ic], length = dims$count[ic]) ## create_dimvar means we can var_get it ## test checks if there's actuall a variable of the dim name if (dims$name[ic] %in% ncmeta::nc_vars(nc)$name) { coords[[ic]] <- RNetCDF::var.get.nc(nc, variable = dims$name[ic])[subidx] attr(coords, "cv") <- c(attr(coords, "cv"), dims$name[ic]) } else { coords[[ic]] <- subidx } } return(coords) } .get_nc_raster <- function(coords) { ## can we create a raster? raster = NULL ## which coords are regular if (length(coords) > 1) { raster = get_raster(affine = c(0, 0), dimensions = names(coords)[1:2], curvilinear = FALSE) } return(raster) } .clean_coords <- function(coords, coord_var, atts, eps) { ## hack longitudes for #277 if (any(lon_coord <- grepl("lon", coord_var$X, ignore.case = TRUE))) { lon_coord <- coord_var$X[lon_coord][1] lons <- coords[[lon_coord]] if (!is.null(lons) && !regular_intervals(lons, epsilon = eps) && .is_degrees(atts, lon_coord) && lons[1L] > 180 && min(lons) > 0) { coords[[lon_coord]] <- ((coords[[lon_coord]] + 180) %% 360) - 180 } else if(!is.null(lons) && .is_degrees(atts, lon_coord) && max(lons) > 180 && min(lons) > 180) { coords[[lon_coord]] <- ((coords[[lon_coord]] + 180) %% 360) - 180 } else if(!is.null(lons) && .is_degrees(atts, lon_coord) && max(lons) > 180) { message(paste("0-360 longitude crossing the international date", "line encountered.\nLongitude coordinates will be", "0-360 in output.")) } } coords } .get_nc_dimensions <- function(dimensions, coord_var, coords, nc, dims, var_names, curvilinear, eps, ignore_bounds, atts) { to_rectilinear = FALSE regular <- .is_regular(coords, eps) for (i in seq_along(coords)) { from_to <- dimensions[[i]]$from:dimensions[[i]]$to try_bounds <- names(coords)[i] %in% var_names && !ignore_bounds && length(bounds <- coord_var[coord_var$variable == names(coords)[i], ]$bounds) > 0 && bounds %in% var_names if (try_bounds) { bounds = RNetCDF::var.get.nc(nc, bounds) if (!is.matrix(bounds)) # single instance, returns a vector bounds = matrix(bounds, nrow = 2) if(!bounds[1, 1] < coords[[i]][1] | !bounds[2, 1] > coords[[i]][1]) { warning(paste("bounds not enveloping", names(coords)[i], "coordinates. Ignoring.")) try_bounds <- FALSE } else { is_reg = ncol(bounds) > 1 && length(u <- .unique_fuzz(apply(bounds, 2, diff), eps)) == 1 && length(v <- .unique_fuzz(diff(bounds[1,]), eps)) == 1 if (is_reg && abs(u + v) < eps) { warning(paste("bounds for", names(coords)[i], "seem to be reversed; reverting them")) bounds = apply(bounds, 2, sort) # should not be needed according to CF, but see #133 u = v } if (is_reg && abs(v - u) < eps) { dimensions[[i]]$offset = bounds[1,1] dimensions[[i]]$delta = v } else { dimensions[[i]]$values = make_intervals(bounds[1, from_to], bounds[2,from_to]) dimensions[[i]]$point = FALSE if (i %in% 1:2 && length(curvilinear) < 1) # FIXME: ? hard-coding here that lon lat are in the first two dimensions: to_rectilinear = TRUE } } } if (regular[i] & !try_bounds) { mdc = mean(diff(coords[[i]])) coord_name = names(coords)[i] t_dim = coord_var[coord_var$variable == coord_name,]$T if (is.null(t_dim) || length(t_dim) == 0) t_dim = NA_character_ # https://github.com/r-spatial/stars/issues/378 if (!is.na(t_dim) && names(coords)[i] == t_dim) dimensions[[i]]$offset[1L] = coords[[i]][1] else dimensions[[i]]$offset[1L] = coords[[i]][1] - mdc/2 ## NaN for singleton dims, but that seems ok unless we have explicit interval? dimensions[[i]]$delta[1L] = mdc } else { dimensions[[i]]$values = coords[[i]][from_to] ## offset/delta for fall-back index (and for NA test ) ## https://github.com/r-spatial/stars/blob/master/R/dimensions.R#L294-L303 dimensions[[i]]$offset[1L] = NA_real_ dimensions[[i]]$delta[1L] = NA_real_ } } if (to_rectilinear) dimensions = .as_rectilinear(dimensions) return(dimensions) } .get_time_meta <- function(coord_var, var, meta) { TIME_name = as.character(na.omit(unlist( coord_var[coord_var$variable == var, ][c("T")]))) if(!length(TIME_name)) { list(tname = NULL, calendar = NULL, tunit = NULL) } else { atts = meta$attribute[meta$attribute$variable == TIME_name, ] ## might not exist, so default to NULL calendar = unlist(atts$value[atts$name == "calendar"])[1L] tunit = unlist(atts$value[atts$name == "units"])[1L] list(tname = TIME_name, calendar = calendar, tunit = tunit) } } .get_nc_time <- function(dimensions, make_time, coord_var, var, meta) { # sort out time -> POSIXct: if (make_time) { if (all("T" %in% names(coord_var))) { tmeta <- .get_time_meta(coord_var, var, meta) if (!is.na(tmeta$tname) && length(tmeta$tname) == 1L && meta$variable$ndims[match(tmeta$tname, meta$variable$name)] == 1) { if (tmeta$tname %in% names(dimensions)) { tdim <- NULL if (is.null(tmeta$tunit) || inherits(tmeta$tunit, "try-error")) { warning("ignoring units of time dimension, not able to interpret") } else { tdim = make_cal_time2(dimensions, time_name = tmeta$tname, time_unit = tmeta$tunit, cal = tmeta$calendar) } if (!is.null(tdim)) dimensions[[tmeta$tname]] <- tdim } } } } return(dimensions) } .get_curvilinear_coords <- function(curvilinear, dimensions, nc, dims) { curvi_coords <- lapply(curvilinear, function(.v) { cv_matcher <- RNetCDF::var.inq.nc(nc, .v)$dimids cv_matcher <- match(cv_matcher, dims$id[1:2]) RNetCDF::var.get.nc(nc, variable = .v, start = dims[1:2, "start", drop = TRUE][cv_matcher], count = dims[1:2, "count", drop = TRUE][cv_matcher], collapse = FALSE, unpack = TRUE) }) names(curvi_coords)[1:2] <- names(dimensions)[1:2] expected_shape <- c(dimensions[[1]]$to, dimensions[[2]]$to) if(!all(dim(curvi_coords[[1]]) == expected_shape)) { curvi_coords[1] <- t(curvi_coords[1]) } if(!all(dim(curvi_coords[2]) == expected_shape)) { curvi_coords[2] <- t(curvi_coords[2]) } return(curvi_coords) } make_cal_time2 <- function(x, time_name = NULL, time_unit = NULL, cal = NULL) { if(inherits(x, "dimensions")) { tm = st_get_dimension_values(x, time_name) dimension <- x[[time_name]] } else { tm <- x$values dimension <- x } if(! is.null(cal) && cal %in% c("360_day", "365_day", "noleap")) { if (!requireNamespace("PCICt", quietly = TRUE)) { stop("package PCICt required, please install it first") # nocov } t01 = set_units(0:1, time_unit, mode = "standard") delta = if (grepl("months", time_unit)) { if (cal == "360_day") set_units(30 * 24 * 3600, "s", mode = "standard") else set_units((365/12) * 24 * 3600, "s", mode = "standard") } else set_units(as_units(diff(as.POSIXct(t01))), "s", mode = "standard") origin = as.character(as.POSIXct(t01[1])) v.pcict = PCICt::as.PCICt(tm * as.numeric(delta), cal, origin) if (!is.null(dimension$values)) { v = dimension$values if (inherits(v, "intervals")) { start = PCICt::as.PCICt(v$start * as.numeric(delta), cal, origin) end = PCICt::as.PCICt(v$end * as.numeric(delta), cal, origin) dimension$values = make_intervals(start, end) } else dimension$values = v.pcict } else { dimension$offset = v.pcict[1] dimension$delta = diff(v.pcict[1:2]) } dimension$refsys = "PCICt" } else { # Gregorian/Julian, POSIXct: if (!is.null(dimension$values)) { v = dimension$values if (inherits(v, "intervals")) { start = as.POSIXct(units::set_units(v$start, time_unit, mode = "standard")) # or: RNetCDF::utcal.nc(u, tm, "c") end = as.POSIXct(units::set_units(v$end, time_unit, mode = "standard")) # or: RNetCDF::utcal.nc(u, tm, "c") dimension$values = make_intervals(start, end) } else dimension$values = as.POSIXct(units::set_units(tm, time_unit, mode = "standard")) # or: RNetCDF::utcal.nc(u, tm, "c") } else { t0 = dimension$offset t1 = dimension$offset + dimension$delta t.posix = as.POSIXct(units::set_units(c(t0, t1), time_unit, mode = "standard")) # or: utcal.nc(u, c(t0,t1), "c") dimension$offset = t.posix[1] dimension$delta = diff(t.posix) } dimension$refsys = "POSIXct" } dimension } .is_netcdf_cf_dsg <- function(meta) { featuretype <- .get_attributes(meta$attribute, "featureType", "NC_GLOBAL") if(!is.null(featuretype)) { if(grepl( "timeseries", featuretype$value, ignore.case = TRUE)) { return(TRUE) } } return(FALSE) } .get_geom_name <- function(meta) { geometry <- unlist(unique(.get_attributes(meta$attribute, "geometry")$value)) if(length(geometry) > 1) { warning(paste("Only a single geometry is supported. Using", geometry[1])) #nocov geometry <- geometry[1] } if(!is.null(geometry)) { ncdfgeom::read_geometry(meta$source$source) } else { NA } } #' @param sf_geometry sf data.frame with geometry and attributes to be added to stars object. #' Must have same number of rows as timeseries instances. #' @details For the \code{ncdfgeom} method: objects are point-timeseries with optional line or polygon geometry for each timeseries specified with the \code{sf_geometry} parameter. See \pkg{ncdfgeom} for more about this NetCDF-based format for geometry and timeseries. #' @name st_as_stars #' @export #' st_as_stars.ncdfgeom <- function(.x, ..., sf_geometry = NA) { crs <- sf::st_crs('OGC:CRS84') if(length(.x$alts) == 0) { ts_points <- data.frame(X = .x$lons, Y = .x$lats) ts_points <- sf::st_as_sf(ts_points, coords = c("X", "Y"), crs = crs) } else { ts_points <- data.frame(X = .x$lons, Y = .x$lats, Z = .x$alts) ts_points <- sf::st_as_sf(ts_points, coords = c("X", "Y", "Z"), crs = crs) } data <- .x$data_frames[[1]] gdim <- create_dimension(from = 1, to = length(.x$lats), refsys = crs, point = TRUE, values = ts_points$geometry) tdim <- create_dimension(from = 1, to = length(.x$time), refsys = "POSIXct", point = FALSE, values = as.POSIXct(.x$time)) dim <- list(time = tdim, points = gdim) if(inherits(sf_geometry, "sf")) { if(length(gdim$values) != length(st_geometry(sf_geometry))) stop("geometry must be same length as instance dimension of timeseries") is_point <- any(grepl("point", class(st_geometry(sf_geometry)), ignore.case = TRUE)) sf_dim <- create_dimension(from = 1, to = length(gdim$values), refsys = st_crs(sf_geometry), point = is_point, is_raster = FALSE, values = st_geometry(sf_geometry)) dim <- c(dim, list(geometry = sf_dim)) } st_stars(x = setNames(list(as.matrix(.x$data_frames[[1]])), .x$varmeta[[1]]$name), dimensions = create_dimensions(dim)) } stars/R/init.R0000644000176200001440000000204514644756201012707 0ustar liggesusers#' @importFrom graphics image.default image par plot title box text axis plot.new plot.window rasterImage layout lcm contour hist strwidth #' @importFrom grDevices dev.capabilities dev.size grey rgb col2rgb cm #' @importFrom utils head tail setTxtProgressBar txtProgressBar packageVersion methods modifyList #' @importFrom stats na.omit runif aggregate setNames predict quantile var complete.cases na.pass time prcomp #' @importFrom tools file_ext #' @importFrom methods as slotNames new slot #' @importFrom abind abind adrop asub #' @importFrom classInt classIntervals #' @importFrom parallel parApply #' @importFrom rlang %||% #' @import sf #' @import units NULL setOldClass("stars") setOldClass("stars_proxy") .onLoad = function(libname, pkgname) { register_all_s3_methods() # dynamically registers non-imported pkgs (tidyverse) # nocov } .onAttach = function(libname, pkgname) { if (nrow(sf::st_drivers("raster", "netCDF")) != 1) packageStartupMessage("Note: sf is linked to a GDAL version without netCDF driver, some tests or examples may fail") } stars/R/ops.R0000644000176200001440000002360414576033423012547 0ustar liggesusersfirst_dimensions_match = function(e1, e2) { d1 = st_dimensions(e1) d2 = st_dimensions(e2) crs1 = st_crs(d1) crs2 = st_crs(d2) st_crs(d1) = st_crs(NA) st_crs(d2) = st_crs(NA) n = min(length(d1), length(d2)) isTRUE(all.equal(d1[1:n], d2[1:n], check.attributes = FALSE)) && crs1 == crs2 } #' S3 Ops Group Generic Functions for stars objects #' #' Ops functions for stars objects, including comparison, product and divide, add, subtract #' #' @param e1 object of class \code{stars} #' @param e2 object of class \code{stars} #' #' @return object of class \code{stars} #' @name ops_stars #' @examples #' tif = system.file("tif/L7_ETMs.tif", package = "stars") #' x = read_stars(tif) #' x * x #' x / x #' x + x #' x + 10 #' all.equal(x * 10, 10 * x) #' @export #' @details if \code{e1} or \code{e2} is is a numeric vector, or \code{e2} #' has less or smaller dimensions than \code{e1}, then \code{e2} is recycled #' such that it fits \code{e1}, using usual R array recycling rules. The user #' needs to make sure this is sensible; it may be needed to use \code{aperm} #' to permutate dimensions first. Ops.stars <- function(e1, e2) { if (!missing(e2)) { if (inherits(e1, "stars") && inherits(e2, "stars") && !first_dimensions_match(e1, e2)) stop("(first) dimensions of e1 and e2 do not match") if (!inherits(e2, "stars")) e1 = drop_units(e1) } ret = if (missing(e2)) lapply(e1, .Generic) else if (!inherits(e2, "stars")) lapply(e1, .Generic, e2 = e2) else { # both e1 and e2 are stars objects: # https://github.com/r-spatial/stars/issues/187#issuecomment-834020710 : if (!is.null(dim(e1)) && !isTRUE(all.equal(dim(e1), dim(e2), check.attributes = FALSE))) { stopifnot(length(e2) == 1) lapply(e1, .Generic, e2 = structure(e2[[1]], dim = NULL)) } else mapply(.Generic, e1, e2, SIMPLIFY = FALSE) } if (any(sapply(ret, function(x) is.null(dim(x))))) # happens if e1[[1]] is a factor; #304 ret = lapply(ret, function(x) { dim(x) = dim(e1); x }) if (! inherits(e1, "stars")) st_as_stars(setNames(ret, names(e2)), dimensions = st_dimensions(e2)) else st_as_stars(ret, dimensions = st_dimensions(e1)) } #' Mathematical operations for stars objects #' #' @param x object of class stars #' @param ... parameters passed on to the Math functions #' #' @export #' @name ops_stars #' #' @examples #' tif = system.file("tif/L7_ETMs.tif", package = "stars") #' x = read_stars(tif) #' a = sqrt(x) #' b = log(x, base = 10) #' @export Math.stars = function(x, ...) { ret = lapply(x, .Generic, ...) st_as_stars(ret, dimensions = st_dimensions(x)) } #' @name ops_stars #' @export Ops.stars_proxy <- function(e1, e2) { if (!inherits(e1, "stars_proxy")) stop("first argument in expression needs to be the stars_proxy object") # FIXME: needed?? #nocov if (missing(e2)) collect(e1, match.call(), .Generic, "e1", env = environment()) else collect(e1, match.call(), .Generic, c("e1", "e2"), env = environment()) } #' @name ops_stars #' @export Math.stars_proxy = function(x, ...) { collect(x, match.call(), .Generic, env = environment()) } # https://github.com/r-spatial/stars/issues/390 has_single_arg = function(fun, dots) { sum(!(names(as.list(args(fun))) %in% c("", "...", names(dots)))) <= 1 } can_single_arg = function(fun) { !inherits(try(fun(1:10), silent = TRUE), "try-error") } #' @export st_apply = function(X, MARGIN, FUN, ...) UseMethod("st_apply") #' st_apply apply a function to one or more array dimensions #' #' st_apply apply a function to array dimensions: aggregate over space, time, or something else #' @name st_apply #' @param X object of class \code{stars} #' @param MARGIN see \link[base]{apply}; index number(s) or name(s) of the dimensions over which \code{FUN} will be applied #' @param FUN see \link[base]{apply} and see Details. #' @param ... arguments passed on to \code{FUN} #' @param CLUSTER cluster to use for parallel apply; see \link[parallel]{makeCluster} #' @param PROGRESS logical; if \code{TRUE}, use \code{pbapply::pbapply} to show progress bar #' @param FUTURE logical;if \code{TRUE}, use \code{future.apply::future_apply} #' @param rename logical; if \code{TRUE} and \code{X} has only one attribute and #' \code{FUN} is a simple function name, rename the attribute of the returned object #' to the function name #' @param .fname function name for the new attribute name (if one or more #' dimensions are reduced) or the new dimension (if a new dimension is created); #' if missing, the name of \code{FUN} is used #' @param single_arg logical; if \code{TRUE}, FUN takes a single argument (like \code{fn_ndvi1} below), #' if \code{FALSE} FUN takes multiple arguments (like \code{fn_ndvi2} below). #' @return object of class \code{stars} with accordingly reduced number of dimensions; #' in case \code{FUN} returns more than one value, a new dimension is created carrying #' the name of the function used; see the examples. Following the logic of #' \link[base]{apply}, This new dimension is put before the #' other dimensions; use \link{aperm} to rearrange this, see last example. #' @param keep logical; if \code{TRUE}, preserve dimension metadata (e.g. time stamps) #' @details FUN is a function which either operates on a single object, which will #' be the data of each iteration step over dimensions MARGIN, or a function that #' has as many arguments as there are elements in such an object. See the NDVI #' examples below. The second form can be VERY much faster e.g. when a trivial #' function is not being called for every pixel, but only once (example). #' #' The heuristics for the default of \code{single_arg} work often, but not always; try #' setting this to the right value when \code{st_apply} gives an error. #' @examples #' tif = system.file("tif/L7_ETMs.tif", package = "stars") #' x = read_stars(tif) #' st_apply(x, 1:2, mean) # mean band value for each pixel #' st_apply(x, c("x", "y"), mean) # equivalent to the above #' st_apply(x, 3, mean) # mean of all pixels for each band #' \dontrun{ #' st_apply(x, "band", mean) # equivalent to the above #' st_apply(x, 1:2, range) # min and max band value for each pixel #' fn_ndvi1 = function(x) (x[4]-x[3])/(x[4]+x[3]) # ONE argument: will be called for each pixel #' fn_ndvi2 = function(red,nir) (nir-red)/(nir+red) # n arguments: will be called only once #' ndvi1 = st_apply(x, 1:2, fn_ndvi1) #' # note that we can select bands 3 and 4 in the first argument: #' ndvi2 = st_apply(x[,,,3:4], 1:2, fn_ndvi2) #' all.equal(ndvi1, ndvi2) #' # compute the (spatial) variance of each band; https://github.com/r-spatial/stars/issues/430 #' st_apply(x, 3, function(x) var(as.vector(x))) # as.vector is required! #' # to get a progress bar also in non-interactive mode, specify: #' if (require(pbapply)) { # install it, if FALSE #' pboptions(type = "timer") #' } #' st_apply(x, 1:2, range) # dimension "range" is first; rearrange by: #' st_apply(x, 1:2, range) %>% aperm(c(2,3,1)) #' } #' @export st_apply.stars = function(X, MARGIN, FUN, ..., CLUSTER = NULL, PROGRESS = FALSE, FUTURE = FALSE, rename = TRUE, .fname, single_arg = has_single_arg(FUN, list(...)) || can_single_arg(FUN), keep = FALSE) { if (missing(.fname)) .fname <- paste(deparse(substitute(FUN), 50), collapse = "\n") if (is.character(MARGIN)) MARGIN = match(MARGIN, names(dim(X))) dX = dim(X)[MARGIN] if (PROGRESS && !requireNamespace("pbapply", quietly = TRUE)) stop("package pbapply required, please install it first") if (FUTURE && !requireNamespace("future.apply", quietly = TRUE)) stop("package future.apply required, please install it first") fn = function(y, ...) { ret = if (PROGRESS) pbapply::pbapply(X = y, MARGIN = MARGIN, FUN = FUN, ..., cl = CLUSTER) else { if (is.null(CLUSTER) && !FUTURE) apply(X = y, MARGIN = MARGIN, FUN = FUN, ...) else if (FUTURE) { oopts = options(future.globals.maxSize = +Inf) on.exit(options(oopts)) future.apply::future_apply(y, MARGIN = MARGIN, FUN = FUN, ...) } else parallel::parApply(CLUSTER, X = y, MARGIN = MARGIN, FUN = FUN, ...) } if (is.array(ret)) ret else array(ret, dX) } no_margin = setdiff(seq_along(dim(X)), MARGIN) ret = if (single_arg) lapply(X, fn, ...) else # call FUN on full chunks: lapply(X, function(a) do.call(FUN, setNames(append(asplit(a, no_margin), list(...)), NULL))) # fix dimensions: dim_ret = dim(ret[[1]]) ret = if (length(dim_ret) == length(MARGIN)) { # FUN returned a single value if (length(ret) == 1 && rename && make.names(.fname) == .fname) ret = setNames(ret, .fname) st_stars(ret, st_dimensions(X)[MARGIN]) } else { # FUN returned multiple values: need to set dimension name & values dim_no_margin = dim(X)[-MARGIN] if (length(no_margin) > 1 && dim(ret[[1]])[1] == prod(dim_no_margin)) { r = attr(st_dimensions(X), "raster") new_dim = c(dim_no_margin, dim(ret[[1]])[-1]) for (i in seq_along(ret)) dim(ret[[i]]) = new_dim # set dims: dims = st_dimensions(X)[c(no_margin, MARGIN)] } else { orig = st_dimensions(X)[MARGIN] r = attr(orig, "raster") dims = if (keep) { c(st_dimensions(X)[no_margin], orig) } else { dim1 = if (!is.null(dimnames(ret[[1]])[[1]])) # FUN returned named vector: create_dimension(values = dimnames(ret[[1]])[[1]]) else create_dimension(to = dim_ret[1]) c(structure(list(dim1), names = .fname), orig) } } st_stars(ret, dimensions = create_dimensions(dims, r)) } for (i in seq_along(ret)) names(dim(ret[[i]])) = names(st_dimensions(ret)) ret } if (!isGeneric("%in%")) setGeneric("%in%", function(x, table) standardGeneric("%in%")) #' evaluate whether cube values are in a given set #' #' evaluate whether cube values are in a given set #' @docType methods #' @rdname in-methods #' @param x data cube value #' @param table values of the set #' @exportMethod "%in%" setMethod("%in%", signature(x = "stars"), function(x, table) { st_stars(lapply(x, function(y) structure(y %in% table, dim = dim(y))), st_dimensions(x)) } ) stars/R/factors.R0000644000176200001440000000333114144300304013364 0ustar liggesusers#' cut methods for stars objects #' #' cut methods for stars objects #' @name cut_stars #' @param x see \link[base]{cut} #' @param breaks see \link[base]{cut} #' @param ... see \link[base]{cut} #' @return an array or matrix with a \code{levels} attribute; see details #' @details R's \code{factor} only works for vectors, not for arrays or matrices. This is a work-around (or hack?) to keep the factor levels generated by \code{cut} and use them in plots. #' @export #' @examples #' tif = system.file("tif/L7_ETMs.tif", package = "stars") #' x = read_stars(tif) #' cut(x, c(0, 50, 100, 255)) #' cut(x[,,,1], c(0, 50, 100, 255)) #' plot(cut(x[,,,1], c(0, 50, 100, 255))) cut.array = function(x, breaks, ...) { structure(cut(as.vector(x), breaks, ...), dim = dim(x)) } #' @name cut_stars #' @export cut.matrix = cut.array #' @name cut_stars #' @export #' @examples #' tif = system.file("tif/L7_ETMs.tif", package = "stars") #' x1 = read_stars(tif) #' (x1_cut = cut(x1, breaks = c(0, 50, 100, Inf))) # shows factor in summary #' plot(x1_cut[,,,c(3,6)]) # propagates through [ and plot cut.stars = function(x, breaks, ...) { my_cut = function(x, breaks, ...) structure(cut(x, breaks, ...), dim = dim(x)) st_stars(lapply(x, my_cut, breaks = breaks, ...), st_dimensions(x)) } #' @export droplevels.stars = function(x, ...) { drop_level = function(x, ...) { d = dim(x) l = levels(x) co = attr(x, "colors") dim(x) = NULL x = droplevels(x, ...) sel = match(levels(x), l) structure(x, dim = d, colors = co[sel]) } for (i in seq_along(x)) { if (inherits(x[[i]], "factor")) x[[i]] = drop_level(x[[i]], ...) } x } #' @export droplevels.stars_proxy = function(x, ...) { collect(x, match.call(), "droplevels", env = environment()) } stars/R/geom.R0000644000176200001440000001226214576033423012673 0ustar liggesusers#' spatial intersect predicate for stars and sfc object #' #' spatial intersect predicate for stars and sfc object #' @param x object of class stars #' @param y object that has an `st_geometry` method: of class `sf` or `sfc`, or `stars` object with an `sfc` dimension #' @param sparse logical; if TRUE, return the a sparse logical matrix (object of class `sgbp`), if FALSE, return a logical matrix #' @param as_points logical, should grid cells be considered as points (TRUE) or polygons (FALSE)? Default: FALSE and warning emitted #' @param ... ignored, or passed on to `st_intersects.sf` for curvilinear grids #' @param transpose logical; should the transpose of the `sgbp` object be returned? #' @return `sgbp` object if sparse = TRUE, logical matrix otherwise #' @details curvilinear grids are always converted to polygons, so points on grid boundaries may intersect with two cells touched; for other grids each cell boundary or corner belongs only to one cell. #' #' @export st_intersects.stars = function(x, y, sparse = TRUE, ..., as_points = NA, transpose = FALSE) { y = st_geometry(y) d = st_dimensions(x) xy = attr(d, "raster")$dimensions if (is.na(as_points)) { p = d[[ xy[1] ]]$point || d[[ xy[2] ]]$point if (is.na(p)) { warning("as_points is NA: assuming here that raster cells are small polygons, not points") as_points = FALSE } else as_points = p } ret = if (has_raster(x) && !is_curvilinear(x) && inherits(y, "sfc_POINT") && !as_points) { cr = colrow_from_xy(st_coordinates(y), x, NA_outside = TRUE) ncells = prod(dim(d[xy])) ncols = dim(x)[ xy[1] ] ix = (cr[,2] - 1) * ncols + cr[,1] # for all points, the 1-based index in (as.vector) x # now create the TRANSPOSE of the sgbp: ret = structure(lapply(as.list(ix), function(x) if (is.na(x)) integer(0) else x), predicate = "intersects", region.id = seq_along(y), ncol = ncells, class = "sgbp") if (! sparse) ret = as.matrix(ret) if (! transpose) t(ret) else ret } else { # curvilinear or !as_points: ret = st_intersects(st_as_sf(x, as_points = as_points, na.rm = FALSE), y, sparse = sparse, ...) if (! sparse) ret = as.matrix(ret) if (transpose) t(ret) else ret } } # FIXME: of_y should be left/right/full? #' Spatially join a stars and an `sf` object #' #' Spatially join a stars and an `sf` object #' @param x object of class stars #' @param y object of class sf, or one that can be coerced into that by \link{st_as_sf} #' @param join the join function, which should return an sgbp object; see details #' @param ... arguments that will be passed on to the join function #' @param as_points logical; controls whether grid cells in \code{x} will be treated as points, or as cell areas; the \link{st_intersects.stars} method by default will derive this from \code{x}'s metadata, or else assume areas. #' @param what "left1", "right" or "inner"; see details #' @param warn logical; if TRUE, warn on 1-to-many matches when \code{what} is \code{"left1"} #' @return If what is "left1", an object of class stars with the (first) value of y at spatial instances of x #' @details When there is more than one match to a single x value, the first matching record from y is taken (and if \code{warn} is TRUE a warning is raised). If what is "inner", an object of class \code{sf} with all matching records of x and y. #' @export st_join.stars = function(x, y, join = st_intersects, ..., what = "left1", as_points = NA, warn = TRUE) { if (!requireNamespace("dplyr", quietly = TRUE)) stop("dplyr needed: install first?") if (!inherits(y, "sf")) { try(y <- st_as_sf(y)) if (inherits(y, "try-error")) stop("argument y should be of class sf, or sfc") } x = st_upfront(x) if (what == "left1") { ix = join(x, y, ..., as_points = as_points) li = lengths(ix) if (warn && any(li > 1)) { warning(paste("st_join found", sum(li > 1),"1-to-n matches, taking the first match for each of those")) ix[li > 1] = lapply(ix[li > 1], head, 1) } sel = li > 0 ix = unlist(ix) # white-out existing cells not matched; No,see https://github.com/r-spatial/stars/issues/643: # for (i in seq_along(x)) # x[[i]][!sel] = NA # add matching fields from y: for (i in setdiff(names(y), attr(y, "sf_column"))) { na_mode = NA storage.mode(na_mode) = storage.mode(y[[i]]) a = array(na_mode, dim(x)) attr(a, "units") = attr(y[[i]], "units") attr(a, "levels") = attr(y[[i]], "levels") a[sel] = y[[i]][ix] x[[ i ]] = a } x } else if (what == "inner") { ix = if (missing(join)) st_intersects(x, y, sparse = TRUE, ..., transpose = TRUE, as_points = as_points) else join(y, st_as_sf(x, as_points = as_points), ...) li = lengths(ix) i = unlist(ix) # sequence of x indexes j = rep(seq_along(ix), li) # possibly repeated y indexes stopifnot(length(i) == length(j)) if (length(dim(x)) > 2) { recycle = prod(dim(x)[-(1:2)]) # 1 in case of no dimensions beyond x and y i = rep(i, recycle) j = rep(j, recycle) # FIXME: create a stars object with an sfc dimension warning("a stars object should probably have been created here; please file an issue") } st_sf(dplyr::bind_cols(as.data.frame(x)[i,], y[j,])) } else stop('value for "what" not supported') } stars/R/datasets.R0000644000176200001440000000201514576033423013547 0ustar liggesusers#' Landsat-7 bands for a selected region around Olinda, BR #' #' Probably containing the six 30 m bands: #' \itemize{ #' \item Band 1 Visible (0.45 - 0.52 µm) 30 m #' \item Band 2 Visible (0.52 - 0.60 µm) 30 m #' \item Band 3 Visible (0.63 - 0.69 µm) 30 m #' \item Band 4 Near-Infrared (0.77 - 0.90 µm) 30 m #' \item Band 5 Short-wave Infrared (1.55 - 1.75 µm) 30 m #' \item Band 7 Mid-Infrared (2.08 - 2.35 µm) 30 m #' } #' "L7_ETMs" #' Monthly Gridded Meteorological Observations #' #' These are the monthly observational data used for BCSD downscaling. See: for more information." ; #' "Atmospheric Temperature, Air Temperature Atmosphere, Precipitation, Rain, Maximum Daily Temperature, Minimum Daily Temperature" ; #' "bcsd_obs" #' Sentinel-2 sample tile #' #' Sentinel-2 sample tile, downloaded from #' reads the four 10-m bands: B2 (490 nm), B3 (560 nm), B4 (665 nm) and B8 (842 nm) "stars_sentinel2" stars/R/spacetime.R0000644000176200001440000001036214246204071013706 0ustar liggesusers#' @export st_as_stars.STSDF = function(.x, ...) st_as_stars(as(.x, "STFDF"), ...) #' @export st_as_stars.STS = function(.x, ...) st_as_stars(as(.x, "STF"), ...) #' @export st_as_stars.STFDF = function(.x, ...) { if (!requireNamespace("sp", quietly = TRUE)) stop("package sp required, please install it first") #nocov if (!requireNamespace("zoo", quietly = TRUE)) stop("package zoo required, please install it first") #nocov #ix = 1:prod(dim(.x)[c("space", "time")]) d = if (sp::gridded(.x@sp)) { gp = sp::gridparameters(.x@sp) nx = gp[1,3] ny = gp[2,3] if (! sp::fullgrid(.x@sp)) { # SpatialPixels lst = vector("list", ncol(.x@data)) for (j in seq_along(.x@data)) { # attributes px2vec = function(pix) { sp::fullgrid(pix) = TRUE as.vector(as.matrix(pix)) } a = array(NA_real_, c(nx, ny, dim(.x)[2])) for (i in seq_len(dim(.x)[2])) # time a[,,i] = px2vec(.x[,i,j]) lst[[j]] = as.vector(a) } .x@data = as.data.frame(setNames(lst, names(.x@data))) } # offs_x dx 0 offs_y 0 dy gt = c(gp[1,1] - gp[1,2]/2, gp[1,2], 0.0, gp[2,1] + (gp[2,3] - 0.5) * gp[2,2], 0.0, -gp[2,2]) vals = lapply(seq_len(nrow(gp)), function(i) seq(gp[i,1], by = gp[i,2], length.out = gp[i,3])) create_dimensions(list( x = create_dimension(values = vals[[1]] - gp[1,2]/2), y = create_dimension(values = rev(vals[[2]]) + gp[2,2]/2), time = create_dimension(values = zoo::index(.x@time))), raster = get_raster()) } else create_dimensions(list( sfc = create_dimension(values = st_as_sfc(.x@sp)), # FIXME: doesn't do SpatialPixels -> x/y time = create_dimension(values = zoo::index(.x@time)))) vals = lapply(.x@data, function(y) { dim(y) = dim(d); y }) st_set_crs(st_as_stars(vals, dimensions = d), st_crs(.x@sp)) } #' @export st_as_stars.STF = function(.x, ...) { if (!requireNamespace("sp", quietly = TRUE)) stop("package sp required, please install it first") #nocov if (!requireNamespace("zoo", quietly = TRUE)) stop("package zoo required, please install it first") #nocov #ix = 1:prod(dim(.x)[c("space", "time")]) d = if (sp::gridded(.x@sp)) { gp = sp::gridparameters(.x@sp) nx = gp[1,3] ny = gp[2,3] # offs_x dx 0 offs_y 0 dy gt = c(gp[1,1] - gp[1,2]/2, gp[1,2], 0.0, gp[2,1] + (gp[2,3] - 0.5) * gp[2,2], 0.0, -gp[2,2]) vals = lapply(seq_len(nrow(gp)), function(i) seq(gp[i,1], by = gp[i,2], length.out = gp[i,3])) create_dimensions(list( x = create_dimension(values = vals[[1]] - gp[1,2]/2), y = create_dimension(values = rev(vals[[2]]) + gp[2,2]/2), time = create_dimension(values = zoo::index(.x@time))), raster = get_raster()) } else create_dimensions(list( sfc = create_dimension(values = st_as_sfc(.x@sp)), # FIXME: doesn't do SpatialPixels -> x/y time = create_dimension(values = zoo::index(.x@time)))) st_set_crs(st_as_stars(list(), dimensions = d), st_crs(.x@sp)) } st_as_STFDF = function(x) { if (!requireNamespace("sp", quietly = TRUE)) stop("package sp required, please install it first") #nocov if (!requireNamespace("spacetime", quietly = TRUE)) stop("package spacetime required, please install it first") #nocov rst = has_raster(x) d = st_dimensions(x) geom = if (rst) { x = st_upfront(x) time_dim = 3 sp::geometry(as(adrop(x[,,,1], drop = 3), "Spatial")) } else { x = st_upfront(x, which_sfc(x)) time_dim = 2 sp::geometry(as(adrop(x[,,1], drop = 2), "Spatial")) } tm = st_get_dimension_values(x, time_dim, center = FALSE) st = suppressWarnings(spacetime::STF(geom, tm)) # would warn for SpatialGrid -> SpatialPixels sp::addAttrToGeom(st, as.data.frame(lapply(x, function(y) structure(y, dim = NULL))), match.ID = FALSE) } setAs("stars", "STFDF", function(from) { if (!requireNamespace("sp", quietly = TRUE)) stop("package sp required, please install it first") #nocov if (!requireNamespace("zoo", quietly = TRUE)) stop("package zoo required, please install it first") #nocov if (!requireNamespace("xts", quietly = TRUE)) stop("package xts required, please install it first") #nocov if (!requireNamespace("spacetime", quietly = TRUE)) stop("package spacetime required, please install it first") #nocov st_as_STFDF(from) }) stars/R/subset.R0000644000176200001440000003503214644747114013256 0ustar liggesusers#' subset stars objects #' #' subset stars objects #' @name stars_subset #' @param x object of class \code{stars} #' @param i first selector: integer, logical or character vector indicating attributes to select, or object of class \code{sf}, \code{sfc}, \code{bbox}, or \code{stars} used as spatial selector; see details #' @param ... further (logical or integer vector) selectors, matched by order, to select on individual dimensions #' @param drop logical; if \code{TRUE}, degenerate dimensions (with only one value) are dropped #' @param crop logical; if \code{TRUE} and parameter \code{i} is a spatial geometry (\code{sf} or \code{sfc}) object, the extent (bounding box) of the result is cropped to match the extent of \code{i} using \link{st_crop}. Cropping curvilinear grids is not supported. #' @details If \code{i} is an object of class \code{sf}, \code{sfc} or \code{bbox}, the spatial subset covering this geometry is selected, possibly followed by cropping the extent. Array values for which the cell centre is not inside the geometry are assigned \code{NA}. If \code{i} is of class \code{stars}, and attributes of \code{i} are \code{logical}, cells in \code{x} corresponding to \code{NA} or \code{FALSE} cells in \code{i} are assigned an \code{NA}. Dimension ranges containing negative values or \code{NA} may be partially supported. #' @export #' @examples #' tif = system.file("tif/L7_ETMs.tif", package = "stars") #' x = read_stars(tif) #' x[,,,1:3] # select bands #' x[,1:100,100:200,] # select x and y by range #' x["L7_ETMs.tif"] # select attribute #' xy = structure(list(x = c(293253.999046018, 296400.196497684), y = c(9113801.64775462, #' 9111328.49619133)), .Names = c("x", "y")) #' pts = st_as_sf(data.frame(do.call(cbind, xy)), coords = c("x", "y"), crs = st_crs(x)) #' image(x, axes = TRUE) #' plot(st_as_sfc(st_bbox(pts)), col = NA, add = TRUE) #' bb = st_bbox(pts) #' (xx = x[bb]) #' image(xx) #' plot(st_as_sfc(bb), add = TRUE, col = NA) #' image(x) #' pt = st_point(c(x = 290462.103109179, y = 9114202.32594085)) #' buf = st_buffer(st_sfc(pt, crs = st_crs(x)), 1500) #' plot(buf, add = TRUE) #' #' buf = st_sfc(st_polygon(list(st_buffer(pt, 1500)[[1]], st_buffer(pt, 1000)[[1]])), #' crs = st_crs(x)) #' image(x[buf]) #' plot(buf, add = TRUE, col = NA) #' image(x[buf, crop=FALSE]) #' plot(buf, add = TRUE, col = NA) #' # with i of class stars: #' x[x > 75] # generates lots of NA's; pattern for each band #' x[x[,,,1] > 75] # recycles a single band template for all bands "[.stars" = function(x, i = TRUE, ..., drop = FALSE, crop = !is_curvilinear(x)) { missing.i = missing(i) # special case: i is sf/sfc/bbox if (! missing.i && inherits(i, c("sf", "sfc", "bbox"))) { x = if (has_raster(x)) st_crop(x, i, crop = crop, ...) else { x = st_upfront(x, which_sfc(x)) sel = which(lengths(st_intersects(st_geometry(x), i)) > 0) x[, sel] } return(x) } # special case: i is stars if (! missing.i && inherits(i, "stars")) { stopifnot(all(sapply(i, is.logical))) fun = function(x, y) { x[is.na(y) | !y] = NA; x } ret = mapply(fun, x, i, SIMPLIFY = FALSE) return(st_as_stars(ret, dimensions = st_dimensions(x))) } d = st_dimensions(x) args = rep(list(rlang::missing_arg()), length(dim(x))) x = unclass(x) if (length(x)) x = x[i] mc = match.call(expand.dots = TRUE) # remove [, x, i from mc: mc = if (missing.i) mc[-(1:2)] else mc[-(1:3)] do_select = FALSE for (i in seq_along(mc)) { if ((is.call(mc[[i]]) || is.name(mc[[i]])) && !identical(as.character(mc[[i]]), "")) # try to "get" it: mc[[i]] = eval(mc[[i]], parent.frame()) if (is.numeric(mc[[i]]) || is.call(mc[[i]]) || is.name(mc[[i]]) || is.character(mc[[i]])) { # FIXME: or something else? args[[i]] = if (is.character(mc[[i]])) { m = match(mc[[i]], d[[i]]$values) if (length(m) == 0 || any(is.na(m))) stop("selecting using invalid value label(s)?") m } else mc[[i]] do_select = TRUE } } if (is_curvilinear(d)) { # fill the row/col selectors for $values matrices: args_xy = rep(list(rlang::missing_arg()), 2) xy = attr(d, "raster")$dimensions for (i in seq_along(mc)) { if (is.numeric(mc[[i]]) || is.call(mc[[i]]) || is.name(mc[[i]])) { # FIXME: or something else? if (names(d)[i] == xy[1]) args_xy[[1]] = mc[[i]] if (names(d)[i] == xy[2]) args_xy[[2]] = mc[[i]] } } } # subset arrays: args[["drop"]] = FALSE for (i in names(x)) x[[i]] = structure(eval(rlang::expr(x[[i]][ !!!args ])), levels = attr(x[[i]], "levels"), colors = attr(x[[i]], "colors"), exclude = attr(x[[i]], "exclude")) # now do dimensions: if (do_select) { ed = expand_dimensions(d) xy = attr(d, "raster")$dimensions if (is_curvilinear(d)) { # subset curvilinear lat/lon matrices/rasters: can't do one-at-a-time! d[[ xy[1] ]]$values = eval(rlang::expr(d[[ xy[1] ]]$values[!!!args_xy])) d[[ xy[2] ]]$values = eval(rlang::expr(d[[ xy[2] ]]$values[!!!args_xy])) } # dimensions: #mc0 = mc[1:3] # "[", x, first dim for (i in seq_along(d)) { # one-at-a-time: name_i = names(d)[i] argi = args[i] if (!(is_curvilinear(d) && name_i %in% xy) && # as that case was handled above !(is.name(argi[[1]]) && all(argi[[1]] == rlang::missing_arg())) && # empty arg is.numeric(e <- eval(argi[[1]])) && !any(is.na(e)) && !all(diff(e) == 1)) # sequence with gaps d[[i]]$values = if (isTRUE(d[[i]]$point) || !is.numeric(unclass(ed[[i]][1]))) ed[[i]] else as_intervals(ed[[i]], add_last = TRUE) d[[i]] = eval(rlang::expr(d[[i]] [!!!argi])) } } x = st_as_stars(x, dimensions = d) if (drop) adrop(x) else x } #' @name stars_subset #' @param downsample downsampling rate used in case \code{i} is a \code{stars_proxy} object #' @param value array of dimensions equal to those in \code{x}, or a vector or value that will be recycled to such an array #' @export #' @details in an assignment (or replacement form, \code{[<-}), argument \code{i} needs to be either (i) a \code{stars} object with logical attribute(s) that has dimensions matching (possibly after recycling) those of \code{x}, in which case the \code{TRUE} cells will be replaced and \code{i} and/or \code{value} will be recycled to the dimensions of the arrays in \code{x}, or (ii) a length-one integer or character vector indicating which array to replace, in which case \code{value} may be stars object or a vector or array (that will be recycled). #' @examples #' x = read_stars(tif) #' # replace, using a logical stars selector: cuts all values above 90 to 90 #' x[x > 90] = 90 #' # replace a single attribute when there are more than one: #' s = split(x) #' names(s) = paste0("band", 1:6) #' # rescale only band 1: #' s[1] = s[1] * 0.75 #' # rescale only attribute named "band2": #' s["band2"] = s["band2"] * 0.85 #' # create a new attribute from a numeric vector: #' s["rnorm"] = rnorm(prod(dim(s))) #' s "[<-.stars" = function(x, i, value) { if (inherits(i, "stars")) { fun = function(x, y, value) { x[y] = value; x } st_as_stars(mapply(fun, x, i, value = value, SIMPLIFY = FALSE), dimensions = st_dimensions(x)) } else if (inherits(i, c("numeric", "character")) && length(i) == 1) { if (inherits(value, "stars")) { stopifnot(length(value) == 1, first_dimensions_match(x, value)) value = value[[1]] } if (is.numeric(i)) stopifnot(i > 0, i <= length(x)) y = unclass(x) y[[i]] = array(value, dim(x)) st_as_stars(y, dimensions = st_dimensions(x)) } else stop("selector i should be a stars object or a lenght-one integer or character vector") } #' @export st_intersects.bbox = function(x, y, ...) { # FIXME: segmentize first if geographic coords? see sf::st_sample if (!inherits(y, "sfc")) y = st_as_sfc(y) st_intersects(st_as_sfc(x), y, ...) } #' crop a stars object #' #' crop a stars object #' @name st_crop #' @export #' @param x object of class \code{stars} #' @param y object of class \code{sf}, \code{sfc} or \code{bbox}; see Details below. #' @param epsilon numeric; factor to shrink the bounding box of \code{y} towards its center before cropping. #' @param as_points logical; only relevant if \code{y} is of class \code{sf} or \code{sfc}: if \code{FALSE}, treat \code{x} as a set of points, else as a set of small polygons. Default: \code{TRUE} if \code{y} is two-dimensional, else \code{FALSE}; see Details #' @param ... ignored #' @param crop logical; if \code{TRUE}, the spatial extent of the returned object is cropped to still cover \code{obj}, if \code{FALSE}, the extent remains the same but cells outside \code{y} are given \code{NA} values. #' @param normalize logical; if \code{TRUE} then pass the cropped object to \code{\link[sf]{st_normalize}} before returning. #' @details for raster \code{x}, \code{st_crop} selects cells that intersect with \code{y}. #' For intersection, are raster cells interpreted as points or as small polygons? #' If \code{y} is of class \code{stars}, \code{x} raster cells are interpreted as points; if \code{y} is of class \code{bbox}, \code{x} cells are interpreted as cells (small polygons). Otherwise, if \code{as_points} is not given, cells are interpreted as points if \code{y} has a two-dimensional geometry. #' #' @examples #' l7 = read_stars(system.file("tif/L7_ETMs.tif", package = "stars")) #' d = st_dimensions(l7) #' #' # area around cells 3:10 (x) and 4:11 (y): #' offset = c(d[["x"]]$offset, d[["y"]]$offset) #' res = c(d[["x"]]$delta, d[["y"]]$delta) #' bb = st_bbox(c(xmin = offset[1] + 2 * res[1], #' ymin = offset[2] + 11 * res[2], #' xmax = offset[1] + 10 * res[1], #' ymax = offset[2] + 3 * res[2]), crs = st_crs(l7)) #' l7[bb] #' # equivalent: #' st_crop(l7, bb) #' #' plot(l7[,1:13,1:13,1], reset = FALSE) #' image(l7[bb,,,1], add = TRUE, col = sf.colors()) #' plot(st_as_sfc(bb), add = TRUE, border = 'green', lwd = 2) #' #' # slightly smaller bbox: #' bb = st_bbox(c(xmin = offset[1] + 2.1 * res[1], #' ymin = offset[2] + 10.9 * res[2], #' xmax = offset[1] + 9.9 * res[1], #' ymax = offset[2] + 3.1 * res[2]), crs = st_crs(l7)) #' l7[bb] #' #' plot(l7[,1:13,1:13,1], reset = FALSE) #' image(l7[bb,,,1], add = TRUE, col = sf.colors()) #' plot(st_as_sfc(bb), add = TRUE, border = 'green', lwd = 2) #' #' # slightly larger bbox: #' bb = st_bbox(c(xmin = offset[1] + 1.9 * res[1], #' ymin = offset[2] + 11.1 * res[2], #' xmax = offset[1] + 10.1 * res[1], #' ymax = offset[2] + 2.9 * res[2]), crs = st_crs(l7)) #' l7[bb] #' #' plot(l7[,1:13,1:13,1], reset = FALSE) #' image(l7[bb,,,1], add = TRUE, col = sf.colors()) #' plot(st_as_sfc(bb), add = TRUE, border = 'green', lwd = 2) #' #' # half a cell size larger bbox: #' bb = st_bbox(c(xmin = offset[1] + 1.49 * res[1], #' ymin = offset[2] + 11.51 * res[2], #' xmax = offset[1] + 10.51 * res[1], #' ymax = offset[2] + 2.49 * res[2]), crs = st_crs(l7)) #' l7[bb] #' #' plot(l7[,1:13,1:13,1], reset = FALSE) #' image(l7[bb,,,1], add = TRUE, col = sf.colors()) #' plot(st_as_sfc(bb), add = TRUE, border = 'green', lwd = 2) st_crop.stars = function(x, y, ..., crop = TRUE, epsilon = sqrt(.Machine$double.eps), as_points = all(st_dimension(y) == 2, na.rm = TRUE), normalize = FALSE) { x = st_upfront(x) # put spatial dimensions up front; https://github.com/r-spatial/stars/issues/457 d = dim(x) dm = st_dimensions(x) args = rep(list(rlang::missing_arg()), length(d)+1) if (inherits(y, c("stars", "sf", "sfc", "bbox")) && st_crs(x) != st_crs(y)) stop("for cropping, the CRS of both objects have to be identical") if (inherits(y, "stars")) { as_points = TRUE y = st_as_sfc(st_bbox(y)) } if (!as.matrix(st_intersects(st_bbox(x), st_bbox(y)))) warning("st_crop: bounding boxes of x and y do not overlap") if (crop && (is_regular_grid(x) || has_rotate_or_shear(x))) { rastxy = attr(dm, "raster")$dimensions xd = rastxy[1] yd = rastxy[2] bb = if (!inherits(y, "bbox")) st_bbox(y) else y if (any(is.na(as.numeric(bb)))) # as.numeric() can go after sf 0.7-5 stop("NA values in bounding box of y") if (epsilon != 0) bb = bb_shrink(bb, epsilon) if (!all(is.na(colrow_from_xy(matrix(bb, 2, byrow = TRUE), dm, NA_outside = TRUE)))) { cr = colrow_from_xy(matrix(bb, 2, byrow = TRUE), dm, NA_outside = FALSE) # FALSE: https://github.com/r-spatial/stars/issues/455 cr[,1] = cr[,1] - dm[[xd]]$from + 1 cr[,2] = cr[,2] - dm[[yd]]$from + 1 for (i in seq_along(d)) { if (names(d[i]) == xd) args[[i+1]] = seq(max(1, cr[1, 1], na.rm = TRUE), min(d[xd], cr[2, 1], na.rm = TRUE)) if (names(d[i]) == yd) { if (dm[[ yd ]]$delta < 0) cr[1:2, 2] = cr[2:1, 2] args[[i+1]] = seq(max(1, cr[1, 2], na.rm = TRUE), min(d[yd], cr[2, 2], na.rm = TRUE)) } } x = eval(rlang::expr(x[!!!args])) } } else if (crop) warning("crop only crops regular grids: maybe use st_warp() first?") if (!inherits(y, "bbox")) { # post-process: burn in geometry mask dxy = attr(dm, "raster")$dimensions xy_grd = if (is_curvilinear(x) || !as_points) # FIXME: for curvilinear as_points should work too! st_as_sfc(st_dimensions(x)[dxy], as_points = as_points, geotransform = st_geotransform(x)) else st_as_sf(do.call(expand.grid, expand_dimensions.stars(x)[dxy]), coords = dxy, crs = st_crs(x)) inside = st_intersects(st_union(y), xy_grd)[[1]] d = dim(x) # cropped x mask = rep(TRUE, prod(d[dxy])) mask[inside] = FALSE mask = array(mask, d) # recycles over dims >= 3 for (i in seq_along(x)) x[[i]][mask] = NA } if (normalize[1]) x = st_normalize(x) x } #' @export st_normalize.stars = function(x, domain = c(0, 0, 1, 1), ...) { stopifnot(all(domain == c(0,0,1,1))) if (has_raster(x)) { x = st_upfront(x) d = st_dimensions(x) if (d[[1]]$from != 1) { d[[1]]$offset = d[[1]]$offset + (d[[1]]$from - 1) * d[[1]]$delta d[[1]]$to = d[[1]]$to - d[[1]]$from + 1 d[[1]]$from = 1 } if (d[[2]]$from != 1) { d[[2]]$offset = d[[2]]$offset + (d[[2]]$from - 1) * d[[2]]$delta d[[2]]$to = d[[2]]$to - d[[2]]$from + 1 d[[2]]$from = 1 } st_stars(x, dimensions = d) } else x } #' @name stars_subset #' @param which character or integer; dimension(s) to be flipped #' @export #' @return \code{st_flip} flips (reverts) the array values along the chosen dimension #' without(s) changing the dimension properties #' @examples #' lc = read_stars(system.file("tif/lc.tif", package = "stars")) #' x = c(orig = lc, #' flip_x = st_flip(lc, "x"), #' flip_y = st_flip(lc, "y"), #' flip_xy = st_flip(lc, c("x", "y")), #' along = 3) #' plot(x) st_flip = function(x, which = 1) { if (is.character(which)) which = match(which, names(dim(x))) stopifnot(all(which %in% seq_along(dim(x)))) dims = lapply(dim(x), seq_len) for (i in which) dims[[ i ]] = rev(dims[[ i ]]) for (i in seq_along(x)) x[[i]] = do.call(`[`, c(list(x[[i]]), dims)) x } stars/R/gt.R0000644000176200001440000000602214576033423012353 0ustar liggesusersget_geotransform = function(x) { r = attr(x, "raster") if (is.null(r)) rep(NA_real_, 6) else { xd = x[[ r$dimensions[1] ]] yd = x[[ r$dimensions[2] ]] as.numeric(c(xd$offset, xd$delta, r$affine[1], yd$offset, r$affine[2], yd$delta)) } } #' get or set the geotransform, or rotation matrix #' @param x object of class stars or dimensions #' @param ... ignored #' @export st_geotransform = function(x, ...) UseMethod("st_geotransform") #' @export st_geotransform.default = function(x, ...) { stop(paste("no st_geotransform method available for objects of class", class(x)[1])) } #' @export st_geotransform.stars = function(x, ...) st_geotransform(st_dimensions(x)) #' @export st_geotransform.dimensions = function(x, ...) get_geotransform(x) #' @export #' @name st_geotransform #' @param value length 6 numeric vector, or 2 x 2 (scaled) rotation matrix `st_geotransform<-` = function(x, value) UseMethod("st_geotransform<-") #' @export #' @name st_geotransform #' @examples #' # using the "classical" rotation matrix, see https://en.wikipedia.org/wiki/Rotation_matrix : #' rot = function(theta, dxdy = c(1., -1.)) { #' th = theta / 180 * pi #' matrix(c(cos(th), sin(th), -sin(th), cos(th)), 2, 2) %*% #' matrix(c(dxdy[2], 0, 0, dxdy[1]), 2, 2) #' } #' l = st_downsample(st_as_stars(L7_ETMs), 9) # save time in plotting #' st_geotransform(l) = rot(20, c(28.5, 28.5)) # clockwise, 20 degrees, scale by cell size #' plot(l[,,,1]) #' m = rot(20, c(1, 2)) #' g = expand.grid(x = 0:4, y = 0:4) #' plot(g[1:2], asp = 1) #' text(g[,1], g[,2], labels = seq_along(g[,1]), pos = 4) #' g = t(m %*% t(as.matrix(g))) #' points(g, col = 'red') #' text(g[,1], g[,2], labels = seq_along(g[,1]), pos = 4, col = 'red') #' #' m = matrix(1:20, 4) #' s0 = st_as_stars(m) #' s = s0 #' # dy > 0, clockwise rotation: #' st_geotransform(s) = rot(10, c(1,1)) #' plot(s0, reset = FALSE) #' plot(s, add = TRUE) #' # dy < 0, counter clockwise rotation, + expansion in x-direction: #' layout(1) #' s0 = st_as_stars(st_bbox(s0), dx = 1) #' s0$values = 1:20 #' s0 #' plot(s0, reset = FALSE) #' s = s0 #' st_geotransform(s) = rot(10, c(2,1)) #' plot(s, add = TRUE) `st_geotransform<-.stars` = function(x, value) { d = st_dimensions(x) r = attr(d, "raster") # https://gdal.org/tutorials/geotransforms_tut.html if (is.matrix(value)) { # gdal has col-row (pixel-line) order, where linear algebra uses row-col stopifnot(all(dim(value) == c(2, 2))) r$affine = c(value[2,1] * sign(d[[ r$dimensions[1] ]]$delta), value[1,2] * sign(d[[ r$dimensions[2] ]]$delta)) d[[ r$dimensions[1] ]]$delta = value[2,2] * sign(d[[ r$dimensions[1] ]]$delta) d[[ r$dimensions[2] ]]$delta = value[1,1] * sign(d[[ r$dimensions[2] ]]$delta) } else { stopifnot(is.numeric(value), length(value) == 6) d[[ r$dimensions[1] ]]$offset = value[1] d[[ r$dimensions[1] ]]$delta = value[2] r$affine[1] = value[3] d[[ r$dimensions[2] ]]$offset = value[4] r$affine[2] = value[5] d[[ r$dimensions[2] ]]$delta = value[6] } attr(d, "raster") = r st_stars(x, d) } stars/R/mosaic.R0000644000176200001440000000553614604601335013220 0ustar liggesusers#' build mosaic (composite) of several spatially disjoint stars objects #' #' build mosaic (composite) of several spatially disjoint stars objects #' @param .x object of class stars, or character vector with input dataset names #' @param ... further input stars objects #' @param dst character; destination file name; this will be a VRT file with references to the source file(s), see details #' @param options character; options to the gdalbuildvrt command #' @param file_ext character; file extension, determining the format used to write to (".tif" implies GeoTIFF) #' @return the stars method returns a stars object with the composite of the input; the \code{character} method returns the file name of the file with the mosaic; see also the GDAL documentation of \code{gdalbuildvrt} #' @details the gdal function buildvrt builds a mosaic of input images; these input images can be multi-band, but not higher-dimensional data cubes or stars objects with multiple attributes; note that for the `stars` method, the `dst` file may contain references to temporary files that are going to be removed at termination of the R session. #' #' uses \link[sf]{gdal_utils} to internally call \code{buildvrt}; no executables external to R are called. #' @export #' @examples #' x = read_stars(system.file("tif/L7_ETMs.tif", package = "stars")) #' x1 = x[,100:200,100:200,] #' x2 = x[,150:300,150:300,] #' plot(st_mosaic(x1, x2)) st_mosaic = function(.x, ...) UseMethod("st_mosaic") #' @export #' @name st_mosaic st_mosaic.stars = function(.x, ..., dst = tempfile(fileext = file_ext), options = c("-vrtnodata", "-9999", "-srcnodata", "nan"), # -srcnodata "nan": see https://github.com/r-spatial/stars/issues/274 file_ext = ".tif") { lst_write = function(obj) { fname = tempfile(fileext = file_ext) write_stars(obj, fname) fname } missing_dst = missing(dst) objs = if (missing(.x)) list(...) else append(list(.x), list(...)) src = sapply(objs, lst_write) st_mosaic(src, dst = dst, file_ext = file_ext, options = options) ret = setNames(read_stars(dst), names(objs[[1]])[1]) if (!inherits(ret, "stars_proxy") && missing_dst) { unlink(src) unlink(dst) } ret } #' @export #' @name st_mosaic st_mosaic.character = function(.x, ..., dst = tempfile(fileext = file_ext), options = c("-vrtnodata", "-9999"), file_ext = ".tif") { sf::gdal_utils("buildvrt", .x, dst, options = options, ...) dst } #' @export #' @name st_mosaic st_mosaic.stars_proxy = function(.x, ..., dst = tempfile(fileext = file_ext), options = c("-vrtnodata", "-9999"), file_ext = ".tif") { if (length(.x) > 1 || length(.x[[1]]) > 1) stop("st_mosaic.stars_proxy only implemented for single-file proxy objects") objs = if (missing(.x)) list(...) else append(list(.x), list(...)) files = sapply(objs, function(sp) sp[[1]]) read_stars(st_mosaic(files, dst = dst, options = options, file_ext = file_ext), proxy = TRUE) } stars/R/sample.R0000644000176200001440000001247114576033423013227 0ustar liggesusersget_index_ranges = function(d, n, offset) { nd = (d - offset) %/% (n + 1) g = do.call(expand.grid, lapply(nd, seq_len)) g = t(apply(g, 1, function(x) offset + 1 + (x - 1) * (n + 1))) dimnames(g) = NULL f = function(x) mapply(seq, x, length.out = n + 1, SIMPLIFY = FALSE) apply(g, 1, f, simplify = FALSE) } # reduce resolution of x, keeping (most of) extent #' @export #' @name st_downsample st_downsample = function(x, n, ...) UseMethod("st_downsample") #' downsample stars or stars_proxy objects #' #' downsample a stars or stars_proxy object either by skipping rows, columns and bands, #' or by computing a single value (e.g. the mean) from the sub-tiles involved #' #' @param x object of class stars or stars_proxy #' @param n integer; for each dimension the number of pixels/lines/bands etc that will be skipped; see Details. #' @param offset integer; offset(s) for downsampling, in pixels, starting at the offset of #' each dimension; should be smaller or equal to \code{n} #' @param FUN function; if given, downsampling will apply FUN to each of the the subtiles #' @param ... arguments passed on to \code{FUN} (e.g., \code{na.rm = TRUE} to ignore missing values if FUN is \code{mean}) #' @details If all n == 0, no downsampling takes place; if it is 1, every second row/column/band #' is skipped, if it is 2, every second+third row/column/band are skipped, etc. #' #' Downsampling a \code{stars_proxy} object returns a \code{stars} object, is #' equivalent to calling \code{st_as_stars(x, downsample = 2)}, and only downsamples #' the first two (x and y) dimensions. #' #' Downsampled regular rasters keep their dimension offsets, have a cell size (delta) that #' is n[i]+1 times larger, and may result in a (slightly) different extent. #' #' Note that terra's \link[terra]{aggregate} with \code{fact=2} corresponds to #' \code{st_downsample(x, n = 1, FUN = mean)}: \code{fact} is one larger than \code{n}. #' @name st_downsample #' @export #' @examples #' (m = matrix(1:121, 11, 11)) #' (s = st_as_stars(m)) #' st_downsample(s, 1) #' st_downsample(s, 1)[[1]] #' st_downsample(s, 1, offset = 1) #' st_downsample(s, 1, offset = 1)[[1]] #' st_downsample(s, 1, offset = c(0,1)) #' st_downsample(s, 1, offset = c(0,1))[[1]] #' st_downsample(s, 1, FUN = mean) #' st_downsample(s, 1, FUN = mean)[[1]] #' st_downsample(s, 1, offset = 1, FUN = mean) #' st_downsample(s, 1, offset = c(0,1), FUN = mean)[[1]] st_downsample.stars = function(x, n, ..., offset = 0, FUN) { d = dim(x) n = rep_len(as.integer(n), length(d)) offset = rep_len(as.integer(offset), length(d)) stopifnot(all(n >= 0), all(offset <= n), all(offset < d)) new_dim = if (! missing(FUN)) (d - offset) %/% (n + 1) else ceiling((d - offset) / (n + 1)) # include last, incomplete tile dims = st_dimensions(x) ix = setNames(vector("list", length(dims)), names(dims)) for (i in seq_along(d)) # need to precompute: ix[[i]] = seq(1 + offset[i], d[i], n[i] + 1) xy = attr(dims, "raster")$dimensions for (i in seq_along(d)) { dims[[i]]$offset = if (offset[i] != 0) dims[[i]]$offset + offset[i] * dims[[i]]$delta else dims[[i]]$offset = dims[[i]]$offset dims[[i]]$delta = dims[[i]]$delta * (n[i] + 1) dims[[i]]$from = 1 dims[[i]]$to = unname(new_dim[i]) if (!is.null(dims[[i]]$values)) { if (is.matrix(dims[[i]]$values) && names(ix)[i] %in% xy) dims[[i]]$values = dims[[i]]$values[ ix[[ xy[1] ]], ix[[ xy[2] ]] ] # speaks for itself else dims[[i]]$values = dims[[i]]$values[ ix[[i]] ] } } if (!all(attr(dims, "raster")$affine == 0.0)) { if (!all(xy %in% names(d)[1:2])) stop("downsampling an affine raster needs to have x/y in dims 1 and 2") attr(dims, "raster")$affine = attr(dims, "raster")$affine * (n[1:2] + 1) } if (all(n == 0)) x else if (!missing(FUN)) { # compute FUN() values over the subtiles: stopifnot(is.function(FUN)) l = get_index_ranges(d, n, offset) x = unclass(x) # so that `[[<-` doesn't go into the stars method, which recycles for (i in seq_along(x)) x[[i]] = structure(sapply(l, function(y) FUN(as.vector(asub(x[[i]], y)), ...)), dim = new_dim) structure(x, class = "stars", dimensions = dims) } else { # downsample using `[`: args = rep(list(rlang::missing_arg()), length(d)+1) for (i in seq_along(d)) if (n[i] > 0) args[[i+1]] = ix[[i]] # i==1 skipped: retain all attributes x = eval(rlang::expr(x[!!!args])) structure(x, dimensions = dims) } } #' @export #' @name st_downsample st_downsample.stars_proxy = function(x, n, ...) { if (length(n) != 2) message("for stars_proxy objects, downsampling only happens for dimensions x and y") st_as_stars(x, downsample = n) } #' @export st_sample.stars = function(x, size, ..., type = "random", replace = FALSE) { if (length(x) > 1) warning("only sampling the first attribute") if (type != "random") warning("only type 'random' supported") v = structure(x[[1]], dim = NULL) if (missing(replace)) replace = size > length(v) v[sample(length(v), size, replace = replace, ...)] } #' @export st_sample.stars_proxy = function(x, size, ..., type = "regular", quiet = TRUE) { if (type != "regular") stop("only type 'regular' for stars_proxy objects supported") # FIXME: tbd d = dim(x) downsampling_rate = c(floor(d[1:2] / sqrt(size)), d[-(1:2)]) if (!quiet) print(downsampling_rate) # nocov st_as_stars(x, downsample = downsampling_rate) # st_sample(st, size, ...) } stars/R/stars.R0000644000176200001440000012377014621476642013114 0ustar liggesuserssplit_strings = function(md, split = "=") { splt = strsplit(md, split) lst = lapply(splt, function(x) if (length(x) <= 1) NA_character_ else x[[2]]) structure(lst, names = sapply(splt, function(x) x[[1]]), class = "gdal_metadata") } .array = function(x, dim) { structure(rep_len(x, prod(dim)), dim = dim) } #' convert objects into a stars object #' #' convert objects into a stars object #' @export #' @param .x object to convert #' @param ... in case \code{.x} is of class \code{bbox}, arguments passed on to #' \link{pretty}. In case \code{.x} is of class \code{nc_proxy}, arguments #' passed on to \code{\link{read_ncdf}}. st_as_stars = function(.x, ...) UseMethod("st_as_stars") #' @name st_as_stars #' @param dimensions object of class dimensions #' @export st_as_stars.list = function(.x, ..., dimensions = NULL) { if (length(.x)) { if (is.null(names(.x))) stop("list should have named elements") for (i in seq_along(.x)[-1]) if (!all(dim(.x[[1]]) == dim(.x[[i]]))) stop("dim attributes not identical") if (!is.null(n <- names(.x)) && (any(n == "") || length(n) != length(unique(n)))) names(.x) = make.names(names(.x), unique = TRUE) # check dimensions, if set: if (!is.null(dimensions)) { dx = dim(.x[[1]]) dd = dim(dimensions) stopifnot(!is.null(dx), !is.null(dd)) for (i in seq_along(dimensions)) { if (dx[i] < dd[i]) { # create_dimension was called with $values one longer than corresponding array dim, v = dimensions[[i]]$values if (is.null(v)) # regularly spaced, meaning offset/delta have replaced $values: dimensions[[i]]$to = dimensions[[i]]$to - 1 else if (length(v) == dx[i] + 1) { # convert the one-too-long values into an intervals object: dimensions[[i]]$values = head(v, -1) dimensions[[i]]$to = dimensions[[i]]$to - 1 } else stop(paste("incorrect length of dimensions values for dimension", i)) dimensions[[i]]$point = FALSE } } } else dimensions = create_dimensions(dim(.x[[1]])) if (is.null(names(dim(.x[[1]])))) for (i in seq_along(.x)) names(dim(.x[[i]])) = names(dimensions) } st_stars(.x, dimensions) } st_stars = function(x, dimensions, class = "stars") { # sanity checks: stopifnot(is.list(x)) stopifnot(inherits(dimensions, "dimensions")) stopifnot(!is.null(attr(dimensions, "raster"))) # for (i in seq_along(x)) # names(dim(x[[i]])) = names(dimensions) structure(x, dimensions = dimensions, class = class) } #' @name st_as_stars #' @export #' @param raster character; the names of the dimensions that denote raster dimensions st_as_stars.default = function(.x = NULL, ..., raster = NULL) { args = if (is.null(.x)) list(...) else append(list(.x), list(...)) if (length(args) == 0) return(st_as_stars(st_bbox())) isdim = sapply(args, inherits, what = "dimensions") dimensions = if (! any(isdim)) { if (is.array(args[[1]]) && !is.null(dimnames(args[[1]]))) st_dimensions(args[[1]]) else do.call(st_dimensions, lapply(dim(args[[1]]), function(x) seq_len(x) - 1)) } else { d = args[[ which(isdim)[1] ]] if (is.null(raster)) raster = attr(d, "raster") d } if (is.null(raster) && !has_sfc(dimensions)) { w = which(sapply(dimensions, function(x) is.null(x$values))) raster = get_raster(dimensions = names(dimensions)[w[1:2]]) } dimensions = create_dimensions(dimensions, raster) if (any(isdim)) args = args[-which(isdim)] if (is.null(names(args))) names(args) = paste0("A", seq_along(args)) for (i in seq_along(args)) names(dim(args[[i]])) = names(dimensions) st_as_stars.list(args, dimensions = dimensions) } #' @param curvilinear only for creating curvilinear grids: named length 2 list holding longitude and latitude matrices or stars arrays, or the names of the corresponding attributes in \code{.x}; the names of this vector should correspond to raster dimensions the matrices are associated with; see Details. #' @param crs object of class \code{crs} with the coordinate reference system of the values in \code{curvilinear}; see details #' @details if \code{curvilinear} is a list with \code{stars} objects with longitude and latitude values, its coordinate reference system is typically not that of the latitude and longitude values. If \code{curvilinear} contains the names of two arrays in \code{.x}, then these are removed from the returned object. #' @export #' @name st_as_stars st_as_stars.stars = function(.x, ..., curvilinear = NULL, crs = st_crs('OGC:CRS84')) { if (is.null(curvilinear)) .x else { stopifnot(names(curvilinear) %in% names(dim(.x))) if (all(sapply(curvilinear, is.character))) curvilinear = unlist(curvilinear) if (is.character(cl <- curvilinear)) { if (!cl[1] %in% names(.x) || !cl[2] %in% names(.x)) stop("curvilinear arrays not present in object") curvilinear = setNames(vector("list", 2), names(curvilinear)) curvilinear[[1]] = .x[ cl[1] ] curvilinear[[2]] = .x[ cl[2] ] .x[[ cl[1] ]] = NULL .x[[ cl[2] ]] = NULL } stopifnot(is.list(curvilinear)) if (inherits(curvilinear[[1]], "stars")) curvilinear[[1]] = curvilinear[[1]][[1]] if (inherits(curvilinear[[2]], "stars")) curvilinear[[2]] = curvilinear[[2]][[1]] dimensions = st_dimensions(.x) xy = names(curvilinear) dimensions[[ xy[1] ]]$values = structure(curvilinear[[1]], dim = setNames(dim(curvilinear[[1]])[1:2], xy)) dimensions[[ xy[2] ]]$values = structure(curvilinear[[2]], dim = setNames(dim(curvilinear[[1]])[1:2], xy)) # erase regular grid coefficients $offset and $delta: dimensions[[ xy[1] ]]$offset = dimensions[[ xy[1] ]]$delta = NA_real_ dimensions[[ xy[2] ]]$offset = dimensions[[ xy[2] ]]$delta = NA_real_ raster = get_raster(dimensions = names(curvilinear), curvilinear = TRUE) st_set_crs(st_stars(.x, create_dimensions(dimensions, raster)), crs) } } pretty_cut = function(lim, n, inside = FALSE, ...) { stopifnot(n > 2) p = pretty(x = lim, n = n, ...) d = diff(p[1:2]) if (! inside) lim = c(lim[1] - 0.5 * d, lim[2] + 0.5 * d) # extended limits half a cell p[p >= lim[1] & p <= lim[2]] } #' @param nx integer; number of cells in x direction; see details #' @param ny integer; number of cells in y direction; see details #' @param nz integer; number of cells in z direction; if missing no z-dimension is created. #' @param dx numeric or object of class units; cell size in x direction; see details #' @param dy numeric or object of class units; cell size in y direction; see details #' @param xlim length 2 numeric vector with extent (min, max) in x direction #' @param ylim length 2 numeric vector with extent (min, max) in y direction #' @param values value(s) to populate the raster values with #' @param n the (approximate) target number of grid cells #' @param pretty logical; should cell coordinates have \link{pretty} values? #' @param inside logical; should all cells entirely fall inside the bbox, potentially not covering it completely (\code{TRUE}), or always cover the bbox (\code{FALSE}), or find a good approximation (\code{NA}, default)? #' @param proxy logical; should a \code{stars_proxy} object be created? (requires gdal_create binary when sf < 1.0-6) #' @details For the \code{bbox} method: if \code{pretty} is \code{TRUE}, raster cells may extend the coordinate range of \code{.x} on all sides. If in addition to \code{nx} and \code{ny}, \code{dx} and \code{dy} are also missing, these are set to a single value computed as \code{sqrt(diff(xlim)*diff(ylim)/n)}. #' #' If \code{nx} and \code{ny} are missing and \code{values} is a matrix, the number of columns and rows of the matrix are taken. #' #' Otherwise, if \code{nx} and \code{ny} are missing, they are computed as the (ceiling, floor, or rounded to integer value) of the ratio of the (x or y) range divided by (dx or dy), depending on the value of \code{inside}. Positive \code{dy} will be made negative. Further named arguments (\code{...}) are passed on to \code{pretty}. If \code{dx} or \code{dy} are \code{units} objects, their value is converted to the units of \code{st_crs(.x)} (only when sf >= 1.0-7). #' #' @export #' @name st_as_stars st_as_stars.bbox = function(.x, ..., nx, ny, dx = dy, dy = dx, xlim = .x[c("xmin", "xmax")], ylim = .x[c("ymin", "ymax")], values = 0., n = 64800, pretty = FALSE, inside = FALSE, nz, proxy = FALSE) { if (xor(missing(nx), missing(ny))) stop("either specify both nx and ny, or none of them") adx = abs(diff(xlim)) ady = abs(diff(ylim)) if (is.matrix(values) && missing(nx) && missing(ny)) { nx = ncol(values) ny = nrow(values) } if (missing(dx) && missing(dy)) { if (missing(nx)) dx = dy = sqrt(adx * ady / n) else { dx = diff(xlim)/nx dy = -diff(ylim)/ny } } else { u <- st_crs(.x)$ud_unit if (inherits(u, "units")) { if (inherits(dx, "units")) units(dx) = u # might convert value if (inherits(dy, "units")) units(dy) = u # might convert value } dx = as.numeric(dx) # drop units if present dy = as.numeric(dy) # drop units if present } consider_inside = function(x, inside) { if (is.na(inside)) round(x) else if (inside) floor(x) else ceiling(x) } if (missing(nx)) nx = consider_inside(diff(xlim) / dx, inside) if (missing(ny)) { if (dy > 0) dy = -dy ny = consider_inside(-diff(ylim) / dy, inside) } if (pretty) { vx = pretty_cut(xlim, nx, inside, ...) nx = length(vx) x = create_dimension(values = vx, refsys = st_crs(.x)) vy = pretty_cut(ylim, ny, inside, ...) ny = length(vy) y = create_dimension(values = vy, refsys = st_crs(.x)) } else { x = create_dimension(from = 1, to = nx, offset = unname(xlim[1]), delta = unname(dx), refsys = st_crs(.x)) y = create_dimension(from = 1, to = ny, offset = unname(ylim[2]), delta = unname(dy), refsys = st_crs(.x)) } if (missing(nz)) { # 2D: if (proxy) { f = tempfile(fileext = ".tif") sf::gdal_create(f, c(nx, ny), values, st_crs(.x), xlim, ylim) read_stars(f, proxy = TRUE) } else st_as_stars(values = .array(values, c(x = nx[[1L]], y = ny[[1L]])), dims = create_dimensions(list(x = x, y = y), get_raster())) } else { stopifnot(proxy == FALSE) z = create_dimension(from = 1, to = nz[[1]]) st_as_stars(values = .array(values, c(x = nx[[1L]], y = ny[[1L]], z = nz[[1]])), dims = create_dimensions(list(x = x, y = y, z = z), get_raster())) } } ## @param x two-column matrix with columns and rows, as understood by GDAL; 0.5 refers to the first cell's centre; xy_from_colrow = function(x, geotransform) { # http://www.gdal.org/classGDALDataset.html , search for geotransform: # 0-based indices: # Xp = geotransform[0] + P*geotransform[1] + L*geotransform[2]; # Yp = geotransform[3] + P*geotransform[4] + L*geotransform[5]; stopifnot(ncol(x) == 2, length(geotransform) == 6, !any(is.na(geotransform))) matrix(geotransform[c(1, 4)], nrow(x), 2, byrow = TRUE) + x %*% matrix(geotransform[c(2, 3, 5, 6)], nrow = 2, ncol = 2) } colrow_from_xy = function(x, obj, NA_outside = FALSE) { if (inherits(obj, "stars")) obj = st_dimensions(obj) xy = attr(obj, "raster")$dimensions if (inherits(obj, "dimensions")) gt = st_geotransform(obj) if (isTRUE(st_is_longlat(st_crs(obj)))) { bb = st_bbox(obj) # see https://github.com/r-spatial/stars/issues/519 where this is problematic; # not sure whether this introduces new problems. # sign = ifelse(x[,1] < bb["xmin"], 1., ifelse(x[,1] > bb["xmax"], -1., 0.)) # x[,1] = x[,1] + sign * 360. # one more try: https://github.com/r-spatial/stars/issues/563 ix = x[,1] > bb["xmax"] & !is.na(x[,1]) x[ix,1] = x[ix,1] - 360. ix = x[,1] < bb["xmin"] & !is.na(x[,1]) x[ix,1] = x[ix,1] + 360. } if (!any(is.na(gt))) { # have geotransform inv_gt = gdal_inv_geotransform(gt) if (any(is.na(inv_gt))) stop("geotransform not invertible") ret = floor(xy_from_colrow(x, inv_gt) + 1.) # will return floating point col/row numbers!! if (NA_outside) ret[ ret[,1] < 1 | ret[,1] > obj[[ xy[1] ]]$to | ret[,2] < 1 | ret[,2] > obj[[ xy[2] ]]$to, ] = NA ret } else if (is_rectilinear(obj)) { ix = obj[[ xy[1] ]]$values if (!inherits(ix, "intervals")) ix = as_intervals(ix, add_last = length(ix) == dim(obj)[ xy[1] ]) cols = find_interval(x[,1], ix) iy = obj[[ xy[2] ]]$values if (!inherits(iy, "intervals")) iy = as_intervals(iy, add_last = length(iy) == dim(obj)[ xy[2] ]) rows = find_interval(x[,2], iy) # always NA_outside cbind(cols, rows) } else if (is_curvilinear(obj)) { stop("colrow_from_xy not supported for curvilinear objects") } else stop("colrow_from_xy not supported for this object") } st_cells_from_row_col = function(x, rows, cols) { nc = dim(x)[1] # ncols (rows - 1) * nc + cols } st_cells_from_xy = function(x, xy) { x = st_upfront(x) cr = colrow_from_xy(xy, x, NA_outside = TRUE) st_cells_from_row_col(x, cr[,2], cr[,1]) } #' return the cell index corresponding to the location of a set of points #' #' If the object has been cropped without normalization, then the indices return #' are relative to the original uncropped extent. See \code{\link{st_crop}} #' @param x object of class \code{stars} #' @param sf object of class \code{sf} or \code{sfc} #' @examples #' set.seed(1345) #' st_bbox(L7_ETMs) |> #' st_as_sfc() |> #' st_sample(10) -> pts #' (x <- st_cells(L7_ETMs, pts)) #' # get the pixel values (first band only): #' st_as_stars(L7_ETMs)[[1]][x] #' # get pixel values for all bands: #' st_as_stars(L7_ETMs) |> split() |> sapply(`[`, x) #' # compare with st_extract(): #' st_as_stars(L7_ETMs) |> split() |> st_extract(pts) #' @export st_cells = function(x, sf) { stopifnot(inherits(x, "stars"), inherits(sf, c("sf", "sfc")), st_crs(x) == st_crs(sf)) st_cells_from_xy(x, st_coordinates(sf)) } has_rotate_or_shear = function(x) { dimensions = st_dimensions(x) if (has_raster(x)) { r = attr(dimensions, "raster") !any(is.na(r$affine)) && any(r$affine != 0.0) } else FALSE } has_raster = function(x) { if (inherits(x, "stars")) x = st_dimensions(x) !is.null(r <- attr(x, "raster")) && all(r$dimensions %in% names(x)) } is_regular_grid = function(x) { has_raster(x) && !(has_rotate_or_shear(x) || is_rectilinear(x) || is_curvilinear(x)) } is_rectilinear = function(x) { d = st_dimensions(x) if (has_raster(x) && !is_curvilinear(x)) { xy = attr(d, "raster")$dimensions dimx = d[[ xy[1] ]] dimy = d[[ xy[2] ]] (is.na(dimx$delta) || is.na(dimy$delta)) && (!regular_intervals(dimx$values) || !regular_intervals(dimy$values)) } else FALSE } is_curvilinear = function(x) { d = st_dimensions(x) has_raster(x) && isTRUE(attr(d, "raster")$curvilinear) } which_sfc = function(x) { if (inherits(x, "stars")) x = st_dimensions(x) which(sapply(x, function(i) inherits(i$values, "sfc"))) } which_time = function(x) { if (inherits(x, "stars")) x = st_dimensions(x) which(sapply(x, function(i) inherits(i$values, c("POSIXct", "Date", "PCICt")) || (is.character(i$refsys) && (i$refsys %in% c("POSIXct", "Date", "PCICt") || grepl("PCICt", i$refsys))))) } #' @export time.stars = function(x, ..., which = 1) { w = which_time(x) if (length(w) > 1 && missing(which)) warning(paste("using the first of", length(w), "time dimensions")) if (length(w) == 0) stop("object does not have a time dimensions") stopifnot(length(which) == 1) expand_dimensions(x)[[ w[which] ]] } has_sfc = function(x) { length(which_sfc(x)) > 0 } #' retrieve coordinates for raster or vector cube cells #' #' retrieve coordinates for raster or vector cube cells #' @param x object of class \code{stars} #' @param add_max logical; if \code{TRUE}, dimensions are given with a min (x) and max (x_max) value #' @param center logical; (only if \code{add_max} is FALSE): should grid cell center coordinates be returned (TRUE) or offset values (FALSE)? \code{center} can be a named logical vector or list to specify values for each dimension. #' @name st_coordinates #' @param ... ignored #' @export st_coordinates.stars = function(x, ..., add_max = FALSE, center = TRUE) { dims = st_dimensions(x) xy = attr(dims, "raster")$dimensions if (is_curvilinear(x)) { x = st_upfront(x) # x and y first... cc = setNames(data.frame(as.vector(dims[[ xy[1] ]]$values), as.vector(dims[[ xy[2] ]]$values)), xy) dims[ xy ] = NULL # remove out = do.call(expand.grid, append(list(ix = seq_len(nrow(cc))), expand_dimensions(dims, center = center))) # cell offsets ix = out$ix out$ix = NULL out = cbind(cc[[1]][ix], cc[[2]][ix], out) names(out)[1:2] = xy out } else if (has_rotate_or_shear(x)) { if (add_max) stop("add_max will not work for rotated/shared rasters") if (isTRUE(!center)) # center = FALSE warning("center values are given for spatial coordinates") d = dim(x) nx = d[ xy[1] ] ny = d[ xy[2] ] setNames(as.data.frame(xy_from_colrow(as.matrix(expand.grid(seq_len(nx), seq_len(ny))) - 0.5, st_geotransform(x))), xy) # gives cell centers } else { if (add_max) { cbind( do.call(expand.grid, expand_dimensions(x, center = FALSE)), # cell offsets setNames(do.call(expand.grid, expand_dimensions(dims[xy], max = TRUE)), paste0(xy, "_max")) ) } else { ed = expand_dimensions(x, center = center) # cell centers for x/y if raster if (length(ed) > 1) do.call(expand.grid, ed) else data.frame(ed) } } } #' @export st_coordinates.dimensions = function(x, ...) { st_coordinates(st_as_stars(list(), dimensions = x), ...) } #' @name st_coordinates #' @export #' @param add_coordinates logical; if `TRUE`, columns with dimension values preceed the array values, #' otherwise they are omitted as.data.frame.stars = function(x, ..., add_max = FALSE, center = NA, add_coordinates = TRUE) { if (add_coordinates) data.frame(st_coordinates(x, add_max = add_max, center = center, ...), lapply(x, function(y) structure(y, dim = NULL))) else as.data.frame(lapply(x, function(y) structure(y, dim = NULL))) } add_units = function(x) { f = function(obj) if (inherits(obj, "units")) paste0("[", enc2utf8(as.character(units(obj))), "]") else "" paste(names(x), sapply(x, f)) } #' print stars or dimensions object #' #' print stars or dimensions object #' @name print_stars #' @param x object of class stars or of class dimensions #' @param n when prod(dim(x)) > 10 * n, the first n cells are used for attribute summary statistics #' @param abbrev number of characters to abbreviate attribute names to #' @param ... passed on to \code{as.data.frame.dimensions} #' @export print.stars = function(x, ..., n = 1e5, abbrev = 30) { shorten = function(s) { if (nchar(s) > abbrev) paste0(substr(s, 1, abbrev - 3), "...") else s } cat("stars object with", length(dim(x)), "dimensions and", length(x), if (length(x) != 1) "attributes\n" else "attribute\n") if (length(x)) { cat("attribute(s)") names(x) = sapply(names(x), shorten) df = if (prod(dim(x)) > 10 * n) { cat(paste0(", summary of first ", n, " cells:\n")) # nocov as.data.frame(lapply(x, function(y) structure(y, dim = NULL)[1:n]), optional = TRUE) # nocov } else { cat(":\n") as.data.frame(lapply(x, function(y) structure(y, dim = NULL)), optional = TRUE) } names(df) = add_units(x) if (all(sapply(x, is.numeric))) { m_summary = function(x) { s = summary(x); if (!"NA's" %in% names(s)) s["NA's"] = 0; s } sums = lapply(df, summary) if (length(unique(lengths(sums))) > 1) sums = lapply(df, m_summary) print(do.call(rbind, sums)) } else print(summary(df)) } cat("dimension(s):\n") print(st_dimensions(x), ...) invisible(x) } #' @export aperm.stars = function(a, perm = NULL, ...) { if (is.null(perm)) perm = rev(seq_along(dim(a))) if (all(perm == seq_along(dim(a))) || isTRUE(all(match(perm, names(dim(a))) == seq_along(dim(a))))) return(a) d = st_dimensions(a) if (is.character(perm)) perm = match(perm, names(d)) st_stars(lapply(a, aperm, perm = perm, ...), d[perm]) } #' @export dim.stars = function(x) { d = st_dimensions(x) if (length(x) == 0) dim(d) else { stopifnot(length(d) == length(dim(x[[1]]))) structure(dim(x[[1]]), names = names(d)) } } propagate_units = function(new, old) { for (i in seq_along(new)) if (inherits(old[[i]], "units")) units(new[[i]]) <- units(old[[i]]) new } setNamesIfnn = function(x, nms) { # set names if not NULL stopifnot(is.character(nms) || is.null(nms)) if (is.null(nms) || length(nms) != length(x)) x else setNames(x, nms) } #' combine multiple stars objects, or combine multiple attributes in a single stars object into a single array #' #' combine multiple stars objects, or combine multiple attributes in a single stars object into a single array #' @param ... object(s) of class \code{star}: in case of multiple arguments, these are combined into a single stars object, in case of a single argument, its attributes are combined into a single attribute. In case of multiple objects, all objects should have the same dimensionality. #' @param along integer; see \link{read_stars} #' @param try_hard logical; if \code{TRUE} and some arrays have different dimensions, combine those that dimensions matching to the first array #' @param tolerance numeric; values used in \link{all.equal} to compare dimension values #' combine those that dimensions matching to the first array #' @param nms character; vector with array names #' @returns a single \code{stars} object with merged (binded) arrays. #' @details An error is raised when attempting to combine arrays with different #' measurement units into a single array. If this was intentded, \code{drop_units} #' can be used to remove units of a \code{stars} object before merging. #' @export #' @examples #' tif = system.file("tif/L7_ETMs.tif", package = "stars") #' x = read_stars(tif) #' (new = c(x, x)) #' c(new) # collapses two arrays into one with an additional dimension #' c(x, x, along = 3) c.stars = function(..., along = NA_integer_, try_hard = FALSE, nms = names(list(...)), tolerance = sqrt(.Machine$double.eps)) { dots = list(...) if (!all(sapply(dots, inherits, "stars"))) stop("all arguments to c() should be stars objects") if (any(sapply(dots, inherits, "stars_proxy"))) stop("convert stars_proxy objects to stars first using st_as_stars()") if (length(dots) == 1 && length(along) == 1 && missing(along)) dots[[1]] else if (length(along) == 1 && is.na(along)) { # Case 1: merge attributes of several objects by simply putting them together in a single stars object; # dim does not change: if (identical_dimensions(dots, tolerance = tolerance)) st_as_stars(setNamesIfnn(do.call(c, lapply(dots, unclass)), nms), dimensions = st_dimensions(dots[[1]])) else { # currently catches only the special case of ... being a broken up time series: along = sort_out_along(dots) if (!is.na(along)) do.call(c, c(dots, along = along)) else if (!try_hard) stop("don't know how to merge arrays: please specify parameter along") else { d = lapply(dots, st_dimensions) ident = c(TRUE, sapply(d[-1], identical, d[[1]])) if (!all(ident)) { warning(paste( "ignored subdataset(s) with dimensions different from first subdataset:", paste(which(!ident), collapse = ", "), "\nuse gdal_subdatasets() to find all subdataset names")) if (!is.null(nms)) nms = nms[ident] } st_as_stars(setNamesIfnn(do.call(c, lapply(dots[ident], unclass)), nms), dimensions = st_dimensions(dots[[1]])) } } } else { if (is.list(along)) { # custom ordering of ... over dimension(s) with values specified if (prod(lengths(along)) != length(dots)) stop("number of objects does not match the product of lenghts of the along argument", call. = FALSE) # abind all: d = st_dimensions(dots[[1]]) ret = mapply(abind, ..., along = length(d) + 1, SIMPLIFY = FALSE) # make dims: newdim = c(dim(dots[[1]]), lengths(along)) ret = lapply(ret, function(x) { dim(x) = newdim; x }) ret = propagate_units(ret, dots[[1]]) # make dimensions: for (i in seq_along(along)) d[[ names(along)[i] ]] = create_dimension(values = along[[i]]) st_as_stars(ret, dimensions = d) } else { # loop over attributes, abind them: # along_dim: the number of the dimension along which we merge arrays d = st_dimensions(dots[[1]]) along_dim = if (is.character(along)) { along_dim = which(along == names(d)) if (length(along_dim) == 0) length(d) + 1 else along_dim } else along stopifnot_identical_units(dots) ret = propagate_units(mapply(abind, ..., along = along_dim, SIMPLIFY = FALSE), dots[[1]]) dims = combine_dimensions(dots, along_dim) if (along_dim == length(d) + 1) names(dims)[along_dim] = if (is.character(along)) along else "new_dim" st_as_stars(ret, dimensions = dims) } } } stopifnot_identical_units = function(lst) { a1 = lst[[1]][[1]] for (i in seq_along(lst[-1])) { ai = lst[[i+1]][[1]] if (inherits(a1, "units") && !identical(units(a1), units(ai))) stop("cannot merge subarrays with different units") } TRUE } #' @export adrop.stars = function(x, drop = which(dim(x) == 1), ..., drop_xy = FALSE) { if (missing(drop) && !drop_xy) { # hanlde drop_xy: by default, don't drop x/y d = st_dimensions(x) xy = attr(d, "raster")$dimensions drop = setdiff(drop, match(xy, names(d))) } if (is.logical(drop)) drop = which(drop) if (any(dim(x) > 1) && length(drop) > 0) { l = vector("list", length = length(x)) f = sapply(x, is.factor) l[!f] = lapply(x[!f], adrop, drop = drop, one.d.array = TRUE, ...) l[f] = lapply(x[f], function(x) structure(x, dim = dim(x)[-drop])) st_as_stars(setNames(l, names(x)), dimensions = st_dimensions(x)[-drop]) } else x } #' @export st_bbox.default = function(obj, ...) { if (!missing(obj)) stop(paste("no st_bbox method available for object of class", class(obj))) obj = st_sfc(st_point(c(-180,-90)), st_point(c(180, 90)), crs = st_crs('OGC:CRS84')) st_bbox(obj) } #' @export st_bbox.dimensions = function(obj, ...) { if (has_raster(obj)) { # raster r = attr(obj, "raster") x = obj[[ r$dimensions[1] ]] y = obj[[ r$dimensions[2] ]] bb = if (is.null(x$values) && is.null(y$values)) { gt = st_geotransform(obj) if (length(gt) == 6 && !any(is.na(gt))) { bb = rbind(c(x$from - 1, y$from - 1), c(x$to, y$from - 1), c(x$to, y$to), c(x$from - 1, y$to)) xy = xy_from_colrow(bb, gt) c(xmin = min(xy[,1]), ymin = min(xy[,2]), xmax = max(xy[,1]), ymax = max(xy[,2])) } else c(xmin = x$from - 0.5, ymin = y$from - 0.5, xmax = x$to + 0.5, ymax = y$to + 0.5) } else { if (is_curvilinear(obj)) c(xmin = min(x$values, na.rm = TRUE), ymin = min(y$values, na.rm = TRUE), xmax = max(x$values, na.rm = TRUE), ymax = max(y$values, na.rm = TRUE)) else { rx = range(x) # dispatches into range.dimension ry = range(y) c(xmin = rx[1], ymin = ry[1], xmax = rx[2], ymax = ry[2]) } } structure(bb, crs = st_crs(x$refsys), class = "bbox") } else { if (! has_sfc(obj)) stop("dimensions table does not have x & y, nor an sfc dimension") # nocov ix = which_sfc(obj) if (length(ix) > 1) warning("returning the bounding box of the first geometry dimension") st_bbox(obj[[ ix[1] ]]$values) } } #' @export st_bbox.stars = function(obj, ...) { st_bbox(st_dimensions(obj), ...) } #' set bounding box parameters of regular grid #' @param x object of class dimensions, stars or stars_proxy #' @param value object of class bbox #' @param ... ignored #' @export st_set_bbox = function(x, value, ...) UseMethod("st_set_bbox") #' @export st_set_bbox.dimensions = function(x, value, ...) { stopifnot(inherits(value, "bbox"), is_regular_grid(x)) xy = attr(x, "raster")$dimensions if (x[[ xy[1] ]]$from != 1 || x[[ xy[2] ]]$from != 1) stop("use st_normalize first so that dimensions start at index 1") d = dim(x) xsign = sign(x[[ xy[1] ]]$delta) ysign = sign(x[[ xy[2] ]]$delta) x[[ xy[1] ]]$offset = ifelse(xsign < 0, value[["xmax"]], value[["xmin"]]) x[[ xy[2] ]]$offset = ifelse(ysign < 0, value[["ymax"]], value[["ymin"]]) x[[ xy[1] ]]$delta = xsign * (value[["xmax"]] - value[["xmin"]]) / d[[ xy[1] ]] x[[ xy[2] ]]$delta = ysign * (value[["ymax"]] - value[["ymin"]]) / d[[ xy[2] ]] if (!is.na(st_crs(value))) st_crs(x) = st_crs(value) x } #' @export st_set_bbox.stars = function(x, value, ...) { structure(x, dimensions = st_set_bbox(st_dimensions(x), value)) } #' @export st_set_bbox.stars_proxy = function(x, value, ...) { structure(x, dimensions = st_set_bbox(st_dimensions(x), value)) } #' @export st_crs.stars = function(x, ...) { st_crs(st_dimensions(x), ...) } #' @export st_crs.dimensions = function(x, ...) { xy = attr(x, "raster")$dimensions if (!all(is.na(xy))) st_crs(x[[ xy[1] ]]$refsys) else if (has_sfc(x)) # search for simple features: st_crs(x[[ which_sfc(x)[1] ]]$values) else NA_crs_ } #' @export `st_crs<-.stars` = function(x, value) { structure(x, dimensions = st_set_crs(st_dimensions(x), value)) } #' @export `st_crs<-.dimensions` = function(x, value) { value = if (is.na(value)) NA_crs_ else if (is.numeric(value) || is.character(value)) st_crs(value) else if (inherits(value, "crs")) value else stop(paste("crs of class", class(value), "not recognized")) # set CRS in dimensions: xy = attr(x, "raster")$dimensions if (!all(is.na(xy))) { # has x/y spatial dimensions: x[[ xy[1] ]]$refsys = value x[[ xy[2] ]]$refsys = value } if (!all(is.na(xy)) && !is.na(x[[ xy[1] ]]$refsys) && !is.na(value) && st_crs(x) != value) warning("replacing CRS does not reproject data: use st_transform, or st_warp to warp to a new CRS") # set crs of sfc's, if any: for (j in which_sfc(x)) { x[[ j ]]$refsys = value st_crs(x[[ j ]]$values) = value } x } #' @export st_geometry.stars = function(obj,...) { if (!has_sfc(obj)) stop("stars object does not have a simple feature dimension") d = st_dimensions(obj) d[[ which_sfc(obj) ]]$values } # make sure asub works for factor too: #' @export asub.factor = function(x, idx, dims, drop = NULL, ...) { l = levels(x) x = unclass(x) ret = NextMethod() structure(ret, class = class(x), levels = l) } #' @name merge #' @aliases split #' @param f the name or index of the dimension to split; by default the last dimension #' @param drop ignored #' @details split.stars works on the first attribute, and will give an error when more than one attribute is present #' @export split.stars = function(x, f = length(dim(x)), drop = TRUE, ...) { if (length(x) > 1) { l = lapply(seq_along(x), function(i) split(x[i], f, drop = drop, ...)) do.call(c, setNames(l, names(x))) } else { d = st_dimensions(x) if (is.character(f)) f = which(names(d) == f) ret = lapply(seq_len(dim(x)[f]), function(y) asub(x[[1]], y, f, drop = TRUE)) nm = if (!is.null(d[[f]]$values)) d[[f]]$values else make.names(seq_along(ret)) st_as_stars(setNames(ret, nm), dimensions = d[-f]) } } #' merge or split stars object #' #' merge attributes into a dimension, or split a dimension over attributes #' @param x object of class \code{stars} #' @param y needs to be missing #' @param name name for the new dimension #' @param ... if defined, the first unnamed argument is used for dimension values, if not defined, attribute names are used for dimension values #' @returns merge merges attributes of a stars object into a new dimension; split splits a dimension over attributes #' @name merge #' @export merge.stars = function(x, y, ..., name = "attributes") { dots = list(...) if (!missing(y)) stop("argument y needs to be missing: merging attributes of x") old_dim = st_dimensions(x) out = st_redimension(x, name = name) new_dim = if (length(dots)) create_dimension(values = dots[[1]]) else create_dimension(values = names(x)) dims = setNames(c(old_dim, list(new_dim)), make.unique(c(names(old_dim), name))) d = create_dimensions(dims, raster = attr(old_dim, "raster")) if (!is.null(names(dots))) names(d)[length(d)] = names(dots) st_stars(out, dimensions = d) # overwrite dimensions of out } sort_out_along = function(ret) { d1 = st_dimensions(ret[[1]]) d2 = st_dimensions(ret[[2]]) if ("time" %in% names(d1) && (isTRUE(d1$time$offset != d2$time$offset) || !any(d1$time$values %in% d2$time$values))) "time" else NA_integer_ } #' @export is.na.stars = function(x, ...) { st_as_stars(lapply(x, is.na), dimensions = st_dimensions(x)) } #' redimension array, or collapse attributes into a new dimension #' #' redimension array, or collapse attributes into a new dimension #' @name redimension #' @export st_redimension = function(x, new_dims, along, ...) UseMethod("st_redimension") #' @export #' @name redimension #' @param x object of class \code{stars} #' @param new_dims target dimensions: either a `dimensions` object or an integer vector with the dimensions' sizes #' @param along named list with new dimension name and values #' @param name character name of the new dimension #' @param ... ignored st_redimension.stars = function(x, new_dims = st_dimensions(x), along = setNames(list(names(x)), name), ..., name = "new_dim") { d = st_dimensions(x) if (inherits(new_dims, "dimensions")) { di = dim(new_dims) } else { di = new_dims new_dims = create_dimensions(di) } if (!isTRUE(all.equal(di, dim(x), check.attributes = FALSE))) { if (prod(dim(x)) != prod(di)) stop("product of dim(new_dim) does not match that of x") for (i in seq_len(min(length(di), length(dim(x))))) if (di[i] == dim(x)[i]) { new_dims[[i]] = d[[i]] names(new_dims)[i] = names(d[i]) } x = unclass(x) raster = attr(d, "raster") if (all(raster$dimensions %in% names(new_dims))) attr(new_dims, "raster") = raster for (i in seq_along(x)) dim(x[[i]]) = di st_stars(x, dimensions = new_dims) } else { # collapse attributes into dimension if (length(x) == 1) # only one attribute: do nothing x else { new_dim = create_dimension(values = along[[1]]) dims = create_dimensions(c(d, new_dim = list(new_dim)), attr(d, "raster")) if (length(names(along)) == 1) names(dims)[names(dims) == "new_dim"] = names(along) ret = structure(do.call(c, x), dim = dim(dims)) st_stars(setNames(list(ret), paste(names(x), collapse = ".")), dimensions = dims) } } } #' @export "$<-.stars" = function(x, i, value) { x[[i]] = value x } #' @export "[[<-.stars" = function(x, i, value) { if (!is.null(value)) { if (prod(dim(x)) %% length(value) != 0) { # error: if (is.null(dim(value))) stop(paste("replacement has length", length(value), ", data has dim", paste(dim(x), collapse = ", "))) else stop(paste("replacement has dim", paste(dim(value), collapse = ", "), ", data has dim", paste(dim(x), collapse = ", "))) } if (inherits(value, "stars")) { stopifnot(length(value) == 1) value = value[[1]] } value = if (inherits(value, c("factor", "POSIXct"))) structure(rep(value, length.out = prod(dim(x))), dim = dim(x), colors = attr(value, "colors"), rgba = attr(value, "rgba"), exclude = attr(value, "exclude")) else if (!is.array(value) || !isTRUE(all.equal(dim(value), dim(x), check.attributes = FALSE))) array(value, dim(x)) else value } NextMethod() } st_upfront = function(x, first = attr(st_dimensions(x), "raster")$dimensions) { if (!is.character(first)) first = names(st_dimensions(x))[first] if (!any(is.na(first))) aperm(x, c(first, setdiff(names(st_dimensions(x)), first))) else x } #' @export st_area.stars = function(x, ...) { crs = st_crs(x) if (is.na(crs)) message("Missing coordinate reference system: assuming Cartesian coordinates") d = st_dimensions(st_upfront(x))[1:2] a = if (isTRUE(st_is_longlat(x)) || is_curvilinear(x)) st_area(st_as_sfc(x, as_points = FALSE)) # has units else { a = if (is_regular_grid(x)) d[[1]]$delta * d[[2]]$delta else { # rectilinear: x = if (inherits(d[[1]]$values, "intervals")) d[[1]]$values else as_intervals(d[[1]]$values) y = if (inherits(d[[2]]$values, "intervals")) d[[2]]$values else as_intervals(d[[2]]$values) apply(do.call(cbind, x), 1, diff) %o% apply(do.call(cbind, y), 1, diff) } if (!is.na(crs)) units::set_units(abs(a), paste0(crs$units, "^2"), mode = "standard") else abs(a) } lst = if (inherits(a, "units")) list(area = `units<-`(array(a, dim(d)), units(a))) else list(area = array(a, dim(d))) st_stars(lst, dimensions = d) } #' @export drop_units.stars = function(x) { try_drop_units = function(x) { if (inherits(x, "units")) units::drop_units(x) else x } st_stars(lapply(x, try_drop_units), dimensions = st_dimensions(x)) } #' Predict values, given a model object, for a stars or stars_proxy object #' @export #' @name predict.stars #' @param object object of class `stars` #' @param model model object of a class that has a predict method; check with `methods(class = class(object))` #' @param drop_dimensions logical; if `TRUE`, remove dimensions (coordinates etc) from `data.frame` with predictors #' @param ... arguments passed on to this predict method #' @details separate predictors in object need to be separate attributes in object; #' in case they are e.g. in a band dimension, use `split(object)` predict.stars = function(object, model, ..., drop_dimensions = FALSE) { obj_df = as.data.frame(st_as_stars(object)) if (drop_dimensions) obj_df = obj_df[-seq_along(dim(object))] na_ids = which(is.na(obj_df), arr.ind = TRUE) # identify rows with NA's in the predictors obj_df[na_ids] = 0 # fill with something valid (e.g. 0) pr = try(predict(model, obj_df, ...), silent = TRUE) has_method = function(generic, cls) { m = row.names(attr(methods(generic), "info")) cls %in% substring(m, nchar(generic) + 2, 1e4) } if (inherits(pr, "try-error") && !has_method("predict", class(model))) stop(paste("No predict method found for objects of class", class(model))) if (inherits(pr, "try-error")) { # https://github.com/r-spatial/stars/issues/448 m = paste0("prediction on array(s) `", paste(names(object), collapse = ","), "' failed; will try to split() dimension `", tail(names(dim(object)), 1), "' over attributes") message(m) predict(split(object), model, ..., drop_dimensions = drop_dimensions) # returns } else { if (!inherits(pr, "data.frame")) pr = if (is.null(colnames(pr))) data.frame(prediction = pr) else as.data.frame(pr) pr[unique(data.frame(na_ids)[,1]), ] = NA # Mask with original NA's st_stars(lapply(pr, function(y) structure(y, dim = dim(object))), st_dimensions(object)) } } #' create an array with dimension values #' #' create an array with dimension values #' @param x object of class \code{stars} #' @param which integer; indices of the dimensions to address (default: all) #' @return \code{stars} object with dimension values as attributes #' @export #' @examples #' tif = system.file("tif/L7_ETMs.tif", package = "stars") #' x1 = read_stars(tif) #' (x = st_dim_to_attr(x1)) #' plot(x) #' (x = st_dim_to_attr(x1, 2:3)) #' plot(x) #' (x= st_dim_to_attr(x1, 3)) #' plot(x) st_dim_to_attr = function(x, which = seq_along(dim(x))) { d = dim(x) l = vector("list", length = length(which)) e = expand_dimensions(x) for (i in seq_along(which)) { l [[i]] = if (is.null(dim(e[[i]]))) { dp = c(which[i], setdiff(seq_along(dim(x)), which[i])) aperm(array(e[[ which[i] ]], d[dp]), order(dp)) } else # curvilinear: array(e[[i]], d) } st_stars(setNames(l, names(d)[which]), st_dimensions(x)) } #' @export st_interpolate_aw.stars = function(x, to, extensive, ...) { ret = sf::st_interpolate_aw(st_as_sf(x), to, extensive, ...) geom = attr(ret, "sf_column") dx = dim(x) if (length(dx) > 2 && length(x) == 1 && length(ret) > 2) { ret = merge(st_as_stars(ret)) nd = names(st_dimensions(x)) ret = st_set_dimensions(ret, seq_along(dx), names = c(geom, paste0(nd[-(1:2)], collapse = "."))) setNames(ret, names(x)) } else ret } #' get the raster type (if any) of a stars object #' @param x object of class \code{stars} #' @param dimension optional: numbers or names of dimension(s) to get per-dimension type #' @return if \code{dimension} is not specified, return the spatial raster type: #' one of \code{NA} (if the object does not have raster dimensions), #' \code{"curvilinear"}, \code{"rectilinear"}, \code{"affine"}, or \code{"regular"}. #' In case dimension(s) are specified, return one of \code{"regular"}, \code{"rectilinear"} #' (irregular but numeric), or \code{"discrete"} (anything else). #' @details categories \code{"curvilinear"} and \code{"affine"} only refer to #' the relationship between a pair of spatial (raster) dimensions. #' @examples #' tif = system.file("tif/L7_ETMs.tif", package = "stars") #' x = read_stars(tif) #' st_raster_type(x) #' st_raster_type(x, 1:3) #' @export st_raster_type = function(x, dimension = character(0)) { stopifnot(inherits(x, "stars")) if (!missing(dimension)) stopifnot(all(dimension >= 1), all(dimension <= length(dim(x)))) dimension_type = function(d) { if (!any(is.na(c(d$offset, d$delta)))) "regular" else if (!is.null(d$values) && is.numeric(d$values) && is.matrix(d$values)) "curvilinear" else if (!is.null(d$values) && is.numeric(d$values)) "rectilinear" else "discrete" } if (length(dimension)) sapply(st_dimensions(x)[dimension], dimension_type) else if (!has_raster(x)) NA_character_ else if (is_curvilinear(x)) "curvilinear" else if (is_rectilinear(x)) "rectilinear" else if (has_rotate_or_shear(x)) "affine" else "regular" } #' obtain (spatial) resolution of a stars object #' #' obtain resolution(s) of a stars object: by default only the (absolute) x/y raster dimensions, optionally all \code{delta} dimension parameters #' @param x an object of class \code{stars} #' @param all logical; if FALSE return a vector with the x/y raster resolution #' @param absolute logical; only works when \code{all = FALSE}; if TRUE return absolute resolution values, if FALSE return \code{delta} values #' @returns if \code{all = FALSE} a vector with x/y raster resolutions, otherwise a list with delta values #' @examples #' st_res(L7_ETMs) #' st_res(L7_ETMs, absolute = FALSE) #' st_res(L7_ETMs, all = TRUE) #' if (require(starsdata)) { #' paste0("netcdf/", c("avhrr-only-v2.19810901.nc", #' "avhrr-only-v2.19810902.nc", #' "avhrr-only-v2.19810903.nc", #' "avhrr-only-v2.19810904.nc")) |> #' system.file(package = "starsdata") |> #' read_stars(quiet = TRUE) -> x #' st_res(x) |> print() #' st_res(x, all = TRUE) |> print() #' } #' @export st_res = function(x, all = FALSE, absolute = !all) { stopifnot(inherits(x, "stars")) d = st_dimensions(x) l = lapply(d, `[[`, "delta") if (!all) { xy = attr(d, "raster")$dimensions l = unlist(l[xy]) if (absolute) l = abs(l) } l } stars/R/dimensions.R0000644000176200001440000006755114576033423014127 0ustar liggesusers#' get dimensions from stars object #' @name st_dimensions #' @export #' @param .x object to retrieve dimensions information from #' @param ... further arguments #' @param value new object of class \code{dimensions}, with matching dimensions #' @return the \code{dimensions} attribute of \code{x}, of class \code{dimensions} st_dimensions = function(.x, ...) UseMethod("st_dimensions") #' @export #' @name st_dimensions st_dimensions.stars = function(.x, ...) attr(.x, "dimensions") #' @export st_dimensions.dimensions = function(.x, ...) .x #' @export #' @name st_dimensions `st_dimensions<-` = function(x, value) UseMethod("st_dimensions<-") #' @export #' @name st_dimensions #' @param x object of class \code{dimensions} `st_dimensions<-.stars` = function(x, value) { stopifnot(inherits(value, "dimensions"), length(x) && all(dim(x[[1]]) == dim(value))) st_stars(x, value) } #' @export #' @name st_dimensions `st_dimensions<-.stars_proxy` = function(x, value) { if (!is.null(attr(x, "call_list"))) stop("st_dimensions<- on a stars_proxy object only works if there is no call list") structure(NextMethod(), class = class(x)) } #' @export #' @name st_dimensions `st_dimensions<-.list` = function(x, value) { st_as_stars(x, dimensions = value) } #' @export #' @name st_dimensions st_dimensions.array = function(.x, ...) { if (length(list(...)) > 0) stop("only one argument expected") dn = dimnames(.x) # raster? r = if (all(c("x", "y") %in% names(dn))) get_raster(affine = c(0.0, 0.0), dimensions = c("x", "y"), curvilinear = FALSE) else get_raster(affine = c(0.0, 0.0), dimensions = rep(NA_character_, 2), curvilinear = FALSE) ret = if (is.null(dn)) st_dimensions(list(.x)) # default else # try to get dimension names and default values from dimnames(.x): create_dimensions(lapply(dn, function(y) create_dimension(values = y)), r) if (is.null(names(ret)) || any(names(ret) == "")) names(ret) = make.names(seq_along(ret)) ret } #' @export st_dimensions.matrix = st_dimensions.array #' @export #' @name st_dimensions #' @param .raster length 2 character array with names (if any) of the raster dimensions #' @param affine numeric; specify parameters of the affine transformation #' @param cell_midpoints logical; if \code{TRUE} AND the dimension values are strictly regular, the values are interpreted as the cell midpoint values rather than the cell offset values when calculating offset (i.e., the half-cell-size correction is applied); can have a value for each dimension, or else is recycled #' @param point logical; does the pixel value (measure) refer to a point (location) value or to an pixel (area) summary value? #' @details dimensions can be specified in two ways. The simplest is to pass a vector with numeric values for a numeric dimension, or character values for a categorical dimension. Parameter \code{cell_midpoints} is used to specify whether numeric values refer to the offset (start) of a dimension interval (default), or to the center; the center case is only available for regular dimensions. For rectilinear numeric dimensions, one can specify either a vector with cell borders (start values), or a data.frame with two columns named "start" and "end", with the respective interval start and end values. In the first case, the end values are computed from the start values by assuming the last two intervals have equal width. #' st_dimensions.default = function(.x, ..., .raster, affine = c(0, 0), cell_midpoints = FALSE, point = FALSE) { d = list(...) if (! missing(.x)) d = append(list(.x), d) if (missing(.raster)) { .raster = if (all(c("x", "y") %in% names(d)) && is.numeric(d$x) && is.numeric(d$y)) c("x", "y") else rep(NA_character_, 2) } create_dimensions(mapply(create_dimension, values = d, is_raster = cell_midpoints, point = point, SIMPLIFY = FALSE), raster = get_raster(dimensions = .raster, affine = affine)) } #' @name st_dimensions #' @param which integer or character; index or name of the dimension to be changed #' @param values values for this dimension (e.g. \code{sfc} list-column), or length-1 \code{dimensions} object; setting special value \code{NULL} removes dimension values, for instance to remove curvilinear raster coordinates #' @param names character; vector with new names for all dimensions, or with the single new name for the dimension indicated by \code{which} #' @param xy length-2 character vector; (new) names for the \code{x} and \code{y} raster dimensions #' @export #' @examples #' x = read_stars(system.file("tif/L7_ETMs.tif", package = "stars")) #' # Landsat 7 ETM+ band semantics: https://landsat.gsfc.nasa.gov/the-enhanced-thematic-mapper-plus/ #' # set bands to values 1,2,3,4,5,7: #' (x1 = st_set_dimensions(x, "band", values = c(1,2,3,4,5,7), names = "band_number", point = TRUE)) #' # set band values as bandwidth #' rbind(c(0.45,0.515), c(0.525,0.605), c(0.63,0.69), c(0.775,0.90), c(1.55,1.75), c(2.08,2.35)) %>% #' units::set_units("um") -> bw # or: units::set_units(µm) -> bw #' # set bandwidth midpoint: #' (x2 = st_set_dimensions(x, "band", values = 0.5 * (bw[,1]+bw[,2]), #' names = "bandwidth_midpoint", point = TRUE)) #' # set bandwidth intervals: #' (x3 = st_set_dimensions(x, "band", values = make_intervals(bw), names = "bandwidth")) st_set_dimensions = function(.x, which, values = NULL, point = NULL, names = NULL, xy, ...) { d = st_dimensions(.x) if (!missing(which) && is.character(which)) which = match(which, base::names(d)) if (! missing(values)) { if (is.na(which)) stop("which should be a name or index of an existing dimensions") if (inherits(values, "units") && names(d)[which] %in% attr(d, "raster")$xy && !is.na(st_crs(.x))) stop("units in x/y dimension values only allowed if object has no CRS") if (inherits(values, "dimensions")) { if (is.null(names)) names <- names(values) values <- values[[1]]$values } if (!is.null(values) && !inherits(values, "intervals") && dim(.x)[which] != length(values)) { if (dim(.x)[which] == length(values) - 1) # create intervals: values = as_intervals(values) else stop(paste("length of values (", length(values), ") does not match length of dimension", which, "(", dim(.x)[which], ")")) } if (is.null(values)) d[[which]]["values"] = list(NULL) # avoid removing element values else d[[which]] = create_dimension(values = values, point = point %||% d[[which]]$point, ...) r = attr(d, "raster") if (isTRUE(r$curvilinear)) { # FIXME: there's much more that should be checked for curvilinear grids... # https://github.com/r-spatial/stars/issues/460 if (base::names(d)[which] %in% r$dimensions && !any(sapply(d, function(v) is.matrix(v$values)))) { attr(d, "raster")$curvilinear = FALSE st_crs(d) = NA } } # else if (inherits(values, "sfc")) # base::names(d)[which] = "sfc" } if (!is.null(point) && is.logical(point)) { d[[which]]$point = point } if (! missing(xy)) { if (is.null(xy) || (length(xy) == 1 && is.na(xy))) xy = c(NA, NA) stopifnot(length(xy) == 2) r = attr(d, "raster") r$dimensions = as.character(xy) attr(d, "raster") = r } if (!missing(names)) { if (length(d) != length(names) && length(names) != 1) stop("length of names should match number of dimensions") r = attr(d, "raster") if (length(d) == length(names)) { # handle names in raster attribute, #46 # problematic again in #379 if (any(!is.na(r$dimensions))) r$dimensions = names[match(r$dimensions, names(d))] new_names = names } else { # replace the name of dimension `which`, #354 if (names(d)[which] %in% r$dimensions) r$dimensions[match(names(d)[which], r$dimensions)] = names new_names = names(d) new_names[which] = names } attr(d, "raster") = r base::names(d) = new_names } if (length(list(...))) d[[which]] = create_dimension(from = 1, to = dim(.x)[which], ...) if (inherits(.x, "stars_proxy")) structure(.x, dimensions = d) else st_as_stars(unclass(.x), dimensions = d) } #' @name st_dimensions #' @param where character, one of 'start', 'center' or 'end'. Set to NA (default) to ignore and use \code{max} and \code{center} explictly. This argument provides a convenient alternative to setting \code{max} and \code{center}. #' @param max logical; if \code{TRUE} return the end, rather than the beginning of an interval #' @param center logical; if \code{TRUE} return the center of an interval; if \code{NA} return the center for raster dimensions, and the start of intervals in other cases #' @export #' @examples #' m = matrix(1:20, nrow = 5, ncol = 4) #' dim(m) = c(x = 5, y = 4) # named dim #' (s = st_as_stars(m)) #' st_get_dimension_values(s, 'x', where = "start") #' st_get_dimension_values(s, 'x', center = FALSE) #' st_get_dimension_values(s, 'x', where = "center") #' st_get_dimension_values(s, 'x', center = TRUE) #' st_get_dimension_values(s, 'x', where = "end") #' st_get_dimension_values(s, 'x', max = TRUE) st_get_dimension_values = function(.x, which, ..., where = NA, max = FALSE, center = NA) { if ((!is.numeric(which) && !is.character(which)) || length(which) != 1) stop("argument which should be a length 1 dimension index or name") # nocov if (!is.na(where)){ w = tolower(where[1]) if (w == 'center'){ max = FALSE center = TRUE } else if (w == 'start'){ max = FALSE center = FALSE } else if (w == 'end'){ max = TRUE center = NA } else { stop("where, if not NA, must be 'start', 'center' or 'end': ", where) } } expand_dimensions(.x, ..., max = max, center = center)[[which]] } #' @export "[.dimensions" = function(x, i, j,..., drop = FALSE) { raster = attr(x, "raster") ret = unclass(x)[i] if (isTRUE(all(raster$dimensions %in% names(ret)))) create_dimensions(ret, raster) else create_dimensions(ret) # drop raster } regular_intervals = function(x, epsilon = 1e-10) { if (length(x) <= 1) FALSE else { ud = if (is.atomic(x) && (is.numeric(x) || inherits(x, c("POSIXt", "Date", "PCICt")))) unique(diff(x)) else { if (inherits(x, "intervals") && identical(tail(x$end, -1), head(x$start, -1))) x$end - x$start else return(FALSE) } isTRUE(as.numeric(abs(diff(range(ud)) / mean(ud))) < epsilon) } } create_dimension = function(from = 1, to, offset = NA_real_, delta = NA_real_, refsys = NA_character_, point = NA, values = NULL, is_raster = FALSE) { example = NA if (! is.null(values)) { # figure out from values whether we have sth regular: from = 1 to = length(values) if (!(is.character(values) || is.factor(values)) && is.atomic(values)) { if (! all(is.finite(values))) warning("dimension value(s) non-finite") else { if (regular_intervals(values)) { offset = values[1] if (length(values) > 1) { delta = diff(values[1:2]) # shift half grid cell size if x or y raster dim cell midpoints: if (is_raster) offset = offset - 0.5 * delta } values = NULL example = offset } else example = values } } if (inherits(values, "intervals")) example = values$start # refsys: if (inherits(example, "POSIXct")) refsys = "POSIXct" else if (inherits(example, "Date")) refsys = "Date" else if (inherits(example, "PCICt")) refsys = paste0("PCICt_", attr(example, "cal")) else if (inherits(example, "units")) refsys = "udunits" if (inherits(values, "sfc")) { point = inherits(values, c("sfc_POINT", "sfc_MULTIPOINT")) if (!is.na(st_crs(values)) && is.na(refsys)) # inherit: refsys = st_crs(values) } if (is.numeric(values) && (is.na(point) || !point)) { values = if (is_raster) set_dimension_values(centers = values) else set_dimension_values(start = values) } } structure(list(from = unname(from), to = unname(to), offset = unname(offset), delta = unname(delta), refsys = refsys, point = unname(point), values = values), class = "dimension") } create_dimensions = function(lst, raster = NULL) { if (is.numeric(lst)) # when called with a dim(array) argument: lst = setNames(lapply(seq_along(lst), function(i) create_dimension(from = 1, to = lst[i])), names(lst)) if (is.null(names(lst))) names(lst) = make.names(seq_along(lst)) if (any(names(lst) == "")) { sel = which(names(lst) == "") names(lst)[sel] = make.names(seq_along(sel)) } if (is.null(raster)) structure(lst, raster = get_raster(dimensions = c(NA_character_, NA_character_)), class = "dimensions") else { d = structure(lst, raster = raster, class = "dimensions") rd = raster$dimensions if (identical(d[[rd[1]]]$refsys, "udunits") && identical(d[[rd[2]]]$refsys, "udunits")) { deg = units(as_units("degree")) ux = units(d[[rd[1]]]) uy = units(d[[rd[2]]]) u1 = units(units::as_units("1")) if (inherits(ux, "symbolic_units") && inherits(uy, "symbolic_units") && (!identical(ux, u1) && !identical(uy, u1)) && units::ud_are_convertible(ux, deg) && units::ud_are_convertible(uy, deg)) { d[[rd[1]]] = drop_units(d[[rd[1]]]) d[[rd[2]]] = drop_units(d[[rd[2]]]) d[[rd[1]]]$refsys = d[[rd[2]]]$refsys = st_crs('OGC:CRS84') # specifies units } } d } } get_crs = function(pr) { if (!is.null(pr$crs)) pr$crs else if (!is.null(pr$wkt)) # newer sf, but GDAL < 3.0.0 st_crs(pr$wkt) else if (!is.null(pr$proj_wkt)) st_crs(pr$proj_wkt) else st_crs(NA) } create_dimensions_from_gdal_meta = function(dims, pr) { #if (all(is.na(pr$geotransform))) # pr$geotransform = c(0.0, 1.0, 0.0, 0.0, 0.0, -1.0) # some GTiffs... lst = vector("list", length(dims)) names(lst) = names(dims) for (i in names(lst)) { lst[[i]] = switch(i, x = create_dimension(from = pr$cols[1], to = pr$cols[2], offset = pr$geotransform[1], delta = pr$geotransform[2], point = pr$point, refsys = get_crs(pr)), y = create_dimension(from = pr$rows[1], to = pr$rows[2], offset = pr$geotransform[4], delta = pr$geotransform[6], point = pr$point, refsys = get_crs(pr)), # default: create_dimension(from = 1, to = dims[i]) # time? depth+units? To be filled in later... ) } if (! is.null(pr$dim_extra)) { # netcdf... for (d in names(pr$dim_extra)) { refsys = if (inherits(pr$dim_extra[[d]], "POSIXct")) "POSIXct" else if (inherits(pr$dim_extra[[d]], "PCICt")) "PCICt" else NA_character_ de = pr$dim_extra[[d]] diff.de = diff(de) lst[[d]] = if (length(unique(diff.de)) <= 1) { delta = if (length(diff.de)) diff.de[1] else NA_real_ create_dimension(from = 1, to = length(de), offset = de[1], delta = delta, refsys = refsys) } else create_dimension(from = 1, to = length(de), values = de, refsys = refsys) } lst[["band"]] = NULL } # handle band descriptions, if present: if (!is.null(lst$band) && !is.null(pr$descriptions) && all(pr$descriptions != "")) lst$band$values = pr$descriptions else if (!is.null(pr$band_meta)) { bm = unlist(pr$band_meta) if (any(a <- grepl("DESCRIPTION=", bm)) && length(which(a)) > 1) lst$band$values = substring(bm[a], 13) } # set up raster: raster = get_raster(affine = pr$geotransform[c(3,5)], dimensions = c("x", "y"), curvilinear = FALSE, blocksizes = pr$blocksizes) create_dimensions(lst, raster) } get_raster = function(affine = rep(0, 2), dimensions = c("x", "y"), curvilinear = FALSE, blocksizes = NULL) { if (any(is.na(affine))) affine = c(0, 0) if (!is.null(blocksizes)) colnames(blocksizes) = dimensions # columns, rows! structure(list(affine = affine, dimensions = dimensions, curvilinear = curvilinear, blocksizes = blocksizes), class = "stars_raster") } #' @export print.stars_raster = function(x, ...) { if (any(is.na(x$affine))) cat(paste("affine parameters:", x$affine[1], x$affine[2], "\n")) else if (any(x$affine != 0.0)) cat(paste("sheared raster with parameters:", format(x$affine[1], ...), format(x$affine[2], ...), "\n")) if (x$curvilinear) cat("curvilinear grid\n") invisible(x) } get_val = function(pattern, meta) { i = grep(pattern, meta) if (length(i)) strsplit(meta[i], "=")[[1]][2] else NA_character_ } get_pcict = function(values, unts, calendar) { stopifnot(calendar %in% c("360_day", "365_day", "noleap")) if (!requireNamespace("PCICt", quietly = TRUE)) stop("package PCICt required, please install it first") # nocov origin = 0:1 units(origin) = try_as_units(unts) delta = if (grepl("months", unts)) { # for these calendars, length of months are different from udunits representation if (calendar == "360_day") set_units(30 * 24 * 3600, "s", mode = "standard") else set_units((365./12) * 24 * 3600, "s", mode = "standard") } else set_units(as_units(diff(as.POSIXct(origin))), "s", mode = "standard") origin_txt = as.character(as.POSIXct(origin[1])) PCICt::as.PCICt(values * as.numeric(delta), calendar, origin_txt) } parse_netcdf_meta = function(pr, name) { meta = pr$meta spl = strsplit(name, ":")[[1]] # "C:\..." results in length 2: name = if (length(spl) < 3) # name is not the variable, but the file name; FIXME: how to make it the variable? "zzzzz40163a99980" # bogus string, to avoid match else tail(spl, 1) # last one contains # longname: pr$long_name = get_val(paste0(get_val("NC_GLOBAL#variable_id", meta), "#long_name"), meta) # unit: pr$units = get_val(paste0(name, "#units"), meta) # extra dims: NETCDF_DIM_EXTRA={time,zlev} val = get_val("NETCDF_DIM_EXTRA", meta) if (! is.na(val)) { val = substr(val, 2, nchar(val)-1) # e.g. "{time,depth}" removes { } val = strsplit(val, ",")[[1]] if (length(val)) { pr$dim_extra = vector("list", length(val)) names(pr$dim_extra) = val for (v in val) { rhs = get_val(paste0("NETCDF_DIM_", v, "_VALUES"), meta) if (!is.na(rhs)) { rhs = strsplit(gsub("[\\{\\}]" , "", rhs), ",") pr$dim_extra[[v]] = as.numeric(rhs[[1]]) } else { rhs = get_val(paste0("NETCDF_DIM_", v), meta) # nocov # FIXME: find example? pr$dim_extra[[v]] = as.numeric(rhs) # nocov } cal = get_val(paste0(v, "#calendar"), meta) u = get_val(paste0(v, "#units"), meta) if (! is.na(u)) { if (v %in% c("t", "time") && !is.na(cal) && cal %in% c("360_day", "365_day", "noleap")) pr$dim_extra[[v]] = get_pcict(pr$dim_extra[[v]], u, cal) else { units(pr$dim_extra[[v]]) = try_as_units(u) if (v %in% c("t", "time") && !inherits(try(as.POSIXct(pr$dim_extra[[v]]), silent = TRUE), "try-error")) pr$dim_extra[[v]] = as.POSIXct(pr$dim_extra[[v]]) } } } pr$dim_extra = rev(pr$dim_extra) } } pr } try_as_units = function(u) { un = try(suppressWarnings(as_units(u)), silent = TRUE) if (inherits(un, "try-error")) # try without ^: un = try(suppressWarnings(as_units(gsub("^", "", u, fixed = TRUE))), silent = TRUE) if (inherits(un, "try-error")) # try without ** un = try(suppressWarnings(as_units(gsub("**", "", u, fixed = TRUE))), silent = TRUE) if (inherits(un, "try-error")) { warning(paste("ignoring unrecognized unit:", u), call. = FALSE) NULL } else un } parse_gdal_meta = function(properties) { point = get_val("AREA_OR_POINT", properties$meta) properties$point = switch(point, Area=FALSE, Point=TRUE, NA) properties } #' expand the dimension values into a list #' #' expand the dimension values into a list #' @param x object of class `stars` or `dimensions` #' @param ... ignored #' @export expand_dimensions = function(x, ...) UseMethod("expand_dimensions") #' @export expand_dimensions.stars = function(x, ...) { expand_dimensions(st_dimensions(x), ...) } #' @export #' @name expand_dimensions #' @param max logical; if `TRUE` return the max (end) values of the dimensions intervals #' @param center logical; if `TRUE` return the center values of intervals, otherwise return offset (start) of intervals; if `NA` (default) return centers for x/y dimensions, offsets for all others expand_dimensions.dimensions = function(x, ..., max = FALSE, center = NA) { # returns, in case of numeric dimensions: # center = TRUE: return center values for x and y coordinates, interval start values otherwise # center = FALSE: return start values # center = NA: return centers for x/y raster, otherwise start values # add_max = TRUE: add in addition to x and y start values an x_max and y_max end values if (length(center) == 1) center = setNames(rep(center, length(x)), names(x)) if (length(max) == 1) max = setNames(rep(max, length(x)), names(x)) if (is.list(max)) max = unlist(max) if (is.list(center)) center = unlist(center) if (any(max & center, na.rm = TRUE)) stop("only one of max and center can be TRUE, not both") where = setNames(rep(0.0, length(x)), names(x)) # offset for (i in seq_along(x)) { if (max[i]) where[i] = 1.0 if (isTRUE(center[i])) where[i] = 0.5 } dimensions = x xy = attr(x, "raster")$dimensions gt = st_geotransform(x) lst = vector("list", length(dimensions)) names(lst) = names(dimensions) if (! is.null(xy) && all(!is.na(xy))) { # we have raster: where defaulting to 0.5 where[xy] = ifelse(!max[xy] & (is.na(center[xy]) | center[xy]), 0.5, where[xy]) if (xy[1] %in% names(lst)) # x lst[[ xy[1] ]] = get_dimension_values(dimensions[[ xy[1] ]], where[[ xy[1] ]], gt, "x") if (xy[2] %in% names(lst)) # y lst[[ xy[2] ]] = get_dimension_values(dimensions[[ xy[2] ]], where[[ xy[2] ]], gt, "y") } if ("crs" %in% names(lst)) lst[[ "crs" ]] = dimensions[[ "crs" ]]$values for (nm in setdiff(names(lst), c(xy, "crs"))) # non-xy, non-crs dimensions lst[[ nm ]] = get_dimension_values(dimensions[[ nm ]], where[[nm]], NA, NA) lst } #' @export dim.dimensions = function(x) { if (is_curvilinear(x)) setNames(sapply(x, function(x) { x$to - x$from + 1 } ), names(x)) else lengths(expand_dimensions(x)) # FIXME: optimise? } #' @name print_stars #' @param digits number of digits to print numbers #' @param usetz logical; used to format \code{PCICt} or \code{POSIXct} values #' @param stars_crs maximum width of string for CRS objects #' @param all logical; if \code{TRUE} print also fields entirely filled with \code{NA} or \code{NULL} #' @export as.data.frame.dimensions = function(x, ..., digits = max(3, getOption("digits")-3), usetz = TRUE, stars_crs = getOption("stars.crs") %||% 28, all = FALSE) { mformat = function(x, ..., digits) { if (inherits(x, c("PCICt", "POSIXct"))) format(x, ..., usetz = usetz) else format(x, digits = digits, ...) } abbrev_dim = function(y) { if (length(y$values) > 3 || (inherits(y$values, "sfc") && length(y$values) > 2)) { y$values = if (is.array(y$values)) paste0("[", paste(dim(y$values), collapse = "x"), "] ", mformat(min(y$values), digits = digits), ",...,", mformat(max(y$values), digits = digits)) else if (inherits(y$values[[1]], "crs")) paste0(format(y$values[[1]]), ",...,", format(y$values[[length(y$values)]])) else if (inherits(y$values, "sfc")) paste0(format(y$values[[1]], width = stars_crs), ",...,", format(y$values[[length(y$values)]], width = stars_crs)) else paste0(format(head(y$values, 1)), ",...,", format(tail(y$values, 1))) } if (is.na(y$refsys)) y$refsys = NA_character_ else if (nchar(tail(format(y$refsys), 1)) > stars_crs) y$refsys = paste0(substr(tail(format(y$refsys), 1), 1L, stars_crs - 3), "...") y } lst = lapply(x, abbrev_dim) lst = lapply(lst, function(x) sapply(x, mformat, digits = digits)) ret = data.frame(do.call(rbind, lst), stringsAsFactors = FALSE) if (! all) { # remove fields entirely NA or NULL: if (all(ret$offset == "NA")) ret$offset = NULL if (all(ret$delta == "NA")) ret$delta = NULL if (all(ret$refsys == "NA")) ret$refsys = NULL if (all(ret$point == "NA")) ret$point = NULL if (all(ret$values == "NULL")) ret$values = NULL } r = attr(x, "raster") if (! any(is.na(r$dimensions))) { ret$raster = rep("", nrow(ret)) ret[r$dimensions[1], "raster"] = "[x]" ret[r$dimensions[2], "raster"] = "[y]" names(ret)[ncol(ret)] = "x/y" } ret } #' @name print_stars #' @export print.dimensions = function(x, ...) { ret = as.data.frame(x, ...) print(ret) print(attr(x, "raster")) invisible(x) } identical_dimensions = function(lst, ignore_resolution = FALSE, tolerance = 0) { if (length(lst) > 1) { d1 = attr(lst[[1]], "dimensions") for (i in 2:length(lst)) { di = attr(lst[[i]], "dimensions") if (ignore_resolution) { for (j in seq_along(d1)) d1[[j]]$delta = d1[[j]]$to = NA_real_ for (j in seq_along(di)) di[[j]]$delta = di[[j]]$to = NA_real_ attr(d1, "raster")$blocksizes = NULL attr(di, "raster")$blocksizes = NULL } if (! isTRUE(all.equal(d1, di, tolerance = tolerance, check.attributes = FALSE))) return(FALSE) } } TRUE } #' @export all.equal.dimensions = function(target, current, ..., ignore_blocksizes = TRUE) { if (ignore_blocksizes) { attr(target, "raster")$blocksizes = NULL attr(current, "raster")$blocksizes = NULL } NextMethod() } combine_dimensions = function(dots, along, check_dims_identical = TRUE) { dims = st_dimensions(dots[[1]]) if (along > length(dims)) { if (length(dots) > 1 && check_dims_identical) { for (i in 2:length(dots)) if (! isTRUE(all.equal(dims, st_dimensions(dots[[i]])))) stop(paste("dimensions of element", 1, "and", i, "are not identical")) } dims[[along]] = create_dimension(from = 1, to = length(dots), values = names(dots)) } else { offset = lapply(dots, function(x) attr(x, "dimensions")[[along]]$offset) if (any(is.na(offset))) { # concatenate values if non-NULL: dims[[along]]$from = 1 dims[[along]]$to = sum(sapply(dots, function(x) { d = st_dimensions(x)[[along]]; d$to - d$from + 1} )) if (!is.null(dims[[along]]$values)) dims[[along]]$values = do.call(c, lapply(dots, function(x) attr(x, "dimensions")[[along]]$values)) } else { values = do.call(c, lapply(dots, function(y) st_get_dimension_values(y, along))) dims[[along]] = create_dimension(values = values) } } dims } #' @export seq.dimension = function(from, ..., center = FALSE) { # does what expand_dimensions also does, for single dimension get_dimension_values(from, where = ifelse(center, 0.5, 0.0), NA, what = NA_character_) } #' @export `[.dimension` = function(x, i, ...) { if (!missing(i)) { if (!is.null(x$values) && !is.matrix(x$values)) x$values = x$values[i] if (!is.na(x$from)) { rang = x$from:x$to # valid range if (!all(is.na(i)) && max(i, na.rm = TRUE) > -1 && min(i, na.rm = TRUE) < 0) stop("cannot mix positive and negative indexes") if (is.logical(i)) i = which(i) else if (all(i < 0)) i = setdiff(rang, abs(i)) # subtract if (!any(is.na(i)) && all(diff(i) == 1)) { if (max(i) > length(rang)) stop("invalid range selected") sel = rang[i] x$from = min(sel) x$to = max(sel) } else { # invalidate offset & delta x$delta = x$offset = NA x$from = 1 x$to = length(i) } } } x } #' @export `[<-.dimensions` = function(x, i, value) { # stopifnot(length(i) == length(value)) if (!is.null(value)) create_dimensions(NextMethod(), raster = attr(x, "raster")) else NextMethod() } #' @export dimnames.stars = function(x) { names(st_dimensions(x)) } #' @export `dimnames<-.stars` = function(x, value) { for (i in seq_along(x)) names(dim(x[[i]])) = value names(attr(x, "dimensions")) = value x } #' @export as.POSIXct.stars = function(x, ...) { d = st_dimensions(x) e = expand_dimensions(d) for (i in seq_along(d)) { p = try(as.POSIXct(e[[i]]), silent = TRUE) if (!inherits(p, "try-error")) d[[i]] = create_dimension(values = p) } structure(x, dimensions = d) } #' @export drop_units.dimension = function(x) { du = function(y) { if (inherits(y, "units")) units::drop_units(y) else if (inherits(y, "intervals")) { y$start = du(y$start) y$end = du(y$end) y } else y } if (!is.null(x$offset)) x$offset = du(x$offset) if (!is.null(x$delta)) x$delta = du(x$delta) if (!is.null(x$values)) x$values = du(x$values) x } #' @export units.dimension = function(x) { if (inherits(x$offset, "units")) units(x$offset) else if (inherits(x$delta, "units")) units(x$delta) else if (inherits(x$values, "units")) units(x$values) else if (inherits(x$values$start, "units")) units(x$values$start) else NULL } stars/R/sp.R0000644000176200001440000000426714576556631012407 0ustar liggesusersstars_to_Spatial = function(from) { if (!requireNamespace("sp", quietly = TRUE)) stop("package sp required, please install it first") #nocov geom = if (has_raster(from)) { from = st_normalize(from) if (length(dim(from)) > 2) stop("stars object must have two (raster: x, y) dimensions") if (!is_regular_grid(from)) stop("only regular rasters can be converted to Spatial") d = st_dimensions(from) xy = attr(d, "raster")$dimensions if (!all(match(xy, names(d)) == 1:2)) stop("x/y dimensions should be at pos 1 and 2") offset = c( d[[ 1 ]]$offset, d[[ 2 ]]$offset ) delta = c( d[[ 1 ]]$delta, d[[ 2 ]]$delta ) cells.dim = dim(from) if (delta[1] < 0) stop("negative x cell size not supported") if (delta[2] > 0) stop("only negative y delta supported") cellcentre.offset = setNames(c(offset[1] + 0.5 * delta[1], offset[2] + (cells.dim[2] - 0.5) * delta[2]), xy) gt = sp::GridTopology(cellcentre.offset, abs(delta), cells.dim) sp::SpatialGrid(gt, as(st_crs(d), "CRS")) } else { if (!has_sfc(from)) stop("no feature dimension in stars object") as(st_dimensions(from)[[1]]$values, "Spatial") } if (length(from) == 0) geom else sp::addAttrToGeom(geom, as.data.frame(lapply(from, function(y) structure(y, dim = NULL))), match.ID = FALSE) } setAs("stars", "Spatial", stars_to_Spatial) #' @export st_as_stars.Spatial = function(.x, ...) { if (!requireNamespace("sp", quietly = TRUE)) stop("package sp required, please install it first") # nocov if (sp::gridded(.x)) { sp::fullgrid(.x) = TRUE gt = sp::gridparameters(.x) # st_as_stars(raster::stack(.x)) --- we can do better # UL corner: x = create_dimension(1, gt$cells.dim[1], offset = gt$cellcentre.offset[1] - 0.5 * gt$cellsize[1], delta = gt$cellsize[1], refsys = st_crs(.x)) y = create_dimension(1, gt$cells.dim[2], offset = gt$cellcentre.offset[2] + (gt$cells.dim[2] - 0.5) * gt$cellsize[2], delta = -gt$cellsize[2], refsys = st_crs(.x)) d = create_dimensions(list(x = x, y = y), raster = get_raster(dimensions = c("x", "y"))) lst = lapply(.x@data, function(x, dims) structure(x, dim = dims), dims = dim(d)) st_stars(lst, d) } else st_as_stars(st_as_sf(.x)) } stars/demo/0000755000176200001440000000000013777653456012361 5ustar liggesusersstars/demo/udf_demo.R0000644000176200001440000000406213777653456014270 0ustar liggesuserslibrary(stars) library(xts) x.orig = read_stars(system.file("tif/L7_ETMs.tif", package = "stars")) x = x.orig (x = st_set_dimensions(x, 3, Sys.Date() + 1:6)) (x = st_set_dimensions(x, 3, names = c("x", "y", "time"))) x = as.xts(x) x[1:3,1:5] y = st_as_stars(x) # has other dimensions collapsed y = st_as_stars(x, dimensions = st_dimensions(x.orig)[c("band","x","y")]) setNames(y, "L7_ETMs.tif") %>% aperm(c(2,3,1)) -> yy all.equal(yy, x.orig) # xts -> equal-sized xts (reverse order) f1 = function(x) { xts(as.matrix(x)[rev(seq_len(nrow(x))),], index(x)) } # xts -> single number (compute time mean) f2 = function(x) { apply(x, 2, mean) } # xts -> multiple numbers (return mean and variance) f3 = function(x) { apply(x, 2, function(x) c(mean = mean(x), var = var(x))) } # xts -> monthly maxima f4 = function(x, by = "months") { a = aggregate(x, cut(index(x), breaks = by), max) xts(as.matrix(a), as.POSIXct(index(a))) } # univariate examples: x = xts(1:5, Sys.Date() + 1:5) f1(x) f2(x) f3(x) x = xts(rnorm(50), Sys.Date() + 1:50) f4(x, "weeks") # multivariate examples, only working on time: x = xts(cbind(1:5,5:1,runif(5)), Sys.Date() + 1:5) f1(x) f2(x) f3(x) x = xts(cbind(rnorm(50), runif(50)), Sys.Date() + 1:50) f4(x) # running them on a 4-d cube: # create 4-dimensional cube: x/y/band/time c(x.orig, 2*x.orig, 3*x.orig, 4*x.orig, along = "time") %>% st_set_dimensions(4, Sys.Date()+1:4) -> d4 d4 # apply a time reducer to a 4d cube, either dropping time, or adding a new dimension reduce_4d_time_xts = function(x, f, time = "time", ...) { out = f(as.xts(x), ...) t = which(names(dim(x)) == time) if (!is.null(dim(out))) { out = t(out) dim(out) = c(dim(x)[-t], new = prod(dim(out))/prod(dim(x)[-t])) st_as_stars(list(x = out)) # but this looses all the dimension metadata } else { # f() returns a vector, not a matrix dim(out) = dim(x)[-t] st_as_stars(list(x = out), dimensions = st_dimensions(x)[-t]) } } reduce_4d_time_xts(d4, f2) reduce_4d_time_xts(d4, f1) reduce_4d_time_xts(d4, f3) # reduce_4d_time_xts(d4[,1:20,1:20], f4, by = "days") -->> takes ages!! stars/demo/00Index0000644000176200001440000000016213777653456013512 0ustar liggesusersspacetime examples from the spacetime package, redone with stars udf_demo demo scripts for (possible) openEO udfs stars/demo/spacetime.R0000644000176200001440000000227713777653456014466 0ustar liggesuserslibrary(stars) # wind: library(gstat) data(wind) library(sp) library(sf) wind.loc$y = as.numeric(char2dms(as.character(wind.loc[["Latitude"]]))) wind.loc$x = as.numeric(char2dms(as.character(wind.loc[["Longitude"]]))) locs = st_as_sf(wind.loc, coords = c("x", "y"), crs = st_crs(4326)) # match order: data = as.data.frame(t(wind[,-c(1:3)])) data$geom = st_geometry(locs[match(names(wind)[-c(1:3)], locs$Code),]) wind_ = st_sf(data) wind.st = merge(st_as_stars(wind_), times = ISOdate(wind$year+1900, wind$month, wind$day, 0)) plot(wind.st) # Produc: data(Produc, package = "plm") library(maps) states = st_as_sf(map('state', plot=FALSE, fill=TRUE))[-8,] yrs = 1970:1986 #time = as.POSIXct(paste(yrs, "-01-01", sep=""), tz = "GMT") Pr = lapply(Produc[order(Produc[2], Produc[1]),-(1:3)], matrix, nrow = nrow(states)) (Produc.st = st_as_stars(Pr, dimensions = st_dimensions(sfc = st_geometry(states[-8]), times = yrs))) plot(Produc.st[1], max.plot = 17) plot(Produc.st[2], max.plot = 17) # gridded example: library(gstat) example(krigeST) library(stars) s = st_as_stars(DE_kriged) stplot(DE_kriged,scales=list(draw=T)) plot(s, axes = TRUE) f = as(s, "STFDF") # roundtrip: summary(f@data - DE_kriged@data) stars/vignettes/0000755000176200001440000000000014644756275013442 5ustar liggesusersstars/vignettes/stars1.Rmd0000644000176200001440000003260614644756215015324 0ustar liggesusers--- title: "1. introduction" author: "Edzer Pebesma" output: rmarkdown::html_vignette vignette: > %\VignetteIndexEntry{1. introduction} %\VignetteEncoding{UTF-8} %\VignetteEngine{knitr::rmarkdown} --- **For a better version of the stars vignettes see** https://r-spatial.github.io/stars/articles/ ```{r setup, include=FALSE} knitr::opts_chunk$set(echo = TRUE, collapse = TRUE, dev = "png") ev = suppressWarnings(require(starsdata, quietly = TRUE)) knitr::opts_chunk$set(fig.height = 4.5) knitr::opts_chunk$set(fig.width = 6) ``` Package `stars` provides infrastructure for _data cubes_, array data with labeled dimensions, with emphasis on arrays where some of the dimensions relate to time and/or space. Spatial data cubes are arrays with one or more spatial dimensions. Raster data cubes have at least two spatial dimensions that form the raster tesselation. Vector data cubes have at least one spatial dimension that may for instance reflect a polygon tesselation, or a set of point locations. Conversions between the two (rasterization, polygonization) are provided. Vector data are represented by simple feature geometries (packages `sf`). Tidyverse methods are provided. The `stars` package is loaded by ```{r} library(stars) ``` Spatiotemporal arrays are stored in objects of class `stars`; methods for class `stars` currently available are ``` {r} methods(class = "stars") ``` (tidyverse methods are only visible after loading package `tidyverse`). # Reading a satellite image We can read a satellite image through GDAL, e.g. from a GeoTIFF file in the package: ```{r} tif = system.file("tif/L7_ETMs.tif", package = "stars") x = read_stars(tif) plot(x, axes = TRUE) ``` We see that the image is geographically referenced (has coordinate values along axes), and that the object returned (`x`) has three dimensions called `x`, `y` and `band`, and has one attribute: ```{r} x ``` Each dimension has a name; the meaning of the fields of a single dimension are: |*field* |*meaning* | |--------|------------------------------------------------------------| | from | the origin index (1) | | to | the final index (dim(x)[i]) | | offset | the start value for this dimension (pixel boundary), if regular | | delta | the step (pixel, cell) size for this dimension, if regular | | refsys | the reference system, or proj4string | | point | logical; whether cells refer to points, or intervals | | values | the sequence of values for this dimension (e.g., geometries), if irregular | This means that for an index i (starting at $i=1$) along a certain dimension, the corresponding dimension value (coordinate, time) is $\mbox{offset} + (i-1) \times \mbox{delta}$. This value then refers to the start (edge) of the cell or interval; in order to get the interval middle or cell centre, one needs to add half an offset. Dimension `band` is a simple sequence from 1 to 6. Since bands refer to colors, one could put their wavelength values in the `values` field. For this particular dataset (and most other raster datasets), we see that delta for dimension `y` is negative: this means that consecutive array values have decreasing $y$ values: cell indexes increase from top to bottom, in the direction opposite to the $y$ axis. `read_stars` reads all bands from a raster dataset, or optionally a subset of raster datasets, into a single `stars` array structure. While doing so, raster values (often UINT8 or UINT16) are converted to double (numeric) values, and scaled back to their original values if needed if the file encodes the scaling parameters. The data structure `stars` is a generalization of the `tbl_cube` found in `cubelyr`; we can convert to that by ```{r eval=ev} library(cubelyr) as.tbl_cube(x) ``` but this will cause a loss of certain properties (cell size, reference system, vector geometries) ## Switching attributes to dimensions and back ```{r} (x.spl = split(x, "band")) merge(x.spl) ``` We see that the newly created dimension lost its name, and the single attribute got a default name. We can set attribute names with `setNames`, and dimension names and values with `st_set_dimensions`: ```{r} merge(x.spl) |> setNames(names(x)) |> st_set_dimensions(3, values = paste0("band", 1:6)) |> st_set_dimensions(names = c("x", "y", "band")) ``` ## Subsetting Besides the `tidyverse` subsetting and selection operators explained in [this vignette](stars3.html), we can also use `[` and `[[`. Since `stars` objects are a list of `array`s with a metadata table describing dimensions, list extraction (and assignment) works as expected: ```{r} class(x[[1]]) dim(x[[1]]) x$two = 2 * x[[1]] x ``` At this level, we can work with `array` objects directly. The `stars` subset operator `[` works a bit different: its * first argument selects attributes * second argument selects the first dimension * third argument selects the second dimension, etc Thus, ```{r} x["two", 1:10, , 2:4] ``` selects the second attribute, the first 10 columns (x-coordinate), all rows, and bands 2-4. Alternatively, when `[` is given a single argument of class `sf`, `sfc` or `bbox`, `[` will work as a crop operator: ```{r} circle = st_sfc(st_buffer(st_point(c(293749.5, 9115745)), 400), crs = st_crs(x)) plot(x[circle][, , , 1], reset = FALSE) plot(circle, col = NA, border = 'red', add = TRUE, lwd = 2) ``` ## Overviews We can read rasters at a lower resolution when they contain so-called overviews. For this GeoTIFF file, they were created with the `gdaladdo` utility, in particular ``` gdaladdo -r average L7_ETMs.tif 2 4 8 16 ``` which adds coarse resolution versions by using the _average_ resampling method to compute values based on blocks of pixels. These can be read by ```{r} x1 = read_stars(tif, options = c("OVERVIEW_LEVEL=1")) x2 = read_stars(tif, options = c("OVERVIEW_LEVEL=2")) x3 = read_stars(tif, options = c("OVERVIEW_LEVEL=3")) dim(x1) dim(x2) dim(x3) par(mfrow = c(1, 3), mar = rep(0.2, 4)) image(x1[,,,1]) image(x2[,,,1]) image(x3[,,,1]) ``` # Reading a raster time series: NetCDF Another example is when we read raster time series model outputs in a NetCDF file, e.g. by ```{r eval=ev} system.file("nc/bcsd_obs_1999.nc", package = "stars") |> read_stars() -> w ``` We see that ```{r eval=ev} w ``` For this dataset we can see that * variables have units associated (and a wrong unit, `C` is assigned to temperature) * time is now a dimension, with proper units and time steps Alternatively, this dataset can be read using `read_ncdf`, as in ```{r} system.file("nc/bcsd_obs_1999.nc", package = "stars") |> read_ncdf() ``` The difference between `read_ncdf` and `read_stars` for NetCDF files is that the former uses package RNetCDF to directly read the NetCDF file, where the latter uses the GDAL driver for NetCDF files. ## Reading datasets from multiple files Model data are often spread across many files. An example of a 0.25 degree grid, global daily sea surface temperature product is found [here](https://psl.noaa.gov/data/gridded/data.noaa.oisst.v2.highres.html); the subset from 1981 used below was downloaded from a NOAA ftp site that is no longer available in this form. (ftp site used to be eclipse.ncdc.noaa.gov/pub/OI-daily-v2/NetCDF/1981/AVHRR/). We read the data by giving `read_stars` a vector with character names: ```{r eval=ev} x = c( "avhrr-only-v2.19810901.nc", "avhrr-only-v2.19810902.nc", "avhrr-only-v2.19810903.nc", "avhrr-only-v2.19810904.nc", "avhrr-only-v2.19810905.nc", "avhrr-only-v2.19810906.nc", "avhrr-only-v2.19810907.nc", "avhrr-only-v2.19810908.nc", "avhrr-only-v2.19810909.nc" ) # see the second vignette: # install.packages("starsdata", repos = "http://pebesma.staff.ifgi.de", type = "source") file_list = system.file(paste0("netcdf/", x), package = "starsdata") (y = read_stars(file_list, quiet = TRUE)) ``` Next, we select sea surface temperature (`sst`), and drop the singular `zlev` (depth) dimension using `adrop`: ```{r eval=ev} library(dplyr) library(abind) z <- y |> select(sst) |> adrop() ``` We can now graph the sea surface temperature (SST) using `ggplot`, which needs data in a long table form, and without units: ```{r eval=ev} # convert POSIXct time to character, to please ggplot's facet_wrap() z1 = st_set_dimensions(z, 3, values = as.character(st_get_dimension_values(z, 3))) library(ggplot2) library(viridis) library(ggthemes) ggplot() + geom_stars(data = z1[1], alpha = 0.8, downsample = c(10, 10, 1)) + facet_wrap("time") + scale_fill_viridis() + coord_equal() + theme_map() + theme(legend.position = "bottom") + theme(legend.key.width = unit(2, "cm")) ``` # Writing stars objects to disk We can write a stars object to disk by using `write_stars`; this used the GDAL write engine. Writing NetCDF files without going through the GDAL interface is currently not supported. `write_stars` currently writes only a single attribute: ```{r eval=ev} write_stars(adrop(y[1]), "sst.tif") ``` See the explanation of `merge` above to see how multiple attributes can be merged (folded) into a dimension. # Cropping a raster's extent Using a curvilinear grid, taken from the example of `read_ncdf`: ```{r} prec_file = system.file("nc/test_stageiv_xyt.nc", package = "stars") prec = read_ncdf(prec_file, curvilinear = c("lon", "lat")) ##plot(prec) ## gives error about unique breaks ## remove NAs, zeros, and give a large number ## of breaks (used for validating in detail) qu_0_omit = function(x, ..., n = 22) { if (inherits(x, "units")) x = units::drop_units(na.omit(x)) c(0, quantile(x[x > 0], seq(0, 1, length.out = n))) } library(dplyr) # loads slice generic prec_slice = slice(prec, index = 17, along = "time") plot(prec_slice, border = NA, breaks = qu_0_omit(prec_slice[[1]]), reset = FALSE) nc = sf::read_sf(system.file("gpkg/nc.gpkg", package = "sf"), "nc.gpkg") plot(st_geometry(nc), add = TRUE, reset = FALSE, col = NA, border = 'red') ``` We can now crop the grid to those cells falling in ```{r} nc = st_transform(nc, st_crs(prec_slice)) # datum transformation plot(prec_slice[nc], border = NA, breaks = qu_0_omit(prec_slice[[1]]), reset = FALSE) plot(st_geometry(nc), add = TRUE, reset = FALSE, col = NA, border = 'red') ``` The selection `prec_slice[nc]` essentially calls `st_crop(prec_slice, nc)` to get a cropped selection. What happened here is that all cells not intersecting with North Carolina (sea) are set to `NA` values. For regular grids, the extent of the resulting `stars` object is also be reduced (cropped) by default; this can be controlled with the `crop` parameter to `st_crop` and `[.stars`. # Vector data cube example Like `tbl_cube`, `stars` arrays have no limits to the number of dimensions they handle. An example is the origin-destination (OD) matrix, by time and travel mode. ## OD: space x space x travel mode x time x time We create a 5-dimensional matrix of traffic between regions, by day, by time of day, and by travel mode. Having day and time of day each as dimension is an advantage when we want to compute patterns over the day, for a certain period. ```{r} nc = st_read(system.file("gpkg/nc.gpkg", package="sf")) to = from = st_geometry(nc) # 100 polygons: O and D regions mode = c("car", "bike", "foot") # travel mode day = 1:100 # arbitrary library(units) units(day) = as_units("days since 2015-01-01") hour = set_units(0:23, h) # hour of day dims = st_dimensions(origin = from, destination = to, mode = mode, day = day, hour = hour) (n = dim(dims)) traffic = array(rpois(prod(n), 10), dim = n) # simulated traffic counts (st = st_as_stars(list(traffic = traffic), dimensions = dims)) ``` This array contains the simple feature geometries of origin and destination so that we can directly plot every slice without additional table joins. If we want to represent such an array as a `tbl_cube`, the simple feature geometry dimensions need to be replaced by indexes: ```{r eval=ev} st |> as.tbl_cube() ``` The following demonstrates how we can use `dplyr` to filter travel mode `bike`, and compute mean bike traffic by hour of day: ```{r eval=ev} b <- st |> as.tbl_cube() |> filter(mode == "bike") |> group_by(hour) |> summarise(traffic = mean(traffic)) |> as.data.frame() require(ggforce) # for plotting a units variable ggplot() + geom_line(data = b, aes(x = hour, y = traffic)) ``` # Extracting at point locations, aggregating over polygons Data cube values at point location can be extracted by `st_extract`, an example is found in [vignette 7](stars7.html) Aggregates, such as mean, maximum or modal values can be obtained by `aggregate`. In this example we use a categorical raster, and try to find the modal (most frequent) class within two circular polygons: ```{r} s = system.file("tif/lc.tif", package = "stars") r = read_stars(s, proxy = FALSE) |> droplevels() levels(r[[1]]) = abbreviate(levels(r[[1]]), 10) # shorten text labels st_point(c(3190631, 3125)) |> st_sfc(crs = st_crs(r)) |> st_buffer(25000) -> pol1 st_point(c(3233847, 21027)) |> st_sfc(crs = st_crs(r)) |> st_buffer(10000) -> pol2 if (isTRUE(dev.capabilities()$rasterImage == "yes")) { plot(r, reset = FALSE, key.pos = 4) plot(c(pol1, pol2), col = NA, border = c('yellow', 'green'), lwd = 2, add = TRUE) } ``` To find the modal value, we need a function that gives back the label corresponding to the class which is most frequent, using `table`: ```{r} f = function(x) { tb = table(x); names(tb)[which.max(tb)] } ``` We can then call `aggregate` on the raster map, and the set of the two circular polygons `pol1` and `pol2`, and pass the function `f`: ```{r} aggregate(r, c(pol1, pol2), f) |> st_as_sf() ``` stars/vignettes/stars8.Rmd0000644000176200001440000001020514644756215015322 0ustar liggesusers--- title: "8. NetCDF Proxy Workflows" author: "David Blodgett" output: rmarkdown::html_vignette vignette: > %\VignetteIndexEntry{8. NetCDF Proxy Workflows} %\VignetteEngine{knitr::rmarkdown} %\VignetteEncoding{UTF-8} --- **For a better version of the stars vignettes see** https://r-spatial.github.io/stars/articles/ ```{r, include = FALSE} knitr::opts_chunk$set(echo = TRUE, collapse = TRUE, dev = "png") suppressPackageStartupMessages(library(dplyr)) knitr::opts_chunk$set(fig.height = 4.5) knitr::opts_chunk$set(fig.width = 6) EVAL = suppressWarnings(require(starsdata, quietly = TRUE)) ``` NetCDF data sources are available via more _and_ less granular files and/or OPeNDAP endpoints. This article demonstrates how `stars` enables discovery, access, and processing of NetCDF data across a wide range of such source-data organization schemes. We'll start with some basics using datasets included with the `stars` installation. A call to `read_ncdf()`, for a dataset smaller than the default threshold, will just read in all the data. Below we read in and display the `reduced.nc` NetCDF file. ```{r} library(stars) f <- system.file("nc/reduced.nc", package = "stars") (nc <- read_ncdf(f)) ``` Let's assume `reduced.nc` was 10 years of hourly data, rather than 1 time step. It would be over 10GB rather than about 130KB and we would not be able to just read it all into memory. In this case, we need a way to read the file's metadata such that we could iterate over it in a way that meets the needs of our workflow objectives. This is where `proxy = TRUE` comes in. Below, we'll lower the option that controls whether `read_ncdf()` defaults to proxy _and_ use `proxy = TRUE` to show both ways of getting the same result. ```{r} old_options <- options("stars.n_proxy" = 100) (nc <- read_ncdf(f, proxy = TRUE)) options(old_options) ``` The above shows that we have a NetCDF sourced stars proxy derived from the `reduced.nc` file. We see it has four variables and their units are displayed. The normal `stars` `dimension(s)` are available and a `nc_request` object is also available. The `nc_request` object contains the information needed to make requests for data according to the dimensions of the NetCDF data source. With this information, we have what we need to request a chunk of data that is what we want and not too large. ```{r} (nc <- read_ncdf(f, var = "sst", ncsub = cbind(start = c(90, 45, 1 , 1), count = c(90, 45, 1, 1)))) plot(nc) ``` The ability to view NetCDF metadata so we can make well formed requests against the data is useful, but the real power of a proxy object is that we can use it in a "lazy evaluation" coding style. That is, we can do virtual operations on the object, like subsetting with another dataset, prior to actually accessing the data volume. Lazy operations. There are two kinds of lazy operations possible with `stars_proxy` objects. Some can be applied to the `stars_proxy` object itself without accessing underlying data. Others must be composed as a chain of calls that will be applied when data is actually required. Methods applied to a `stars_proxy` object: - `[` - Nearly the same as stars_proxy - `[[<-` - stars_proxy method works - `print` - unique method for nc_proxy to facilitate unique workflows - `dim` - stars_proxy method works - `c` - stars_proxy method works - `st_redimension` - Not sure what this entails but it might not make sense for nc_proxy. - `st_mosaic` * Calls read_stars on assembled list. Not supported for now. - `st_set_bbox` Methods that add a call to the `call_list`. - `[<-` - `adrop` - `aperm` - `is.na` - `split` - `st_apply` - `predict` - `merge` - `st_crop` - `drop_levels` - `Ops` (group generic for +, -, etc.) - `Math` (group generic for abs, sqrt, tan, etc.) - `filter` - `mutate` - `tansmute` - `select` - `rename` - `pull` - `slice` * hyperslabbing for NetCDF could be as above? - `pull` - `replace_na` Methods that cause a `stars_proxy` object to be fetched and turned into a `stars` object. - `as.data.frame` - `plot` - `st_as_stars` - `aggregate` - `st_dimensions<-` * https://github.com/r-spatial/stars/issues/494 - `hist` - `st_downsample` - `st_sample` - `st_as_sf` - `write_stars` stars/vignettes/stars6.Rmd0000644000176200001440000002040014644756215015316 0ustar liggesusers--- title: "6. How `raster` functions map to `stars` functions" author: "Sébastien Rochette, Adriano Fantini, Edzer Pebesma" output: rmarkdown::html_vignette vignette: > %\VignetteIndexEntry{6. How raster functions map to stars functions} %\VignetteEngine{knitr::rmarkdown} %\VignetteEncoding{UTF-8} --- **For a better version of the stars vignettes see** https://r-spatial.github.io/stars/articles/ ```{r, include = FALSE} knitr::opts_chunk$set(echo = FALSE, collapse = TRUE, dev = "png") suppressPackageStartupMessages(library(dplyr)) knitr::opts_chunk$set(fig.height = 4.5) knitr::opts_chunk$set(fig.width = 6) ``` Here's an attempt at the table describing how `raster` functions map to `stars` functions, discussed in [issue #122](https://github.com/r-spatial/stars/issues/122). This table uses the functionality of the `raster` package as a template; it may be incomplete, imprecise or plain wrong, so take it with a pinch of salt. Any comment or correction is hugely appreciated, please contribute! Some of the functions (`filter`, `slice`, `mutate`, `select`, `pull`, ...) are provided via `dplyr`, which must be loaded. See `?stars::dplyr`. ## COMMENT LEGEND ? = Not sure / unknown \* = Not present, low priority \# = Not present, high priority NA = Not available by design ## Creating objects ```{r} tribble( ~raster, ~stars, ~`Note/comment`, "{raster, stack, brick} (read)", "read_stars or read_stars(along = ...)","", "{stack, brick} (concatenate layers)", "c or c(along = ...)","", "subset", "{[ ] , slice, filter}","", "addLayer", "c() or c(along = ...)","", "dropLayer", "{[ ] , slice, filter}","", "unstack", "combine lapply and {[ ] , slice, filter}","" ) %>% knitr::kable() ``` ## Changing spatial extent and/or resolution of objects ```{r} tribble( ~raster, ~stars, ~`Note/comment`, "merge", "c", "#, currently only works for adjacent objects", "mosaic", "st_mosaic", "these are not identical, read the docs carefully", "crop", "filter, st_crop", "", "setExtent", "", "# maybe use st_warp?", "trim", "", "#", "aggregate", "aggregate", "WIP; raster's aggregate with fact=2 will not work, use st_warp in that case?", "disaggregate", "", "# use st_warp(use_gdal = TRUE)?", "resample", "{st_transform, st_warp}", "", "projectRaster", "{st_transform, st_warp}", "", "shift", "", "#, now use st_set_dimensions", "flip", "[] with reversed index", "#", "rotate", "", "*", "t", "", "NA" ) %>% knitr::kable() ``` ## Cell based computation ```{r} tribble( ~raster, ~stars, ~`Note/comment`, "calc", "st_apply", "", "overlay", "c(along = , ...) %>% st_apply(...)", "", "cover", "[ ] <-", "", "mask", "[ ], st_crop", "when using an sf polygon as mask", "mask", "[ ]<- NA", "when using a stars object to mask", "cut", "cut", "", "subs", "", "", "reclassify", "mutate with case_when", "or forcats::fct_recode ?", "reclassify", "cut", "see https://stackoverflow.com/questions/70619002/reclassify-2d-stars-array/", "init", "[ ] <-", "", "stackApply", "{[ ] , slice, filter} %>% st_apply", "", "stackSelect", "", "", ) %>% knitr::kable() ``` ## Spatial contextual computation ```{r} tribble( ~raster, ~stars, ~`Note/comment`, "distance", "", "#", "gridDistance", "", "*", "distanceFromPoints", "", "#", "direction", "", "*", "focal", "f = st_apply(x1, 3, foc, w = matrix(1, 3, 3))", "See. [issue 176](https://github.com/r-spatial/stars/issues/176)", "localFun", "", "*", "boundaries", "st_as_sf(as_points=FALSE, merge=TRUE, connect8=TRUE)", "", "clump", "st_as_sf(r, merge = TRUE)", "`st_as_sf` returns polygons, `clump` a raster", "adjacent", "", "*", "area", "st_area", "", "terrain", "", "#", "Moran", "", "" ) %>% knitr::kable() ``` ## Model predictions ```{r} tribble( ~raster, ~stars, ~`Note/comment`, "predict", "predict", "", "interpolate", "gstat::idw, gstat::krige", "`st_warp` has raster-raster interpolations of `gdalwarp`" ) %>% knitr::kable() ``` ## Data type conversion ```{r} tribble( ~raster, ~stars, ~`Note/comment`, "rasterize", "st_as_stars", "", "rasterToPoints", "st_as_sf(as_points=TRUE)", "", "rasterToPolygons", "st_as_sf(as_points=FALSE, ...), st_polygonize", "", "rasterToContour", "st_contour", "requires GDAL >= 2.4.0", "rasterFromXYZ", "", "", "rasterFromCells", "", "", ) %>% knitr::kable() ``` ## Summarizing ```{r} tribble( ~raster, ~stars, ~`Note/comment`, "cellStats", "st_apply", "", "summary", "print, summary(as.vector(. %>% pull))", "", "freq", "table", "*", "crosstab", "", "", "unique", "unique(as.vector(. %>% pull))", "", "zonal", "", "*" ) %>% knitr::kable() ``` ## Accessing values of objects ```{r} tribble( ~raster, ~stars, ~`Note/comment`, "getValues", "{pull, [[ ]]}", "", "getValuesBlock", "{[ ] , slice, filter} %>% pull", "", "getValuesFocal", "{[ ] , slice, filter} %>% pull", "", "as.matrix", "[[ ]]", "currently behaves somewhat unexpectedly *", "as.array", "[[ ]]", "currently behaves somewhat unexpectedly *", "extract (by cell)", "{[ ] , slice, filter}", "", "extract (by polygon)", "x[sf_object]", "", "extract (by point)", "aggregate(stars_object, sf_object, function(x) x[1], as_points = FALSE)", "", "sampleRandom", "", "*", "sampleRegular", "", "*", "minValue", "purrr::map(x, min)", "", "maxValue", "purrr::map(x, max)", "", "setMinMax", "", "," ) %>% knitr::kable() ``` ## Plotting ```{r} tribble( ~raster, ~stars, ~`Note/comment`, "plot", "plot, geom_stars", "", "plotRGB", "plot(x, rgb =...)", "", "spplot", "-", "", "image", "image", "", "persp", "-", "", "contour", "(st_contour, then sf::plot)", "", "filledContour", "(same)", "", "text", "text", "", "`hist`", "`hist(x[[1]])`", "", "barplot", "", "", "density", "", "", "pairs", "", "", "boxplot", "", "" ) %>% knitr::kable() ``` ## Getting and setting dimensions ```{r} tribble( ~raster, ~stars, ~`Note/comment`, "ncol", "dim(x)[1]", "or use name instead of 1; cols may be the second dimension!", "nrow", "dim(x)[2]", "or use name instead of 2; rows may be the first dimension!", "ncell", "prod(dim(x))", "", "res", "st_dimensions", "can also not be a constant in case of rectilinear or curvilinear grids", "nlayers", "-", "there is no concept of layers in stars", "names", "names", "", "xres", "st_res(x)[1]", "may not be a constant in case of rectilinear or curvilinear grids", "yres", "st_res(x)[2]", "may not be a constant in case of rectilinear or curvilinear grids", "xmin", "st_bbox(x)[1]", "", "xmax", "st_bbox(x)[3]", "", "ymin", "st_bbox(x)[2]", "", "ymax", "st_bbox(x)[4]", "", "extent", "st_bbox(x)", "different ordering of numbers", "origin", "-", "", "projection", "st_crs(x)", "", "isLonLat", "st_is_longlat(st_crs(x))", "", "filename", "", "stars_proxy objects carry file names where otherwise the array data is", "bandnr", "", "stars has no general concept of bands", "nbands", "dim(x)[3]", "may also be time; bands may also be in another dimension, or have another name", "compareRaster", "all.equal(st_dimensions(x), st_dimensions(y))", "*", "NAvalue", "", "-" ) %>% knitr::kable() ``` ## Computing row, column, cell numbers and coordinates ```{r} tribble( ~raster, ~stars, ~`Note/comment`, "xFromCol", "st_get_dimension_values(., 'x')[col]", "I am not sure how to generally get the x dimension name - here it is x", "yFromRow", "st_get_dimension_values(., 'y')[row]", "I am not sure how to generally get the y dimension name - here it is y", "xFromCell", "", "", "yFromCell", "", "", "xyFromCell", "", "", "colFromX", "", "*", "rowFromY", "", "*", "rowColFromCell", "", "", "cellFromXY", "st_cells()", "", "cellFromRowCol", "", "", "cellsFromExtent", "", "", "coordinates", "st_coordinates", "", "validCell", "", "", "validCol", "col %>% between(st_dimensions(.)$x$from, st_dimensions(.)$x$to)", "raster columns are not always named 'x'", "validRow", "row %>% between(st_dimensions(.)$y$from, st_dimensions(.)$y$to)", "raster columns are not always named 'y'", "setValues", "[ ] <-", "", "writeRaster", "write_stars", "currently uses GDAL, somewhat limited", "KML", "", "" ) %>% knitr::kable() ``` The format of this table follows the `raster-package` entry in the raster manual, found at `https://cran.r-project.org/web/packages/raster/raster.pdf`. stars/vignettes/stars5.Rmd0000644000176200001440000001200514644756215015317 0ustar liggesusers--- title: "5. vector-raster conversions, reprojection, warping" author: "Edzer Pebesma" output: rmarkdown::html_vignette vignette: > %\VignetteIndexEntry{5. vector-raster conversions, reprojection, warping} %\VignetteEngine{knitr::rmarkdown} %\VignetteEncoding{UTF-8} --- **For a better version of the stars vignettes see** https://r-spatial.github.io/stars/articles/ ```{r setup, include=FALSE} knitr::opts_chunk$set(echo = TRUE, collapse = TRUE, dev = "png") suppressPackageStartupMessages(library(sf)) knitr::opts_chunk$set(fig.height = 4.5) knitr::opts_chunk$set(fig.width = 6) ev = TRUE ``` This vignette shows how `stars` object can be moved from vector and raster representations. # Rasterizing an `sf` vector object ```{r} library(stars) system.file("gpkg/nc.gpkg", package = "sf") %>% read_sf() %>% st_transform(32119) -> nc nc$dens = nc$BIR79 / units::set_units(st_area(nc), km^2) (nc.st = st_rasterize(nc["dens"], dx = 5000, dy = 5000)) plot(nc.st) ``` The algorithm used is the GDAL `rasterize` utility, all options of this utility can be passed to `st_rasterize`. The geometry of the final raster can be controlled by passing a target bounding box and either the raster dimensions `nx` and `ny`, or pixel size by the `dx` and `dy` parameters. # Vectorizing a raster object to an `sf` object `stars` objects can be converted into an `sf` object using `st_as_sf`. It has a number of options, depending on whether pixels represent the point value at the pixel center, or small square polygons with a single value. We will work again with the landsat-7 6-band image, but will select the first band and round the values: ```{r} tif = system.file("tif/L7_ETMs.tif", package = "stars") x = read_stars(tif)[, 1:50, 1:50, 1:2] x[[1]] = round(x[[1]]/5) ``` ## Polygonizing In case raster cells reflect point values and we want to get a vector representation of the whole field, we can draw contour lines and export the contour sets (only available when the GDAL version is at least 2.4.0): ```{r eval=ev} l = st_contour(x, contour_lines = TRUE, breaks = 11:15) plot(l[1], key.pos = 1, pal = sf.colors, lwd = 2, key.length = 0.8) ``` ## Exporting to points Alternatively, we can simply export all the pixels as points, and get them either as a wide table with all bands per point, and no replicated `POINT` geometries: ```{r} st_as_sf(x, as_points = TRUE, merge = FALSE) ``` or as a long table with a single attribute and all points replicated: ```{r} st_as_sf(x, as_points = TRUE, merge = FALSE, long = TRUE) ``` as we can see, an additional attribute `band` now indicates which band is concerned. ## Exporting to polygons Alternatively, we can export to polygons and either get a single polygon per pixel, as in ```{r} st_as_sf(x[1], as_points = FALSE, merge = FALSE) ``` or merge polygons that have identical pixel values; ```{r} p = st_as_sf(x, as_points = FALSE, merge = TRUE) ``` When plotted with boundaries, we see the resolved boundaries of areas with the same pixel value: ```{r} plot(p) ``` A further option `connect8` can be set to `TRUE` to use 8 connectedness, rather than the default 4 connectedness algorithm. In both cases, the polygons returned will often be invalid according to the simple feature standard, but can be made valid using `lwgeom::st_make_valid`. # Switching between vector and raster in `stars` objects We can convert a raster dimension to a vector dimension while keeping other dimensions as they are in a `stars` object by ```{r} x.sf = st_xy2sfc(x, as_points = TRUE) x.sf ``` which also requires setting the `as_points` arguments as in `st_as_sf`. # Reprojecting a raster If we accept that curvilinear rasters are rasters too, and that regular and rectilinear grids are special cases of curvilinear grids, reprojecting a raster is no longer a "problem", it just recomputes new coordinates for every raster cell, and generally results in a curvilinear grid (that sometimes can be brought back to a regular or rectilinear grid). If curvilinear grid cells are represented by coordinates of the cell center, the actual shape of a grid cell gets lost, and this may be a larger effect if grid cells are large or if the transformation is stronger non-linear. An example of the reprojection of the grid created above is ```{r} nc.st %>% st_transform("+proj=laea +lat_0=34 +lon_0=-60") -> nc.curv nc.curv plot(nc.curv, border = NA, graticule = TRUE) ``` where it should be noted that the dimensionality of the grid didn't change: the same set of raster cells has been replotted in the new CRS, but now in a curvilinear grid. # Warping a raster Warping a raster means creating a new _regular_ grid in a new CRS, based on a (usually regular) grid in another CRS. We can do the transformation of the previous section by first creating a target grid: ```{r} nc %>% st_transform("+proj=laea +lat_0=34 +lon_0=-60") %>% st_bbox() %>% st_as_stars() -> newgrid ``` and then warping the old raster to the new ```{r} nc.st %>% st_warp(newgrid) -> nc.new nc.new plot(nc.new) ``` This new object has a regular grid in the new CRS, aligned with the new x- and y-axes. stars/vignettes/stars2.Rmd0000644000176200001440000003207514644756215015325 0ustar liggesusers--- title: "2. stars proxy objects" author: "Edzer Pebesma" output: rmarkdown::html_vignette vignette: > %\VignetteIndexEntry{2. stars proxy objects} %\VignetteEngine{knitr::rmarkdown} %\VignetteEncoding{UTF-8} --- **For a better version of the stars vignettes see** https://r-spatial.github.io/stars/articles/ ```{r setup, include=FALSE} knitr::opts_chunk$set(echo = TRUE, collapse = TRUE, dev = "png") set.seed(13579) knitr::opts_chunk$set(fig.height = 4.5) knitr::opts_chunk$set(fig.width = 6) EVAL = x = suppressWarnings(require(starsdata, quietly = TRUE)) ``` When your imagery or array data easily fits a couple of times in R's working memory (RAM), consider yourself lucky. This document was not written for you. If your imagery is too large, or for other reasons you want to work with smaller chunks of data than the files in which they come, read on about your options. First, we will discuss the low-level interface for this, then the higher level, using stars proxy objects that delay all reading. # Preamble: the starsdata package To run all of the examples in this vignette, you must install a package with datasets that are too large (1 Gb) to be held in the `stars` package. They are in a [drat repo](https://github.com/eddelbuettel/drat), installation is done by ```{r eval=FALSE} install.packages("starsdata", repos = "http://gis-bigdata.uni-muenster.de/pebesma", type = "source") # possibly after: options(timeout = 100) # or from an alternative repository: # install.packages("starsdata", repos = "http://pebesma.staff.ifgi.de", type = "source") ``` # Reading chunks, change resolution, select bands `read_stars` has an argument called `RasterIO` which controls how a GDAL dataset is being read. By default, all pixels and all bands are read in memory. This can consume a lot of time and require a lot of memory. Remember that your file may be compressed, and that pixel values represented in the file by bytes are converted to 8-byte doubles in R. The reason for using `RasterIO` for this is that the parameters we use are directly mapped to the GDAL RasterIO function used (after adapting the 1-based offset index in R to 0-based offset in C++). ## Reading a particular chunk An example of using `RasterIO` is ```{r} library(stars) tif = system.file("tif/L7_ETMs.tif", package = "stars") rasterio = list(nXOff = 6, nYOff = 6, nXSize = 100, nYSize = 100, bands = c(1, 3, 4)) (x = read_stars(tif, RasterIO = rasterio)) dim(x) ``` Compare this to ```{r} st_dimensions(read_stars(tif)) ``` and we see that * the `delta` values remain the same, * the offset (x/y coordinates of origin) of the grid remain the same * the `from` and `to` reflect the new area, and relate to the new `delta` values * `dim(x)` reflects the new size, and * only three bands were read ## Reading at a different resolution Reading datasets at a lower (but also higher!) resolution can be done by setting `nBufXSize` and `nBufYSize` ```{r} rasterio = list(nXOff = 6, nYOff = 6, nXSize = 100, nYSize = 100, nBufXSize = 20, nBufYSize = 20, bands = c(1, 3, 4)) (x = read_stars(tif, RasterIO = rasterio)) ``` and we see that in addition: * the `delta` (raster cell size) values have increased a factor 5, because `nBufXSize` and `nBufYSize` were set to values a factor 5 smaller than `nXSize` and `nYSize` * the offset coordinates of the grid are still the same * the `from` and `to` reflect the new area, but relate to the new `delta` cell size values We can also read at higher resolution; here we read a 3 x 3 area and blow it up to 100 x 100: ```{r} rasterio = list(nXOff = 6, nYOff = 6, nXSize = 3, nYSize = 3, nBufXSize = 100, nBufYSize = 100, bands = 1) x = read_stars(tif, RasterIO = rasterio) dim(x) plot(x) ``` The reason we "see" only three grid cells is that the default sampling method is "nearest neighbour". We can modify this by ```{r} rasterio = list(nXOff = 6, nYOff = 6, nXSize = 3, nYSize = 3, nBufXSize = 100, nBufYSize = 100, bands = 1, resample = "cubic_spline") x = read_stars(tif, RasterIO = rasterio) dim(x) plot(x) ``` The following methods are allowed for parameter `resample`: | `resample` | method used | |------------|------------------------------------| |`nearest_neighbour`| Nearest neighbour (default) | |`bilinear` | Bilinear (2x2 kernel) | |`cubic` | Cubic Convolution Approximation (4x4 kernel) | |`cubic_spline` | Cubic B-Spline Approximation (4x4 kernel) | |`lanczos` | Lanczos windowed sinc interpolation (6x6 kernel) | |`average` | Average | |`mode` | Mode (selects the value which appears most often of all the sampled points) | |`Gauss` | Gauss blurring | All these methods are implemented in GDAL; for what these methods exactly do, we refer to the GDAL documentation or source code. # Stars proxy objects Stars proxy objects take another approach: upon creation they contain no data at all, but only pointers to where the data can be read. Data is only read when it is needed, and only as much as is needed: if we plot a proxy objects, the data are read at the resolution of pixels on the screen, rather than at the native resolution, so that if we have e.g. a 10000 x 10000 Sentinel 2 (level 1C) image, we can open it by ```{r, eval=EVAL} granule = system.file("sentinel/S2A_MSIL1C_20180220T105051_N0206_R051_T32ULE_20180221T134037.zip", package = "starsdata") s2 = paste0("SENTINEL2_L1C:/vsizip/", granule, "/S2A_MSIL1C_20180220T105051_N0206_R051_T32ULE_20180221T134037.SAFE/MTD_MSIL1C.xml:10m:EPSG_32632") (p = read_stars(s2, proxy = TRUE)) ``` and this happens _instantly_, because no data is read. When we plot this object, ```{r,eval=EVAL} system.time(plot(p)) ``` This takes only around 1 second, since only those pixels are read that can be seen on the plot. If we read the entire image in memory first, as we would do with ```{r eval = FALSE} p = read_stars(s2, proxy = FALSE) ``` then only the reading would take over a minute, and require 5 Gb memory. ## Methods for stars proxy objects ```{r} methods(class = "stars_proxy") ``` ## Select attributes We can select attributes as with regular `stars` objects, by using the first argument to `[`: ```{r,eval=EVAL} x = c("avhrr-only-v2.19810901.nc", "avhrr-only-v2.19810902.nc", "avhrr-only-v2.19810903.nc", "avhrr-only-v2.19810904.nc", "avhrr-only-v2.19810905.nc", "avhrr-only-v2.19810906.nc", "avhrr-only-v2.19810907.nc", "avhrr-only-v2.19810908.nc", "avhrr-only-v2.19810909.nc") file_list = system.file(paste0("netcdf/", x), package = "starsdata") y = read_stars(file_list, quiet = TRUE, proxy = TRUE) names(y) y["sst"] ``` Note that this selection limits the reading from 4 to 1 subdataset from all 9 NetCDF files. ## Select an area Another possibility is to crop, or select a rectangular region based on a spatial object. This can be done by passing a `bbox` object, or an `sf`, `sfc` or `stars` object from which the bounding box will be taken. An example: ```{r,eval=EVAL} bb = st_bbox(c(xmin = 10.125, ymin = 0.125, xmax = 70.125, ymax = 70.125)) ysub = y[bb] st_dimensions(ysub) class(ysub) # still no data here!! plot(ysub, reset = FALSE) # plot reads the data, at resolution that is relevant plot(st_as_sfc(bb), add = TRUE, lwd = .5, border = 'red') ``` ## Lazy evaluation, changing evaluation order Some other actions can be carried out on `stars_proxy` objects, but their effect is delayed until the data are actually needed (`plot`, `write_stars`). For instance, range selections on dimensions other than shown above first need data, and can only then be carried out. Such functions are added to the object, in an attribute called `call_list`: ```{r,eval=EVAL} yy = adrop(y) yyy = yy[,1:10,1:10,] class(yyy) # still no data st_dimensions(yyy) # and dimensions not adjusted attr(yyy, "call_list") # the name of object in the call (y) is replaced with x: ``` Doing this allows for optimizing the order in which operations are done. As an example, for `st_apply`, reading can be done sequentially over the dimensions over which the function is applied: * If for example a function is applied to each band (such as: compute band quantiles), bands can be read sequentially, and discarded after the quantiles have been computed. * If a time series function is applied to pixel time series and the result is plotted on a map, the time series function is only evaluated on the pixels actually plotted. This means that e.g. in ```{r eval = FALSE} plot(st_apply(x, c("x", "y"), range)) ``` the order of evaluation is reversed: `plot` knows which pixels are going to be shown, and controls how `x` is downsampled *before* `st_apply` is carried out on this subset. ### Fetching the data Fetching the data now involves reading the whole array and then evaluating the `call_list` on it, sequentially: ```{r,eval=EVAL} (x = st_as_stars(yyy)) # read, adrop, subset ``` ### Plotting with changed evaluation order For the Sentinel 2 data, band 4 represents NIR and band 1 red, so we can compute NDVI by ```{r,eval=EVAL} # S2 10m: band 4: near infrared, band 1: red. #ndvi = function(x) (x[4] - x[1])/(x[4] + x[1]) ndvi = function(x1, x2, x3, x4) (x4 - x1)/(x4 + x1) rm(x) (s2.ndvi = st_apply(p, c("x", "y"), ndvi)) system.time(plot(s2.ndvi)) # read - compute ndvi - plot ``` # Multi-resolution proxy objects This sections shows some examples how `stars_proxy` objects deal with the situation where the different maps have dissimilar resolution. The assumptions here are: * all maps need to have the same origin coordinates (typically upper-left corner) and CRS. * the first map determines the "working" resolution, to which e.g. native or downsampled resolutions refer We'll create four maps with cells size 1, 2 and 3: ```{r} s1 = st_as_stars(matrix(1:16, 4)) s2 = st_as_stars(matrix(1:16, 4)) s3 = st_as_stars(matrix(1:16, 4)) attr(s1, "dimensions")$X1$offset = 0 attr(s1, "dimensions")$X2$offset = 4 attr(s2, "dimensions")$X1$offset = 0 attr(s2, "dimensions")$X2$offset = 4 attr(s3, "dimensions")$X1$offset = 0 attr(s3, "dimensions")$X2$offset = 4 attr(s1, "dimensions")$X1$delta = 1 attr(s1, "dimensions")$X2$delta = -1 attr(s2, "dimensions")$X1$delta = 2 attr(s2, "dimensions")$X2$delta = -2 attr(s3, "dimensions")$X1$delta = 3 attr(s3, "dimensions")$X2$delta = -3 plot(s1, axes = TRUE, text_values = TRUE, text_color = 'orange') plot(s2, axes = TRUE, text_values = TRUE, text_color = 'orange') plot(s3, axes = TRUE, text_values = TRUE, text_color = 'orange') ``` We created three rasters with identical cell values and dimensions, but different cell sizes, and hence extents. If we bind them in a single proxy object, with ```{r eval=TRUE} fn1 = paste0(tempdir(), .Platform$file.sep, "img1.tif") fn2 = paste0(tempdir(), .Platform$file.sep, "img2.tif") fn3 = paste0(tempdir(), .Platform$file.sep, "img3.tif") write_stars(s1, fn1) write_stars(s2, fn2) write_stars(s3, fn3) (r1 = read_stars(c(fn1, fn2, fn3), proxy = TRUE)) ``` We see that **multi-resolution** is mentioned in the printed summary. When converting this to a `stars` object, the secondary rasters are resampled to the cellsize + extent of the first: ```{r eval=TRUE} st_as_stars(r1) %>% merge() %>% plot(breaks = "equal", text_values = TRUE, text_color = 'orange', axes = TRUE) ``` If we do this for a sub-range, defined for the object resolutions, we get: ```{r eval=TRUE} st_as_stars(r1[,2:4,2:4]) %>% merge() %>% plot(breaks = "equal", text_values = TRUE, text_color = 'orange', axes = TRUE) ``` We now create four maps, all over the same region ([0,4] x [0,4]), with different resolutions (cell size 1, 1/2 and 1/3): ```{r} s4 = st_as_stars(matrix(1: 16, 4)) s5 = st_as_stars(matrix(1: 64, 8)) s6 = st_as_stars(matrix(1:144,12)) attr(s4, "dimensions")$X1$offset = 0 attr(s4, "dimensions")$X2$offset = 4 attr(s5, "dimensions")$X1$offset = 0 attr(s5, "dimensions")$X2$offset = 4 attr(s6, "dimensions")$X1$offset = 0 attr(s6, "dimensions")$X2$offset = 4 attr(s4, "dimensions")$X1$delta = 1 attr(s4, "dimensions")$X2$delta = -1 attr(s5, "dimensions")$X1$delta = 1/2 attr(s5, "dimensions")$X2$delta = -1/2 attr(s6, "dimensions")$X1$delta = 1/3 attr(s6, "dimensions")$X2$delta = -1/3 plot(s4, axes = TRUE, text_values = TRUE, text_color = 'orange') plot(s5, axes = TRUE, text_values = TRUE, text_color = 'orange') plot(s6, axes = TRUE, text_values = TRUE, text_color = 'orange') ``` ```{r eval=TRUE} fn4 = paste0(tempdir(), .Platform$file.sep, "img4.tif") fn5 = paste0(tempdir(), .Platform$file.sep, "img5.tif") fn6 = paste0(tempdir(), .Platform$file.sep, "img6.tif") write_stars(s4, fn4) write_stars(s5, fn5) write_stars(s6, fn6) (r2 = read_stars(c(fn4, fn5, fn6), proxy = TRUE)) st_as_stars(r2) %>% merge() %>% plot(breaks = "equal", text_values = TRUE, text_color = 'orange', axes = TRUE) st_as_stars(r2[,2:4,2:4]) %>% merge() %>% plot(breaks = "equal", text_values = TRUE, text_color = 'orange', axes = TRUE) ``` Finally, an example where the first raster has the higher resolution: ```{r eval=TRUE} (r3 = read_stars(c(fn6, fn5, fn4), proxy = TRUE)) st_as_stars(r3) %>% merge() %>% plot(breaks = "equal", text_values = TRUE, text_color = 'orange', axes = TRUE) st_as_stars(r3[,2:6,3:6]) %>% merge() %>% plot(breaks = "equal", text_values = TRUE, text_color = 'orange', axes = TRUE) ``` stars/vignettes/stars7.Rmd0000644000176200001440000000055114644756215015324 0ustar liggesusers--- title: "7. Statistical modelling with stars objects" author: "Edzer Pebesma" output: rmarkdown::html_vignette vignette: > %\VignetteIndexEntry{7. Statistical modelling with stars objects} %\VignetteEngine{knitr::rmarkdown} %\VignetteEncoding{UTF-8} --- **For a better version of the stars vignettes see** https://r-spatial.github.io/stars/articles/ stars/vignettes/stars4.Rmd0000644000176200001440000002652214644756215015327 0ustar liggesusers--- title: '4. stars data model' author: "Edzer Pebesma" output: rmarkdown::html_vignette vignette: > %\VignetteIndexEntry{4. stars data model} %\VignetteEngine{knitr::rmarkdown} %\VignetteEncoding{UTF-8} --- **For a better version of the stars vignettes see** https://r-spatial.github.io/stars/articles/ ```{r setup, include=FALSE} knitr::opts_chunk$set(echo = TRUE, collapse = TRUE, dev = "png") set.seed(13579) knitr::opts_chunk$set(fig.height = 4.5) knitr::opts_chunk$set(fig.width = 6) ``` This vignette explains the data model of `stars` objects, illustrated using artificial and real datasets. ## Stars objects `stars` objects consist of * a (possibly empty) named list of arrays, each having named dimensions (`dim`) attribute * an attribute called `dimensions` of class `dimensions` that carries dimension metadata * a class name that includes `stars` A `dimensions` object is a named list of `dimension` elements, each describing the semantics a dimension of the data arrays (space, time, type etc). In addition to that, a `dimensions` object has an attribute called `raster` of class `stars_raster`, which is a named list with three elements: * `dimensions` length 2 character; the dimension names that constitute a spatial raster (or NA) * `affine` length 2 numeric; the two affine parameters of the geotransform (or NA) * `curvilinear` a boolean indicating whether a raster is a curvilinear raster (or NA) The `affine` and `curvilinear` values are only relevant in case of raster data, indicated by `dimensions` to have non-NA values. A `dimension` object describes a _single_ dimension; it is a list with named elements * `from`: (numeric length 1): the start index of the array * `to`: (numeric length 1): the end index of the array * `offset`: (numeric length 1): the start coordinate (or time) value of the first pixel (i.e., a pixel/cell boundary) * `delta`: (numeric length 1): the increment, or cell size * `refsys`: (character, or `crs`): object describing the reference system; e.g. the PROJ string, or string `POSIXct` or `PCICt` (for 360 and 365 days/year calendars), or object of class `crs` (containing both EPSG code and proj4string) * `point`: (logical length 1): boolean indicating whether cells/pixels refer to areas/periods, or to points/instances (may be NA) * `values`: one of * `NULL` (missing), * a vector with coordinate values (numeric, `POSIXct`, `PCICt`, or `sfc`), * an object of class `intervals` (a list with two vectors, `start` and `end`, with interval start- and end-values), or * a matrix with longitudes or latitudes for all cells (in case of curvilinear grids) `from` and `to` will usually be 1 and the dimension size, but `from` may be larger than 1 in case a sub-grid got was selected (or cropped). `offset` and `delta` only apply to _regularly_ discretized dimensions, and are `NA` if this is not the case. If they are `NA`, dimension values may be held in the `values` field. Rectilinear and curvilinear grids need grid values in `values` that can be either: * for rectilinear grids: irregularly _spaced_ coordinate values, or coordinate _intervals_ of irregular width (a rectilinear grid _can_ have one dimension that is regular), * for curvilinear grids: or a matrix with grid cell centre values for _all_ row/col combinations (usually in longitude or latitude). Alternatively, `values` can contains a set of spatial geometries encoded in an `sfc` vector ("list-column"), in which case we have a [vector data cube](https://r-spatial.org/r/2022/09/12/vdc.html). ## Grid type ### Regular grids With a very simple file created from a $4 \times 5$ matrix ```{r fig.width=4.5, fig.height=4} suppressPackageStartupMessages(library(stars)) m = matrix(1:20, nrow = 5, ncol = 4) dim(m) = c(x = 5, y = 4) # named dim (s = st_as_stars(m)) ``` we see that * the rows (5) are mapped to the first dimension, the x-coordinate * the columns (4) are mapped to the second dimension, the y-coordinate * the `from` and `to` fields of each dimension define a range that corresponds to the array dimension: ```{r} dim(s[[1]]) ``` * offset and delta specify how increasing row and column index maps to x and y coordinate values respectively. When we plot this object, using the `image` method for `stars` objects, ```{r fig.width=4.5, fig.height=4} image(s, text_values = TRUE, axes = TRUE) ``` we see that $(0,0)$ is the origin of the grid (grid corner), and $1$ the coordinate value increase from one index (row, col) to the next. It means that consecutive matrix columns represent grid lines, going from south to north. Grids defined this way are **regular**: grid cell size is constant everywhere. Many actual grid datasets have y coordinates (grid rows) going from North to South (top to bottom); this is realised with a negative value for `delta`. We see that the grid origin $(0,0)$ did not change: ```{r fig.width=4.5, fig.height=4} attr(s, "dimensions")[[2]]$delta = -1 image(s, text_values = TRUE, axes = TRUE) ``` An example is the GeoTIFF carried in the package, which, as probably all data sources read through GDAL, has a negative `delta` for the `y`-coordinate: ```{r} tif = system.file("tif/L7_ETMs.tif", package = "stars") st_dimensions(read_stars(tif))["y"] ``` ### Raster attributes, rotated and sheared grids Dimension tables of `stars` objects carry a `raster` attribute: ```{r} str(attr(st_dimensions(s), "raster")) ``` which is a list that holds * `dimensions`: character, the names of raster dimensions (if any), as opposed to e.g. spectral, temporal or other dimensions * `affine`: numeric, the affine parameters * `curvilinear`: a logical indicating whether the raster is curvilinear These fields are needed at this level, because they describe properties of the array at a higher level than individual dimensions do: a pair of dimensions forms a raster, both `affine` and `curvilinear` describe how x and y _as a pair_ are derived from grid indexes (see below) when this cannot be done on a per-dimension basis. With two affine parameters $a_1$ and $a_2$, $x$ and $y$ coordinates are derived from (1-based) grid indexes $i$ and $j$, grid offset values $o_x$ and $o_y$, and grid cell sizes $d_x$ and $d_y$ by $$x = o_x + (i-1) d_x + (j-1) a_1$$ $$y = o_y + (i-1) a_2 + (j-1) d_y$$ Clearly, when $a_1=a_2=0$, $x$ and $y$ are entirely derived from their respective index, offset and cellsize. Note that for integer indexes, the coordinates are that of the starting edge of a grid cell; to get the grid cell center of the top left grid cell (in case of a negative $d_y$), use $i=1.5$ and $j=1.5$. We can rotate grids by setting $a_1$ and $a_2$ to a non-zero value: ```{r} attr(attr(s, "dimensions"), "raster")$affine = c(0.1, 0.1) plot(st_as_sf(s, as_points = FALSE), axes = TRUE, nbreaks = 20) ``` The rotation angle, in degrees, is ```{r} atan2(0.1, 1) * 180 / pi ``` Sheared grids are obtained when the two rotation coefficients, $a_1$ and $a_2$, are unequal: ```{r} attr(attr(s, "dimensions"), "raster")$affine = c(0.1, 0.2) plot(st_as_sf(s, as_points = FALSE), axes = TRUE, nbreaks = 20) ``` Now, the y-axis and x-axis have different rotation in degrees of respectively ```{r} atan2(c(0.1, 0.2), 1) * 180 / pi ``` ## Rectilinear grids [Rectilinear grids](https://en.wikipedia.org/wiki/Regular_grid) have orthogonal axes, but do not have congruent (equally sized and shaped) cells: each axis has its own irregular subdivision. We can define a rectilinear grid by specifying the cell _boundaries_, meaning for every dimension we specify _one more_ value than the dimension size: ```{r} x = c(0, 0.5, 1, 2, 4, 5) # 6 numbers: boundaries! y = c(0.3, 0.5, 1, 2, 2.2) # 5 numbers: boundaries! (r = st_as_stars(list(m = m), dimensions = st_dimensions(x = x, y = y))) st_bbox(r) image(r, axes = TRUE, col = grey((1:20)/20)) ``` Would we leave out the last value, than `stars` may come up with a _different_ cell boundary for the last cell, as this is now derived from the width of the one-but-last cell: ```{r} x = c(0, 0.5, 1, 2, 4) # 5 numbers: offsets only! y = c(0.3, 0.5, 1, 2) # 4 numbers: offsets only! (r = st_as_stars(list(m = m), dimensions = st_dimensions(x = x, y = y))) st_bbox(r) ``` This is not problematic if cells have a constant width, in which case the boundaries are reduced to an `offset` and `delta` value, irrespective whether an upper boundary is given: ```{r} x = c(0, 1, 2, 3, 4) # 5 numbers: offsets only! y = c(0.5, 1, 1.5, 2) # 4 numbers: offsets only! (r = st_as_stars(list(m = m), dimensions = st_dimensions(x = x, y = y))) st_bbox(r) ``` Alternatively, one can also set the _cell midpoints_ by specifying arguments `cell_midpoints` to the `st_dimensions` call: ```{r} x = st_as_stars(matrix(1:9, 3, 3), st_dimensions(x = c(1, 2, 3), y = c(2, 3, 10), cell_midpoints = TRUE)) ``` When the dimension is regular, this results in `offset` being shifted back with half a `delta`, or else in intervals derived from the distances between cell centers. This should obviously not be done when cell boundaries are specified. ## Curvilinear grids Curvilinear grids are grids whose grid lines are not straight. Rather than describing the curvature parametrically, the typical (HDF5 or NetCDF) files in which they are found have two raster layers with the longitudes and latitudes for every corresponding pixel of remaining layers. As an example, we will use a Sentinel 5P dataset available from package `starsdata`; this package can be installed with ```{r eval=FALSE} install.packages("starsdata", repos = "http://pebesma.staff.ifgi.de", type = "source") ``` The dataset is found here: ```{r} (s5p = system.file("sentinel5p/S5P_NRTI_L2__NO2____20180717T120113_20180717T120613_03932_01_010002_20180717T125231.nc", package = "starsdata")) ``` ```{r echo=FALSE} EVAL = s5p != "" ``` We can construct the curvilinear `stars` raster by calling `read_stars` on the right sub-array: ```{r eval=EVAL} subs = gdal_subdatasets(s5p) subs[[6]] ``` For this array, we can see the GDAL metadata under item `GEOLOCATION`: ```{r eval=EVAL} gdal_metadata(subs[[6]], "GEOLOCATION") ``` which reveals where, in this dataset, the longitude and latitude arrays are kept. ```{r eval=EVAL} nit.c = read_stars(subs[[6]]) threshold = units::set_units(9e+36, mol/m^2) nit.c[[1]][nit.c[[1]] > threshold] = NA nit.c ``` The curvilinear array has the actual arrays (raster layers, matrices) with longitude and latitude values read in its dimension table. We can plot this file: ```{r eval=EVAL} plot(nit.c, breaks = "equal", reset = FALSE, axes = TRUE, as_points = TRUE, pch = 16, logz = TRUE, key.length = 1) maps::map('world', add = TRUE, col = 'red') ``` ```{r eval=EVAL} plot(nit.c, breaks = "equal", reset = FALSE, axes = TRUE, as_points = FALSE, border = NA, logz = TRUE, key.length = 1) maps::map('world', add = TRUE, col = 'red') ``` We can downsample the data by ```{r eval=EVAL} (nit.c_ds = stars:::st_downsample(nit.c, 8)) plot(nit.c_ds, breaks = "equal", reset = FALSE, axes = TRUE, as_points = TRUE, pch = 16, logz = TRUE, key.length = 1) maps::map('world', add = TRUE, col = 'red') ``` which doesn't look nice, but plotting the cells as polygons looks better: ```{r eval=EVAL} plot(nit.c_ds, breaks = "equal", reset = FALSE, axes = TRUE, as_points = FALSE, border = NA, logz = TRUE, key.length = 1) maps::map('world', add = TRUE, col = 'red') ``` Another approach would be to warp the curvilinear grid to a regular grid, e.g. by ```{r eval=EVAL} w = st_warp(nit.c, crs = 4326, cellsize = 0.25) plot(w) ``` stars/vignettes/stars3.Rmd0000644000176200001440000000532114644756215015320 0ustar liggesusers--- title: "3. stars tidyverse methods" author: "Edzer Pebesma" output: rmarkdown::html_vignette vignette: > %\VignetteIndexEntry{3. stars tidyverse methods} %\VignetteEngine{knitr::rmarkdown} %\VignetteEncoding{UTF-8} --- **For a better version of the stars vignettes see** https://r-spatial.github.io/stars/articles/ ```{r setup, include=FALSE} knitr::opts_chunk$set(echo = TRUE, collapse = TRUE, dev = "png") ev = TRUE knitr::opts_chunk$set(fig.height = 4.5) knitr::opts_chunk$set(fig.width = 6) ``` This vignette shows how some of the tidyverse verbs can be used on `stars` objects. The `stars` and `tidyverse` packages are loaded by ```{r} library(stars) library(dplyr) ``` Methods now available for class `stars` are ``` {r} methods(class = "stars") ``` We will work with a three-band section of a landsat image: ```{r} system.file("tif/L7_ETMs.tif", package = "stars") %>% read_stars -> x x ``` ## `slice` `slice` slices a sub-array out of the cube; this is done by specifying the dimension on which to act, and the slice number. ```{r} x %>% slice(band, 6) -> x6 x6 ``` It returns a lower-dimensional array if a single element is selected along the slice dimension. ## `filter` Similar to `slice`, `filter` selects on dimensions but evaluates their values rather than their index: in ```{r} x %>% filter(x > 289000, x < 291000, band > 3) -> x7 x7 ``` the subarray is created based on the x _coordinate_ values. Note that `filter` converts the object to a `tbl_cube`, and uses the `dplyr` `filter` method for `tbl_cube` objects. This has the limitation that `stars` objects with rectilinear, curvilinear or simple feature geometries cannot be handled. For such objects, using regular `[` selection or using `st_crop` may be an alternative. ## `pull` `pull` pulls out an array from a stars object: ```{r} x %>% pull(1) -> x8 class(x8) dim(x8) ``` ## `mutate` ```{r} x %>% mutate(band2 = 2 * L7_ETMs.tif) -> x2 x2 ``` ## `select` `select` selects an attribute, or a set of attributes: ```{r} x2 %>% select(band2) -> x9 x9 ``` ## `geom_stars` `geom_raster` is a `ggplot2` geom function that accepts `stars` objects as its `data` argument and * sets up the raster or vector spatial coordinates as plot dimensions, and the first attribute as the fill variable * allows for downsampling (without choosing a suitable downsampling level) * chooses between using `geom_raster`, `geom_rect` and `geom_sf` depending on whether the geometry is regular, rectilinear or has vector geometries An example use is ```{r} library(ggplot2) library(viridis) ggplot() + geom_stars(data = x) + coord_equal() + facet_wrap(~band) + theme_void() + scale_fill_viridis() + scale_x_discrete(expand = c(0, 0)) + scale_y_discrete(expand = c(0, 0)) ``` stars/data/0000755000176200001440000000000014144045432012320 5ustar liggesusersstars/data/bcsd_obs.rda0000644000176200001440000000156214144045432014572 0ustar liggesusersBZh91AY&SY˓0!`"D@ @L#o(IP iPAFi4=M4m @@@@@d8  2 p4 dMHh {IM6(hiz mHl:d+GOA؏ʔLb1fC.IYs0ccK \;'9S*F!],*B+@C1P\(Tg!Я+ JANRb)zPd2iYզj*́S! AA eSc;c&db9m<8DD2:s!o%'NYƄAC 0_\J] "`C@bIB$D^x̍S؅%2-L&z#SQ[uHKR1$1 l&C0m0Nb BS؈Н H@66Lm$ Y1aޅ8ps`6cHc: LnQY 4n@+p|A ]m߱S^dA0)A}L5}tHeH}\*jkzAy$jgKQ9EXxVӸRE0d̾9СjUG+<h:UZ%)+1BuJBFձiU/ѥV% ed ]ޒw"A2_Saer63vyLE<+Z,/ @1Bϰ90oAz#^e&}-5 F]ī aVjElKM$Fy .^Y7W#QYX]BBTcw`stars/data/stars_sentinel2.rda0000644000176200001440000000316714144045432016136 0ustar liggesusersBZh91AY&SY>SgBL/@f4zhA~6hmM2=G 1hhɠ8i @44 h2dC@M FMOU?SʞOSiO<=&OSA=''=OS{ UVMA:7 f D0.._!=>#!:;4rݫU zRUU!u¢&` +wu}lv ei]!BC˧#?sI }7eÞ6v/"TF(ջ! rAۭɄn8%eK&wdE]ۓqv4Tү..FR<8cjk~nOa>|3zTTUQU"MT THZ-jSs.]4p{ef͕ˆ*QTQ"K#.=a;%>/_gROu)rS\8ԉ8JiOJo[&xcx5<)(H<'/1(4}Ԟ%n{Fc8γěDEdS'+jnN3mS4E-ě.dhJS2k/LYq3KIe4h&RE'k9>;{YSyt#R[YyglX~;|o/k{{B9 ?315III9m'cJypS51n8S;ni߳&ZblЙ pŜW9pH`jG#r3̤:ڲf;;MyNuc .;v_j|F%E"oKȽq{^bs9-i.u4S}C7$s.ǁRo|7&${Kq`:W3i'qz2ތ?t1a*fJkpM.LO})hbt8]N8L'.}';h`ڹ>zkޜwNy)~fv BͬN)5LN~Qz_Ώ6i`%i~ DC ))lsgϛ]LDZ]=N0`،XNFvKt5#KzSYi[̗L!gS\ə4w8if:u%aGf,E%RxjW`=$D>Ӻ4NG*>ݧqrwjXNҹ&=:rm<͉c7kG) O 2ǀ)JQ4=-Zw\NWYnyD:+Ds8qkzuř,741bj^b0SnzMzZ24h`bڔbԚ)4% WLX^ioSZh-j k>G yQ=$^Ӹgo/bl\kroЇipGQqb1r2M!kr?;) pCK6'lah6p6SW&+V)0Cx-q7:jh:Ч"ݱjsL'io/fGkaqqXhb5ɹ鶗}{G#f/ \%u,Z;eyč}b$\Mw/nz1ZGOo 'SǛΘ==s1~Ƥ޽/b;L2ULa*}딴O2WRb-8_5OKՓsO}s_v1`-Y+l'7غw0,1\Z\ SirN"kf%+42ju\C nrq.QQY/\t^S/o䗭~)R=eȽrd-brS6LgI>&Zͼ▾?oKj q'cdE0,ĉ }FDbaXWug4W[o;jLНo^ Zf>9|ũR6& v kq-fũjXYt9f cMYh]εPo2ȫF c9хj]<$ rE8PWstars/NAMESPACE0000644000176200001440000001506714576035525012652 0ustar liggesusers# Generated by roxygen2: do not edit by hand S3method("$<-",stars) S3method("[",dimension) S3method("[",dimensions) S3method("[",intervals) S3method("[",intervals_list) S3method("[",nc_proxy) S3method("[",stars) S3method("[",stars_proxy) S3method("[<-",dimensions) S3method("[<-",stars) S3method("[<-",stars_proxy) S3method("[[<-",stars) S3method("[[<-",stars_proxy) S3method("dimnames<-",stars) S3method("st_crs<-",dimensions) S3method("st_crs<-",stars) S3method("st_dimensions<-",list) S3method("st_dimensions<-",stars) S3method("st_dimensions<-",stars_proxy) S3method("st_geotransform<-",stars) S3method(Math,stars) S3method(Math,stars_proxy) S3method(Ops,stars) S3method(Ops,stars_proxy) S3method(adrop,stars) S3method(adrop,stars_proxy) S3method(aggregate,stars) S3method(aggregate,stars_proxy) S3method(all,equal.dimensions) S3method(aperm,stars) S3method(aperm,stars_proxy) S3method(as.POSIXct,stars) S3method(as.data.frame,dimensions) S3method(as.data.frame,stars) S3method(as.data.frame,stars_proxy) S3method(as.list,intervals) S3method(asub,factor) S3method(c,intervals) S3method(c,nc_proxy) S3method(c,stars) S3method(c,stars_proxy) S3method(contour,stars) S3method(cut,array) S3method(cut,matrix) S3method(cut,stars) S3method(dim,dimensions) S3method(dim,stars) S3method(dim,stars_proxy) S3method(dimnames,stars) S3method(drop_units,dimension) S3method(drop_units,stars) S3method(droplevels,stars) S3method(droplevels,stars_proxy) S3method(expand_dimensions,dimensions) S3method(expand_dimensions,stars) S3method(format,intervals) S3method(format,intervals_list) S3method(head,intervals) S3method(hist,stars) S3method(hist,stars_proxy) S3method(image,stars) S3method(image,stars_proxy) S3method(is.na,stars) S3method(is.na,stars_proxy) S3method(length,intervals) S3method(merge,stars) S3method(merge,stars_proxy) S3method(plot,nc_proxy) S3method(plot,stars) S3method(plot,stars_proxy) S3method(prcomp,stars) S3method(prcomp,stars_proxy) S3method(predict,stars) S3method(predict,stars_proxy) S3method(print,dimensions) S3method(print,nc_proxy) S3method(print,stars) S3method(print,stars_proxy) S3method(print,stars_raster) S3method(range,dimension) S3method(seq,dimension) S3method(split,stars) S3method(split,stars_proxy) S3method(st_apply,stars) S3method(st_apply,stars_proxy) S3method(st_area,stars) S3method(st_as_sf,dimensions) S3method(st_as_sf,stars) S3method(st_as_sf,stars_proxy) S3method(st_as_sfc,stars) S3method(st_as_stars,OpenStreetMap) S3method(st_as_stars,Raster) S3method(st_as_stars,STF) S3method(st_as_stars,STFDF) S3method(st_as_stars,STS) S3method(st_as_stars,STSDF) S3method(st_as_stars,SpatRaster) S3method(st_as_stars,Spatial) S3method(st_as_stars,bbox) S3method(st_as_stars,cubble_df) S3method(st_as_stars,data.frame) S3method(st_as_stars,default) S3method(st_as_stars,im) S3method(st_as_stars,list) S3method(st_as_stars,nc_proxy) S3method(st_as_stars,ncdfgeom) S3method(st_as_stars,sf) S3method(st_as_stars,sfc) S3method(st_as_stars,stars) S3method(st_as_stars,stars_proxy) S3method(st_as_stars,xts) S3method(st_bbox,default) S3method(st_bbox,dimensions) S3method(st_bbox,stars) S3method(st_coordinates,dimensions) S3method(st_coordinates,stars) S3method(st_crop,nc_proxy) S3method(st_crop,stars) S3method(st_crop,stars_proxy) S3method(st_crs,dimensions) S3method(st_crs,stars) S3method(st_dimensions,array) S3method(st_dimensions,default) S3method(st_dimensions,dimensions) S3method(st_dimensions,matrix) S3method(st_dimensions,stars) S3method(st_downsample,stars) S3method(st_downsample,stars_proxy) S3method(st_extract,stars) S3method(st_geometry,stars) S3method(st_geotransform,default) S3method(st_geotransform,dimensions) S3method(st_geotransform,stars) S3method(st_interpolate_aw,stars) S3method(st_intersects,bbox) S3method(st_intersects,stars) S3method(st_join,stars) S3method(st_mosaic,character) S3method(st_mosaic,nc_proxy) S3method(st_mosaic,stars) S3method(st_mosaic,stars_proxy) S3method(st_normalize,stars) S3method(st_normalize,stars_proxy) S3method(st_redimension,nc_proxy) S3method(st_redimension,stars) S3method(st_redimension,stars_proxy) S3method(st_rotate,sf) S3method(st_rotate,sfc) S3method(st_rotate,stars) S3method(st_sample,stars) S3method(st_sample,stars_proxy) S3method(st_set_bbox,dimensions) S3method(st_set_bbox,stars) S3method(st_set_bbox,stars_proxy) S3method(st_transform,stars) S3method(st_write,stars) S3method(tail,intervals) S3method(time,stars) S3method(units,dimension) S3method(write_stars,stars) S3method(write_stars,stars_proxy) export("st_dimensions<-") export("st_geotransform<-") export(as.tbl_cube.stars) export(detect.driver) export(expand_dimensions) export(geom_stars) export(make_intervals) export(read_mdim) export(read_ncdf) export(read_stars) export(st_apply) export(st_as_stars) export(st_cells) export(st_contour) export(st_dim_to_attr) export(st_dimensions) export(st_downsample) export(st_extract) export(st_flip) export(st_geotransform) export(st_get_dimension_values) export(st_mosaic) export(st_raster_type) export(st_rasterize) export(st_redimension) export(st_res) export(st_rgb) export(st_rotate) export(st_set_bbox) export(st_set_dimensions) export(st_sfc2xy) export(st_tile) export(st_warp) export(st_xy2sfc) export(write_mdim) export(write_stars) exportMethods("%in%") import(sf) import(units) importFrom(abind,abind) importFrom(abind,adrop) importFrom(abind,asub) importFrom(classInt,classIntervals) importFrom(grDevices,cm) importFrom(grDevices,col2rgb) importFrom(grDevices,dev.capabilities) importFrom(grDevices,dev.size) importFrom(grDevices,grey) importFrom(grDevices,rgb) importFrom(graphics,axis) importFrom(graphics,box) importFrom(graphics,contour) importFrom(graphics,hist) importFrom(graphics,image) importFrom(graphics,image.default) importFrom(graphics,layout) importFrom(graphics,lcm) importFrom(graphics,par) importFrom(graphics,plot) importFrom(graphics,plot.new) importFrom(graphics,plot.window) importFrom(graphics,rasterImage) importFrom(graphics,strwidth) importFrom(graphics,text) importFrom(graphics,title) importFrom(methods,as) importFrom(methods,new) importFrom(methods,slot) importFrom(methods,slotNames) importFrom(parallel,parApply) importFrom(rlang,"%||%") importFrom(stats,aggregate) importFrom(stats,complete.cases) importFrom(stats,na.omit) importFrom(stats,na.pass) importFrom(stats,prcomp) importFrom(stats,predict) importFrom(stats,quantile) importFrom(stats,runif) importFrom(stats,setNames) importFrom(stats,time) importFrom(stats,var) importFrom(tools,file_ext) importFrom(utils,head) importFrom(utils,methods) importFrom(utils,modifyList) importFrom(utils,packageVersion) importFrom(utils,setTxtProgressBar) importFrom(utils,tail) importFrom(utils,txtProgressBar) stars/NEWS.md0000644000176200001440000005270614642514221012517 0ustar liggesusers# version 0.6-6 * skip `cubble` tests for cubble version 0.3.1; https://github.com/huizezhang-sherry/cubble/issues/30 * `st_transform.stars` transforms geometries in array elements * `mutate.stars` (and others) handle attribute names with spaces in them; #689 * `st_crop()` gains an argument `normalize`; when set to `TRUE` `st_normalize()` is called on the returned value; #685, #686 * constrain reading full GEOLOCATION arrays to the case where they are 2-D; #678 # version 0.6-5 * fix `st_as_stars.Spatial()` for `Spatial` gridded objects with non-square grid cells, see https://github.com/r-spatial/gstat/issues/123 * add `prcomp()` methods for `stars` and `stars_proxy` objects, working on attributes or last dimension * `st_rasterize()` with `align=TRUE` returns `NA` values where there are no data; #668 * `read_mdim()` reads tables with composity type, returning a `data.frame` in that case; #659 * `st_rotate()` transforms a rotated grid back to a curvilinear grid in unrotated coordinates. * `aggregate.stars()` deals with functions that return more than one number, putting these in a new dimension like `st_apply()` does * `st_as_stars.data.frame()` and `st_as_stars.sf()` better handle non-raster data cubes * `plot.stars()` only resets layout when needed (more than one sub-plot, or key present) * fixed `st_as_stars.im()`; #648 * `st_crs<-.stars()` is less critical on existing CRS being of class `crs` * `c.stars()` with a single (valid) argument and `along` specified adds a dimension; #646 * `st_join.stars()` keeps attributes from `x` complete; #643 * `st_as_stars.list()` requires a _named_ list, and will set names of array dimensions if not present # version 0.6-4 * `plot.stars()` has a `fill` argument that shifts unused plotting space between sub-maps to the bottom or right side of the plotting area * in `plot.stars()`, the `key.width` default is sensitive to `par("ps")`, the pointsize graphics parameter * `plot.stars()` and `image.stars()` are sensitive to `cex.axis`, for axes and key (requires sf >= 1.0-14); #642 * move `lwgeom` dependency to Suggests; using `st_transform_proj()` on vector data cubes requires loading `lwgeom` first # version 0.6-3 * `st_downsample()` has argument `offset` to pixel-shift downsampled images * `st_downsample()` has argument `FUN` to compute sub-tile aggregates; #604 * `st_as_stars.bbox()` retains `factor` values; #640 * fix CRAN error in test script * `st_crop()` works (and warns) for the case when the crop area does not overlap with the area of the object; #638 # version 0.6-2 * `split.stars()` accepts `stars` objects with multiple attributes; #635 * `[.stars()` supports `NA` values in dimension ranges for vector geometry (`sfc`) dimensions, resulting in empty geometries * `st_extract()` supports extracting points values from curvilinear grids (when not proxy); #632 * `read_mdim()` reads curvilinear rasters (geolocation arrays), so far only at full extent/resolution * `st_as_stars.stars()` accepts curvilinear argument with lon/lat array names present in `.x` * consistently use `OGC:CRS84` instead of `EPSG:4326` * setting `values = NULL` in `st_set_dimensions()` removes dimension values * more gracefully handle plotting of global coverage curvilinear grids; #632 * `image.stars()` plots images (e.g. of cross sections) when x and/or y are singular or absent; #628 * `st_as_stars.cubble_df()` adds conversion from cubble; `cubble::as_cubble()` methods converts back * `[<-.stars()` accepts for `i` an indicator (numeric length character) to the array to be replaced; #626 * `plot.stars()` gains an argument `key.lab` to set the legend key label (requires sf >= 1.0-13) # version 0.6-1 * remove `rgdal` dependency * `read_stars()` fixes combining bands with different block sizes; #623 * `st_warp()` gets (proper) default value for `threshold`; #618 * `read_mdim()` reads "raster" with single pixel (as a point) * `[.stars()`, as in `r[x]` allows `x` to be a logical stars object * `[<-.stars_proxy()` clones environment, so that after `r[r > 100] = NA` we don't get infinite recursion when realizing `r` * `read_stars()` avoids reading a raster twice to determine how to choose `proxy`; `proxy` can now be set as (and defaults to) the number of cells (bands * rows * columns) above which data will not be read in memory but returned as `stars_proxy` object; #609 * fix using `RasterIO` in `read_stars()` when `proxy=TRUE`; #608 * `plot.stars()` hook function can handle arguments `row`, `col`, `nrow`, `ncol`, `nr`, `value` and `bbox`; #600 * fix handling of categorical rasters with colors but without category labels; #595, fallout of #565 * fix subsetting of proxy objects over a time range; #596 # version 0.6-0 * `write_stars()` writes scaled and/or shifted values when using argument `scale_offset`; #589 * `aggregate.stars_proxy()` implements aggregation with non-spatial `by` objects (lazily) * fix `[.stars_proxy()` when selecting dimension 3 and higher; #561 * in `plot.stars()`, `col` can also be a palette function * `st_res()` returns spatial resolutions, and optionally all dimension resolutions; #557 thanks to Krzysztof Dyba * `read_stars()` shortens band or array names that contain a common start or ending, unless names would become empty or `shorten=FALSE` was set; e.g. `shorten="B"` puts a `B` before shortened array names * printing `stars` dimension tables omits fields with only `NULL` or `NA` values, unless `print(..., all = TRUE)` is given * improve reading categorical rasters, which now avoids calling `factor()`; #565 thanks to Krzysztof Dyba * `read_mdim()` will read bounds arrays for coordinates using the `bounds` attribute, and accepts a `bounds` argument to specify them when that attribute is missing * `time()` returns time stamps of a time dimension * `st_cells()` returns the cell index for a set of point coordinates, provided as `sf` or `sfc` object; #558 * reading & writing vector data cubes: `read_mdim()` reads CF compliant vector geometries, and reconstructs them into an `sfc` dimension; `write_mdim()` writes them. * `write_mdim()` uses GDAL multidimensional array API; * `read_mdim()` uses arguments `offset`, `count` and `step` to read sub-arrays or strided arrays (requires sf >= 1.0-9) # version 0.5-6 * export `read_mdim()`, a reader using GDAL's multidimensional array API (for sf <= 1.0-8) * remove `tos_O1_2001-2002.nc` from packaged datasets to keep source package size below 5 Mb * `as.POSIXct.stars()` converts `PCICt` dimensions to `POSIXct` values. * improve handling of `PCICt` 360 or 365 day calendars; read them in `read_mdim` (requires sf >= 1.0-9) * `read_stars()` reads factor levels better from attribute table; #484 thanks to @ailich * `read_stars()` puts band names from `band_meta` DESCRIPTION= tags into `values`; * improve handling of categorical rasters, and their exchange with `terra`; #484 * `plot()` handles auto colors better for factor arrays * `read_ncdf()` handles units more formally in setting crs; #533 * print message that dimensions of proxy objects do not reflect unevaluated operations; #530 * passing `na.action = na.omit` to `geom_stars()` removes `NA` values; #532 * `read_stars()` detects curvilinear grids automatically; #513 * `st_warp()` warps curvilinear grids (using Euclidean distances only on coordinates); #513 * `Ops.stars()` errors when (common) dimension are not identical; #506 * `guess_raster()` accepts empty rows/columns and sparse grids; #509 * speed up `rgb` plotting; #503 * Added a new helper function `st_tile()` to specify the block parameters (`nXOff`, `nYOff`, `nXsize`, `nYSize`) required by `RasterIO` argument in `read_stars()`; #492 thanks to Krzysztof Dyba # version 0.5-5 * `st_as_stars.bbox()` creates an empy raster file if `proxy = TRUE`; #489 * `st_rasterize()` has option `align = TRUE` to use a template for aligning the new raster to; #489 * `adrop.stars()` with missing dimensions no longer drops x/y raster dimensions; #485 * `aggregate.stars()` propagates units of arrays; #477 # version 0.5-4 * `c.stars()` fails if it tries to merge arrays with different units; #475 * For NetCDF files, `read_stars()` uses the `long_name` as array name; #475 * add `rename()` method; #470 * refresh CRS of packaged `L7_ETMs.tif`; #466 * `as.data.frame.stars()` works for mixed regular and rectilinear dimension; #458 * `plot.stars()` plots curvilinear rasters with color table, or without table but `col` argument passed; #456 * `st_extract()` accepts a matrix with points as `at` argument, for when performance is important; see e.g. https://github.com/rspatial/terra/issues/341 * fix bug in `st_crop()` when cropping area is larger than grid; #455 * export `st_downsample()`, e.g. to be used by `tmap`; https://github.com/r-tmap/tmap/issues/597 * argument `downsample` in `plot.stars()` and `st_as_stars.stars_proxy()` and `st_downsample()` has the same effect (removed a one-offset between them). * `st_redimension()` works for curvilinear grids; #441 * `downsample` is propagated to subexpressions like `r[r < 50] = NA` * `predict.stars()` obtains an argument `drop_dimensions` that, if `TRUE`, drops dimensions from the prediction `data.frame`; #362 * extend options in `st_rgb()`, #432, by Gabo Gaona * allow subsetting with `[` by using labels, e.g. of band names. # version 0.5-3 * `read_stars()` accepts a function (or list with functions) as first argument, allowing for saving `stars` objects that read from package directories resolving platform-dependent paths at run-time * handle categorical rasters starting at value 0 (by adding 1, and warning); #428 * add `%in%` method; #424 * `read_stars()` gains an argument `tolerance` to control tolerance in dimension value comparisons; #414 * binary Ops (like `+`, `-`, `*` etc.) work for `stars_proxy` objects; #390 * `st_rasterize()` rasterizes multiple attributes, and handles factors (when sf >= 0.9-9) * `write_stars()` deals better with `stars_proxy` objects; #404 * fix regression in reading some `stars_proxy` objects; #379 * add `[<-` (partially) and `is.na` methods for `stars_proxy` objects; #402 * add `replace_na()` methods; #402 # version 0.5-2 * read and write factor levels as GDAL category names; write color table; #392 * handle `normalize_path` for choosing to `proxy`; #391 * ignore units when there are different units across bands of a subdataset * speed up `st_rgb()` using faster `st_apply()` approach; #315, #390 * improve handling of crs in Spatial objects (avoid loss of wkt comments) * correctly write band subsets for smaller proxy objects; #291 * write arbitrarily cropped proxy objects; #291 * speed up `st_apply()` when a function is provided that works on chunks at a time; #390 * warn when breaks = "quantile" results in a single class; #388 * fix `[` bug selecting bands in proxy objects; #388 * for `stars_proxy` objects, `write_stars()` writes all objects into a multi-layer file; #385 * multi-file proxy objects can be `st_warp()`ed with `use_gdal = TRUE`; #385 # version 0.5-1 * fix weird GDAL-related bug in stars2 vignette * `read_ncdf()` does not take time as mid-points of regular intervals, but as starting points; #378 # version 0.5-0 * fix handling of rasters with color tables; #375 * `st_apply()` and other methods for `stars_proxy` objects handle ... ; #374 * add `st_bbox()`, `st_crs()` methods for terra's `SpatVector` objects; https://github.com/r-tmap/tmap/issues/536 * add `st_bbox()`, `st_crs()` and `st_as_stars()` methods for terra's `SpatRaster` objects; https://github.com/r-tmap/tmap/issues/536 * allow for multi-resolution attributes in `stars_proxy` objects (e.g., all gray scale sentinel-2 bands); see vignettes 2 and 7 for examples. * `plot()` defaults to a categorical color scale when plotting a factor variable; https://github.com/r-tmap/tmap/issues/526 * `st_extract()` extracts space-time points if `time_column` is specified, and handles time intervals; #352 * add `[[<-.stars` method, which is now called by `$<-.stars`, so that array names can be set programmatically * add `transmute()` methods * `plot.stars()` calls `droplevels` if a factor array has any `NA` levels; #339 * `read_stars()` reads `NaN`s as `NA`; #333 * improve `st_extract()` method for both `stars` and `stars_proxy` objects; interpolation options are reduced to bilinear; #322, #279, #290 * better handle categorical rasters that do not start at value 1; #329 * plot layout can be controlled with `mfrow = c(nr, nc)` argument * `stars_proxy` objects have a normalized path; #331 * cropping or selecting with `bbox` treats cells always as small polygons; #330 * add faster `st_extract()` method for `stars` objects; #322 * added vignette: "How `raster` functions map to `stars` functions", by Sebastien Rochette; #122, #325 * fix bug in dimension `values` field when downsampling; #324 * `write_stars()` also writes out band names; #323 * add `rgdal` to Suggests: * each `call_list` entry of a `stars_proxy` object carries its proper calling environment; #309 * `st_as_sf.stars()` copes with zero attribute (empty) stars objects * add `st_set_bbox()` generic, to set raster extent, motivated by #315 * set up tic, with great help from @pat-s, #313 * get rid of more `proj4string`s for representing coordinate reference systems; #312 * as(x, "Spatial") correctly handles `from` dimension values different from one * `read_stars()` now sets the `BANDNAME` GDAL metadata item, or else the band's GetDescription() as the band's dimension values * `st_as_stars.data.frame()` reads simple tables (non-raster data) if `dims` has length less than 2 * band descriptions are in the band dimension values * dimension tables are simpler, and are shown properly in Rstudio * `st_rgb()` gains a `probs` argument, to cut off and stretch based on quantiles * `as(x, "Raster")` merges multiple attributes before converting to raster brick # version 0.4-3 * fix bug in `st_as_stars.Raster`; set crs to the one assigned by raster; https://github.com/r-tmap/tmap/issues/471 * add `s2` to Suggests: * new function `st_rgb()` collapses (reduces) a dimension to rgb hex value; #302 # version 0.4-2 * `aggregate.stars()` handles arrays with NA values now correctly; brought up in #299 by Thorsten Simon * `aggregate.stars()` gains an argument `exact` which, if `TRUE`, calls `exactextractr` for polygonal aggregation; #289 * `read_stars()` reads all subdatasets with dimensions equal to first, and warns when ignoring others; #296 * make copying over of dimensions somewhat easier; #295 * `st_as_stars.Raster()` tries to read from file if the raster object is not an in-memory object. * `write_stars()` normalizes path, as `read_stars` already did; #293 * `merge()` for proxy objects acts, and is no longer lazy; #290 * `st_as_stars.Raster()` returns a proxy object if the raster layer is on disk * add `st_extract()` to extract e.g. time series from grids at point locations; #279; #290 * `read_stars()` chooses a value for `proxy` that depends on the data dimensions; #281 * x/y range subsetting of `stars_proxy` objects now only reads that range, similar to how crop already did this. * `st_warp()` preserves levels and colors; https://github.com/r-tmap/tmap/issues/429 * `st_crop()` works with bounding boxes larger than the downsampled bounding box; #276 * `st_crop()` has a non-zero default for `epsilon` (bounding box shrinkage) to exclude cells touching the crop bounding box; #275 * `image.stars()` (and hence `plot.stars`) gains an `extent` argument for setting the extent of a plot; https://github.com/r-spatial/sf/issues/1193 # version 0.4-1 * `st_warp()` (stars native) flips longitudes a full cycle; #256, #264, #269 * handle axis order in `st_transform` (requires sf >= 0.9-1) * depend on sf 0.9-0 * adapt to cubelyr split-off from dplyr; add cubelyr to Suggests:; https://github.com/hadley/cubelyr/issues/2 * add `droplevels()` method * handle color tables, category tables and raster attribute tables read through GDAL; #128, #245; https://github.com/r-spatial/mapview/issues/208 * handle dimension `crs` specially, for proxy objects now. * handle new-style `crs` objects from upcoming sf, moving away from proj4strings * handle full `crs` objects as `refsys` element in a spatial dimensions, rather than proj4string only * `st_raster_type(x)` reveals the raster type of `x`; #248, https://github.com/r-tmap/tmap/issues/368 * add `st_as_stars.OpenStreetMap()` method; #241 by @mtennekes * add `st_flip()` to flip arrays along one or more dimensions without changing dimension properties * add `as.owin()` method to convert (2D raster) stars objects to spatstat `owin`; https://github.com/r-spatial/sf/issues/1233 * for temporal aggregation, `aggregate.stars` now also takes `by` arguments like "week", "month", or "5 days" * add `st_as_stars()` method for `xts` objects; improve `as.xts` for `stars` objects * skip some tests on solaris # version 0.4-0 * `plot()` now uses all data to figure out breaks, in order to also find extremes; #216 * `st_mosaic()` creates mosaics from spatially disjoint rasters; #210 * #205 large refactoring of `read_ncdf`, by David Blodgett and Mike Sumner, affecting #199, #89, #30, #86, #175 * allow for funny units like `m s**-1`; #201 * add `contour()` method for `stars` objects; #196 * plot uses `rasterImage` by default if available; #194 * the `x` and `y` raster dimensions can be set and unset with `xy` argument in `st_set_dimensions`; #190 * retain `factor` levels with dimension values when set in `st_set_dimensions`; #188 * add conversion from `stars_proxy` to `Raster`: #193 * make plotting of multiple curvilinear grids work * plot by default no cell borders in case of curvilinear, rotated or sheared grids * robustify handling of units * allow `read_ncdf()` to ignore bounds * scale was applied wrongly on multi-band images; #189, this requires sf >= 0.7-5 * `.nc` is now recognized correctly by `write_stars` and will write a NetCDF file; #186 * `[` subset now works correctly with negative or logical indices; #184, #185 * `NA` values for float32 grids are now correctly detected; #182, this requires sf >= 0.7-5 * cropping of a `stars_proxy` object now works; #179 * `st_apply()` can now loop over Raster layers; examples in #176 # version 0.3-1 * `st_as_stars.bbox` now has an `ncells` and `pretty` argument, to better choose default raster dimensions * `geom_stars` now works with `stars_proxy` objects, but needs `downsample` to be set; #21 * `NA` values in Float32 rasters are now read correctly with `read_stars`; #182 * handle bounds, when given, in `read_ncdf` * provide time parsing (POSIXct, PCICt) for `read_ncdf`; #115 # version 0.3-0 * add `st_area` method to return raster grid cell sizes; #99 * fix `st_warp` with `use_gdal=TRUE`, allowing for multi-band warps * add `st_get_dimension_values` to get the values of a particular dimension (if meaningful); #100 * allow for setting intervals as dimension values; see examples of `st_dimensions` * add `st_contour`, and clean up `st_as_sf`; #99 * experimental color table support; https://github.com/r-spatial/mapview/issues/208 * rewrote vignettes, added vignettes; #99 * deprecate `st_write.stars` for `write_stars`; #96 * use "native" R array-factor support * support for `PCICt` 360- and 365-day calendars; #29 * remove import of `ncdf4` in favor of `RNetCDF`, now in line with practice in `ncmeta` package. Thanks to David Blodgett for motivation and testing (see #87, #94). * `st_as_sf` uses date/time column names when appropriate * allow missing trailing comma's when subsetting: `a[1,,]` and `a[1,]` now do the same. * move `rlang` to Imports: ; rewrite `[` subset using rlang. * add conversion to and from `Spatial*` classes, including the gridded ones, taking care of `factor` variables * depend on sf 0.7-2 * add `logz` support for log-scale keys to `plot` and `image` # version 0.2-0 * vignettes now use an external package, `starsdata`, for larger dataset examples * support `[<-.stars` e.g. to mask out values; support `is.na.stars` to replace NA masks * support `cut` methods and factor arrays (plot, subset); #56 * add `st_rasterize`, which uses `GDALRasterize` to rasterize an sf object; #13 * `st_as_sf.stars` now uses `GDAL(F)Polygonize` when give a regular or sheared grid grid cells are not points, and returns contour bands using `GDALContourGenerateEx` (requiring GDAL 2.4.0) in case cells are points; #13 * support curvilinear grids; see #54 and the `data_model` vignette * add vignette about how `stars_proxy` objects work * `stars_proxy` objects defer processing of `st_apply` jobs until after subsampling; #50 * allow reading sections of a raster, raster at a lower resolution, selected bands; #48 * allow reading vectors (arrays) with more than 2^31 elements; #48 * fold all higher dimensions into the third dimension before coercing to `Raster`; #40 # version 0.1-1 * add meta data reader to `read_stars` # version 0.1-0 * add `merge` (merge attributes into array dimension) and `split` (split dimension over attributes) * interface to sf, raster and spacetime * improve plotting * handle `logical` arrays in plot * add `st_apply`, analogous to `apply` * add cropping/masking when used as x[buf] with buf an `sf`, `sfc` or `bbox` object; masking when `x[buf, crop = FALSE]` * add Ops (+,-,/ etc) and Math (sqrt, pow, sin etc) methods * add `dimnames` and `dimnames<-` methods for stars objects * downsample large grids to only plot pixels actually shown * can plot rectilinear grids (but will plot rgb images as regular grids) * `rgb` argument to `image` works * `[` array-like subsetting works; first index is attribute selector # version 0.0 * interface the 9 C++ gdal utils through `gdal_utils()` (now part of `sf`) stars/inst/0000755000176200001440000000000014644756275012407 5ustar liggesusersstars/inst/CITATION0000644000176200001440000000057514405651246013536 0ustar liggesuserscitHeader("To cite package stars in publications, please use:") bibentry(bibtype = "Book", title = "{Spatial Data Science: With applications in R}", author = c(person("Edzer", "Pebesma"), person("Roger", "Bivand")), year = "2023", publisher = "Chapman and Hall/CRC", address = "London", url = "https://r-spatial.org/book/", pages = 352, doi = "10.1201/9780429459016") stars/inst/plumber/0000755000176200001440000000000013777653456014060 5ustar liggesusersstars/inst/plumber/server.R0000644000176200001440000000100013777653456015500 0ustar liggesusers# script.R library(jsonlite) # base64_enc # load stars library(stars) # load some imagery data x = read_stars(system.file("tif/L7_ETMs.tif", package = "stars")) # global database data = list(x = x, y = "foo", z = 0:10) #* @get /data get_data <- function(expr = NULL) { if (is.null(expr)) names(data) else base64_enc(serialize( eval(parse(text = expr), data), NULL)) # to char } #* @put /data put_data <- function(req, name, value) { data[[name]] <<- unserialize(base64_dec(fromJSON(value))) NULL } stars/inst/plumber/api.R0000644000176200001440000000025713777653456014760 0ustar liggesuserslibrary(plumber) r <- plumb("server.R") # Where 'script.R' is the location of the file shown above r$registerHook("exit", function(){ print("Bye bye!") }) r$run(port=8000) stars/inst/plumber/client.R0000644000176200001440000000211213777653456015455 0ustar liggesuserslibrary(httr) # GET library(jsonlite) # base64_dec library(stars) url = "http://localhost:8000/data" get_data = function(url, expr = NULL) { if (is.null(expr)) fromJSON( content(GET(url), "text", encoding = "UTF-8")) else { url = paste0(url, "?expr=", expr) unserialize(base64_dec(fromJSON( content(GET(url), "text", encoding = "UTF-8")))) } } put_data = function(url, name, value) { value = toJSON(base64_enc(serialize(value, NULL))) PUT(url, body = list(name = name, value = value), encode = "json") } get_data(url) get_data(url, "y") get_data(url, "z") xx = get_data(url, "x") tif = system.file("tif/L7_ETMs.tif", package = "stars") x = read_stars(tif) all.equal(x, xx) #get_data(url, "x[,,,1]") #get_data(url, "adrop(x[,,,1])") put_data(url, "foo", "bar") get_data(url) get_data(url, "foo") put_data(url, "z3", "3 * z") get_data(url, "z3") #put_data(url, "tif", x) #get_data(url) put_data(url, "xx", matrix(1:4,2)) get_data(url) get_data(url, "xx") library(sf) pt = st_sf(a = 1, geom = st_sfc(st_point(3:4))) put_data(url, "xx", pt) get_data(url) get_data(url, "xx") stars/inst/plumber/server_blog.R0000644000176200001440000000253313777653456016517 0ustar liggesusers# script.R library(jsonlite) # base64_enc # load stars library(stars) # load some imagery meta data # from a S2 .zip file, create a readable gdal source: s2_expand_zip = function(s) { paste0("/vsizip/", s, "/", sub(".zip", ".SAFE", s), "/MTD_MSIL1C.xml") } lst = list.files(pattern = "*.zip") lst = lst[c(grep("S2A_MSIL1C", lst), grep("S2B_MSIL1C", lst))] time = strptime(substr(lst, 12, 26), "%Y%m%dT%H%M%S") l = lapply(s2_expand_zip(lst), read_stars, sub = 1, proxy = TRUE) bb = do.call(c, lapply(l, function(x) st_as_sfc(st_bbox(st_dimensions(x))))) epsg = sapply(l, function(x) st_crs(x)$epsg) library(tibble) md = st_sf(tibble(proxy = l, time = as.POSIXct(time), epsg = epsg, geom = bb)) # create global database data = list(md = md) rm(md) #* Log some information about the incoming request #* @filter logger function(req) { cat(as.character(Sys.time()), "-", req$REQUEST_METHOD, req$PATH_INFO, "-", req$HTTP_USER_AGENT, "@", req$REMOTE_ADDR, ":", req$postBody, "\n") plumber::forward() } # plumber REST end points /data: #* @get /data #* @post /data get_data <- function(req, expr = NULL) { print(expr) if (is.null(expr)) names(data) else base64_enc(serialize( eval(parse(text = expr), data), NULL)) # to char } #* @put /data put_data <- function(req, name, value) { data[[name]] <<- unserialize(base64_dec(fromJSON(value))) NULL } stars/inst/plumber/client_blog.R0000644000176200001440000000317513777653456016472 0ustar liggesuserslibrary(httr) # GET, POST, PUT library(jsonlite) # base64_dec, base64_enc, toJSON, fromJSON library(tibble) # print get_data = function(url, expr = NULL) { if (is.null(expr)) fromJSON( content(GET(url), "text", encoding = "UTF-8")) else unserialize(base64_dec(fromJSON( content(POST(url, body = list(expr = expr), encode = "json"), "text", encoding = "UTF-8") ))) } put_data = function(url, name, value) { value = toJSON(base64_enc(serialize(value, NULL))) PUT(url, body = list(name = name, value = value), encode = "json") } # from where? url = "http://localhost:8000/data" get_data(url) # list library(stars) md = get_data(url, "md") # select a country: nl = st_as_sf(raster::getData("GADM", country = "NLD", level = 0)) %>% st_transform(st_crs(md)) get_data(url) # any data there? md = get_data(url, "md") proxy = md[nl,]$proxy plot(st_geometry(nl), axes = TRUE) s = sapply(proxy, function(x) { r = st_as_stars(x, downsample = 100, url = url) image(r, add = TRUE, rgb = 1:3, maxColorValue = 30000) }) plot(st_geometry(md), border = 'yellow', add = TRUE, lwd = .8) plot(st_geometry(nl), add=TRUE, border = 'orange', lwd = 2) ndvi = function(x) (x[4]-x[1])/(x[4]+x[1]) put_data(url, "ndvi", ndvi) get_data(url, "ndvi") plot(st_geometry(nl), axes = TRUE) x = st_apply(proxy[[10]], 1:2, ndvi) xs = st_as_stars(x, url = url, downsample = 50) image(xs, add = TRUE) s = sapply(proxy, function(x) { x = st_apply(x, 1:2, ndvi) r = st_as_stars(x, url = url, downsample = 100) image(r, add = TRUE, zlim = c(-1,1), col = colorRampPalette(c(grey(.1), grey(.5), 'green'))(10)) }) plot(st_geometry(nl), add = TRUE, border = 'red') stars/inst/doc/0000755000176200001440000000000014644756275013154 5ustar liggesusersstars/inst/doc/stars1.Rmd0000644000176200001440000003260614644756215015036 0ustar liggesusers--- title: "1. introduction" author: "Edzer Pebesma" output: rmarkdown::html_vignette vignette: > %\VignetteIndexEntry{1. introduction} %\VignetteEncoding{UTF-8} %\VignetteEngine{knitr::rmarkdown} --- **For a better version of the stars vignettes see** https://r-spatial.github.io/stars/articles/ ```{r setup, include=FALSE} knitr::opts_chunk$set(echo = TRUE, collapse = TRUE, dev = "png") ev = suppressWarnings(require(starsdata, quietly = TRUE)) knitr::opts_chunk$set(fig.height = 4.5) knitr::opts_chunk$set(fig.width = 6) ``` Package `stars` provides infrastructure for _data cubes_, array data with labeled dimensions, with emphasis on arrays where some of the dimensions relate to time and/or space. Spatial data cubes are arrays with one or more spatial dimensions. Raster data cubes have at least two spatial dimensions that form the raster tesselation. Vector data cubes have at least one spatial dimension that may for instance reflect a polygon tesselation, or a set of point locations. Conversions between the two (rasterization, polygonization) are provided. Vector data are represented by simple feature geometries (packages `sf`). Tidyverse methods are provided. The `stars` package is loaded by ```{r} library(stars) ``` Spatiotemporal arrays are stored in objects of class `stars`; methods for class `stars` currently available are ``` {r} methods(class = "stars") ``` (tidyverse methods are only visible after loading package `tidyverse`). # Reading a satellite image We can read a satellite image through GDAL, e.g. from a GeoTIFF file in the package: ```{r} tif = system.file("tif/L7_ETMs.tif", package = "stars") x = read_stars(tif) plot(x, axes = TRUE) ``` We see that the image is geographically referenced (has coordinate values along axes), and that the object returned (`x`) has three dimensions called `x`, `y` and `band`, and has one attribute: ```{r} x ``` Each dimension has a name; the meaning of the fields of a single dimension are: |*field* |*meaning* | |--------|------------------------------------------------------------| | from | the origin index (1) | | to | the final index (dim(x)[i]) | | offset | the start value for this dimension (pixel boundary), if regular | | delta | the step (pixel, cell) size for this dimension, if regular | | refsys | the reference system, or proj4string | | point | logical; whether cells refer to points, or intervals | | values | the sequence of values for this dimension (e.g., geometries), if irregular | This means that for an index i (starting at $i=1$) along a certain dimension, the corresponding dimension value (coordinate, time) is $\mbox{offset} + (i-1) \times \mbox{delta}$. This value then refers to the start (edge) of the cell or interval; in order to get the interval middle or cell centre, one needs to add half an offset. Dimension `band` is a simple sequence from 1 to 6. Since bands refer to colors, one could put their wavelength values in the `values` field. For this particular dataset (and most other raster datasets), we see that delta for dimension `y` is negative: this means that consecutive array values have decreasing $y$ values: cell indexes increase from top to bottom, in the direction opposite to the $y$ axis. `read_stars` reads all bands from a raster dataset, or optionally a subset of raster datasets, into a single `stars` array structure. While doing so, raster values (often UINT8 or UINT16) are converted to double (numeric) values, and scaled back to their original values if needed if the file encodes the scaling parameters. The data structure `stars` is a generalization of the `tbl_cube` found in `cubelyr`; we can convert to that by ```{r eval=ev} library(cubelyr) as.tbl_cube(x) ``` but this will cause a loss of certain properties (cell size, reference system, vector geometries) ## Switching attributes to dimensions and back ```{r} (x.spl = split(x, "band")) merge(x.spl) ``` We see that the newly created dimension lost its name, and the single attribute got a default name. We can set attribute names with `setNames`, and dimension names and values with `st_set_dimensions`: ```{r} merge(x.spl) |> setNames(names(x)) |> st_set_dimensions(3, values = paste0("band", 1:6)) |> st_set_dimensions(names = c("x", "y", "band")) ``` ## Subsetting Besides the `tidyverse` subsetting and selection operators explained in [this vignette](stars3.html), we can also use `[` and `[[`. Since `stars` objects are a list of `array`s with a metadata table describing dimensions, list extraction (and assignment) works as expected: ```{r} class(x[[1]]) dim(x[[1]]) x$two = 2 * x[[1]] x ``` At this level, we can work with `array` objects directly. The `stars` subset operator `[` works a bit different: its * first argument selects attributes * second argument selects the first dimension * third argument selects the second dimension, etc Thus, ```{r} x["two", 1:10, , 2:4] ``` selects the second attribute, the first 10 columns (x-coordinate), all rows, and bands 2-4. Alternatively, when `[` is given a single argument of class `sf`, `sfc` or `bbox`, `[` will work as a crop operator: ```{r} circle = st_sfc(st_buffer(st_point(c(293749.5, 9115745)), 400), crs = st_crs(x)) plot(x[circle][, , , 1], reset = FALSE) plot(circle, col = NA, border = 'red', add = TRUE, lwd = 2) ``` ## Overviews We can read rasters at a lower resolution when they contain so-called overviews. For this GeoTIFF file, they were created with the `gdaladdo` utility, in particular ``` gdaladdo -r average L7_ETMs.tif 2 4 8 16 ``` which adds coarse resolution versions by using the _average_ resampling method to compute values based on blocks of pixels. These can be read by ```{r} x1 = read_stars(tif, options = c("OVERVIEW_LEVEL=1")) x2 = read_stars(tif, options = c("OVERVIEW_LEVEL=2")) x3 = read_stars(tif, options = c("OVERVIEW_LEVEL=3")) dim(x1) dim(x2) dim(x3) par(mfrow = c(1, 3), mar = rep(0.2, 4)) image(x1[,,,1]) image(x2[,,,1]) image(x3[,,,1]) ``` # Reading a raster time series: NetCDF Another example is when we read raster time series model outputs in a NetCDF file, e.g. by ```{r eval=ev} system.file("nc/bcsd_obs_1999.nc", package = "stars") |> read_stars() -> w ``` We see that ```{r eval=ev} w ``` For this dataset we can see that * variables have units associated (and a wrong unit, `C` is assigned to temperature) * time is now a dimension, with proper units and time steps Alternatively, this dataset can be read using `read_ncdf`, as in ```{r} system.file("nc/bcsd_obs_1999.nc", package = "stars") |> read_ncdf() ``` The difference between `read_ncdf` and `read_stars` for NetCDF files is that the former uses package RNetCDF to directly read the NetCDF file, where the latter uses the GDAL driver for NetCDF files. ## Reading datasets from multiple files Model data are often spread across many files. An example of a 0.25 degree grid, global daily sea surface temperature product is found [here](https://psl.noaa.gov/data/gridded/data.noaa.oisst.v2.highres.html); the subset from 1981 used below was downloaded from a NOAA ftp site that is no longer available in this form. (ftp site used to be eclipse.ncdc.noaa.gov/pub/OI-daily-v2/NetCDF/1981/AVHRR/). We read the data by giving `read_stars` a vector with character names: ```{r eval=ev} x = c( "avhrr-only-v2.19810901.nc", "avhrr-only-v2.19810902.nc", "avhrr-only-v2.19810903.nc", "avhrr-only-v2.19810904.nc", "avhrr-only-v2.19810905.nc", "avhrr-only-v2.19810906.nc", "avhrr-only-v2.19810907.nc", "avhrr-only-v2.19810908.nc", "avhrr-only-v2.19810909.nc" ) # see the second vignette: # install.packages("starsdata", repos = "http://pebesma.staff.ifgi.de", type = "source") file_list = system.file(paste0("netcdf/", x), package = "starsdata") (y = read_stars(file_list, quiet = TRUE)) ``` Next, we select sea surface temperature (`sst`), and drop the singular `zlev` (depth) dimension using `adrop`: ```{r eval=ev} library(dplyr) library(abind) z <- y |> select(sst) |> adrop() ``` We can now graph the sea surface temperature (SST) using `ggplot`, which needs data in a long table form, and without units: ```{r eval=ev} # convert POSIXct time to character, to please ggplot's facet_wrap() z1 = st_set_dimensions(z, 3, values = as.character(st_get_dimension_values(z, 3))) library(ggplot2) library(viridis) library(ggthemes) ggplot() + geom_stars(data = z1[1], alpha = 0.8, downsample = c(10, 10, 1)) + facet_wrap("time") + scale_fill_viridis() + coord_equal() + theme_map() + theme(legend.position = "bottom") + theme(legend.key.width = unit(2, "cm")) ``` # Writing stars objects to disk We can write a stars object to disk by using `write_stars`; this used the GDAL write engine. Writing NetCDF files without going through the GDAL interface is currently not supported. `write_stars` currently writes only a single attribute: ```{r eval=ev} write_stars(adrop(y[1]), "sst.tif") ``` See the explanation of `merge` above to see how multiple attributes can be merged (folded) into a dimension. # Cropping a raster's extent Using a curvilinear grid, taken from the example of `read_ncdf`: ```{r} prec_file = system.file("nc/test_stageiv_xyt.nc", package = "stars") prec = read_ncdf(prec_file, curvilinear = c("lon", "lat")) ##plot(prec) ## gives error about unique breaks ## remove NAs, zeros, and give a large number ## of breaks (used for validating in detail) qu_0_omit = function(x, ..., n = 22) { if (inherits(x, "units")) x = units::drop_units(na.omit(x)) c(0, quantile(x[x > 0], seq(0, 1, length.out = n))) } library(dplyr) # loads slice generic prec_slice = slice(prec, index = 17, along = "time") plot(prec_slice, border = NA, breaks = qu_0_omit(prec_slice[[1]]), reset = FALSE) nc = sf::read_sf(system.file("gpkg/nc.gpkg", package = "sf"), "nc.gpkg") plot(st_geometry(nc), add = TRUE, reset = FALSE, col = NA, border = 'red') ``` We can now crop the grid to those cells falling in ```{r} nc = st_transform(nc, st_crs(prec_slice)) # datum transformation plot(prec_slice[nc], border = NA, breaks = qu_0_omit(prec_slice[[1]]), reset = FALSE) plot(st_geometry(nc), add = TRUE, reset = FALSE, col = NA, border = 'red') ``` The selection `prec_slice[nc]` essentially calls `st_crop(prec_slice, nc)` to get a cropped selection. What happened here is that all cells not intersecting with North Carolina (sea) are set to `NA` values. For regular grids, the extent of the resulting `stars` object is also be reduced (cropped) by default; this can be controlled with the `crop` parameter to `st_crop` and `[.stars`. # Vector data cube example Like `tbl_cube`, `stars` arrays have no limits to the number of dimensions they handle. An example is the origin-destination (OD) matrix, by time and travel mode. ## OD: space x space x travel mode x time x time We create a 5-dimensional matrix of traffic between regions, by day, by time of day, and by travel mode. Having day and time of day each as dimension is an advantage when we want to compute patterns over the day, for a certain period. ```{r} nc = st_read(system.file("gpkg/nc.gpkg", package="sf")) to = from = st_geometry(nc) # 100 polygons: O and D regions mode = c("car", "bike", "foot") # travel mode day = 1:100 # arbitrary library(units) units(day) = as_units("days since 2015-01-01") hour = set_units(0:23, h) # hour of day dims = st_dimensions(origin = from, destination = to, mode = mode, day = day, hour = hour) (n = dim(dims)) traffic = array(rpois(prod(n), 10), dim = n) # simulated traffic counts (st = st_as_stars(list(traffic = traffic), dimensions = dims)) ``` This array contains the simple feature geometries of origin and destination so that we can directly plot every slice without additional table joins. If we want to represent such an array as a `tbl_cube`, the simple feature geometry dimensions need to be replaced by indexes: ```{r eval=ev} st |> as.tbl_cube() ``` The following demonstrates how we can use `dplyr` to filter travel mode `bike`, and compute mean bike traffic by hour of day: ```{r eval=ev} b <- st |> as.tbl_cube() |> filter(mode == "bike") |> group_by(hour) |> summarise(traffic = mean(traffic)) |> as.data.frame() require(ggforce) # for plotting a units variable ggplot() + geom_line(data = b, aes(x = hour, y = traffic)) ``` # Extracting at point locations, aggregating over polygons Data cube values at point location can be extracted by `st_extract`, an example is found in [vignette 7](stars7.html) Aggregates, such as mean, maximum or modal values can be obtained by `aggregate`. In this example we use a categorical raster, and try to find the modal (most frequent) class within two circular polygons: ```{r} s = system.file("tif/lc.tif", package = "stars") r = read_stars(s, proxy = FALSE) |> droplevels() levels(r[[1]]) = abbreviate(levels(r[[1]]), 10) # shorten text labels st_point(c(3190631, 3125)) |> st_sfc(crs = st_crs(r)) |> st_buffer(25000) -> pol1 st_point(c(3233847, 21027)) |> st_sfc(crs = st_crs(r)) |> st_buffer(10000) -> pol2 if (isTRUE(dev.capabilities()$rasterImage == "yes")) { plot(r, reset = FALSE, key.pos = 4) plot(c(pol1, pol2), col = NA, border = c('yellow', 'green'), lwd = 2, add = TRUE) } ``` To find the modal value, we need a function that gives back the label corresponding to the class which is most frequent, using `table`: ```{r} f = function(x) { tb = table(x); names(tb)[which.max(tb)] } ``` We can then call `aggregate` on the raster map, and the set of the two circular polygons `pol1` and `pol2`, and pass the function `f`: ```{r} aggregate(r, c(pol1, pol2), f) |> st_as_sf() ``` stars/inst/doc/stars8.Rmd0000644000176200001440000001020514644756215015034 0ustar liggesusers--- title: "8. NetCDF Proxy Workflows" author: "David Blodgett" output: rmarkdown::html_vignette vignette: > %\VignetteIndexEntry{8. NetCDF Proxy Workflows} %\VignetteEngine{knitr::rmarkdown} %\VignetteEncoding{UTF-8} --- **For a better version of the stars vignettes see** https://r-spatial.github.io/stars/articles/ ```{r, include = FALSE} knitr::opts_chunk$set(echo = TRUE, collapse = TRUE, dev = "png") suppressPackageStartupMessages(library(dplyr)) knitr::opts_chunk$set(fig.height = 4.5) knitr::opts_chunk$set(fig.width = 6) EVAL = suppressWarnings(require(starsdata, quietly = TRUE)) ``` NetCDF data sources are available via more _and_ less granular files and/or OPeNDAP endpoints. This article demonstrates how `stars` enables discovery, access, and processing of NetCDF data across a wide range of such source-data organization schemes. We'll start with some basics using datasets included with the `stars` installation. A call to `read_ncdf()`, for a dataset smaller than the default threshold, will just read in all the data. Below we read in and display the `reduced.nc` NetCDF file. ```{r} library(stars) f <- system.file("nc/reduced.nc", package = "stars") (nc <- read_ncdf(f)) ``` Let's assume `reduced.nc` was 10 years of hourly data, rather than 1 time step. It would be over 10GB rather than about 130KB and we would not be able to just read it all into memory. In this case, we need a way to read the file's metadata such that we could iterate over it in a way that meets the needs of our workflow objectives. This is where `proxy = TRUE` comes in. Below, we'll lower the option that controls whether `read_ncdf()` defaults to proxy _and_ use `proxy = TRUE` to show both ways of getting the same result. ```{r} old_options <- options("stars.n_proxy" = 100) (nc <- read_ncdf(f, proxy = TRUE)) options(old_options) ``` The above shows that we have a NetCDF sourced stars proxy derived from the `reduced.nc` file. We see it has four variables and their units are displayed. The normal `stars` `dimension(s)` are available and a `nc_request` object is also available. The `nc_request` object contains the information needed to make requests for data according to the dimensions of the NetCDF data source. With this information, we have what we need to request a chunk of data that is what we want and not too large. ```{r} (nc <- read_ncdf(f, var = "sst", ncsub = cbind(start = c(90, 45, 1 , 1), count = c(90, 45, 1, 1)))) plot(nc) ``` The ability to view NetCDF metadata so we can make well formed requests against the data is useful, but the real power of a proxy object is that we can use it in a "lazy evaluation" coding style. That is, we can do virtual operations on the object, like subsetting with another dataset, prior to actually accessing the data volume. Lazy operations. There are two kinds of lazy operations possible with `stars_proxy` objects. Some can be applied to the `stars_proxy` object itself without accessing underlying data. Others must be composed as a chain of calls that will be applied when data is actually required. Methods applied to a `stars_proxy` object: - `[` - Nearly the same as stars_proxy - `[[<-` - stars_proxy method works - `print` - unique method for nc_proxy to facilitate unique workflows - `dim` - stars_proxy method works - `c` - stars_proxy method works - `st_redimension` - Not sure what this entails but it might not make sense for nc_proxy. - `st_mosaic` * Calls read_stars on assembled list. Not supported for now. - `st_set_bbox` Methods that add a call to the `call_list`. - `[<-` - `adrop` - `aperm` - `is.na` - `split` - `st_apply` - `predict` - `merge` - `st_crop` - `drop_levels` - `Ops` (group generic for +, -, etc.) - `Math` (group generic for abs, sqrt, tan, etc.) - `filter` - `mutate` - `tansmute` - `select` - `rename` - `pull` - `slice` * hyperslabbing for NetCDF could be as above? - `pull` - `replace_na` Methods that cause a `stars_proxy` object to be fetched and turned into a `stars` object. - `as.data.frame` - `plot` - `st_as_stars` - `aggregate` - `st_dimensions<-` * https://github.com/r-spatial/stars/issues/494 - `hist` - `st_downsample` - `st_sample` - `st_as_sf` - `write_stars` stars/inst/doc/stars3.R0000644000176200001440000000277314644756253014523 0ustar liggesusers## ----setup, include=FALSE----------------------------------------------------- knitr::opts_chunk$set(echo = TRUE, collapse = TRUE, dev = "png") ev = TRUE knitr::opts_chunk$set(fig.height = 4.5) knitr::opts_chunk$set(fig.width = 6) ## ----------------------------------------------------------------------------- library(stars) library(dplyr) ## ----------------------------------------------------------------------------- methods(class = "stars") ## ----------------------------------------------------------------------------- system.file("tif/L7_ETMs.tif", package = "stars") %>% read_stars -> x x ## ----------------------------------------------------------------------------- x %>% slice(band, 6) -> x6 x6 ## ----------------------------------------------------------------------------- x %>% filter(x > 289000, x < 291000, band > 3) -> x7 x7 ## ----------------------------------------------------------------------------- x %>% pull(1) -> x8 class(x8) dim(x8) ## ----------------------------------------------------------------------------- x %>% mutate(band2 = 2 * L7_ETMs.tif) -> x2 x2 ## ----------------------------------------------------------------------------- x2 %>% select(band2) -> x9 x9 ## ----------------------------------------------------------------------------- library(ggplot2) library(viridis) ggplot() + geom_stars(data = x) + coord_equal() + facet_wrap(~band) + theme_void() + scale_fill_viridis() + scale_x_discrete(expand = c(0, 0)) + scale_y_discrete(expand = c(0, 0)) stars/inst/doc/stars2.html0000644000176200001440000207052314644756252015264 0ustar liggesusers 2. stars proxy objects

2. stars proxy objects

Edzer Pebesma

For a better version of the stars vignettes see https://r-spatial.github.io/stars/articles/

When your imagery or array data easily fits a couple of times in R’s working memory (RAM), consider yourself lucky. This document was not written for you. If your imagery is too large, or for other reasons you want to work with smaller chunks of data than the files in which they come, read on about your options. First, we will discuss the low-level interface for this, then the higher level, using stars proxy objects that delay all reading.

Preamble: the starsdata package

To run all of the examples in this vignette, you must install a package with datasets that are too large (1 Gb) to be held in the stars package. They are in a drat repo, installation is done by

install.packages("starsdata", repos = "http://gis-bigdata.uni-muenster.de/pebesma", type = "source") 
# possibly after: options(timeout = 100)
# or from an alternative repository:
# install.packages("starsdata", repos = "http://pebesma.staff.ifgi.de", type = "source")

Reading chunks, change resolution, select bands

read_stars has an argument called RasterIO which controls how a GDAL dataset is being read. By default, all pixels and all bands are read in memory. This can consume a lot of time and require a lot of memory. Remember that your file may be compressed, and that pixel values represented in the file by bytes are converted to 8-byte doubles in R.

The reason for using RasterIO for this is that the parameters we use are directly mapped to the GDAL RasterIO function used (after adapting the 1-based offset index in R to 0-based offset in C++).

Reading a particular chunk

An example of using RasterIO is

library(stars)
tif = system.file("tif/L7_ETMs.tif", package = "stars")
rasterio = list(nXOff = 6, nYOff = 6, nXSize = 100, nYSize = 100, bands = c(1, 3, 4))
(x = read_stars(tif, RasterIO = rasterio))
## stars object with 3 dimensions and 1 attribute
## attribute(s):
##              Min. 1st Qu. Median     Mean 3rd Qu. Max.
## L7_ETMs.tif    23      54     63 62.05977   73.25  235
## dimension(s):
##      from  to  offset delta                     refsys point x/y
## x       6 105  288776  28.5 SIRGAS 2000 / UTM zone 25S FALSE [x]
## y       6 105 9120761 -28.5 SIRGAS 2000 / UTM zone 25S FALSE [y]
## band    1   3      NA    NA                         NA    NA
dim(x)
##    x    y band 
##  100  100    3

Compare this to

st_dimensions(read_stars(tif))
##      from  to  offset delta                     refsys point x/y
## x       1 349  288776  28.5 SIRGAS 2000 / UTM zone 25S FALSE [x]
## y       1 352 9120761 -28.5 SIRGAS 2000 / UTM zone 25S FALSE [y]
## band    1   6      NA    NA                         NA    NA

and we see that

  • the delta values remain the same,
  • the offset (x/y coordinates of origin) of the grid remain the same
  • the from and to reflect the new area, and relate to the new delta values
  • dim(x) reflects the new size, and
  • only three bands were read

Reading at a different resolution

Reading datasets at a lower (but also higher!) resolution can be done by setting nBufXSize and nBufYSize

rasterio = list(nXOff = 6, nYOff = 6, nXSize = 100, nYSize = 100,
                nBufXSize = 20, nBufYSize = 20, bands = c(1, 3, 4))
(x = read_stars(tif, RasterIO = rasterio))
## stars object with 3 dimensions and 1 attribute
## attribute(s):
##              Min. 1st Qu. Median     Mean 3rd Qu. Max.
## L7_ETMs.tif    27      53     63 62.09417      74  151
## dimension(s):
##      from to  offset  delta                     refsys point x/y
## x       2 21  288776  142.5 SIRGAS 2000 / UTM zone 25S FALSE [x]
## y       2 21 9120761 -142.5 SIRGAS 2000 / UTM zone 25S FALSE [y]
## band    1  3      NA     NA                         NA    NA

and we see that in addition:

  • the delta (raster cell size) values have increased a factor 5, because nBufXSize and nBufYSize were set to values a factor 5 smaller than nXSize and nYSize
  • the offset coordinates of the grid are still the same
  • the from and to reflect the new area, but relate to the new delta cell size values

We can also read at higher resolution; here we read a 3 x 3 area and blow it up to 100 x 100:

rasterio = list(nXOff = 6, nYOff = 6, nXSize = 3, nYSize = 3,
   nBufXSize = 100, nBufYSize = 100, bands = 1)
x = read_stars(tif, RasterIO = rasterio)
dim(x)
##   x   y 
## 100 100
plot(x)

The reason we “see” only three grid cells is that the default sampling method is “nearest neighbour”. We can modify this by

rasterio = list(nXOff = 6, nYOff = 6, nXSize = 3, nYSize = 3,
   nBufXSize = 100, nBufYSize = 100, bands = 1, resample = "cubic_spline")
x = read_stars(tif, RasterIO = rasterio)
dim(x)
##   x   y 
## 100 100
plot(x)

The following methods are allowed for parameter resample:

resample method used
nearest_neighbour Nearest neighbour (default)
bilinear Bilinear (2x2 kernel)
cubic Cubic Convolution Approximation (4x4 kernel)
cubic_spline Cubic B-Spline Approximation (4x4 kernel)
lanczos Lanczos windowed sinc interpolation (6x6 kernel)
average Average
mode Mode (selects the value which appears most often of all the sampled points)
Gauss Gauss blurring

All these methods are implemented in GDAL; for what these methods exactly do, we refer to the GDAL documentation or source code.

Stars proxy objects

Stars proxy objects take another approach: upon creation they contain no data at all, but only pointers to where the data can be read. Data is only read when it is needed, and only as much as is needed: if we plot a proxy objects, the data are read at the resolution of pixels on the screen, rather than at the native resolution, so that if we have e.g. a 10000 x 10000 Sentinel 2 (level 1C) image, we can open it by

granule = system.file("sentinel/S2A_MSIL1C_20180220T105051_N0206_R051_T32ULE_20180221T134037.zip", package = "starsdata")
s2 = paste0("SENTINEL2_L1C:/vsizip/", granule, "/S2A_MSIL1C_20180220T105051_N0206_R051_T32ULE_20180221T134037.SAFE/MTD_MSIL1C.xml:10m:EPSG_32632")
(p = read_stars(s2, proxy = TRUE))
## stars_proxy object with 1 attribute in 1 file(s):
## $EPSG_32632
## [1] "[...]/MTD_MSIL1C.xml:10m:EPSG_32632"
## 
## dimension(s):
##      from    to offset delta                refsys    values x/y
## x       1 10980  3e+05    10 WGS 84 / UTM zone 32N      NULL [x]
## y       1 10980  6e+06   -10 WGS 84 / UTM zone 32N      NULL [y]
## band    1     4     NA    NA                    NA B4,...,B8

and this happens instantly, because no data is read. When we plot this object,

system.time(plot(p))
## downsample set to 43

##    user  system elapsed 
##   0.766   0.356   0.166

This takes only around 1 second, since only those pixels are read that can be seen on the plot. If we read the entire image in memory first, as we would do with

p = read_stars(s2, proxy = FALSE)

then only the reading would take over a minute, and require 5 Gb memory.

Methods for stars proxy objects

methods(class = "stars_proxy")
##  [1] Math            Ops             [               [<-            
##  [5] [[<-            adrop           aggregate       aperm          
##  [9] as.data.frame   c               coerce          dim            
## [13] droplevels      filter          hist            image          
## [17] initialize      is.na           merge           mutate         
## [21] plot            prcomp          predict         print          
## [25] pull            rename          select          show           
## [29] slice           slotsFromS3     split           st_apply       
## [33] st_as_sf        st_as_stars     st_crop         st_dimensions<-
## [37] st_downsample   st_mosaic       st_normalize    st_redimension 
## [41] st_sample       st_set_bbox     transmute       write_stars    
## see '?methods' for accessing help and source code

Select attributes

We can select attributes as with regular stars objects, by using the first argument to [:

x = c("avhrr-only-v2.19810901.nc",
"avhrr-only-v2.19810902.nc",
"avhrr-only-v2.19810903.nc",
"avhrr-only-v2.19810904.nc",
"avhrr-only-v2.19810905.nc",
"avhrr-only-v2.19810906.nc",
"avhrr-only-v2.19810907.nc",
"avhrr-only-v2.19810908.nc",
"avhrr-only-v2.19810909.nc")
file_list = system.file(paste0("netcdf/", x), package = "starsdata")
y = read_stars(file_list, quiet = TRUE, proxy = TRUE)
names(y)
## [1] "sst"  "anom" "err"  "ice"
y["sst"]
## stars_proxy object with 1 attribute in 9 file(s):
## $sst
## [1] "[...]/avhrr-only-v2.19810901.nc:sst" "[...]/avhrr-only-v2.19810902.nc:sst"
## [3] "[...]/avhrr-only-v2.19810903.nc:sst" "[...]/avhrr-only-v2.19810904.nc:sst"
## [5] "[...]/avhrr-only-v2.19810905.nc:sst" "[...]/avhrr-only-v2.19810906.nc:sst"
## [7] "[...]/avhrr-only-v2.19810907.nc:sst" "[...]/avhrr-only-v2.19810908.nc:sst"
## [9] "[...]/avhrr-only-v2.19810909.nc:sst"
## 
## dimension(s):
##      from   to         offset  delta  refsys x/y
## x       1 1440              0   0.25      NA [x]
## y       1  720             90  -0.25      NA [y]
## zlev    1    1          0 [m]     NA      NA    
## time    1    9 1981-09-01 UTC 1 days POSIXct

Note that this selection limits the reading from 4 to 1 subdataset from all 9 NetCDF files.

Select an area

Another possibility is to crop, or select a rectangular region based on a spatial object. This can be done by passing a bbox object, or an sf, sfc or stars object from which the bounding box will be taken. An example:

bb = st_bbox(c(xmin = 10.125, ymin = 0.125, xmax = 70.125, ymax = 70.125))
ysub = y[bb]
st_dimensions(ysub)
##      from  to         offset  delta  refsys x/y
## x      41 281              0   0.25      NA [x]
## y      80 360             90  -0.25      NA [y]
## zlev    1   1          0 [m]     NA      NA    
## time    1   9 1981-09-01 UTC 1 days POSIXct
class(ysub) # still no data here!!
## [1] "stars_proxy" "stars"
plot(ysub, reset = FALSE) # plot reads the data, at resolution that is relevant
plot(st_as_sfc(bb), add = TRUE, lwd = .5, border = 'red')

Lazy evaluation, changing evaluation order

Some other actions can be carried out on stars_proxy objects, but their effect is delayed until the data are actually needed (plot, write_stars). For instance, range selections on dimensions other than shown above first need data, and can only then be carried out. Such functions are added to the object, in an attribute called call_list:

yy = adrop(y)
yyy = yy[,1:10,1:10,]
class(yyy) # still no data
## [1] "stars_proxy" "stars"
st_dimensions(yyy) # and dimensions not adjusted
##      from   to         offset  delta  refsys x/y
## x       1 1440              0   0.25      NA [x]
## y       1  720             90  -0.25      NA [y]
## zlev    1    1          0 [m]     NA      NA    
## time    1    9 1981-09-01 UTC 1 days POSIXct
attr(yyy, "call_list") # the name of object in the call (y) is replaced with x:
## [[1]]
## adrop(x = x, drop = drop)
## attr(,".Environment")
## <environment: 0x62e8bab6dbf8>
## 
## [[2]]
## x[i = i, 1:10, 1:10, , drop = drop, crop = crop]
## attr(,".Environment")
## <environment: 0x62e8bab1ff68>

Doing this allows for optimizing the order in which operations are done. As an example, for st_apply, reading can be done sequentially over the dimensions over which the function is applied:

  • If for example a function is applied to each band (such as: compute band quantiles), bands can be read sequentially, and discarded after the quantiles have been computed.
  • If a time series function is applied to pixel time series and the result is plotted on a map, the time series function is only evaluated on the pixels actually plotted. This means that e.g. in
plot(st_apply(x, c("x", "y"), range))

the order of evaluation is reversed: plot knows which pixels are going to be shown, and controls how x is downsampled before st_apply is carried out on this subset.

Fetching the data

Fetching the data now involves reading the whole array and then evaluating the call_list on it, sequentially:

(x = st_as_stars(yyy)) # read, adrop, subset
## stars object with 3 dimensions and 4 attributes
## attribute(s):
##                Min. 1st Qu. Median       Mean 3rd Qu.  Max.
## sst [C*°]     -1.28   -1.17  -1.11 -1.1163555   -1.06 -0.95
## anom [C*°]     0.48    0.62   0.69  0.6649555    0.72  0.77
## err [C*°]      0.30    0.30   0.30  0.3000000    0.30  0.30
## ice [percent]  0.76    0.79   0.81  0.8062889    0.82  0.85
## dimension(s):
##      from to         offset  delta  refsys x/y
## x       1 10              0   0.25      NA [x]
## y       1 10             90  -0.25      NA [y]
## time    1  9 1981-09-01 UTC 1 days POSIXct

Plotting with changed evaluation order

For the Sentinel 2 data, band 4 represents NIR and band 1 red, so we can compute NDVI by

# S2 10m: band 4: near infrared, band 1: red.
#ndvi = function(x) (x[4] - x[1])/(x[4] + x[1])
ndvi = function(x1, x2, x3, x4) (x4 - x1)/(x4 + x1)
rm(x)
(s2.ndvi = st_apply(p, c("x", "y"), ndvi))
## stars_proxy object with 1 attribute in 1 file(s):
## $EPSG_32632
## [1] "[...]/MTD_MSIL1C.xml:10m:EPSG_32632"
## 
## dimension(s):
##      from    to offset delta                refsys    values x/y
## x       1 10980  3e+05    10 WGS 84 / UTM zone 32N      NULL [x]
## y       1 10980  6e+06   -10 WGS 84 / UTM zone 32N      NULL [y]
## band    1     4     NA    NA                    NA B4,...,B8    
## call_list:
## [[1]]
## st_apply(X = X, MARGIN = MARGIN, FUN = FUN, CLUSTER = CLUSTER, 
##     PROGRESS = PROGRESS, FUTURE = FUTURE, rename = rename, .fname = .fname)
## attr(,".Environment")
## <environment: 0x62e8a9034ad8>
## 
## This object has pending lazy operations: dimensions as printed may not reflect this.
system.time(plot(s2.ndvi)) # read - compute ndvi - plot 
## downsample set to 43

##    user  system elapsed 
##   0.736   0.282   0.127

Multi-resolution proxy objects

This sections shows some examples how stars_proxy objects deal with the situation where the different maps have dissimilar resolution. The assumptions here are:

  • all maps need to have the same origin coordinates (typically upper-left corner) and CRS.
  • the first map determines the “working” resolution, to which e.g. native or downsampled resolutions refer

We’ll create four maps with cells size 1, 2 and 3:

s1 = st_as_stars(matrix(1:16, 4))
s2 = st_as_stars(matrix(1:16, 4))
s3 = st_as_stars(matrix(1:16, 4))
attr(s1, "dimensions")$X1$offset = 0
attr(s1, "dimensions")$X2$offset = 4
attr(s2, "dimensions")$X1$offset = 0
attr(s2, "dimensions")$X2$offset = 4
attr(s3, "dimensions")$X1$offset = 0
attr(s3, "dimensions")$X2$offset = 4
attr(s1, "dimensions")$X1$delta =  1
attr(s1, "dimensions")$X2$delta = -1
attr(s2, "dimensions")$X1$delta =  2
attr(s2, "dimensions")$X2$delta = -2
attr(s3, "dimensions")$X1$delta =  3
attr(s3, "dimensions")$X2$delta = -3
plot(s1, axes = TRUE, text_values = TRUE, text_color = 'orange')

plot(s2, axes = TRUE, text_values = TRUE, text_color = 'orange')

plot(s3, axes = TRUE, text_values = TRUE, text_color = 'orange')

We created three rasters with identical cell values and dimensions, but different cell sizes, and hence extents. If we bind them in a single proxy object, with

fn1 = paste0(tempdir(), .Platform$file.sep, "img1.tif")
fn2 = paste0(tempdir(), .Platform$file.sep, "img2.tif")
fn3 = paste0(tempdir(), .Platform$file.sep, "img3.tif")
write_stars(s1, fn1)
write_stars(s2, fn2)
write_stars(s3, fn3) 
(r1 = read_stars(c(fn1, fn2, fn3), proxy = TRUE))
## multi-resolution stars_proxy object with 3 attributes in 3 file(s):
## $`1`
## [1] "[...]/img1.tif"
## 
## $`2`
## [1] "[...]/img2.tif"
## 
## $`3`
## [1] "[...]/img3.tif"
## 
## dimension(s):
##   from to offset delta x/y
## x    1  4      0     1 [x]
## y    1  4      4    -1 [y]

We see that multi-resolution is mentioned in the printed summary. When converting this to a stars object, the secondary rasters are resampled to the cellsize + extent of the first:

st_as_stars(r1) %>%
  merge() %>%
  plot(breaks = "equal", text_values = TRUE, text_color = 'orange', axes = TRUE)

If we do this for a sub-range, defined for the object resolutions, we get:

st_as_stars(r1[,2:4,2:4]) %>%
  merge() %>%
  plot(breaks = "equal", text_values = TRUE, text_color = 'orange', axes = TRUE)

We now create four maps, all over the same region ([0,4] x [0,4]), with different resolutions (cell size 1, 1/2 and 1/3):

s4 = st_as_stars(matrix(1: 16, 4))
s5 = st_as_stars(matrix(1: 64, 8))
s6 = st_as_stars(matrix(1:144,12))
attr(s4, "dimensions")$X1$offset = 0
attr(s4, "dimensions")$X2$offset = 4
attr(s5, "dimensions")$X1$offset = 0
attr(s5, "dimensions")$X2$offset = 4
attr(s6, "dimensions")$X1$offset = 0
attr(s6, "dimensions")$X2$offset = 4
attr(s4, "dimensions")$X1$delta =  1
attr(s4, "dimensions")$X2$delta = -1
attr(s5, "dimensions")$X1$delta =  1/2
attr(s5, "dimensions")$X2$delta = -1/2
attr(s6, "dimensions")$X1$delta =  1/3
attr(s6, "dimensions")$X2$delta = -1/3
plot(s4, axes = TRUE, text_values = TRUE, text_color = 'orange')

plot(s5, axes = TRUE, text_values = TRUE, text_color = 'orange')

plot(s6, axes = TRUE, text_values = TRUE, text_color = 'orange')

fn4 = paste0(tempdir(), .Platform$file.sep, "img4.tif")
fn5 = paste0(tempdir(), .Platform$file.sep, "img5.tif")
fn6 = paste0(tempdir(), .Platform$file.sep, "img6.tif")
write_stars(s4, fn4)
write_stars(s5, fn5)
write_stars(s6, fn6) 
(r2 = read_stars(c(fn4, fn5, fn6), proxy = TRUE))
## multi-resolution stars_proxy object with 3 attributes in 3 file(s):
## $`4`
## [1] "[...]/img4.tif"
## 
## $`5`
## [1] "[...]/img5.tif"
## 
## $`6`
## [1] "[...]/img6.tif"
## 
## dimension(s):
##   from to offset delta x/y
## x    1  4      0     1 [x]
## y    1  4      4    -1 [y]

st_as_stars(r2) %>%
  merge() %>%
  plot(breaks = "equal", text_values = TRUE, text_color = 'orange', axes = TRUE)

st_as_stars(r2[,2:4,2:4]) %>%
  merge() %>%
  plot(breaks = "equal", text_values = TRUE, text_color = 'orange', axes = TRUE)

Finally, an example where the first raster has the higher resolution:

(r3 = read_stars(c(fn6, fn5, fn4), proxy = TRUE))
## multi-resolution stars_proxy object with 3 attributes in 3 file(s):
## $`6`
## [1] "[...]/img6.tif"
## 
## $`5`
## [1] "[...]/img5.tif"
## 
## $`4`
## [1] "[...]/img4.tif"
## 
## dimension(s):
##   from to offset   delta x/y
## x    1 12      0  0.3333 [x]
## y    1 12      4 -0.3333 [y]

st_as_stars(r3) %>%
  merge() %>%
  plot(breaks = "equal", text_values = TRUE, text_color = 'orange', axes = TRUE)

st_as_stars(r3[,2:6,3:6]) %>%
  merge() %>%
  plot(breaks = "equal", text_values = TRUE, text_color = 'orange', axes = TRUE)

stars/inst/doc/stars2.R0000644000176200001440000001700514644756251014512 0ustar liggesusers## ----setup, include=FALSE----------------------------------------------------- knitr::opts_chunk$set(echo = TRUE, collapse = TRUE, dev = "png") set.seed(13579) knitr::opts_chunk$set(fig.height = 4.5) knitr::opts_chunk$set(fig.width = 6) EVAL = x = suppressWarnings(require(starsdata, quietly = TRUE)) ## ----eval=FALSE--------------------------------------------------------------- # install.packages("starsdata", repos = "http://gis-bigdata.uni-muenster.de/pebesma", type = "source") # # possibly after: options(timeout = 100) # # or from an alternative repository: # # install.packages("starsdata", repos = "http://pebesma.staff.ifgi.de", type = "source") ## ----------------------------------------------------------------------------- library(stars) tif = system.file("tif/L7_ETMs.tif", package = "stars") rasterio = list(nXOff = 6, nYOff = 6, nXSize = 100, nYSize = 100, bands = c(1, 3, 4)) (x = read_stars(tif, RasterIO = rasterio)) dim(x) ## ----------------------------------------------------------------------------- st_dimensions(read_stars(tif)) ## ----------------------------------------------------------------------------- rasterio = list(nXOff = 6, nYOff = 6, nXSize = 100, nYSize = 100, nBufXSize = 20, nBufYSize = 20, bands = c(1, 3, 4)) (x = read_stars(tif, RasterIO = rasterio)) ## ----------------------------------------------------------------------------- rasterio = list(nXOff = 6, nYOff = 6, nXSize = 3, nYSize = 3, nBufXSize = 100, nBufYSize = 100, bands = 1) x = read_stars(tif, RasterIO = rasterio) dim(x) plot(x) ## ----------------------------------------------------------------------------- rasterio = list(nXOff = 6, nYOff = 6, nXSize = 3, nYSize = 3, nBufXSize = 100, nBufYSize = 100, bands = 1, resample = "cubic_spline") x = read_stars(tif, RasterIO = rasterio) dim(x) plot(x) ## ----eval=EVAL---------------------------------------------------------------- granule = system.file("sentinel/S2A_MSIL1C_20180220T105051_N0206_R051_T32ULE_20180221T134037.zip", package = "starsdata") s2 = paste0("SENTINEL2_L1C:/vsizip/", granule, "/S2A_MSIL1C_20180220T105051_N0206_R051_T32ULE_20180221T134037.SAFE/MTD_MSIL1C.xml:10m:EPSG_32632") (p = read_stars(s2, proxy = TRUE)) ## ----eval=EVAL---------------------------------------------------------------- system.time(plot(p)) ## ----eval = FALSE------------------------------------------------------------- # p = read_stars(s2, proxy = FALSE) ## ----------------------------------------------------------------------------- methods(class = "stars_proxy") ## ----eval=EVAL---------------------------------------------------------------- x = c("avhrr-only-v2.19810901.nc", "avhrr-only-v2.19810902.nc", "avhrr-only-v2.19810903.nc", "avhrr-only-v2.19810904.nc", "avhrr-only-v2.19810905.nc", "avhrr-only-v2.19810906.nc", "avhrr-only-v2.19810907.nc", "avhrr-only-v2.19810908.nc", "avhrr-only-v2.19810909.nc") file_list = system.file(paste0("netcdf/", x), package = "starsdata") y = read_stars(file_list, quiet = TRUE, proxy = TRUE) names(y) y["sst"] ## ----eval=EVAL---------------------------------------------------------------- bb = st_bbox(c(xmin = 10.125, ymin = 0.125, xmax = 70.125, ymax = 70.125)) ysub = y[bb] st_dimensions(ysub) class(ysub) # still no data here!! plot(ysub, reset = FALSE) # plot reads the data, at resolution that is relevant plot(st_as_sfc(bb), add = TRUE, lwd = .5, border = 'red') ## ----eval=EVAL---------------------------------------------------------------- yy = adrop(y) yyy = yy[,1:10,1:10,] class(yyy) # still no data st_dimensions(yyy) # and dimensions not adjusted attr(yyy, "call_list") # the name of object in the call (y) is replaced with x: ## ----eval = FALSE------------------------------------------------------------- # plot(st_apply(x, c("x", "y"), range)) ## ----eval=EVAL---------------------------------------------------------------- (x = st_as_stars(yyy)) # read, adrop, subset ## ----eval=EVAL---------------------------------------------------------------- # S2 10m: band 4: near infrared, band 1: red. #ndvi = function(x) (x[4] - x[1])/(x[4] + x[1]) ndvi = function(x1, x2, x3, x4) (x4 - x1)/(x4 + x1) rm(x) (s2.ndvi = st_apply(p, c("x", "y"), ndvi)) system.time(plot(s2.ndvi)) # read - compute ndvi - plot ## ----------------------------------------------------------------------------- s1 = st_as_stars(matrix(1:16, 4)) s2 = st_as_stars(matrix(1:16, 4)) s3 = st_as_stars(matrix(1:16, 4)) attr(s1, "dimensions")$X1$offset = 0 attr(s1, "dimensions")$X2$offset = 4 attr(s2, "dimensions")$X1$offset = 0 attr(s2, "dimensions")$X2$offset = 4 attr(s3, "dimensions")$X1$offset = 0 attr(s3, "dimensions")$X2$offset = 4 attr(s1, "dimensions")$X1$delta = 1 attr(s1, "dimensions")$X2$delta = -1 attr(s2, "dimensions")$X1$delta = 2 attr(s2, "dimensions")$X2$delta = -2 attr(s3, "dimensions")$X1$delta = 3 attr(s3, "dimensions")$X2$delta = -3 plot(s1, axes = TRUE, text_values = TRUE, text_color = 'orange') plot(s2, axes = TRUE, text_values = TRUE, text_color = 'orange') plot(s3, axes = TRUE, text_values = TRUE, text_color = 'orange') ## ----eval=TRUE---------------------------------------------------------------- fn1 = paste0(tempdir(), .Platform$file.sep, "img1.tif") fn2 = paste0(tempdir(), .Platform$file.sep, "img2.tif") fn3 = paste0(tempdir(), .Platform$file.sep, "img3.tif") write_stars(s1, fn1) write_stars(s2, fn2) write_stars(s3, fn3) (r1 = read_stars(c(fn1, fn2, fn3), proxy = TRUE)) ## ----eval=TRUE---------------------------------------------------------------- st_as_stars(r1) %>% merge() %>% plot(breaks = "equal", text_values = TRUE, text_color = 'orange', axes = TRUE) ## ----eval=TRUE---------------------------------------------------------------- st_as_stars(r1[,2:4,2:4]) %>% merge() %>% plot(breaks = "equal", text_values = TRUE, text_color = 'orange', axes = TRUE) ## ----------------------------------------------------------------------------- s4 = st_as_stars(matrix(1: 16, 4)) s5 = st_as_stars(matrix(1: 64, 8)) s6 = st_as_stars(matrix(1:144,12)) attr(s4, "dimensions")$X1$offset = 0 attr(s4, "dimensions")$X2$offset = 4 attr(s5, "dimensions")$X1$offset = 0 attr(s5, "dimensions")$X2$offset = 4 attr(s6, "dimensions")$X1$offset = 0 attr(s6, "dimensions")$X2$offset = 4 attr(s4, "dimensions")$X1$delta = 1 attr(s4, "dimensions")$X2$delta = -1 attr(s5, "dimensions")$X1$delta = 1/2 attr(s5, "dimensions")$X2$delta = -1/2 attr(s6, "dimensions")$X1$delta = 1/3 attr(s6, "dimensions")$X2$delta = -1/3 plot(s4, axes = TRUE, text_values = TRUE, text_color = 'orange') plot(s5, axes = TRUE, text_values = TRUE, text_color = 'orange') plot(s6, axes = TRUE, text_values = TRUE, text_color = 'orange') ## ----eval=TRUE---------------------------------------------------------------- fn4 = paste0(tempdir(), .Platform$file.sep, "img4.tif") fn5 = paste0(tempdir(), .Platform$file.sep, "img5.tif") fn6 = paste0(tempdir(), .Platform$file.sep, "img6.tif") write_stars(s4, fn4) write_stars(s5, fn5) write_stars(s6, fn6) (r2 = read_stars(c(fn4, fn5, fn6), proxy = TRUE)) st_as_stars(r2) %>% merge() %>% plot(breaks = "equal", text_values = TRUE, text_color = 'orange', axes = TRUE) st_as_stars(r2[,2:4,2:4]) %>% merge() %>% plot(breaks = "equal", text_values = TRUE, text_color = 'orange', axes = TRUE) ## ----eval=TRUE---------------------------------------------------------------- (r3 = read_stars(c(fn6, fn5, fn4), proxy = TRUE)) st_as_stars(r3) %>% merge() %>% plot(breaks = "equal", text_values = TRUE, text_color = 'orange', axes = TRUE) st_as_stars(r3[,2:6,3:6]) %>% merge() %>% plot(breaks = "equal", text_values = TRUE, text_color = 'orange', axes = TRUE) stars/inst/doc/stars8.R0000644000176200001440000000160514644756274014524 0ustar liggesusers## ----include = FALSE---------------------------------------------------------- knitr::opts_chunk$set(echo = TRUE, collapse = TRUE, dev = "png") suppressPackageStartupMessages(library(dplyr)) knitr::opts_chunk$set(fig.height = 4.5) knitr::opts_chunk$set(fig.width = 6) EVAL = suppressWarnings(require(starsdata, quietly = TRUE)) ## ----------------------------------------------------------------------------- library(stars) f <- system.file("nc/reduced.nc", package = "stars") (nc <- read_ncdf(f)) ## ----------------------------------------------------------------------------- old_options <- options("stars.n_proxy" = 100) (nc <- read_ncdf(f, proxy = TRUE)) options(old_options) ## ----------------------------------------------------------------------------- (nc <- read_ncdf(f, var = "sst", ncsub = cbind(start = c(90, 45, 1 , 1), count = c(90, 45, 1, 1)))) plot(nc) stars/inst/doc/stars6.Rmd0000644000176200001440000002040014644756215015030 0ustar liggesusers--- title: "6. How `raster` functions map to `stars` functions" author: "Sébastien Rochette, Adriano Fantini, Edzer Pebesma" output: rmarkdown::html_vignette vignette: > %\VignetteIndexEntry{6. How raster functions map to stars functions} %\VignetteEngine{knitr::rmarkdown} %\VignetteEncoding{UTF-8} --- **For a better version of the stars vignettes see** https://r-spatial.github.io/stars/articles/ ```{r, include = FALSE} knitr::opts_chunk$set(echo = FALSE, collapse = TRUE, dev = "png") suppressPackageStartupMessages(library(dplyr)) knitr::opts_chunk$set(fig.height = 4.5) knitr::opts_chunk$set(fig.width = 6) ``` Here's an attempt at the table describing how `raster` functions map to `stars` functions, discussed in [issue #122](https://github.com/r-spatial/stars/issues/122). This table uses the functionality of the `raster` package as a template; it may be incomplete, imprecise or plain wrong, so take it with a pinch of salt. Any comment or correction is hugely appreciated, please contribute! Some of the functions (`filter`, `slice`, `mutate`, `select`, `pull`, ...) are provided via `dplyr`, which must be loaded. See `?stars::dplyr`. ## COMMENT LEGEND ? = Not sure / unknown \* = Not present, low priority \# = Not present, high priority NA = Not available by design ## Creating objects ```{r} tribble( ~raster, ~stars, ~`Note/comment`, "{raster, stack, brick} (read)", "read_stars or read_stars(along = ...)","", "{stack, brick} (concatenate layers)", "c or c(along = ...)","", "subset", "{[ ] , slice, filter}","", "addLayer", "c() or c(along = ...)","", "dropLayer", "{[ ] , slice, filter}","", "unstack", "combine lapply and {[ ] , slice, filter}","" ) %>% knitr::kable() ``` ## Changing spatial extent and/or resolution of objects ```{r} tribble( ~raster, ~stars, ~`Note/comment`, "merge", "c", "#, currently only works for adjacent objects", "mosaic", "st_mosaic", "these are not identical, read the docs carefully", "crop", "filter, st_crop", "", "setExtent", "", "# maybe use st_warp?", "trim", "", "#", "aggregate", "aggregate", "WIP; raster's aggregate with fact=2 will not work, use st_warp in that case?", "disaggregate", "", "# use st_warp(use_gdal = TRUE)?", "resample", "{st_transform, st_warp}", "", "projectRaster", "{st_transform, st_warp}", "", "shift", "", "#, now use st_set_dimensions", "flip", "[] with reversed index", "#", "rotate", "", "*", "t", "", "NA" ) %>% knitr::kable() ``` ## Cell based computation ```{r} tribble( ~raster, ~stars, ~`Note/comment`, "calc", "st_apply", "", "overlay", "c(along = , ...) %>% st_apply(...)", "", "cover", "[ ] <-", "", "mask", "[ ], st_crop", "when using an sf polygon as mask", "mask", "[ ]<- NA", "when using a stars object to mask", "cut", "cut", "", "subs", "", "", "reclassify", "mutate with case_when", "or forcats::fct_recode ?", "reclassify", "cut", "see https://stackoverflow.com/questions/70619002/reclassify-2d-stars-array/", "init", "[ ] <-", "", "stackApply", "{[ ] , slice, filter} %>% st_apply", "", "stackSelect", "", "", ) %>% knitr::kable() ``` ## Spatial contextual computation ```{r} tribble( ~raster, ~stars, ~`Note/comment`, "distance", "", "#", "gridDistance", "", "*", "distanceFromPoints", "", "#", "direction", "", "*", "focal", "f = st_apply(x1, 3, foc, w = matrix(1, 3, 3))", "See. [issue 176](https://github.com/r-spatial/stars/issues/176)", "localFun", "", "*", "boundaries", "st_as_sf(as_points=FALSE, merge=TRUE, connect8=TRUE)", "", "clump", "st_as_sf(r, merge = TRUE)", "`st_as_sf` returns polygons, `clump` a raster", "adjacent", "", "*", "area", "st_area", "", "terrain", "", "#", "Moran", "", "" ) %>% knitr::kable() ``` ## Model predictions ```{r} tribble( ~raster, ~stars, ~`Note/comment`, "predict", "predict", "", "interpolate", "gstat::idw, gstat::krige", "`st_warp` has raster-raster interpolations of `gdalwarp`" ) %>% knitr::kable() ``` ## Data type conversion ```{r} tribble( ~raster, ~stars, ~`Note/comment`, "rasterize", "st_as_stars", "", "rasterToPoints", "st_as_sf(as_points=TRUE)", "", "rasterToPolygons", "st_as_sf(as_points=FALSE, ...), st_polygonize", "", "rasterToContour", "st_contour", "requires GDAL >= 2.4.0", "rasterFromXYZ", "", "", "rasterFromCells", "", "", ) %>% knitr::kable() ``` ## Summarizing ```{r} tribble( ~raster, ~stars, ~`Note/comment`, "cellStats", "st_apply", "", "summary", "print, summary(as.vector(. %>% pull))", "", "freq", "table", "*", "crosstab", "", "", "unique", "unique(as.vector(. %>% pull))", "", "zonal", "", "*" ) %>% knitr::kable() ``` ## Accessing values of objects ```{r} tribble( ~raster, ~stars, ~`Note/comment`, "getValues", "{pull, [[ ]]}", "", "getValuesBlock", "{[ ] , slice, filter} %>% pull", "", "getValuesFocal", "{[ ] , slice, filter} %>% pull", "", "as.matrix", "[[ ]]", "currently behaves somewhat unexpectedly *", "as.array", "[[ ]]", "currently behaves somewhat unexpectedly *", "extract (by cell)", "{[ ] , slice, filter}", "", "extract (by polygon)", "x[sf_object]", "", "extract (by point)", "aggregate(stars_object, sf_object, function(x) x[1], as_points = FALSE)", "", "sampleRandom", "", "*", "sampleRegular", "", "*", "minValue", "purrr::map(x, min)", "", "maxValue", "purrr::map(x, max)", "", "setMinMax", "", "," ) %>% knitr::kable() ``` ## Plotting ```{r} tribble( ~raster, ~stars, ~`Note/comment`, "plot", "plot, geom_stars", "", "plotRGB", "plot(x, rgb =...)", "", "spplot", "-", "", "image", "image", "", "persp", "-", "", "contour", "(st_contour, then sf::plot)", "", "filledContour", "(same)", "", "text", "text", "", "`hist`", "`hist(x[[1]])`", "", "barplot", "", "", "density", "", "", "pairs", "", "", "boxplot", "", "" ) %>% knitr::kable() ``` ## Getting and setting dimensions ```{r} tribble( ~raster, ~stars, ~`Note/comment`, "ncol", "dim(x)[1]", "or use name instead of 1; cols may be the second dimension!", "nrow", "dim(x)[2]", "or use name instead of 2; rows may be the first dimension!", "ncell", "prod(dim(x))", "", "res", "st_dimensions", "can also not be a constant in case of rectilinear or curvilinear grids", "nlayers", "-", "there is no concept of layers in stars", "names", "names", "", "xres", "st_res(x)[1]", "may not be a constant in case of rectilinear or curvilinear grids", "yres", "st_res(x)[2]", "may not be a constant in case of rectilinear or curvilinear grids", "xmin", "st_bbox(x)[1]", "", "xmax", "st_bbox(x)[3]", "", "ymin", "st_bbox(x)[2]", "", "ymax", "st_bbox(x)[4]", "", "extent", "st_bbox(x)", "different ordering of numbers", "origin", "-", "", "projection", "st_crs(x)", "", "isLonLat", "st_is_longlat(st_crs(x))", "", "filename", "", "stars_proxy objects carry file names where otherwise the array data is", "bandnr", "", "stars has no general concept of bands", "nbands", "dim(x)[3]", "may also be time; bands may also be in another dimension, or have another name", "compareRaster", "all.equal(st_dimensions(x), st_dimensions(y))", "*", "NAvalue", "", "-" ) %>% knitr::kable() ``` ## Computing row, column, cell numbers and coordinates ```{r} tribble( ~raster, ~stars, ~`Note/comment`, "xFromCol", "st_get_dimension_values(., 'x')[col]", "I am not sure how to generally get the x dimension name - here it is x", "yFromRow", "st_get_dimension_values(., 'y')[row]", "I am not sure how to generally get the y dimension name - here it is y", "xFromCell", "", "", "yFromCell", "", "", "xyFromCell", "", "", "colFromX", "", "*", "rowFromY", "", "*", "rowColFromCell", "", "", "cellFromXY", "st_cells()", "", "cellFromRowCol", "", "", "cellsFromExtent", "", "", "coordinates", "st_coordinates", "", "validCell", "", "", "validCol", "col %>% between(st_dimensions(.)$x$from, st_dimensions(.)$x$to)", "raster columns are not always named 'x'", "validRow", "row %>% between(st_dimensions(.)$y$from, st_dimensions(.)$y$to)", "raster columns are not always named 'y'", "setValues", "[ ] <-", "", "writeRaster", "write_stars", "currently uses GDAL, somewhat limited", "KML", "", "" ) %>% knitr::kable() ``` The format of this table follows the `raster-package` entry in the raster manual, found at `https://cran.r-project.org/web/packages/raster/raster.pdf`. stars/inst/doc/stars3.html0000644000176200001440000141044314644756253015264 0ustar liggesusers 3. stars tidyverse methods

3. stars tidyverse methods

Edzer Pebesma

For a better version of the stars vignettes see https://r-spatial.github.io/stars/articles/

This vignette shows how some of the tidyverse verbs can be used on stars objects.

The stars and tidyverse packages are loaded by

library(stars)
library(dplyr)

Methods now available for class stars are

methods(class = "stars")
##  [1] $<-               %in%              Math              Ops              
##  [5] [                 [<-               [[<-              adrop            
##  [9] aggregate         aperm             as.POSIXct        as.data.frame    
## [13] as.tbl_cube       as_tibble         c                 coerce           
## [17] contour           cut               dim               dimnames         
## [21] dimnames<-        drop_units        droplevels        expand_dimensions
## [25] filter            hist              image             initialize       
## [29] is.na             merge             mutate            plot             
## [33] prcomp            predict           print             pull             
## [37] rename            select            show              slice            
## [41] slotsFromS3       split             st_apply          st_area          
## [45] st_as_sf          st_as_sfc         st_as_stars       st_bbox          
## [49] st_coordinates    st_crop           st_crs            st_crs<-         
## [53] st_dimensions     st_dimensions<-   st_downsample     st_extract       
## [57] st_geometry       st_geotransform   st_geotransform<- st_interpolate_aw
## [61] st_intersects     st_join           st_mosaic         st_normalize     
## [65] st_redimension    st_rotate         st_sample         st_set_bbox      
## [69] st_transform      st_write          time              transmute        
## [73] write_stars      
## see '?methods' for accessing help and source code

We will work with a three-band section of a landsat image:

system.file("tif/L7_ETMs.tif", package = "stars") %>%
    read_stars -> x
x
## stars object with 3 dimensions and 1 attribute
## attribute(s):
##              Min. 1st Qu. Median     Mean 3rd Qu. Max.
## L7_ETMs.tif     1      54     69 68.91242      86  255
## dimension(s):
##      from  to  offset delta                     refsys point x/y
## x       1 349  288776  28.5 SIRGAS 2000 / UTM zone 25S FALSE [x]
## y       1 352 9120761 -28.5 SIRGAS 2000 / UTM zone 25S FALSE [y]
## band    1   6      NA    NA                         NA    NA

slice

slice slices a sub-array out of the cube; this is done by specifying the dimension on which to act, and the slice number.

x %>% slice(band, 6) -> x6
x6
## stars object with 2 dimensions and 1 attribute
## attribute(s):
##              Min. 1st Qu. Median     Mean 3rd Qu. Max.
## L7_ETMs.tif     1      32     60 59.97521      88  255
## dimension(s):
##   from  to  offset delta                     refsys point x/y
## x    1 349  288776  28.5 SIRGAS 2000 / UTM zone 25S FALSE [x]
## y    1 352 9120761 -28.5 SIRGAS 2000 / UTM zone 25S FALSE [y]

It returns a lower-dimensional array if a single element is selected along the slice dimension.

filter

Similar to slice, filter selects on dimensions but evaluates their values rather than their index: in

x %>% filter(x > 289000, x < 291000, band > 3) -> x7
x7
## stars object with 3 dimensions and 1 attribute
## attribute(s):
##              Min. 1st Qu. Median     Mean 3rd Qu. Max.
## L7_ETMs.tif     5      54     70 71.79194      88  252
## dimension(s):
##      from  to  offset delta                     refsys point x/y
## x       1  70  289004  28.5 SIRGAS 2000 / UTM zone 25S FALSE [x]
## y       1 352 9120761 -28.5 SIRGAS 2000 / UTM zone 25S FALSE [y]
## band    1   3       4     1                         NA    NA

the subarray is created based on the x coordinate values.

Note that filter converts the object to a tbl_cube, and uses the dplyr filter method for tbl_cube objects. This has the limitation that stars objects with rectilinear, curvilinear or simple feature geometries cannot be handled. For such objects, using regular [ selection or using st_crop may be an alternative.

pull

pull pulls out an array from a stars object:

x %>% pull(1) -> x8
class(x8)
## [1] "array"
dim(x8)
##    x    y band 
##  349  352    6

mutate

x %>% mutate(band2 = 2 * L7_ETMs.tif) -> x2 
x2
## stars object with 3 dimensions and 2 attributes
## attribute(s):
##              Min. 1st Qu. Median      Mean 3rd Qu. Max.
## L7_ETMs.tif     1      54     69  68.91242      86  255
## band2           2     108    138 137.82484     172  510
## dimension(s):
##      from  to  offset delta                     refsys point x/y
## x       1 349  288776  28.5 SIRGAS 2000 / UTM zone 25S FALSE [x]
## y       1 352 9120761 -28.5 SIRGAS 2000 / UTM zone 25S FALSE [y]
## band    1   6      NA    NA                         NA    NA

select

select selects an attribute, or a set of attributes:

x2 %>% select(band2) -> x9
x9
## stars object with 3 dimensions and 1 attribute
## attribute(s):
##        Min. 1st Qu. Median     Mean 3rd Qu. Max.
## band2     2     108    138 137.8248     172  510
## dimension(s):
##      from  to  offset delta                     refsys point x/y
## x       1 349  288776  28.5 SIRGAS 2000 / UTM zone 25S FALSE [x]
## y       1 352 9120761 -28.5 SIRGAS 2000 / UTM zone 25S FALSE [y]
## band    1   6      NA    NA                         NA    NA

geom_stars

geom_raster is a ggplot2 geom function that accepts stars objects as its data argument and

  • sets up the raster or vector spatial coordinates as plot dimensions, and the first attribute as the fill variable
  • allows for downsampling (without choosing a suitable downsampling level)
  • chooses between using geom_raster, geom_rect and geom_sf depending on whether the geometry is regular, rectilinear or has vector geometries

An example use is

library(ggplot2)
library(viridis)
ggplot() + 
  geom_stars(data = x) +
  coord_equal() +
  facet_wrap(~band) +
  theme_void() +
  scale_fill_viridis() +
  scale_x_discrete(expand = c(0, 0)) +
  scale_y_discrete(expand = c(0, 0))

stars/inst/doc/stars5.Rmd0000644000176200001440000001200514644756215015031 0ustar liggesusers--- title: "5. vector-raster conversions, reprojection, warping" author: "Edzer Pebesma" output: rmarkdown::html_vignette vignette: > %\VignetteIndexEntry{5. vector-raster conversions, reprojection, warping} %\VignetteEngine{knitr::rmarkdown} %\VignetteEncoding{UTF-8} --- **For a better version of the stars vignettes see** https://r-spatial.github.io/stars/articles/ ```{r setup, include=FALSE} knitr::opts_chunk$set(echo = TRUE, collapse = TRUE, dev = "png") suppressPackageStartupMessages(library(sf)) knitr::opts_chunk$set(fig.height = 4.5) knitr::opts_chunk$set(fig.width = 6) ev = TRUE ``` This vignette shows how `stars` object can be moved from vector and raster representations. # Rasterizing an `sf` vector object ```{r} library(stars) system.file("gpkg/nc.gpkg", package = "sf") %>% read_sf() %>% st_transform(32119) -> nc nc$dens = nc$BIR79 / units::set_units(st_area(nc), km^2) (nc.st = st_rasterize(nc["dens"], dx = 5000, dy = 5000)) plot(nc.st) ``` The algorithm used is the GDAL `rasterize` utility, all options of this utility can be passed to `st_rasterize`. The geometry of the final raster can be controlled by passing a target bounding box and either the raster dimensions `nx` and `ny`, or pixel size by the `dx` and `dy` parameters. # Vectorizing a raster object to an `sf` object `stars` objects can be converted into an `sf` object using `st_as_sf`. It has a number of options, depending on whether pixels represent the point value at the pixel center, or small square polygons with a single value. We will work again with the landsat-7 6-band image, but will select the first band and round the values: ```{r} tif = system.file("tif/L7_ETMs.tif", package = "stars") x = read_stars(tif)[, 1:50, 1:50, 1:2] x[[1]] = round(x[[1]]/5) ``` ## Polygonizing In case raster cells reflect point values and we want to get a vector representation of the whole field, we can draw contour lines and export the contour sets (only available when the GDAL version is at least 2.4.0): ```{r eval=ev} l = st_contour(x, contour_lines = TRUE, breaks = 11:15) plot(l[1], key.pos = 1, pal = sf.colors, lwd = 2, key.length = 0.8) ``` ## Exporting to points Alternatively, we can simply export all the pixels as points, and get them either as a wide table with all bands per point, and no replicated `POINT` geometries: ```{r} st_as_sf(x, as_points = TRUE, merge = FALSE) ``` or as a long table with a single attribute and all points replicated: ```{r} st_as_sf(x, as_points = TRUE, merge = FALSE, long = TRUE) ``` as we can see, an additional attribute `band` now indicates which band is concerned. ## Exporting to polygons Alternatively, we can export to polygons and either get a single polygon per pixel, as in ```{r} st_as_sf(x[1], as_points = FALSE, merge = FALSE) ``` or merge polygons that have identical pixel values; ```{r} p = st_as_sf(x, as_points = FALSE, merge = TRUE) ``` When plotted with boundaries, we see the resolved boundaries of areas with the same pixel value: ```{r} plot(p) ``` A further option `connect8` can be set to `TRUE` to use 8 connectedness, rather than the default 4 connectedness algorithm. In both cases, the polygons returned will often be invalid according to the simple feature standard, but can be made valid using `lwgeom::st_make_valid`. # Switching between vector and raster in `stars` objects We can convert a raster dimension to a vector dimension while keeping other dimensions as they are in a `stars` object by ```{r} x.sf = st_xy2sfc(x, as_points = TRUE) x.sf ``` which also requires setting the `as_points` arguments as in `st_as_sf`. # Reprojecting a raster If we accept that curvilinear rasters are rasters too, and that regular and rectilinear grids are special cases of curvilinear grids, reprojecting a raster is no longer a "problem", it just recomputes new coordinates for every raster cell, and generally results in a curvilinear grid (that sometimes can be brought back to a regular or rectilinear grid). If curvilinear grid cells are represented by coordinates of the cell center, the actual shape of a grid cell gets lost, and this may be a larger effect if grid cells are large or if the transformation is stronger non-linear. An example of the reprojection of the grid created above is ```{r} nc.st %>% st_transform("+proj=laea +lat_0=34 +lon_0=-60") -> nc.curv nc.curv plot(nc.curv, border = NA, graticule = TRUE) ``` where it should be noted that the dimensionality of the grid didn't change: the same set of raster cells has been replotted in the new CRS, but now in a curvilinear grid. # Warping a raster Warping a raster means creating a new _regular_ grid in a new CRS, based on a (usually regular) grid in another CRS. We can do the transformation of the previous section by first creating a target grid: ```{r} nc %>% st_transform("+proj=laea +lat_0=34 +lon_0=-60") %>% st_bbox() %>% st_as_stars() -> newgrid ``` and then warping the old raster to the new ```{r} nc.st %>% st_warp(newgrid) -> nc.new nc.new plot(nc.new) ``` This new object has a regular grid in the new CRS, aligned with the new x- and y-axes. stars/inst/doc/stars2.Rmd0000644000176200001440000003207514644756215015037 0ustar liggesusers--- title: "2. stars proxy objects" author: "Edzer Pebesma" output: rmarkdown::html_vignette vignette: > %\VignetteIndexEntry{2. stars proxy objects} %\VignetteEngine{knitr::rmarkdown} %\VignetteEncoding{UTF-8} --- **For a better version of the stars vignettes see** https://r-spatial.github.io/stars/articles/ ```{r setup, include=FALSE} knitr::opts_chunk$set(echo = TRUE, collapse = TRUE, dev = "png") set.seed(13579) knitr::opts_chunk$set(fig.height = 4.5) knitr::opts_chunk$set(fig.width = 6) EVAL = x = suppressWarnings(require(starsdata, quietly = TRUE)) ``` When your imagery or array data easily fits a couple of times in R's working memory (RAM), consider yourself lucky. This document was not written for you. If your imagery is too large, or for other reasons you want to work with smaller chunks of data than the files in which they come, read on about your options. First, we will discuss the low-level interface for this, then the higher level, using stars proxy objects that delay all reading. # Preamble: the starsdata package To run all of the examples in this vignette, you must install a package with datasets that are too large (1 Gb) to be held in the `stars` package. They are in a [drat repo](https://github.com/eddelbuettel/drat), installation is done by ```{r eval=FALSE} install.packages("starsdata", repos = "http://gis-bigdata.uni-muenster.de/pebesma", type = "source") # possibly after: options(timeout = 100) # or from an alternative repository: # install.packages("starsdata", repos = "http://pebesma.staff.ifgi.de", type = "source") ``` # Reading chunks, change resolution, select bands `read_stars` has an argument called `RasterIO` which controls how a GDAL dataset is being read. By default, all pixels and all bands are read in memory. This can consume a lot of time and require a lot of memory. Remember that your file may be compressed, and that pixel values represented in the file by bytes are converted to 8-byte doubles in R. The reason for using `RasterIO` for this is that the parameters we use are directly mapped to the GDAL RasterIO function used (after adapting the 1-based offset index in R to 0-based offset in C++). ## Reading a particular chunk An example of using `RasterIO` is ```{r} library(stars) tif = system.file("tif/L7_ETMs.tif", package = "stars") rasterio = list(nXOff = 6, nYOff = 6, nXSize = 100, nYSize = 100, bands = c(1, 3, 4)) (x = read_stars(tif, RasterIO = rasterio)) dim(x) ``` Compare this to ```{r} st_dimensions(read_stars(tif)) ``` and we see that * the `delta` values remain the same, * the offset (x/y coordinates of origin) of the grid remain the same * the `from` and `to` reflect the new area, and relate to the new `delta` values * `dim(x)` reflects the new size, and * only three bands were read ## Reading at a different resolution Reading datasets at a lower (but also higher!) resolution can be done by setting `nBufXSize` and `nBufYSize` ```{r} rasterio = list(nXOff = 6, nYOff = 6, nXSize = 100, nYSize = 100, nBufXSize = 20, nBufYSize = 20, bands = c(1, 3, 4)) (x = read_stars(tif, RasterIO = rasterio)) ``` and we see that in addition: * the `delta` (raster cell size) values have increased a factor 5, because `nBufXSize` and `nBufYSize` were set to values a factor 5 smaller than `nXSize` and `nYSize` * the offset coordinates of the grid are still the same * the `from` and `to` reflect the new area, but relate to the new `delta` cell size values We can also read at higher resolution; here we read a 3 x 3 area and blow it up to 100 x 100: ```{r} rasterio = list(nXOff = 6, nYOff = 6, nXSize = 3, nYSize = 3, nBufXSize = 100, nBufYSize = 100, bands = 1) x = read_stars(tif, RasterIO = rasterio) dim(x) plot(x) ``` The reason we "see" only three grid cells is that the default sampling method is "nearest neighbour". We can modify this by ```{r} rasterio = list(nXOff = 6, nYOff = 6, nXSize = 3, nYSize = 3, nBufXSize = 100, nBufYSize = 100, bands = 1, resample = "cubic_spline") x = read_stars(tif, RasterIO = rasterio) dim(x) plot(x) ``` The following methods are allowed for parameter `resample`: | `resample` | method used | |------------|------------------------------------| |`nearest_neighbour`| Nearest neighbour (default) | |`bilinear` | Bilinear (2x2 kernel) | |`cubic` | Cubic Convolution Approximation (4x4 kernel) | |`cubic_spline` | Cubic B-Spline Approximation (4x4 kernel) | |`lanczos` | Lanczos windowed sinc interpolation (6x6 kernel) | |`average` | Average | |`mode` | Mode (selects the value which appears most often of all the sampled points) | |`Gauss` | Gauss blurring | All these methods are implemented in GDAL; for what these methods exactly do, we refer to the GDAL documentation or source code. # Stars proxy objects Stars proxy objects take another approach: upon creation they contain no data at all, but only pointers to where the data can be read. Data is only read when it is needed, and only as much as is needed: if we plot a proxy objects, the data are read at the resolution of pixels on the screen, rather than at the native resolution, so that if we have e.g. a 10000 x 10000 Sentinel 2 (level 1C) image, we can open it by ```{r, eval=EVAL} granule = system.file("sentinel/S2A_MSIL1C_20180220T105051_N0206_R051_T32ULE_20180221T134037.zip", package = "starsdata") s2 = paste0("SENTINEL2_L1C:/vsizip/", granule, "/S2A_MSIL1C_20180220T105051_N0206_R051_T32ULE_20180221T134037.SAFE/MTD_MSIL1C.xml:10m:EPSG_32632") (p = read_stars(s2, proxy = TRUE)) ``` and this happens _instantly_, because no data is read. When we plot this object, ```{r,eval=EVAL} system.time(plot(p)) ``` This takes only around 1 second, since only those pixels are read that can be seen on the plot. If we read the entire image in memory first, as we would do with ```{r eval = FALSE} p = read_stars(s2, proxy = FALSE) ``` then only the reading would take over a minute, and require 5 Gb memory. ## Methods for stars proxy objects ```{r} methods(class = "stars_proxy") ``` ## Select attributes We can select attributes as with regular `stars` objects, by using the first argument to `[`: ```{r,eval=EVAL} x = c("avhrr-only-v2.19810901.nc", "avhrr-only-v2.19810902.nc", "avhrr-only-v2.19810903.nc", "avhrr-only-v2.19810904.nc", "avhrr-only-v2.19810905.nc", "avhrr-only-v2.19810906.nc", "avhrr-only-v2.19810907.nc", "avhrr-only-v2.19810908.nc", "avhrr-only-v2.19810909.nc") file_list = system.file(paste0("netcdf/", x), package = "starsdata") y = read_stars(file_list, quiet = TRUE, proxy = TRUE) names(y) y["sst"] ``` Note that this selection limits the reading from 4 to 1 subdataset from all 9 NetCDF files. ## Select an area Another possibility is to crop, or select a rectangular region based on a spatial object. This can be done by passing a `bbox` object, or an `sf`, `sfc` or `stars` object from which the bounding box will be taken. An example: ```{r,eval=EVAL} bb = st_bbox(c(xmin = 10.125, ymin = 0.125, xmax = 70.125, ymax = 70.125)) ysub = y[bb] st_dimensions(ysub) class(ysub) # still no data here!! plot(ysub, reset = FALSE) # plot reads the data, at resolution that is relevant plot(st_as_sfc(bb), add = TRUE, lwd = .5, border = 'red') ``` ## Lazy evaluation, changing evaluation order Some other actions can be carried out on `stars_proxy` objects, but their effect is delayed until the data are actually needed (`plot`, `write_stars`). For instance, range selections on dimensions other than shown above first need data, and can only then be carried out. Such functions are added to the object, in an attribute called `call_list`: ```{r,eval=EVAL} yy = adrop(y) yyy = yy[,1:10,1:10,] class(yyy) # still no data st_dimensions(yyy) # and dimensions not adjusted attr(yyy, "call_list") # the name of object in the call (y) is replaced with x: ``` Doing this allows for optimizing the order in which operations are done. As an example, for `st_apply`, reading can be done sequentially over the dimensions over which the function is applied: * If for example a function is applied to each band (such as: compute band quantiles), bands can be read sequentially, and discarded after the quantiles have been computed. * If a time series function is applied to pixel time series and the result is plotted on a map, the time series function is only evaluated on the pixels actually plotted. This means that e.g. in ```{r eval = FALSE} plot(st_apply(x, c("x", "y"), range)) ``` the order of evaluation is reversed: `plot` knows which pixels are going to be shown, and controls how `x` is downsampled *before* `st_apply` is carried out on this subset. ### Fetching the data Fetching the data now involves reading the whole array and then evaluating the `call_list` on it, sequentially: ```{r,eval=EVAL} (x = st_as_stars(yyy)) # read, adrop, subset ``` ### Plotting with changed evaluation order For the Sentinel 2 data, band 4 represents NIR and band 1 red, so we can compute NDVI by ```{r,eval=EVAL} # S2 10m: band 4: near infrared, band 1: red. #ndvi = function(x) (x[4] - x[1])/(x[4] + x[1]) ndvi = function(x1, x2, x3, x4) (x4 - x1)/(x4 + x1) rm(x) (s2.ndvi = st_apply(p, c("x", "y"), ndvi)) system.time(plot(s2.ndvi)) # read - compute ndvi - plot ``` # Multi-resolution proxy objects This sections shows some examples how `stars_proxy` objects deal with the situation where the different maps have dissimilar resolution. The assumptions here are: * all maps need to have the same origin coordinates (typically upper-left corner) and CRS. * the first map determines the "working" resolution, to which e.g. native or downsampled resolutions refer We'll create four maps with cells size 1, 2 and 3: ```{r} s1 = st_as_stars(matrix(1:16, 4)) s2 = st_as_stars(matrix(1:16, 4)) s3 = st_as_stars(matrix(1:16, 4)) attr(s1, "dimensions")$X1$offset = 0 attr(s1, "dimensions")$X2$offset = 4 attr(s2, "dimensions")$X1$offset = 0 attr(s2, "dimensions")$X2$offset = 4 attr(s3, "dimensions")$X1$offset = 0 attr(s3, "dimensions")$X2$offset = 4 attr(s1, "dimensions")$X1$delta = 1 attr(s1, "dimensions")$X2$delta = -1 attr(s2, "dimensions")$X1$delta = 2 attr(s2, "dimensions")$X2$delta = -2 attr(s3, "dimensions")$X1$delta = 3 attr(s3, "dimensions")$X2$delta = -3 plot(s1, axes = TRUE, text_values = TRUE, text_color = 'orange') plot(s2, axes = TRUE, text_values = TRUE, text_color = 'orange') plot(s3, axes = TRUE, text_values = TRUE, text_color = 'orange') ``` We created three rasters with identical cell values and dimensions, but different cell sizes, and hence extents. If we bind them in a single proxy object, with ```{r eval=TRUE} fn1 = paste0(tempdir(), .Platform$file.sep, "img1.tif") fn2 = paste0(tempdir(), .Platform$file.sep, "img2.tif") fn3 = paste0(tempdir(), .Platform$file.sep, "img3.tif") write_stars(s1, fn1) write_stars(s2, fn2) write_stars(s3, fn3) (r1 = read_stars(c(fn1, fn2, fn3), proxy = TRUE)) ``` We see that **multi-resolution** is mentioned in the printed summary. When converting this to a `stars` object, the secondary rasters are resampled to the cellsize + extent of the first: ```{r eval=TRUE} st_as_stars(r1) %>% merge() %>% plot(breaks = "equal", text_values = TRUE, text_color = 'orange', axes = TRUE) ``` If we do this for a sub-range, defined for the object resolutions, we get: ```{r eval=TRUE} st_as_stars(r1[,2:4,2:4]) %>% merge() %>% plot(breaks = "equal", text_values = TRUE, text_color = 'orange', axes = TRUE) ``` We now create four maps, all over the same region ([0,4] x [0,4]), with different resolutions (cell size 1, 1/2 and 1/3): ```{r} s4 = st_as_stars(matrix(1: 16, 4)) s5 = st_as_stars(matrix(1: 64, 8)) s6 = st_as_stars(matrix(1:144,12)) attr(s4, "dimensions")$X1$offset = 0 attr(s4, "dimensions")$X2$offset = 4 attr(s5, "dimensions")$X1$offset = 0 attr(s5, "dimensions")$X2$offset = 4 attr(s6, "dimensions")$X1$offset = 0 attr(s6, "dimensions")$X2$offset = 4 attr(s4, "dimensions")$X1$delta = 1 attr(s4, "dimensions")$X2$delta = -1 attr(s5, "dimensions")$X1$delta = 1/2 attr(s5, "dimensions")$X2$delta = -1/2 attr(s6, "dimensions")$X1$delta = 1/3 attr(s6, "dimensions")$X2$delta = -1/3 plot(s4, axes = TRUE, text_values = TRUE, text_color = 'orange') plot(s5, axes = TRUE, text_values = TRUE, text_color = 'orange') plot(s6, axes = TRUE, text_values = TRUE, text_color = 'orange') ``` ```{r eval=TRUE} fn4 = paste0(tempdir(), .Platform$file.sep, "img4.tif") fn5 = paste0(tempdir(), .Platform$file.sep, "img5.tif") fn6 = paste0(tempdir(), .Platform$file.sep, "img6.tif") write_stars(s4, fn4) write_stars(s5, fn5) write_stars(s6, fn6) (r2 = read_stars(c(fn4, fn5, fn6), proxy = TRUE)) st_as_stars(r2) %>% merge() %>% plot(breaks = "equal", text_values = TRUE, text_color = 'orange', axes = TRUE) st_as_stars(r2[,2:4,2:4]) %>% merge() %>% plot(breaks = "equal", text_values = TRUE, text_color = 'orange', axes = TRUE) ``` Finally, an example where the first raster has the higher resolution: ```{r eval=TRUE} (r3 = read_stars(c(fn6, fn5, fn4), proxy = TRUE)) st_as_stars(r3) %>% merge() %>% plot(breaks = "equal", text_values = TRUE, text_color = 'orange', axes = TRUE) st_as_stars(r3[,2:6,3:6]) %>% merge() %>% plot(breaks = "equal", text_values = TRUE, text_color = 'orange', axes = TRUE) ``` stars/inst/doc/stars7.html0000644000176200001440000001310714644756274015266 0ustar liggesusers 7. Statistical modelling with stars objects

7. Statistical modelling with stars objects

Edzer Pebesma

For a better version of the stars vignettes see https://r-spatial.github.io/stars/articles/

stars/inst/doc/stars7.Rmd0000644000176200001440000000055114644756215015036 0ustar liggesusers--- title: "7. Statistical modelling with stars objects" author: "Edzer Pebesma" output: rmarkdown::html_vignette vignette: > %\VignetteIndexEntry{7. Statistical modelling with stars objects} %\VignetteEngine{knitr::rmarkdown} %\VignetteEncoding{UTF-8} --- **For a better version of the stars vignettes see** https://r-spatial.github.io/stars/articles/ stars/inst/doc/stars6.html0000644000176200001440000006305414644756274015273 0ustar liggesusers 6. How raster functions map to stars functions

6. How raster functions map to stars functions

Sébastien Rochette, Adriano Fantini, Edzer Pebesma

For a better version of the stars vignettes see https://r-spatial.github.io/stars/articles/

Here’s an attempt at the table describing how raster functions map to stars functions, discussed in issue #122. This table uses the functionality of the raster package as a template; it may be incomplete, imprecise or plain wrong, so take it with a pinch of salt. Any comment or correction is hugely appreciated, please contribute!

Some of the functions (filter, slice, mutate, select, pull, …) are provided via dplyr, which must be loaded. See ?stars::dplyr.

COMMENT LEGEND

? = Not sure / unknown

* = Not present, low priority

# = Not present, high priority

NA = Not available by design

Creating objects

raster stars Note/comment
{raster, stack, brick} (read) read_stars or read_stars(along = …)
{stack, brick} (concatenate layers) c or c(along = …)
subset {[ ] , slice, filter}
addLayer c() or c(along = …)
dropLayer {[ ] , slice, filter}
unstack combine lapply and {[ ] , slice, filter}

Changing spatial extent and/or resolution of objects

raster stars Note/comment
merge c #, currently only works for adjacent objects
mosaic st_mosaic these are not identical, read the docs carefully
crop filter, st_crop
setExtent # maybe use st_warp?
trim #
aggregate aggregate WIP; raster’s aggregate with fact=2 will not work, use st_warp in that case?
disaggregate # use st_warp(use_gdal = TRUE)?
resample {st_transform, st_warp}
projectRaster {st_transform, st_warp}
shift #, now use st_set_dimensions
flip [] with reversed index #
rotate *
t NA

Cell based computation

raster stars Note/comment
calc st_apply
overlay c(along = , …) %>% st_apply(…)
cover [ ] <-
mask [ ], st_crop when using an sf polygon as mask
mask [ ]<- NA when using a stars object to mask
cut cut
subs
reclassify mutate with case_when or forcats::fct_recode ?
reclassify cut see https://stackoverflow.com/questions/70619002/reclassify-2d-stars-array/
init [ ] <-
stackApply {[ ] , slice, filter} %>% st_apply
stackSelect

Spatial contextual computation

raster stars Note/comment
distance #
gridDistance *
distanceFromPoints #
direction *
focal f = st_apply(x1, 3, foc, w = matrix(1, 3, 3)) See. issue 176
localFun *
boundaries st_as_sf(as_points=FALSE, merge=TRUE, connect8=TRUE)
clump st_as_sf(r, merge = TRUE) st_as_sf returns polygons, clump a raster
adjacent *
area st_area
terrain #
Moran

Model predictions

raster stars Note/comment
predict predict
interpolate gstat::idw, gstat::krige st_warp has raster-raster interpolations of gdalwarp

Data type conversion

raster stars Note/comment
rasterize st_as_stars
rasterToPoints st_as_sf(as_points=TRUE)
rasterToPolygons st_as_sf(as_points=FALSE, …), st_polygonize
rasterToContour st_contour requires GDAL >= 2.4.0
rasterFromXYZ
rasterFromCells

Summarizing

raster stars Note/comment
cellStats st_apply
summary print, summary(as.vector(. %>% pull))
freq table *
crosstab
unique unique(as.vector(. %>% pull))
zonal *

Accessing values of objects

raster stars Note/comment
getValues {pull, [[ ]]}
getValuesBlock {[ ] , slice, filter} %>% pull
getValuesFocal {[ ] , slice, filter} %>% pull
as.matrix [[ ]] currently behaves somewhat unexpectedly *
as.array [[ ]] currently behaves somewhat unexpectedly *
extract (by cell) {[ ] , slice, filter}
extract (by polygon) x[sf_object]
extract (by point) aggregate(stars_object, sf_object, function(x) x[1], as_points = FALSE)
sampleRandom *
sampleRegular *
minValue purrr::map(x, min)
maxValue purrr::map(x, max)
setMinMax ,

Plotting

raster stars Note/comment
plot plot, geom_stars
plotRGB plot(x, rgb =…)
spplot -
image image
persp -
contour (st_contour, then sf::plot)
filledContour (same)
text text
hist hist(x[[1]])
barplot
density
pairs
boxplot

Getting and setting dimensions

raster stars Note/comment
ncol dim(x)[1] or use name instead of 1; cols may be the second dimension!
nrow dim(x)[2] or use name instead of 2; rows may be the first dimension!
ncell prod(dim(x))
res st_dimensions can also not be a constant in case of rectilinear or curvilinear grids
nlayers - there is no concept of layers in stars
names names
xres st_res(x)[1] may not be a constant in case of rectilinear or curvilinear grids
yres st_res(x)[2] may not be a constant in case of rectilinear or curvilinear grids
xmin st_bbox(x)[1]
xmax st_bbox(x)[3]
ymin st_bbox(x)[2]
ymax st_bbox(x)[4]
extent st_bbox(x) different ordering of numbers
origin -
projection st_crs(x)
isLonLat st_is_longlat(st_crs(x))
filename stars_proxy objects carry file names where otherwise the array data is
bandnr stars has no general concept of bands
nbands dim(x)[3] may also be time; bands may also be in another dimension, or have another name
compareRaster all.equal(st_dimensions(x), st_dimensions(y)) *
NAvalue -

Computing row, column, cell numbers and coordinates

raster stars Note/comment
xFromCol st_get_dimension_values(., ‘x’)[col] I am not sure how to generally get the x dimension name - here it is x
yFromRow st_get_dimension_values(., ‘y’)[row] I am not sure how to generally get the y dimension name - here it is y
xFromCell
yFromCell
xyFromCell
colFromX *
rowFromY *
rowColFromCell
cellFromXY st_cells()
cellFromRowCol
cellsFromExtent
coordinates st_coordinates
validCell
validCol col %>% between(st_dimensions(.)\(x\)from, st_dimensions(.)\(x\)to) raster columns are not always named ‘x’
validRow row %>% between(st_dimensions(.)\(y\)from, st_dimensions(.)\(y\)to) raster columns are not always named ‘y’
setValues [ ] <-
writeRaster write_stars currently uses GDAL, somewhat limited
KML

The format of this table follows the raster-package entry in the raster manual, found at https://cran.r-project.org/web/packages/raster/raster.pdf.

stars/inst/doc/stars1.html0000644000176200001440000172354714644756245015277 0ustar liggesusers 1. introduction

1. introduction

Edzer Pebesma

For a better version of the stars vignettes see https://r-spatial.github.io/stars/articles/

Package stars provides infrastructure for data cubes, array data with labeled dimensions, with emphasis on arrays where some of the dimensions relate to time and/or space.

Spatial data cubes are arrays with one or more spatial dimensions. Raster data cubes have at least two spatial dimensions that form the raster tesselation. Vector data cubes have at least one spatial dimension that may for instance reflect a polygon tesselation, or a set of point locations. Conversions between the two (rasterization, polygonization) are provided. Vector data are represented by simple feature geometries (packages sf). Tidyverse methods are provided.

The stars package is loaded by

library(stars)
## Loading required package: abind
## Loading required package: sf
## Linking to GEOS 3.12.1, GDAL 3.8.4, PROJ 9.3.1; sf_use_s2() is TRUE

Spatiotemporal arrays are stored in objects of class stars; methods for class stars currently available are

methods(class = "stars")
##  [1] $<-               %in%              Math              Ops              
##  [5] [                 [<-               [[<-              adrop            
##  [9] aggregate         aperm             as.POSIXct        as.data.frame    
## [13] c                 coerce            contour           cut              
## [17] dim               dimnames          dimnames<-        droplevels       
## [21] expand_dimensions hist              image             initialize       
## [25] is.na             merge             plot              prcomp           
## [29] predict           print             show              slotsFromS3      
## [33] split             st_apply          st_area           st_as_sf         
## [37] st_as_sfc         st_as_stars       st_bbox           st_coordinates   
## [41] st_crop           st_crs            st_crs<-          st_dimensions    
## [45] st_dimensions<-   st_downsample     st_extract        st_geometry      
## [49] st_geotransform   st_geotransform<- st_interpolate_aw st_intersects    
## [53] st_join           st_mosaic         st_normalize      st_redimension   
## [57] st_rotate         st_sample         st_set_bbox       st_transform     
## [61] st_write          time              write_stars      
## see '?methods' for accessing help and source code

(tidyverse methods are only visible after loading package tidyverse).

Reading a satellite image

We can read a satellite image through GDAL, e.g. from a GeoTIFF file in the package:

tif = system.file("tif/L7_ETMs.tif", package = "stars")
x = read_stars(tif)
plot(x, axes = TRUE)

We see that the image is geographically referenced (has coordinate values along axes), and that the object returned (x) has three dimensions called x, y and band, and has one attribute:

x
## stars object with 3 dimensions and 1 attribute
## attribute(s):
##              Min. 1st Qu. Median     Mean 3rd Qu. Max.
## L7_ETMs.tif     1      54     69 68.91242      86  255
## dimension(s):
##      from  to  offset delta                     refsys point x/y
## x       1 349  288776  28.5 SIRGAS 2000 / UTM zone 25S FALSE [x]
## y       1 352 9120761 -28.5 SIRGAS 2000 / UTM zone 25S FALSE [y]
## band    1   6      NA    NA                         NA    NA

Each dimension has a name; the meaning of the fields of a single dimension are:

field meaning
from the origin index (1)
to the final index (dim(x)[i])
offset the start value for this dimension (pixel boundary), if regular
delta the step (pixel, cell) size for this dimension, if regular
refsys the reference system, or proj4string
point logical; whether cells refer to points, or intervals
values the sequence of values for this dimension (e.g., geometries), if irregular

This means that for an index i (starting at \(i=1\)) along a certain dimension, the corresponding dimension value (coordinate, time) is \(\mbox{offset} + (i-1) \times \mbox{delta}\). This value then refers to the start (edge) of the cell or interval; in order to get the interval middle or cell centre, one needs to add half an offset.

Dimension band is a simple sequence from 1 to 6. Since bands refer to colors, one could put their wavelength values in the values field.

For this particular dataset (and most other raster datasets), we see that delta for dimension y is negative: this means that consecutive array values have decreasing \(y\) values: cell indexes increase from top to bottom, in the direction opposite to the \(y\) axis.

read_stars reads all bands from a raster dataset, or optionally a subset of raster datasets, into a single stars array structure. While doing so, raster values (often UINT8 or UINT16) are converted to double (numeric) values, and scaled back to their original values if needed if the file encodes the scaling parameters.

The data structure stars is a generalization of the tbl_cube found in cubelyr; we can convert to that by

library(cubelyr)
as.tbl_cube(x)
## Source: local array [737,088 x 3]
## D: x [dbl, 349]
## D: y [dbl, 352]
## D: band [int, 6]
## M: L7_ETMs.tif [dbl[,352,6]]

but this will cause a loss of certain properties (cell size, reference system, vector geometries)

Switching attributes to dimensions and back

(x.spl = split(x, "band"))
## stars object with 2 dimensions and 6 attributes
## attribute(s):
##     Min. 1st Qu. Median     Mean 3rd Qu. Max.
## X1    47      67     78 79.14772      89  255
## X2    32      55     66 67.57465      79  255
## X3    21      49     63 64.35886      77  255
## X4     9      52     63 59.23541      75  255
## X5     1      63     89 83.18266     112  255
## X6     1      32     60 59.97521      88  255
## dimension(s):
##   from  to  offset delta                     refsys point x/y
## x    1 349  288776  28.5 SIRGAS 2000 / UTM zone 25S FALSE [x]
## y    1 352 9120761 -28.5 SIRGAS 2000 / UTM zone 25S FALSE [y]
merge(x.spl)
## stars object with 3 dimensions and 1 attribute
## attribute(s):
##                    Min. 1st Qu. Median     Mean 3rd Qu. Max.
## X1.X2.X3.X4.X5.X6     1      54     69 68.91242      86  255
## dimension(s):
##            from  to  offset delta                     refsys point    values
## x             1 349  288776  28.5 SIRGAS 2000 / UTM zone 25S FALSE      NULL
## y             1 352 9120761 -28.5 SIRGAS 2000 / UTM zone 25S FALSE      NULL
## attributes    1   6      NA    NA                         NA    NA X1,...,X6
##            x/y
## x          [x]
## y          [y]
## attributes

We see that the newly created dimension lost its name, and the single attribute got a default name. We can set attribute names with setNames, and dimension names and values with st_set_dimensions:

merge(x.spl) |>
  setNames(names(x)) |> 
  st_set_dimensions(3, values = paste0("band", 1:6)) |>
  st_set_dimensions(names = c("x", "y", "band"))
## stars object with 3 dimensions and 1 attribute
## attribute(s):
##              Min. 1st Qu. Median     Mean 3rd Qu. Max.
## L7_ETMs.tif     1      54     69 68.91242      86  255
## dimension(s):
##      from  to  offset delta                     refsys point          values
## x       1 349  288776  28.5 SIRGAS 2000 / UTM zone 25S FALSE            NULL
## y       1 352 9120761 -28.5 SIRGAS 2000 / UTM zone 25S FALSE            NULL
## band    1   6      NA    NA                         NA    NA band1,...,band6
##      x/y
## x    [x]
## y    [y]
## band

Subsetting

Besides the tidyverse subsetting and selection operators explained in this vignette, we can also use [ and [[.

Since stars objects are a list of arrays with a metadata table describing dimensions, list extraction (and assignment) works as expected:

class(x[[1]])
## [1] "array"
dim(x[[1]])
##    x    y band 
##  349  352    6
x$two = 2 * x[[1]]
x
## stars object with 3 dimensions and 2 attributes
## attribute(s):
##              Min. 1st Qu. Median      Mean 3rd Qu. Max.
## L7_ETMs.tif     1      54     69  68.91242      86  255
## two             2     108    138 137.82484     172  510
## dimension(s):
##      from  to  offset delta                     refsys point x/y
## x       1 349  288776  28.5 SIRGAS 2000 / UTM zone 25S FALSE [x]
## y       1 352 9120761 -28.5 SIRGAS 2000 / UTM zone 25S FALSE [y]
## band    1   6      NA    NA                         NA    NA

At this level, we can work with array objects directly.

The stars subset operator [ works a bit different: its

  • first argument selects attributes
  • second argument selects the first dimension
  • third argument selects the second dimension, etc

Thus,

x["two", 1:10, , 2:4]
## stars object with 3 dimensions and 1 attribute
## attribute(s):
##      Min. 1st Qu. Median     Mean 3rd Qu. Max.
## two    36     100    116 119.7326     136  470
## dimension(s):
##      from  to  offset delta                     refsys point x/y
## x       1  10  288776  28.5 SIRGAS 2000 / UTM zone 25S FALSE [x]
## y       1 352 9120761 -28.5 SIRGAS 2000 / UTM zone 25S FALSE [y]
## band    2   4      NA    NA                         NA    NA

selects the second attribute, the first 10 columns (x-coordinate), all rows, and bands 2-4.

Alternatively, when [ is given a single argument of class sf, sfc or bbox, [ will work as a crop operator:

circle = st_sfc(st_buffer(st_point(c(293749.5, 9115745)), 400), crs = st_crs(x))
plot(x[circle][, , , 1], reset = FALSE)
plot(circle, col = NA, border = 'red', add = TRUE, lwd = 2)

Overviews

We can read rasters at a lower resolution when they contain so-called overviews. For this GeoTIFF file, they were created with the gdaladdo utility, in particular

gdaladdo -r average L7_ETMs.tif  2 4 8 16

which adds coarse resolution versions by using the average resampling method to compute values based on blocks of pixels. These can be read by

x1 = read_stars(tif, options = c("OVERVIEW_LEVEL=1"))
x2 = read_stars(tif, options = c("OVERVIEW_LEVEL=2"))
x3 = read_stars(tif, options = c("OVERVIEW_LEVEL=3"))
dim(x1)
##    x    y band 
##   88   88    6
dim(x2)
##    x    y band 
##   44   44    6
dim(x3)
##    x    y band 
##   22   22    6
par(mfrow = c(1, 3), mar = rep(0.2, 4))
image(x1[,,,1])
image(x2[,,,1])
image(x3[,,,1])

Reading a raster time series: NetCDF

Another example is when we read raster time series model outputs in a NetCDF file, e.g. by

system.file("nc/bcsd_obs_1999.nc", package = "stars") |>
    read_stars() -> w
## pr, tas,

We see that

w
## stars object with 3 dimensions and 2 attributes
## attribute(s):
##                 Min.   1st Qu.   Median      Mean   3rd Qu.      Max. NA's
## pr [mm/m]  0.5900000 56.139999 81.88000 101.26433 121.07250 848.54999 7116
## tas [C]   -0.4209678  8.898887 15.65763  15.48932  21.77979  29.38581 7116
## dimension(s):
##      from to offset  delta  refsys                    values x/y
## x       1 81    -85  0.125      NA                      NULL [x]
## y       1 33  37.12 -0.125      NA                      NULL [y]
## time    1 12     NA     NA POSIXct 1999-01-31,...,1999-12-31

For this dataset we can see that

  • variables have units associated (and a wrong unit, C is assigned to temperature)
  • time is now a dimension, with proper units and time steps

Alternatively, this dataset can be read using read_ncdf, as in

system.file("nc/bcsd_obs_1999.nc", package = "stars") |>
    read_ncdf()
## no 'var' specified, using pr, tas
## other available variables:
##  latitude, longitude, time
## Will return stars object with 32076 cells.
## No projection information found in nc file. 
##  Coordinate variable units found to be degrees, 
##  assuming WGS84 Lat/Lon.
## stars object with 3 dimensions and 2 attributes
## attribute(s):
##                 Min.   1st Qu.   Median      Mean   3rd Qu.      Max. NA's
## pr [mm/m]  0.5900000 56.139999 81.88000 101.26433 121.07250 848.54999 7116
## tas [C]   -0.4209678  8.898887 15.65763  15.48932  21.77979  29.38581 7116
## dimension(s):
##           from to offset delta         refsys                    values x/y
## longitude    1 81    -85 0.125 WGS 84 (CRS84)                      NULL [x]
## latitude     1 33     33 0.125 WGS 84 (CRS84)                      NULL [y]
## time         1 12     NA    NA        POSIXct 1999-01-31,...,1999-12-31

The difference between read_ncdf and read_stars for NetCDF files is that the former uses package RNetCDF to directly read the NetCDF file, where the latter uses the GDAL driver for NetCDF files.

Reading datasets from multiple files

Model data are often spread across many files. An example of a 0.25 degree grid, global daily sea surface temperature product is found here; the subset from 1981 used below was downloaded from a NOAA ftp site that is no longer available in this form. (ftp site used to be eclipse.ncdc.noaa.gov/pub/OI-daily-v2/NetCDF/1981/AVHRR/).

We read the data by giving read_stars a vector with character names:

x = c(
"avhrr-only-v2.19810901.nc",
"avhrr-only-v2.19810902.nc",
"avhrr-only-v2.19810903.nc",
"avhrr-only-v2.19810904.nc",
"avhrr-only-v2.19810905.nc",
"avhrr-only-v2.19810906.nc",
"avhrr-only-v2.19810907.nc",
"avhrr-only-v2.19810908.nc",
"avhrr-only-v2.19810909.nc"
)
# see the second vignette:
# install.packages("starsdata", repos = "http://pebesma.staff.ifgi.de", type = "source")
file_list = system.file(paste0("netcdf/", x), package = "starsdata")
(y = read_stars(file_list, quiet = TRUE))
## stars object with 4 dimensions and 4 attributes
## attribute(s), summary of first 1e+05 cells:
##                Min. 1st Qu. Median       Mean 3rd Qu. Max.  NA's
## sst [C*°]     -1.80   -1.19  -1.05 -0.3201670   -0.20 9.36 13360
## anom [C*°]    -4.69   -0.06   0.52  0.2299385    0.71 3.70 13360
## err [C*°]      0.11    0.30   0.30  0.2949421    0.30 0.48 13360
## ice [percent]  0.01    0.73   0.83  0.7657695    0.87 1.00 27377
## dimension(s):
##      from   to         offset  delta  refsys x/y
## x       1 1440              0   0.25      NA [x]
## y       1  720             90  -0.25      NA [y]
## zlev    1    1          0 [m]     NA      NA    
## time    1    9 1981-09-01 UTC 1 days POSIXct

Next, we select sea surface temperature (sst), and drop the singular zlev (depth) dimension using adrop:

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(abind)
z <- y |> select(sst) |> adrop()

We can now graph the sea surface temperature (SST) using ggplot, which needs data in a long table form, and without units:

# convert POSIXct time to character, to please ggplot's facet_wrap()
z1 = st_set_dimensions(z, 3, values = as.character(st_get_dimension_values(z, 3)))
library(ggplot2)
library(viridis)
## Loading required package: viridisLite
library(ggthemes)
ggplot() +  
  geom_stars(data = z1[1], alpha = 0.8, downsample = c(10, 10, 1)) + 
  facet_wrap("time") +
  scale_fill_viridis() +
  coord_equal() +
  theme_map() +
  theme(legend.position = "bottom") +
  theme(legend.key.width = unit(2, "cm"))

Writing stars objects to disk

We can write a stars object to disk by using write_stars; this used the GDAL write engine. Writing NetCDF files without going through the GDAL interface is currently not supported. write_stars currently writes only a single attribute:

write_stars(adrop(y[1]), "sst.tif")

See the explanation of merge above to see how multiple attributes can be merged (folded) into a dimension.

Cropping a raster’s extent

Using a curvilinear grid, taken from the example of read_ncdf:

prec_file = system.file("nc/test_stageiv_xyt.nc", package = "stars")
prec = read_ncdf(prec_file, curvilinear = c("lon", "lat"))
## no 'var' specified, using Total_precipitation_surface_1_Hour_Accumulation
## other available variables:
##  lat, lon, time
## Will return stars object with 236118 cells.
## No projection information found in nc file. 
##  Coordinate variable units found to be degrees, 
##  assuming WGS84 Lat/Lon.
##plot(prec) ## gives error about unique breaks
## remove NAs, zeros, and give a large number
## of breaks (used for validating in detail)
qu_0_omit = function(x, ..., n = 22) {
  if (inherits(x, "units"))
    x = units::drop_units(na.omit(x))
  c(0, quantile(x[x > 0], seq(0, 1, length.out = n)))
}
library(dplyr) # loads slice generic
prec_slice = slice(prec, index = 17, along = "time")
plot(prec_slice, border = NA, breaks = qu_0_omit(prec_slice[[1]]), reset = FALSE)
nc = sf::read_sf(system.file("gpkg/nc.gpkg", package = "sf"), "nc.gpkg")
plot(st_geometry(nc), add = TRUE, reset = FALSE, col = NA, border = 'red')

We can now crop the grid to those cells falling in

nc = st_transform(nc, st_crs(prec_slice)) # datum transformation
plot(prec_slice[nc], border = NA, breaks = qu_0_omit(prec_slice[[1]]), reset = FALSE)
plot(st_geometry(nc), add = TRUE, reset = FALSE, col = NA, border = 'red')

The selection prec_slice[nc] essentially calls st_crop(prec_slice, nc) to get a cropped selection. What happened here is that all cells not intersecting with North Carolina (sea) are set to NA values. For regular grids, the extent of the resulting stars object is also be reduced (cropped) by default; this can be controlled with the crop parameter to st_crop and [.stars.

Vector data cube example

Like tbl_cube, stars arrays have no limits to the number of dimensions they handle. An example is the origin-destination (OD) matrix, by time and travel mode.

OD: space x space x travel mode x time x time

We create a 5-dimensional matrix of traffic between regions, by day, by time of day, and by travel mode. Having day and time of day each as dimension is an advantage when we want to compute patterns over the day, for a certain period.

nc = st_read(system.file("gpkg/nc.gpkg", package="sf")) 
## Reading layer `nc.gpkg' from data source 
##   `/home/edzer/R/x86_64-pc-linux-gnu-library/4.4/sf/gpkg/nc.gpkg' 
##   using driver `GPKG'
## 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
to = from = st_geometry(nc) # 100 polygons: O and D regions
mode = c("car", "bike", "foot") # travel mode
day = 1:100 # arbitrary
library(units)
## udunits database from /usr/share/xml/udunits/udunits2.xml
units(day) = as_units("days since 2015-01-01")
hour = set_units(0:23, h) # hour of day
dims = st_dimensions(origin = from, destination = to, mode = mode, day = day, hour = hour)
(n = dim(dims))
##      origin destination        mode         day        hour 
##         100         100           3         100          24
traffic = array(rpois(prod(n), 10), dim = n) # simulated traffic counts
(st = st_as_stars(list(traffic = traffic),  dimensions = dims))
## stars object with 5 dimensions and 1 attribute
## attribute(s), summary of first 1e+05 cells:
##          Min. 1st Qu. Median    Mean 3rd Qu. Max.
## traffic     0       8     10 9.99579      12   26
## dimension(s):
##             from  to                      offset                       delta
## origin         1 100                          NA                          NA
## destination    1 100                          NA                          NA
## mode           1   3                          NA                          NA
## day            1 100 1 [(days since 2015-01-01)] 1 [(days since 2015-01-01)]
## hour           1  24                       0 [h]                       1 [h]
##              refsys point
## origin        NAD27 FALSE
## destination   NAD27 FALSE
## mode             NA FALSE
## day         udunits FALSE
## hour        udunits FALSE
##                                                                    values
## origin      MULTIPOLYGON (((-81.47276...,...,MULTIPOLYGON (((-78.65572...
## destination MULTIPOLYGON (((-81.47276...,...,MULTIPOLYGON (((-78.65572...
## mode                                                     car , bike, foot
## day                                                                  NULL
## hour                                                                 NULL

This array contains the simple feature geometries of origin and destination so that we can directly plot every slice without additional table joins. If we want to represent such an array as a tbl_cube, the simple feature geometry dimensions need to be replaced by indexes:

st |> as.tbl_cube()
## Source: local array [72,000,000 x 5]
## D: origin [int, 100]
## D: destination [int, 100]
## D: mode [chr, 3]
## D: day [[(days since 2015-01-01)], 100]
## D: hour [[h], 24]
## M: traffic [int[,100,3,100,24]]

The following demonstrates how we can use dplyr to filter travel mode bike, and compute mean bike traffic by hour of day:

b <- st |> 
  as.tbl_cube() |> 
  filter(mode == "bike") |> 
  group_by(hour) |>
  summarise(traffic = mean(traffic)) |> 
  as.data.frame()
require(ggforce) # for plotting a units variable
## Loading required package: ggforce
ggplot() +  
  geom_line(data = b, aes(x = hour, y = traffic))
## Warning: The `scale_name` argument of `continuous_scale()` is deprecated as of ggplot2
## 3.5.0.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.

Extracting at point locations, aggregating over polygons

Data cube values at point location can be extracted by st_extract, an example is found in vignette 7

Aggregates, such as mean, maximum or modal values can be obtained by aggregate. In this example we use a categorical raster, and try to find the modal (most frequent) class within two circular polygons:

s = system.file("tif/lc.tif", package = "stars")
r = read_stars(s, proxy = FALSE) |> droplevels()
levels(r[[1]]) = abbreviate(levels(r[[1]]), 10) # shorten text labels
st_point(c(3190631, 3125)) |> st_sfc(crs = st_crs(r)) |> st_buffer(25000) -> pol1
st_point(c(3233847, 21027)) |> st_sfc(crs = st_crs(r)) |> st_buffer(10000) -> pol2
if (isTRUE(dev.capabilities()$rasterImage == "yes")) {
  plot(r, reset = FALSE, key.pos = 4)
  plot(c(pol1, pol2), col = NA, border = c('yellow', 'green'), lwd = 2, add = TRUE)
}

To find the modal value, we need a function that gives back the label corresponding to the class which is most frequent, using table:

f = function(x) { tb = table(x); names(tb)[which.max(tb)] }

We can then call aggregate on the raster map, and the set of the two circular polygons pol1 and pol2, and pass the function f:

aggregate(r, c(pol1, pol2), f) |> st_as_sf()
## Simple feature collection with 2 features and 1 field
## Geometry type: POLYGON
## Dimension:     XY
## Bounding box:  xmin: 3165631 ymin: -21875 xmax: 3243847 ymax: 31027
## Projected CRS: Albers Conical Equal Area
##       lc.tif                       geometry
## 1 EvrgrnFrst POLYGON ((3215631 3125, 321...
## 2 Dvlpd,MdmI POLYGON ((3243847 21027, 32...
stars/inst/doc/stars5.R0000644000176200001440000000433714644756273014525 0ustar liggesusers## ----setup, include=FALSE----------------------------------------------------- knitr::opts_chunk$set(echo = TRUE, collapse = TRUE, dev = "png") suppressPackageStartupMessages(library(sf)) knitr::opts_chunk$set(fig.height = 4.5) knitr::opts_chunk$set(fig.width = 6) ev = TRUE ## ----------------------------------------------------------------------------- library(stars) system.file("gpkg/nc.gpkg", package = "sf") %>% read_sf() %>% st_transform(32119) -> nc nc$dens = nc$BIR79 / units::set_units(st_area(nc), km^2) (nc.st = st_rasterize(nc["dens"], dx = 5000, dy = 5000)) plot(nc.st) ## ----------------------------------------------------------------------------- tif = system.file("tif/L7_ETMs.tif", package = "stars") x = read_stars(tif)[, 1:50, 1:50, 1:2] x[[1]] = round(x[[1]]/5) ## ----eval=ev------------------------------------------------------------------ l = st_contour(x, contour_lines = TRUE, breaks = 11:15) plot(l[1], key.pos = 1, pal = sf.colors, lwd = 2, key.length = 0.8) ## ----------------------------------------------------------------------------- st_as_sf(x, as_points = TRUE, merge = FALSE) ## ----------------------------------------------------------------------------- st_as_sf(x, as_points = TRUE, merge = FALSE, long = TRUE) ## ----------------------------------------------------------------------------- st_as_sf(x[1], as_points = FALSE, merge = FALSE) ## ----------------------------------------------------------------------------- p = st_as_sf(x, as_points = FALSE, merge = TRUE) ## ----------------------------------------------------------------------------- plot(p) ## ----------------------------------------------------------------------------- x.sf = st_xy2sfc(x, as_points = TRUE) x.sf ## ----------------------------------------------------------------------------- nc.st %>% st_transform("+proj=laea +lat_0=34 +lon_0=-60") -> nc.curv nc.curv plot(nc.curv, border = NA, graticule = TRUE) ## ----------------------------------------------------------------------------- nc %>% st_transform("+proj=laea +lat_0=34 +lon_0=-60") %>% st_bbox() %>% st_as_stars() -> newgrid ## ----------------------------------------------------------------------------- nc.st %>% st_warp(newgrid) -> nc.new nc.new plot(nc.new) stars/inst/doc/stars5.html0000644000176200001440000066177714644756273015311 0ustar liggesusers 5. vector-raster conversions, reprojection, warping

5. vector-raster conversions, reprojection, warping

Edzer Pebesma

For a better version of the stars vignettes see https://r-spatial.github.io/stars/articles/

This vignette shows how stars object can be moved from vector and raster representations.

Rasterizing an sf vector object

library(stars)
system.file("gpkg/nc.gpkg", package = "sf") %>%
  read_sf() %>%
  st_transform(32119) -> nc
nc$dens = nc$BIR79 / units::set_units(st_area(nc), km^2)
(nc.st = st_rasterize(nc["dens"], dx = 5000, dy = 5000))
## stars object with 2 dimensions and 1 attribute
## attribute(s):
##                    Min.  1st Qu. Median     Mean  3rd Qu.     Max. NA's
## dens [1/km^2] 0.2545128 1.225654 1.9322 3.345956 3.825793 21.24795 4808
## dimension(s):
##   from  to offset delta                 refsys point x/y
## x    1 162 123830  5000 NAD83 / North Carolina FALSE [x]
## y    1  61 318256 -5000 NAD83 / North Carolina FALSE [y]
plot(nc.st)

The algorithm used is the GDAL rasterize utility, all options of this utility can be passed to st_rasterize. The geometry of the final raster can be controlled by passing a target bounding box and either the raster dimensions nx and ny, or pixel size by the dx and dy parameters.

Vectorizing a raster object to an sf object

stars objects can be converted into an sf object using st_as_sf. It has a number of options, depending on whether pixels represent the point value at the pixel center, or small square polygons with a single value.

We will work again with the landsat-7 6-band image, but will select the first band and round the values:

tif = system.file("tif/L7_ETMs.tif", package = "stars")
x = read_stars(tif)[, 1:50, 1:50, 1:2]
x[[1]] = round(x[[1]]/5)

Polygonizing

In case raster cells reflect point values and we want to get a vector representation of the whole field, we can draw contour lines and export the contour sets (only available when the GDAL version is at least 2.4.0):

l =  st_contour(x, contour_lines = TRUE, breaks = 11:15)
plot(l[1], key.pos = 1, pal = sf.colors, lwd = 2, key.length = 0.8)

Exporting to points

Alternatively, we can simply export all the pixels as points, and get them either as a wide table with all bands per point, and no replicated POINT geometries:

st_as_sf(x, as_points = TRUE, merge = FALSE)
## Simple feature collection with 2500 features and 2 fields
## Geometry type: POINT
## Dimension:     XY
## Bounding box:  xmin: 288790.5 ymin: 9119350 xmax: 290187 ymax: 9120747
## Projected CRS: SIRGAS 2000 / UTM zone 25S
## First 10 features:
##    L7_ETMs.tif.V1 L7_ETMs.tif.V2                 geometry
## 1              14             11 POINT (288790.5 9120747)
## 2              14             11   POINT (288819 9120747)
## 3              13             10 POINT (288847.5 9120747)
## 4              12              9   POINT (288876 9120747)
## 5              12             10 POINT (288904.5 9120747)
## 6              12             10   POINT (288933 9120747)
## 7              12             10 POINT (288961.5 9120747)
## 8              12             10   POINT (288990 9120747)
## 9              13             10 POINT (289018.5 9120747)
## 10             13             10   POINT (289047 9120747)

or as a long table with a single attribute and all points replicated:

st_as_sf(x, as_points = TRUE, merge = FALSE, long = TRUE)
## Simple feature collection with 5000 features and 2 fields
## Geometry type: POINT
## Dimension:     XY
## Bounding box:  xmin: 288790.5 ymin: 9119350 xmax: 290187 ymax: 9120747
## Projected CRS: SIRGAS 2000 / UTM zone 25S
## First 10 features:
##    band L7_ETMs.tif                 geometry
## 1     1          14 POINT (288790.5 9120747)
## 2     1          14   POINT (288819 9120747)
## 3     1          13 POINT (288847.5 9120747)
## 4     1          12   POINT (288876 9120747)
## 5     1          12 POINT (288904.5 9120747)
## 6     1          12   POINT (288933 9120747)
## 7     1          12 POINT (288961.5 9120747)
## 8     1          12   POINT (288990 9120747)
## 9     1          13 POINT (289018.5 9120747)
## 10    1          13   POINT (289047 9120747)

as we can see, an additional attribute band now indicates which band is concerned.

Exporting to polygons

Alternatively, we can export to polygons and either get a single polygon per pixel, as in

st_as_sf(x[1], as_points = FALSE, merge = FALSE)
## Simple feature collection with 2500 features and 2 fields
## Geometry type: POLYGON
## Dimension:     XY
## Bounding box:  xmin: 288776.3 ymin: 9119336 xmax: 290201.3 ymax: 9120761
## Projected CRS: SIRGAS 2000 / UTM zone 25S
## First 10 features:
##    L7_ETMs.tif.V1 L7_ETMs.tif.V2                       geometry
## 1              14             11 POLYGON ((288776.3 9120761,...
## 2              14             11 POLYGON ((288804.8 9120761,...
## 3              13             10 POLYGON ((288833.3 9120761,...
## 4              12              9 POLYGON ((288861.8 9120761,...
## 5              12             10 POLYGON ((288890.3 9120761,...
## 6              12             10 POLYGON ((288918.8 9120761,...
## 7              12             10 POLYGON ((288947.3 9120761,...
## 8              12             10 POLYGON ((288975.8 9120761,...
## 9              13             10 POLYGON ((289004.3 9120761,...
## 10             13             10 POLYGON ((289032.8 9120761,...

or merge polygons that have identical pixel values;

p = st_as_sf(x, as_points = FALSE, merge = TRUE)

When plotted with boundaries, we see the resolved boundaries of areas with the same pixel value:

plot(p)

A further option connect8 can be set to TRUE to use 8 connectedness, rather than the default 4 connectedness algorithm. In both cases, the polygons returned will often be invalid according to the simple feature standard, but can be made valid using lwgeom::st_make_valid.

Switching between vector and raster in stars objects

We can convert a raster dimension to a vector dimension while keeping other dimensions as they are in a stars object by

x.sf = st_xy2sfc(x, as_points = TRUE)
x.sf
## stars object with 2 dimensions and 1 attribute
## attribute(s):
##              Min. 1st Qu. Median    Mean 3rd Qu. Max.
## L7_ETMs.tif     7       9     11 11.2548      12   28
## dimension(s):
##          from   to                     refsys point
## geometry    1 2500 SIRGAS 2000 / UTM zone 25S  TRUE
## band        1    2                         NA    NA
##                                                       values
## geometry POINT (288790.5 9120747),...,POINT (290187 9119350)
## band                                                    NULL

which also requires setting the as_points arguments as in st_as_sf.

Reprojecting a raster

If we accept that curvilinear rasters are rasters too, and that regular and rectilinear grids are special cases of curvilinear grids, reprojecting a raster is no longer a “problem”, it just recomputes new coordinates for every raster cell, and generally results in a curvilinear grid (that sometimes can be brought back to a regular or rectilinear grid). If curvilinear grid cells are represented by coordinates of the cell center, the actual shape of a grid cell gets lost, and this may be a larger effect if grid cells are large or if the transformation is stronger non-linear.

An example of the reprojection of the grid created above is

nc.st %>% st_transform("+proj=laea +lat_0=34 +lon_0=-60") -> nc.curv
nc.curv
## stars object with 2 dimensions and 1 attribute
## attribute(s):
##                    Min.  1st Qu. Median     Mean  3rd Qu.     Max. NA's
## dens [1/km^2] 0.2545128 1.225654 1.9322 3.345956 3.825793 21.24795 4808
## dimension(s):
##   from  to                       refsys point                         values
## x    1 162 +proj=laea +lat_0=34 +lon... FALSE [162x61] -2210936,...,-1371611
## y    1  61 +proj=laea +lat_0=34 +lon... FALSE      [162x61] 90646,...,538200
##   x/y
## x [x]
## y [y]
## curvilinear grid
plot(nc.curv, border = NA, graticule = TRUE)

where it should be noted that the dimensionality of the grid didn’t change: the same set of raster cells has been replotted in the new CRS, but now in a curvilinear grid.

Warping a raster

Warping a raster means creating a new regular grid in a new CRS, based on a (usually regular) grid in another CRS. We can do the transformation of the previous section by first creating a target grid:

nc %>% st_transform("+proj=laea +lat_0=34 +lon_0=-60") %>% st_bbox() %>%
    st_as_stars() -> newgrid

and then warping the old raster to the new

nc.st %>% st_warp(newgrid) -> nc.new
nc.new 
## stars object with 2 dimensions and 1 attribute
## attribute(s):
##                    Min.  1st Qu. Median     Mean  3rd Qu.     Max.  NA's
## dens [1/km^2] 0.2545128 1.225654 1.9322 3.344844 3.825793 21.24795 36155
## dimension(s):
##   from  to   offset delta                       refsys x/y
## x    1 380 -2188108  2098 +proj=laea +lat_0=34 +lon... [x]
## y    1 171   494920 -2098 +proj=laea +lat_0=34 +lon... [y]
plot(nc.new)

This new object has a regular grid in the new CRS, aligned with the new x- and y-axes.

stars/inst/doc/stars4.Rmd0000644000176200001440000002652214644756215015041 0ustar liggesusers--- title: '4. stars data model' author: "Edzer Pebesma" output: rmarkdown::html_vignette vignette: > %\VignetteIndexEntry{4. stars data model} %\VignetteEngine{knitr::rmarkdown} %\VignetteEncoding{UTF-8} --- **For a better version of the stars vignettes see** https://r-spatial.github.io/stars/articles/ ```{r setup, include=FALSE} knitr::opts_chunk$set(echo = TRUE, collapse = TRUE, dev = "png") set.seed(13579) knitr::opts_chunk$set(fig.height = 4.5) knitr::opts_chunk$set(fig.width = 6) ``` This vignette explains the data model of `stars` objects, illustrated using artificial and real datasets. ## Stars objects `stars` objects consist of * a (possibly empty) named list of arrays, each having named dimensions (`dim`) attribute * an attribute called `dimensions` of class `dimensions` that carries dimension metadata * a class name that includes `stars` A `dimensions` object is a named list of `dimension` elements, each describing the semantics a dimension of the data arrays (space, time, type etc). In addition to that, a `dimensions` object has an attribute called `raster` of class `stars_raster`, which is a named list with three elements: * `dimensions` length 2 character; the dimension names that constitute a spatial raster (or NA) * `affine` length 2 numeric; the two affine parameters of the geotransform (or NA) * `curvilinear` a boolean indicating whether a raster is a curvilinear raster (or NA) The `affine` and `curvilinear` values are only relevant in case of raster data, indicated by `dimensions` to have non-NA values. A `dimension` object describes a _single_ dimension; it is a list with named elements * `from`: (numeric length 1): the start index of the array * `to`: (numeric length 1): the end index of the array * `offset`: (numeric length 1): the start coordinate (or time) value of the first pixel (i.e., a pixel/cell boundary) * `delta`: (numeric length 1): the increment, or cell size * `refsys`: (character, or `crs`): object describing the reference system; e.g. the PROJ string, or string `POSIXct` or `PCICt` (for 360 and 365 days/year calendars), or object of class `crs` (containing both EPSG code and proj4string) * `point`: (logical length 1): boolean indicating whether cells/pixels refer to areas/periods, or to points/instances (may be NA) * `values`: one of * `NULL` (missing), * a vector with coordinate values (numeric, `POSIXct`, `PCICt`, or `sfc`), * an object of class `intervals` (a list with two vectors, `start` and `end`, with interval start- and end-values), or * a matrix with longitudes or latitudes for all cells (in case of curvilinear grids) `from` and `to` will usually be 1 and the dimension size, but `from` may be larger than 1 in case a sub-grid got was selected (or cropped). `offset` and `delta` only apply to _regularly_ discretized dimensions, and are `NA` if this is not the case. If they are `NA`, dimension values may be held in the `values` field. Rectilinear and curvilinear grids need grid values in `values` that can be either: * for rectilinear grids: irregularly _spaced_ coordinate values, or coordinate _intervals_ of irregular width (a rectilinear grid _can_ have one dimension that is regular), * for curvilinear grids: or a matrix with grid cell centre values for _all_ row/col combinations (usually in longitude or latitude). Alternatively, `values` can contains a set of spatial geometries encoded in an `sfc` vector ("list-column"), in which case we have a [vector data cube](https://r-spatial.org/r/2022/09/12/vdc.html). ## Grid type ### Regular grids With a very simple file created from a $4 \times 5$ matrix ```{r fig.width=4.5, fig.height=4} suppressPackageStartupMessages(library(stars)) m = matrix(1:20, nrow = 5, ncol = 4) dim(m) = c(x = 5, y = 4) # named dim (s = st_as_stars(m)) ``` we see that * the rows (5) are mapped to the first dimension, the x-coordinate * the columns (4) are mapped to the second dimension, the y-coordinate * the `from` and `to` fields of each dimension define a range that corresponds to the array dimension: ```{r} dim(s[[1]]) ``` * offset and delta specify how increasing row and column index maps to x and y coordinate values respectively. When we plot this object, using the `image` method for `stars` objects, ```{r fig.width=4.5, fig.height=4} image(s, text_values = TRUE, axes = TRUE) ``` we see that $(0,0)$ is the origin of the grid (grid corner), and $1$ the coordinate value increase from one index (row, col) to the next. It means that consecutive matrix columns represent grid lines, going from south to north. Grids defined this way are **regular**: grid cell size is constant everywhere. Many actual grid datasets have y coordinates (grid rows) going from North to South (top to bottom); this is realised with a negative value for `delta`. We see that the grid origin $(0,0)$ did not change: ```{r fig.width=4.5, fig.height=4} attr(s, "dimensions")[[2]]$delta = -1 image(s, text_values = TRUE, axes = TRUE) ``` An example is the GeoTIFF carried in the package, which, as probably all data sources read through GDAL, has a negative `delta` for the `y`-coordinate: ```{r} tif = system.file("tif/L7_ETMs.tif", package = "stars") st_dimensions(read_stars(tif))["y"] ``` ### Raster attributes, rotated and sheared grids Dimension tables of `stars` objects carry a `raster` attribute: ```{r} str(attr(st_dimensions(s), "raster")) ``` which is a list that holds * `dimensions`: character, the names of raster dimensions (if any), as opposed to e.g. spectral, temporal or other dimensions * `affine`: numeric, the affine parameters * `curvilinear`: a logical indicating whether the raster is curvilinear These fields are needed at this level, because they describe properties of the array at a higher level than individual dimensions do: a pair of dimensions forms a raster, both `affine` and `curvilinear` describe how x and y _as a pair_ are derived from grid indexes (see below) when this cannot be done on a per-dimension basis. With two affine parameters $a_1$ and $a_2$, $x$ and $y$ coordinates are derived from (1-based) grid indexes $i$ and $j$, grid offset values $o_x$ and $o_y$, and grid cell sizes $d_x$ and $d_y$ by $$x = o_x + (i-1) d_x + (j-1) a_1$$ $$y = o_y + (i-1) a_2 + (j-1) d_y$$ Clearly, when $a_1=a_2=0$, $x$ and $y$ are entirely derived from their respective index, offset and cellsize. Note that for integer indexes, the coordinates are that of the starting edge of a grid cell; to get the grid cell center of the top left grid cell (in case of a negative $d_y$), use $i=1.5$ and $j=1.5$. We can rotate grids by setting $a_1$ and $a_2$ to a non-zero value: ```{r} attr(attr(s, "dimensions"), "raster")$affine = c(0.1, 0.1) plot(st_as_sf(s, as_points = FALSE), axes = TRUE, nbreaks = 20) ``` The rotation angle, in degrees, is ```{r} atan2(0.1, 1) * 180 / pi ``` Sheared grids are obtained when the two rotation coefficients, $a_1$ and $a_2$, are unequal: ```{r} attr(attr(s, "dimensions"), "raster")$affine = c(0.1, 0.2) plot(st_as_sf(s, as_points = FALSE), axes = TRUE, nbreaks = 20) ``` Now, the y-axis and x-axis have different rotation in degrees of respectively ```{r} atan2(c(0.1, 0.2), 1) * 180 / pi ``` ## Rectilinear grids [Rectilinear grids](https://en.wikipedia.org/wiki/Regular_grid) have orthogonal axes, but do not have congruent (equally sized and shaped) cells: each axis has its own irregular subdivision. We can define a rectilinear grid by specifying the cell _boundaries_, meaning for every dimension we specify _one more_ value than the dimension size: ```{r} x = c(0, 0.5, 1, 2, 4, 5) # 6 numbers: boundaries! y = c(0.3, 0.5, 1, 2, 2.2) # 5 numbers: boundaries! (r = st_as_stars(list(m = m), dimensions = st_dimensions(x = x, y = y))) st_bbox(r) image(r, axes = TRUE, col = grey((1:20)/20)) ``` Would we leave out the last value, than `stars` may come up with a _different_ cell boundary for the last cell, as this is now derived from the width of the one-but-last cell: ```{r} x = c(0, 0.5, 1, 2, 4) # 5 numbers: offsets only! y = c(0.3, 0.5, 1, 2) # 4 numbers: offsets only! (r = st_as_stars(list(m = m), dimensions = st_dimensions(x = x, y = y))) st_bbox(r) ``` This is not problematic if cells have a constant width, in which case the boundaries are reduced to an `offset` and `delta` value, irrespective whether an upper boundary is given: ```{r} x = c(0, 1, 2, 3, 4) # 5 numbers: offsets only! y = c(0.5, 1, 1.5, 2) # 4 numbers: offsets only! (r = st_as_stars(list(m = m), dimensions = st_dimensions(x = x, y = y))) st_bbox(r) ``` Alternatively, one can also set the _cell midpoints_ by specifying arguments `cell_midpoints` to the `st_dimensions` call: ```{r} x = st_as_stars(matrix(1:9, 3, 3), st_dimensions(x = c(1, 2, 3), y = c(2, 3, 10), cell_midpoints = TRUE)) ``` When the dimension is regular, this results in `offset` being shifted back with half a `delta`, or else in intervals derived from the distances between cell centers. This should obviously not be done when cell boundaries are specified. ## Curvilinear grids Curvilinear grids are grids whose grid lines are not straight. Rather than describing the curvature parametrically, the typical (HDF5 or NetCDF) files in which they are found have two raster layers with the longitudes and latitudes for every corresponding pixel of remaining layers. As an example, we will use a Sentinel 5P dataset available from package `starsdata`; this package can be installed with ```{r eval=FALSE} install.packages("starsdata", repos = "http://pebesma.staff.ifgi.de", type = "source") ``` The dataset is found here: ```{r} (s5p = system.file("sentinel5p/S5P_NRTI_L2__NO2____20180717T120113_20180717T120613_03932_01_010002_20180717T125231.nc", package = "starsdata")) ``` ```{r echo=FALSE} EVAL = s5p != "" ``` We can construct the curvilinear `stars` raster by calling `read_stars` on the right sub-array: ```{r eval=EVAL} subs = gdal_subdatasets(s5p) subs[[6]] ``` For this array, we can see the GDAL metadata under item `GEOLOCATION`: ```{r eval=EVAL} gdal_metadata(subs[[6]], "GEOLOCATION") ``` which reveals where, in this dataset, the longitude and latitude arrays are kept. ```{r eval=EVAL} nit.c = read_stars(subs[[6]]) threshold = units::set_units(9e+36, mol/m^2) nit.c[[1]][nit.c[[1]] > threshold] = NA nit.c ``` The curvilinear array has the actual arrays (raster layers, matrices) with longitude and latitude values read in its dimension table. We can plot this file: ```{r eval=EVAL} plot(nit.c, breaks = "equal", reset = FALSE, axes = TRUE, as_points = TRUE, pch = 16, logz = TRUE, key.length = 1) maps::map('world', add = TRUE, col = 'red') ``` ```{r eval=EVAL} plot(nit.c, breaks = "equal", reset = FALSE, axes = TRUE, as_points = FALSE, border = NA, logz = TRUE, key.length = 1) maps::map('world', add = TRUE, col = 'red') ``` We can downsample the data by ```{r eval=EVAL} (nit.c_ds = stars:::st_downsample(nit.c, 8)) plot(nit.c_ds, breaks = "equal", reset = FALSE, axes = TRUE, as_points = TRUE, pch = 16, logz = TRUE, key.length = 1) maps::map('world', add = TRUE, col = 'red') ``` which doesn't look nice, but plotting the cells as polygons looks better: ```{r eval=EVAL} plot(nit.c_ds, breaks = "equal", reset = FALSE, axes = TRUE, as_points = FALSE, border = NA, logz = TRUE, key.length = 1) maps::map('world', add = TRUE, col = 'red') ``` Another approach would be to warp the curvilinear grid to a regular grid, e.g. by ```{r eval=EVAL} w = st_warp(nit.c, crs = 4326, cellsize = 0.25) plot(w) ``` stars/inst/doc/stars6.R0000644000176200001440000001702614644756274014526 0ustar liggesusers## ----include = FALSE---------------------------------------------------------- knitr::opts_chunk$set(echo = FALSE, collapse = TRUE, dev = "png") suppressPackageStartupMessages(library(dplyr)) knitr::opts_chunk$set(fig.height = 4.5) knitr::opts_chunk$set(fig.width = 6) ## ----------------------------------------------------------------------------- tribble( ~raster, ~stars, ~`Note/comment`, "{raster, stack, brick} (read)", "read_stars or read_stars(along = ...)","", "{stack, brick} (concatenate layers)", "c or c(along = ...)","", "subset", "{[ ] , slice, filter}","", "addLayer", "c() or c(along = ...)","", "dropLayer", "{[ ] , slice, filter}","", "unstack", "combine lapply and {[ ] , slice, filter}","" ) %>% knitr::kable() ## ----------------------------------------------------------------------------- tribble( ~raster, ~stars, ~`Note/comment`, "merge", "c", "#, currently only works for adjacent objects", "mosaic", "st_mosaic", "these are not identical, read the docs carefully", "crop", "filter, st_crop", "", "setExtent", "", "# maybe use st_warp?", "trim", "", "#", "aggregate", "aggregate", "WIP; raster's aggregate with fact=2 will not work, use st_warp in that case?", "disaggregate", "", "# use st_warp(use_gdal = TRUE)?", "resample", "{st_transform, st_warp}", "", "projectRaster", "{st_transform, st_warp}", "", "shift", "", "#, now use st_set_dimensions", "flip", "[] with reversed index", "#", "rotate", "", "*", "t", "", "NA" ) %>% knitr::kable() ## ----------------------------------------------------------------------------- tribble( ~raster, ~stars, ~`Note/comment`, "calc", "st_apply", "", "overlay", "c(along = , ...) %>% st_apply(...)", "", "cover", "[ ] <-", "", "mask", "[ ], st_crop", "when using an sf polygon as mask", "mask", "[ ]<- NA", "when using a stars object to mask", "cut", "cut", "", "subs", "", "", "reclassify", "mutate with case_when", "or forcats::fct_recode ?", "reclassify", "cut", "see https://stackoverflow.com/questions/70619002/reclassify-2d-stars-array/", "init", "[ ] <-", "", "stackApply", "{[ ] , slice, filter} %>% st_apply", "", "stackSelect", "", "", ) %>% knitr::kable() ## ----------------------------------------------------------------------------- tribble( ~raster, ~stars, ~`Note/comment`, "distance", "", "#", "gridDistance", "", "*", "distanceFromPoints", "", "#", "direction", "", "*", "focal", "f = st_apply(x1, 3, foc, w = matrix(1, 3, 3))", "See. [issue 176](https://github.com/r-spatial/stars/issues/176)", "localFun", "", "*", "boundaries", "st_as_sf(as_points=FALSE, merge=TRUE, connect8=TRUE)", "", "clump", "st_as_sf(r, merge = TRUE)", "`st_as_sf` returns polygons, `clump` a raster", "adjacent", "", "*", "area", "st_area", "", "terrain", "", "#", "Moran", "", "" ) %>% knitr::kable() ## ----------------------------------------------------------------------------- tribble( ~raster, ~stars, ~`Note/comment`, "predict", "predict", "", "interpolate", "gstat::idw, gstat::krige", "`st_warp` has raster-raster interpolations of `gdalwarp`" ) %>% knitr::kable() ## ----------------------------------------------------------------------------- tribble( ~raster, ~stars, ~`Note/comment`, "rasterize", "st_as_stars", "", "rasterToPoints", "st_as_sf(as_points=TRUE)", "", "rasterToPolygons", "st_as_sf(as_points=FALSE, ...), st_polygonize", "", "rasterToContour", "st_contour", "requires GDAL >= 2.4.0", "rasterFromXYZ", "", "", "rasterFromCells", "", "", ) %>% knitr::kable() ## ----------------------------------------------------------------------------- tribble( ~raster, ~stars, ~`Note/comment`, "cellStats", "st_apply", "", "summary", "print, summary(as.vector(. %>% pull))", "", "freq", "table", "*", "crosstab", "", "", "unique", "unique(as.vector(. %>% pull))", "", "zonal", "", "*" ) %>% knitr::kable() ## ----------------------------------------------------------------------------- tribble( ~raster, ~stars, ~`Note/comment`, "getValues", "{pull, [[ ]]}", "", "getValuesBlock", "{[ ] , slice, filter} %>% pull", "", "getValuesFocal", "{[ ] , slice, filter} %>% pull", "", "as.matrix", "[[ ]]", "currently behaves somewhat unexpectedly *", "as.array", "[[ ]]", "currently behaves somewhat unexpectedly *", "extract (by cell)", "{[ ] , slice, filter}", "", "extract (by polygon)", "x[sf_object]", "", "extract (by point)", "aggregate(stars_object, sf_object, function(x) x[1], as_points = FALSE)", "", "sampleRandom", "", "*", "sampleRegular", "", "*", "minValue", "purrr::map(x, min)", "", "maxValue", "purrr::map(x, max)", "", "setMinMax", "", "," ) %>% knitr::kable() ## ----------------------------------------------------------------------------- tribble( ~raster, ~stars, ~`Note/comment`, "plot", "plot, geom_stars", "", "plotRGB", "plot(x, rgb =...)", "", "spplot", "-", "", "image", "image", "", "persp", "-", "", "contour", "(st_contour, then sf::plot)", "", "filledContour", "(same)", "", "text", "text", "", "`hist`", "`hist(x[[1]])`", "", "barplot", "", "", "density", "", "", "pairs", "", "", "boxplot", "", "" ) %>% knitr::kable() ## ----------------------------------------------------------------------------- tribble( ~raster, ~stars, ~`Note/comment`, "ncol", "dim(x)[1]", "or use name instead of 1; cols may be the second dimension!", "nrow", "dim(x)[2]", "or use name instead of 2; rows may be the first dimension!", "ncell", "prod(dim(x))", "", "res", "st_dimensions", "can also not be a constant in case of rectilinear or curvilinear grids", "nlayers", "-", "there is no concept of layers in stars", "names", "names", "", "xres", "st_res(x)[1]", "may not be a constant in case of rectilinear or curvilinear grids", "yres", "st_res(x)[2]", "may not be a constant in case of rectilinear or curvilinear grids", "xmin", "st_bbox(x)[1]", "", "xmax", "st_bbox(x)[3]", "", "ymin", "st_bbox(x)[2]", "", "ymax", "st_bbox(x)[4]", "", "extent", "st_bbox(x)", "different ordering of numbers", "origin", "-", "", "projection", "st_crs(x)", "", "isLonLat", "st_is_longlat(st_crs(x))", "", "filename", "", "stars_proxy objects carry file names where otherwise the array data is", "bandnr", "", "stars has no general concept of bands", "nbands", "dim(x)[3]", "may also be time; bands may also be in another dimension, or have another name", "compareRaster", "all.equal(st_dimensions(x), st_dimensions(y))", "*", "NAvalue", "", "-" ) %>% knitr::kable() ## ----------------------------------------------------------------------------- tribble( ~raster, ~stars, ~`Note/comment`, "xFromCol", "st_get_dimension_values(., 'x')[col]", "I am not sure how to generally get the x dimension name - here it is x", "yFromRow", "st_get_dimension_values(., 'y')[row]", "I am not sure how to generally get the y dimension name - here it is y", "xFromCell", "", "", "yFromCell", "", "", "xyFromCell", "", "", "colFromX", "", "*", "rowFromY", "", "*", "rowColFromCell", "", "", "cellFromXY", "st_cells()", "", "cellFromRowCol", "", "", "cellsFromExtent", "", "", "coordinates", "st_coordinates", "", "validCell", "", "", "validCol", "col %>% between(st_dimensions(.)$x$from, st_dimensions(.)$x$to)", "raster columns are not always named 'x'", "validRow", "row %>% between(st_dimensions(.)$y$from, st_dimensions(.)$y$to)", "raster columns are not always named 'y'", "setValues", "[ ] <-", "", "writeRaster", "write_stars", "currently uses GDAL, somewhat limited", "KML", "", "" ) %>% knitr::kable() stars/inst/doc/stars8.html0000644000176200001440000007747714644756275015314 0ustar liggesusers 8. NetCDF Proxy Workflows

8. NetCDF Proxy Workflows

David Blodgett

For a better version of the stars vignettes see https://r-spatial.github.io/stars/articles/

NetCDF data sources are available via more and less granular files and/or OPeNDAP endpoints. This article demonstrates how stars enables discovery, access, and processing of NetCDF data across a wide range of such source-data organization schemes.

We’ll start with some basics using datasets included with the stars installation. A call to read_ncdf(), for a dataset smaller than the default threshold, will just read in all the data. Below we read in and display the reduced.nc NetCDF file.

library(stars)
f <- system.file("nc/reduced.nc", package = "stars")
(nc <- read_ncdf(f))
## no 'var' specified, using sst, anom, err, ice
## other available variables:
##  lon, lat, zlev, time
## 0-360 longitude crossing the international date line encountered.
## Longitude coordinates will be 0-360 in output.
## Will return stars object with 16200 cells.
## No projection information found in nc file. 
##  Coordinate variable units found to be degrees, 
##  assuming WGS84 Lat/Lon.
## stars object with 4 dimensions and 4 attributes
## attribute(s):
##                Min. 1st Qu. Median       Mean 3rd Qu.  Max.  NA's
## sst [°C]      -1.80   -0.03 13.655 12.9940841 24.8125 32.97  4448
## anom [°C]     -7.95   -0.58 -0.080 -0.1847324  0.2100  2.99  4449
## err [°C]       0.11    0.16  0.270  0.2626872  0.3200  0.84  4448
## ice [percent]  0.01    0.47  0.920  0.7178118  0.9600  1.00 13266
## dimension(s):
##      from  to offset delta         refsys         values x/y
## lon     1 180     -1     2 WGS 84 (CRS84)           NULL [x]
## lat     1  90    -90     2 WGS 84 (CRS84)           NULL [y]
## zlev    1   1     NA    NA             NA              0    
## time    1   1     NA    NA        POSIXct 1981-12-31 UTC

Let’s assume reduced.nc was 10 years of hourly data, rather than 1 time step. It would be over 10GB rather than about 130KB and we would not be able to just read it all into memory. In this case, we need a way to read the file’s metadata such that we could iterate over it in a way that meets the needs of our workflow objectives. This is where proxy = TRUE comes in. Below, we’ll lower the option that controls whether read_ncdf() defaults to proxy and use proxy = TRUE to show both ways of getting the same result.

old_options <- options("stars.n_proxy" = 100)
(nc <- read_ncdf(f, proxy = TRUE))
## no 'var' specified, using sst, anom, err, ice
## other available variables:
##  lon, lat, zlev, time
## 0-360 longitude crossing the international date line encountered.
## Longitude coordinates will be 0-360 in output.
## No projection information found in nc file. 
##  Coordinate variable units found to be degrees, 
##  assuming WGS84 Lat/Lon.
## netcdf source stars proxy object from:
## [1] "[...]/reduced.nc"
## 
## Available nc variables:
## sst
## anom
## err
## ice
## 
## dimension(s):
##      from  to offset delta         refsys         values x/y
## lon     1 180     -1     2 WGS 84 (CRS84)           NULL [x]
## lat     1  90    -90     2 WGS 84 (CRS84)           NULL [y]
## zlev    1   1     NA    NA             NA              0    
## time    1   1     NA    NA        POSIXct 1981-12-31 UTC
options(old_options)

The above shows that we have a NetCDF sourced stars proxy derived from the reduced.nc file. We see it has four variables and their units are displayed. The normal stars dimension(s) are available and a nc_request object is also available. The nc_request object contains the information needed to make requests for data according to the dimensions of the NetCDF data source. With this information, we have what we need to request a chunk of data that is what we want and not too large.

(nc <- read_ncdf(f, 
                 var = "sst", 
                 ncsub = cbind(start = c(90, 45, 1 , 1), 
                              count = c(90, 45, 1, 1))))
## 0-360 longitude crossing the international date line encountered.
## Longitude coordinates will be 0-360 in output.
## Will return stars object with 4050 cells.
## No projection information found in nc file. 
##  Coordinate variable units found to be degrees, 
##  assuming WGS84 Lat/Lon.
## stars object with 4 dimensions and 1 attribute
## attribute(s):
##          Min. 1st Qu. Median     Mean 3rd Qu.  Max. NA's
## sst [°C] -1.8   -1.04     14 12.92722   25.13 29.81  757
## dimension(s):
##      from to offset delta         refsys         values x/y
## lon     1 90    177     2 WGS 84 (CRS84)           NULL [x]
## lat     1 45     -2     2 WGS 84 (CRS84)           NULL [y]
## zlev    1  1     NA    NA             NA              0    
## time    1  1     NA    NA        POSIXct 1981-12-31 UTC

plot(nc)

The ability to view NetCDF metadata so we can make well formed requests against the data is useful, but the real power of a proxy object is that we can use it in a “lazy evaluation” coding style. That is, we can do virtual operations on the object, like subsetting with another dataset, prior to actually accessing the data volume.

Lazy operations.

There are two kinds of lazy operations possible with stars_proxy objects. Some can be applied to the stars_proxy object itself without accessing underlying data. Others must be composed as a chain of calls that will be applied when data is actually required.

Methods applied to a stars_proxy object:

  • [ - Nearly the same as stars_proxy
  • [[<- - stars_proxy method works
  • print - unique method for nc_proxy to facilitate unique workflows
  • dim - stars_proxy method works
  • c - stars_proxy method works
  • st_redimension - Not sure what this entails but it might not make sense for nc_proxy.
  • st_mosaic * Calls read_stars on assembled list. Not supported for now.
  • st_set_bbox

Methods that add a call to the call_list.

  • [<-
  • adrop
  • aperm
  • is.na
  • split
  • st_apply
  • predict
  • merge
  • st_crop
  • drop_levels
  • Ops (group generic for +, -, etc.)
  • Math (group generic for abs, sqrt, tan, etc.)
  • filter
  • mutate
  • tansmute
  • select
  • rename
  • pull
  • slice * hyperslabbing for NetCDF could be as above?
  • pull
  • replace_na

Methods that cause a stars_proxy object to be fetched and turned into a stars object.

stars/inst/doc/stars4.R0000644000176200001440000001152214644756272014515 0ustar liggesusers## ----setup, include=FALSE----------------------------------------------------- knitr::opts_chunk$set(echo = TRUE, collapse = TRUE, dev = "png") set.seed(13579) knitr::opts_chunk$set(fig.height = 4.5) knitr::opts_chunk$set(fig.width = 6) ## ----fig.width=4.5, fig.height=4---------------------------------------------- suppressPackageStartupMessages(library(stars)) m = matrix(1:20, nrow = 5, ncol = 4) dim(m) = c(x = 5, y = 4) # named dim (s = st_as_stars(m)) ## ----------------------------------------------------------------------------- dim(s[[1]]) ## ----fig.width=4.5, fig.height=4---------------------------------------------- image(s, text_values = TRUE, axes = TRUE) ## ----fig.width=4.5, fig.height=4---------------------------------------------- attr(s, "dimensions")[[2]]$delta = -1 image(s, text_values = TRUE, axes = TRUE) ## ----------------------------------------------------------------------------- tif = system.file("tif/L7_ETMs.tif", package = "stars") st_dimensions(read_stars(tif))["y"] ## ----------------------------------------------------------------------------- str(attr(st_dimensions(s), "raster")) ## ----------------------------------------------------------------------------- attr(attr(s, "dimensions"), "raster")$affine = c(0.1, 0.1) plot(st_as_sf(s, as_points = FALSE), axes = TRUE, nbreaks = 20) ## ----------------------------------------------------------------------------- atan2(0.1, 1) * 180 / pi ## ----------------------------------------------------------------------------- attr(attr(s, "dimensions"), "raster")$affine = c(0.1, 0.2) plot(st_as_sf(s, as_points = FALSE), axes = TRUE, nbreaks = 20) ## ----------------------------------------------------------------------------- atan2(c(0.1, 0.2), 1) * 180 / pi ## ----------------------------------------------------------------------------- x = c(0, 0.5, 1, 2, 4, 5) # 6 numbers: boundaries! y = c(0.3, 0.5, 1, 2, 2.2) # 5 numbers: boundaries! (r = st_as_stars(list(m = m), dimensions = st_dimensions(x = x, y = y))) st_bbox(r) image(r, axes = TRUE, col = grey((1:20)/20)) ## ----------------------------------------------------------------------------- x = c(0, 0.5, 1, 2, 4) # 5 numbers: offsets only! y = c(0.3, 0.5, 1, 2) # 4 numbers: offsets only! (r = st_as_stars(list(m = m), dimensions = st_dimensions(x = x, y = y))) st_bbox(r) ## ----------------------------------------------------------------------------- x = c(0, 1, 2, 3, 4) # 5 numbers: offsets only! y = c(0.5, 1, 1.5, 2) # 4 numbers: offsets only! (r = st_as_stars(list(m = m), dimensions = st_dimensions(x = x, y = y))) st_bbox(r) ## ----------------------------------------------------------------------------- x = st_as_stars(matrix(1:9, 3, 3), st_dimensions(x = c(1, 2, 3), y = c(2, 3, 10), cell_midpoints = TRUE)) ## ----eval=FALSE--------------------------------------------------------------- # install.packages("starsdata", repos = "http://pebesma.staff.ifgi.de", type = "source") ## ----------------------------------------------------------------------------- (s5p = system.file("sentinel5p/S5P_NRTI_L2__NO2____20180717T120113_20180717T120613_03932_01_010002_20180717T125231.nc", package = "starsdata")) ## ----echo=FALSE--------------------------------------------------------------- EVAL = s5p != "" ## ----eval=EVAL---------------------------------------------------------------- subs = gdal_subdatasets(s5p) subs[[6]] ## ----eval=EVAL---------------------------------------------------------------- gdal_metadata(subs[[6]], "GEOLOCATION") ## ----eval=EVAL---------------------------------------------------------------- nit.c = read_stars(subs[[6]]) threshold = units::set_units(9e+36, mol/m^2) nit.c[[1]][nit.c[[1]] > threshold] = NA nit.c ## ----eval=EVAL---------------------------------------------------------------- plot(nit.c, breaks = "equal", reset = FALSE, axes = TRUE, as_points = TRUE, pch = 16, logz = TRUE, key.length = 1) maps::map('world', add = TRUE, col = 'red') ## ----eval=EVAL---------------------------------------------------------------- plot(nit.c, breaks = "equal", reset = FALSE, axes = TRUE, as_points = FALSE, border = NA, logz = TRUE, key.length = 1) maps::map('world', add = TRUE, col = 'red') ## ----eval=EVAL---------------------------------------------------------------- (nit.c_ds = stars:::st_downsample(nit.c, 8)) plot(nit.c_ds, breaks = "equal", reset = FALSE, axes = TRUE, as_points = TRUE, pch = 16, logz = TRUE, key.length = 1) maps::map('world', add = TRUE, col = 'red') ## ----eval=EVAL---------------------------------------------------------------- plot(nit.c_ds, breaks = "equal", reset = FALSE, axes = TRUE, as_points = FALSE, border = NA, logz = TRUE, key.length = 1) maps::map('world', add = TRUE, col = 'red') ## ----eval=EVAL---------------------------------------------------------------- w = st_warp(nit.c, crs = 4326, cellsize = 0.25) plot(w) stars/inst/doc/stars1.R0000644000176200001440000001534514644756244014520 0ustar liggesusers## ----setup, include=FALSE----------------------------------------------------- knitr::opts_chunk$set(echo = TRUE, collapse = TRUE, dev = "png") ev = suppressWarnings(require(starsdata, quietly = TRUE)) knitr::opts_chunk$set(fig.height = 4.5) knitr::opts_chunk$set(fig.width = 6) ## ----------------------------------------------------------------------------- library(stars) ## ----------------------------------------------------------------------------- methods(class = "stars") ## ----------------------------------------------------------------------------- tif = system.file("tif/L7_ETMs.tif", package = "stars") x = read_stars(tif) plot(x, axes = TRUE) ## ----------------------------------------------------------------------------- x ## ----eval=ev------------------------------------------------------------------ library(cubelyr) as.tbl_cube(x) ## ----------------------------------------------------------------------------- (x.spl = split(x, "band")) merge(x.spl) ## ----------------------------------------------------------------------------- merge(x.spl) |> setNames(names(x)) |> st_set_dimensions(3, values = paste0("band", 1:6)) |> st_set_dimensions(names = c("x", "y", "band")) ## ----------------------------------------------------------------------------- class(x[[1]]) dim(x[[1]]) x$two = 2 * x[[1]] x ## ----------------------------------------------------------------------------- x["two", 1:10, , 2:4] ## ----------------------------------------------------------------------------- circle = st_sfc(st_buffer(st_point(c(293749.5, 9115745)), 400), crs = st_crs(x)) plot(x[circle][, , , 1], reset = FALSE) plot(circle, col = NA, border = 'red', add = TRUE, lwd = 2) ## ----------------------------------------------------------------------------- x1 = read_stars(tif, options = c("OVERVIEW_LEVEL=1")) x2 = read_stars(tif, options = c("OVERVIEW_LEVEL=2")) x3 = read_stars(tif, options = c("OVERVIEW_LEVEL=3")) dim(x1) dim(x2) dim(x3) par(mfrow = c(1, 3), mar = rep(0.2, 4)) image(x1[,,,1]) image(x2[,,,1]) image(x3[,,,1]) ## ----eval=ev------------------------------------------------------------------ system.file("nc/bcsd_obs_1999.nc", package = "stars") |> read_stars() -> w ## ----eval=ev------------------------------------------------------------------ w ## ----------------------------------------------------------------------------- system.file("nc/bcsd_obs_1999.nc", package = "stars") |> read_ncdf() ## ----eval=ev------------------------------------------------------------------ x = c( "avhrr-only-v2.19810901.nc", "avhrr-only-v2.19810902.nc", "avhrr-only-v2.19810903.nc", "avhrr-only-v2.19810904.nc", "avhrr-only-v2.19810905.nc", "avhrr-only-v2.19810906.nc", "avhrr-only-v2.19810907.nc", "avhrr-only-v2.19810908.nc", "avhrr-only-v2.19810909.nc" ) # see the second vignette: # install.packages("starsdata", repos = "http://pebesma.staff.ifgi.de", type = "source") file_list = system.file(paste0("netcdf/", x), package = "starsdata") (y = read_stars(file_list, quiet = TRUE)) ## ----eval=ev------------------------------------------------------------------ library(dplyr) library(abind) z <- y |> select(sst) |> adrop() ## ----eval=ev------------------------------------------------------------------ # convert POSIXct time to character, to please ggplot's facet_wrap() z1 = st_set_dimensions(z, 3, values = as.character(st_get_dimension_values(z, 3))) library(ggplot2) library(viridis) library(ggthemes) ggplot() + geom_stars(data = z1[1], alpha = 0.8, downsample = c(10, 10, 1)) + facet_wrap("time") + scale_fill_viridis() + coord_equal() + theme_map() + theme(legend.position = "bottom") + theme(legend.key.width = unit(2, "cm")) ## ----eval=ev------------------------------------------------------------------ write_stars(adrop(y[1]), "sst.tif") ## ----------------------------------------------------------------------------- prec_file = system.file("nc/test_stageiv_xyt.nc", package = "stars") prec = read_ncdf(prec_file, curvilinear = c("lon", "lat")) ##plot(prec) ## gives error about unique breaks ## remove NAs, zeros, and give a large number ## of breaks (used for validating in detail) qu_0_omit = function(x, ..., n = 22) { if (inherits(x, "units")) x = units::drop_units(na.omit(x)) c(0, quantile(x[x > 0], seq(0, 1, length.out = n))) } library(dplyr) # loads slice generic prec_slice = slice(prec, index = 17, along = "time") plot(prec_slice, border = NA, breaks = qu_0_omit(prec_slice[[1]]), reset = FALSE) nc = sf::read_sf(system.file("gpkg/nc.gpkg", package = "sf"), "nc.gpkg") plot(st_geometry(nc), add = TRUE, reset = FALSE, col = NA, border = 'red') ## ----------------------------------------------------------------------------- nc = st_transform(nc, st_crs(prec_slice)) # datum transformation plot(prec_slice[nc], border = NA, breaks = qu_0_omit(prec_slice[[1]]), reset = FALSE) plot(st_geometry(nc), add = TRUE, reset = FALSE, col = NA, border = 'red') ## ----------------------------------------------------------------------------- nc = st_read(system.file("gpkg/nc.gpkg", package="sf")) to = from = st_geometry(nc) # 100 polygons: O and D regions mode = c("car", "bike", "foot") # travel mode day = 1:100 # arbitrary library(units) units(day) = as_units("days since 2015-01-01") hour = set_units(0:23, h) # hour of day dims = st_dimensions(origin = from, destination = to, mode = mode, day = day, hour = hour) (n = dim(dims)) traffic = array(rpois(prod(n), 10), dim = n) # simulated traffic counts (st = st_as_stars(list(traffic = traffic), dimensions = dims)) ## ----eval=ev------------------------------------------------------------------ st |> as.tbl_cube() ## ----eval=ev------------------------------------------------------------------ b <- st |> as.tbl_cube() |> filter(mode == "bike") |> group_by(hour) |> summarise(traffic = mean(traffic)) |> as.data.frame() require(ggforce) # for plotting a units variable ggplot() + geom_line(data = b, aes(x = hour, y = traffic)) ## ----------------------------------------------------------------------------- s = system.file("tif/lc.tif", package = "stars") r = read_stars(s, proxy = FALSE) |> droplevels() levels(r[[1]]) = abbreviate(levels(r[[1]]), 10) # shorten text labels st_point(c(3190631, 3125)) |> st_sfc(crs = st_crs(r)) |> st_buffer(25000) -> pol1 st_point(c(3233847, 21027)) |> st_sfc(crs = st_crs(r)) |> st_buffer(10000) -> pol2 if (isTRUE(dev.capabilities()$rasterImage == "yes")) { plot(r, reset = FALSE, key.pos = 4) plot(c(pol1, pol2), col = NA, border = c('yellow', 'green'), lwd = 2, add = TRUE) } ## ----------------------------------------------------------------------------- f = function(x) { tb = table(x); names(tb)[which.max(tb)] } ## ----------------------------------------------------------------------------- aggregate(r, c(pol1, pol2), f) |> st_as_sf() stars/inst/doc/stars3.Rmd0000644000176200001440000000532114644756215015032 0ustar liggesusers--- title: "3. stars tidyverse methods" author: "Edzer Pebesma" output: rmarkdown::html_vignette vignette: > %\VignetteIndexEntry{3. stars tidyverse methods} %\VignetteEngine{knitr::rmarkdown} %\VignetteEncoding{UTF-8} --- **For a better version of the stars vignettes see** https://r-spatial.github.io/stars/articles/ ```{r setup, include=FALSE} knitr::opts_chunk$set(echo = TRUE, collapse = TRUE, dev = "png") ev = TRUE knitr::opts_chunk$set(fig.height = 4.5) knitr::opts_chunk$set(fig.width = 6) ``` This vignette shows how some of the tidyverse verbs can be used on `stars` objects. The `stars` and `tidyverse` packages are loaded by ```{r} library(stars) library(dplyr) ``` Methods now available for class `stars` are ``` {r} methods(class = "stars") ``` We will work with a three-band section of a landsat image: ```{r} system.file("tif/L7_ETMs.tif", package = "stars") %>% read_stars -> x x ``` ## `slice` `slice` slices a sub-array out of the cube; this is done by specifying the dimension on which to act, and the slice number. ```{r} x %>% slice(band, 6) -> x6 x6 ``` It returns a lower-dimensional array if a single element is selected along the slice dimension. ## `filter` Similar to `slice`, `filter` selects on dimensions but evaluates their values rather than their index: in ```{r} x %>% filter(x > 289000, x < 291000, band > 3) -> x7 x7 ``` the subarray is created based on the x _coordinate_ values. Note that `filter` converts the object to a `tbl_cube`, and uses the `dplyr` `filter` method for `tbl_cube` objects. This has the limitation that `stars` objects with rectilinear, curvilinear or simple feature geometries cannot be handled. For such objects, using regular `[` selection or using `st_crop` may be an alternative. ## `pull` `pull` pulls out an array from a stars object: ```{r} x %>% pull(1) -> x8 class(x8) dim(x8) ``` ## `mutate` ```{r} x %>% mutate(band2 = 2 * L7_ETMs.tif) -> x2 x2 ``` ## `select` `select` selects an attribute, or a set of attributes: ```{r} x2 %>% select(band2) -> x9 x9 ``` ## `geom_stars` `geom_raster` is a `ggplot2` geom function that accepts `stars` objects as its `data` argument and * sets up the raster or vector spatial coordinates as plot dimensions, and the first attribute as the fill variable * allows for downsampling (without choosing a suitable downsampling level) * chooses between using `geom_raster`, `geom_rect` and `geom_sf` depending on whether the geometry is regular, rectilinear or has vector geometries An example use is ```{r} library(ggplot2) library(viridis) ggplot() + geom_stars(data = x) + coord_equal() + facet_wrap(~band) + theme_void() + scale_fill_viridis() + scale_x_discrete(expand = c(0, 0)) + scale_y_discrete(expand = c(0, 0)) ``` stars/inst/doc/stars4.html0000644000176200001440000236645414644756272015303 0ustar liggesusers 4. stars data model

4. stars data model

Edzer Pebesma

For a better version of the stars vignettes see https://r-spatial.github.io/stars/articles/

This vignette explains the data model of stars objects, illustrated using artificial and real datasets.

Stars objects

stars objects consist of

  • a (possibly empty) named list of arrays, each having named dimensions (dim) attribute
  • an attribute called dimensions of class dimensions that carries dimension metadata
  • a class name that includes stars

A dimensions object is a named list of dimension elements, each describing the semantics a dimension of the data arrays (space, time, type etc). In addition to that, a dimensions object has an attribute called raster of class stars_raster, which is a named list with three elements:

  • dimensions length 2 character; the dimension names that constitute a spatial raster (or NA)
  • affine length 2 numeric; the two affine parameters of the geotransform (or NA)
  • curvilinear a boolean indicating whether a raster is a curvilinear raster (or NA)

The affine and curvilinear values are only relevant in case of raster data, indicated by dimensions to have non-NA values.

A dimension object describes a single dimension; it is a list with named elements

  • from: (numeric length 1): the start index of the array
  • to: (numeric length 1): the end index of the array
  • offset: (numeric length 1): the start coordinate (or time) value of the first pixel (i.e., a pixel/cell boundary)
  • delta: (numeric length 1): the increment, or cell size
  • refsys: (character, or crs): object describing the reference system; e.g. the PROJ string, or string POSIXct or PCICt (for 360 and 365 days/year calendars), or object of class crs (containing both EPSG code and proj4string)
  • point: (logical length 1): boolean indicating whether cells/pixels refer to areas/periods, or to points/instances (may be NA)
  • values: one of
    • NULL (missing),
    • a vector with coordinate values (numeric, POSIXct, PCICt, or sfc),
    • an object of class intervals (a list with two vectors, start and end, with interval start- and end-values), or
    • a matrix with longitudes or latitudes for all cells (in case of curvilinear grids)

from and to will usually be 1 and the dimension size, but from may be larger than 1 in case a sub-grid got was selected (or cropped).

offset and delta only apply to regularly discretized dimensions, and are NA if this is not the case. If they are NA, dimension values may be held in the values field. Rectilinear and curvilinear grids need grid values in values that can be either:

  • for rectilinear grids: irregularly spaced coordinate values, or coordinate intervals of irregular width (a rectilinear grid can have one dimension that is regular),
  • for curvilinear grids: or a matrix with grid cell centre values for all row/col combinations (usually in longitude or latitude).

Alternatively, values can contains a set of spatial geometries encoded in an sfc vector (“list-column”), in which case we have a vector data cube.

Grid type

Regular grids

With a very simple file created from a \(4 \times 5\) matrix

suppressPackageStartupMessages(library(stars))
m = matrix(1:20, nrow = 5, ncol = 4)
dim(m) = c(x = 5, y = 4) # named dim
(s = st_as_stars(m))
## stars object with 2 dimensions and 1 attribute
## attribute(s):
##     Min. 1st Qu. Median Mean 3rd Qu. Max.
## A1     1    5.75   10.5 10.5   15.25   20
## dimension(s):
##   from to offset delta point x/y
## x    1  5      0     1 FALSE [x]
## y    1  4      0     1 FALSE [y]

we see that

  • the rows (5) are mapped to the first dimension, the x-coordinate
  • the columns (4) are mapped to the second dimension, the y-coordinate
  • the from and to fields of each dimension define a range that corresponds to the array dimension:
dim(s[[1]])
## x y 
## 5 4
  • offset and delta specify how increasing row and column index maps to x and y coordinate values respectively.

When we plot this object, using the image method for stars objects,

image(s, text_values = TRUE, axes = TRUE)

we see that \((0,0)\) is the origin of the grid (grid corner), and \(1\) the coordinate value increase from one index (row, col) to the next. It means that consecutive matrix columns represent grid lines, going from south to north. Grids defined this way are regular: grid cell size is constant everywhere.

Many actual grid datasets have y coordinates (grid rows) going from North to South (top to bottom); this is realised with a negative value for delta. We see that the grid origin \((0,0)\) did not change:

attr(s, "dimensions")[[2]]$delta = -1
image(s, text_values = TRUE, axes = TRUE)

An example is the GeoTIFF carried in the package, which, as probably all data sources read through GDAL, has a negative delta for the y-coordinate:

tif = system.file("tif/L7_ETMs.tif", package = "stars")
st_dimensions(read_stars(tif))["y"]
##   from  to  offset delta                     refsys point
## y    1 352 9120761 -28.5 SIRGAS 2000 / UTM zone 25S FALSE

Raster attributes, rotated and sheared grids

Dimension tables of stars objects carry a raster attribute:

str(attr(st_dimensions(s), "raster"))
## List of 4
##  $ affine     : num [1:2] 0 0
##  $ dimensions : chr [1:2] "x" "y"
##  $ curvilinear: logi FALSE
##  $ blocksizes : NULL
##  - attr(*, "class")= chr "stars_raster"

which is a list that holds

  • dimensions: character, the names of raster dimensions (if any), as opposed to e.g. spectral, temporal or other dimensions
  • affine: numeric, the affine parameters
  • curvilinear: a logical indicating whether the raster is curvilinear

These fields are needed at this level, because they describe properties of the array at a higher level than individual dimensions do: a pair of dimensions forms a raster, both affine and curvilinear describe how x and y as a pair are derived from grid indexes (see below) when this cannot be done on a per-dimension basis.

With two affine parameters \(a_1\) and \(a_2\), \(x\) and \(y\) coordinates are derived from (1-based) grid indexes \(i\) and \(j\), grid offset values \(o_x\) and \(o_y\), and grid cell sizes \(d_x\) and \(d_y\) by

\[x = o_x + (i-1) d_x + (j-1) a_1\]

\[y = o_y + (i-1) a_2 + (j-1) d_y\] Clearly, when \(a_1=a_2=0\), \(x\) and \(y\) are entirely derived from their respective index, offset and cellsize.

Note that for integer indexes, the coordinates are that of the starting edge of a grid cell; to get the grid cell center of the top left grid cell (in case of a negative \(d_y\)), use \(i=1.5\) and \(j=1.5\).

We can rotate grids by setting \(a_1\) and \(a_2\) to a non-zero value:

attr(attr(s, "dimensions"), "raster")$affine = c(0.1, 0.1)
plot(st_as_sf(s, as_points = FALSE), axes = TRUE, nbreaks = 20)

The rotation angle, in degrees, is

atan2(0.1, 1) * 180 / pi
## [1] 5.710593

Sheared grids are obtained when the two rotation coefficients, \(a_1\) and \(a_2\), are unequal:

attr(attr(s, "dimensions"), "raster")$affine = c(0.1, 0.2)
plot(st_as_sf(s, as_points = FALSE), axes = TRUE, nbreaks = 20)

Now, the y-axis and x-axis have different rotation in degrees of respectively

atan2(c(0.1, 0.2), 1) * 180 / pi
## [1]  5.710593 11.309932

Rectilinear grids

Rectilinear grids have orthogonal axes, but do not have congruent (equally sized and shaped) cells: each axis has its own irregular subdivision.

We can define a rectilinear grid by specifying the cell boundaries, meaning for every dimension we specify one more value than the dimension size:

x = c(0, 0.5, 1, 2, 4, 5)  # 6 numbers: boundaries!
y = c(0.3, 0.5, 1, 2, 2.2) # 5 numbers: boundaries!
(r = st_as_stars(list(m = m), dimensions = st_dimensions(x = x, y = y)))
## stars object with 2 dimensions and 1 attribute
## attribute(s):
##    Min. 1st Qu. Median Mean 3rd Qu. Max.
## m     1    5.75   10.5 10.5   15.25   20
## dimension(s):
##   from to point                values x/y
## x    1  5 FALSE     [0,0.5),...,[4,5) [x]
## y    1  4 FALSE [0.3,0.5),...,[2,2.2) [y]
st_bbox(r)
## xmin ymin xmax ymax 
##  0.0  0.3  5.0  2.2
image(r, axes = TRUE, col = grey((1:20)/20))

Would we leave out the last value, than stars may come up with a different cell boundary for the last cell, as this is now derived from the width of the one-but-last cell:

x = c(0, 0.5, 1, 2, 4)  # 5 numbers: offsets only!
y = c(0.3, 0.5, 1, 2)   # 4 numbers: offsets only!
(r = st_as_stars(list(m = m), dimensions = st_dimensions(x = x, y = y)))
## stars object with 2 dimensions and 1 attribute
## attribute(s):
##    Min. 1st Qu. Median Mean 3rd Qu. Max.
## m     1    5.75   10.5 10.5   15.25   20
## dimension(s):
##   from to point              values x/y
## x    1  5 FALSE   [0,0.5),...,[4,6) [x]
## y    1  4 FALSE [0.3,0.5),...,[2,3) [y]
st_bbox(r)
## xmin ymin xmax ymax 
##  0.0  0.3  6.0  3.0

This is not problematic if cells have a constant width, in which case the boundaries are reduced to an offset and delta value, irrespective whether an upper boundary is given:

x = c(0, 1, 2, 3, 4)  # 5 numbers: offsets only!
y = c(0.5, 1, 1.5, 2)   # 4 numbers: offsets only!
(r = st_as_stars(list(m = m), dimensions = st_dimensions(x = x, y = y)))
## stars object with 2 dimensions and 1 attribute
## attribute(s):
##    Min. 1st Qu. Median Mean 3rd Qu. Max.
## m     1    5.75   10.5 10.5   15.25   20
## dimension(s):
##   from to offset delta point x/y
## x    1  5      0     1 FALSE [x]
## y    1  4    0.5   0.5 FALSE [y]
st_bbox(r)
## xmin ymin xmax ymax 
##  0.0  0.5  5.0  2.5

Alternatively, one can also set the cell midpoints by specifying arguments cell_midpoints to the st_dimensions call:

x = st_as_stars(matrix(1:9, 3, 3), 
                st_dimensions(x = c(1, 2, 3), y = c(2, 3, 10), cell_midpoints = TRUE))

When the dimension is regular, this results in offset being shifted back with half a delta, or else in intervals derived from the distances between cell centers. This should obviously not be done when cell boundaries are specified.

Curvilinear grids

Curvilinear grids are grids whose grid lines are not straight. Rather than describing the curvature parametrically, the typical (HDF5 or NetCDF) files in which they are found have two raster layers with the longitudes and latitudes for every corresponding pixel of remaining layers.

As an example, we will use a Sentinel 5P dataset available from package starsdata; this package can be installed with

install.packages("starsdata", repos = "http://pebesma.staff.ifgi.de", type = "source")

The dataset is found here:

(s5p = system.file("sentinel5p/S5P_NRTI_L2__NO2____20180717T120113_20180717T120613_03932_01_010002_20180717T125231.nc", package = "starsdata"))
## [1] "/home/edzer/R/x86_64-pc-linux-gnu-library/4.4/starsdata/sentinel5p/S5P_NRTI_L2__NO2____20180717T120113_20180717T120613_03932_01_010002_20180717T125231.nc"

We can construct the curvilinear stars raster by calling read_stars on the right sub-array:

subs = gdal_subdatasets(s5p)
subs[[6]]
## [1] "NETCDF:\"/home/edzer/R/x86_64-pc-linux-gnu-library/4.4/starsdata/sentinel5p/S5P_NRTI_L2__NO2____20180717T120113_20180717T120613_03932_01_010002_20180717T125231.nc\":/PRODUCT/nitrogendioxide_tropospheric_column"

For this array, we can see the GDAL metadata under item GEOLOCATION:

gdal_metadata(subs[[6]], "GEOLOCATION")
## $GEOREFERENCING_CONVENTION
## [1] "PIXEL_CENTER"
## 
## $LINE_OFFSET
## [1] "0"
## 
## $LINE_STEP
## [1] "1"
## 
## $PIXEL_OFFSET
## [1] "0"
## 
## $PIXEL_STEP
## [1] "1"
## 
## $SRS
## [1] "GEOGCS[\"WGS 84\",DATUM[\"WGS_1984\",SPHEROID[\"WGS 84\",6378137,298.257223563,AUTHORITY[\"EPSG\",\"7030\"]],AUTHORITY[\"EPSG\",\"6326\"]],PRIMEM[\"Greenwich\",0,AUTHORITY[\"EPSG\",\"8901\"]],UNIT[\"degree\",0.0174532925199433,AUTHORITY[\"EPSG\",\"9122\"]],AXIS[\"Latitude\",NORTH],AXIS[\"Longitude\",EAST],AUTHORITY[\"EPSG\",\"4326\"]]"
## 
## $X_BAND
## [1] "1"
## 
## $X_DATASET
## [1] "NETCDF:\"/home/edzer/R/x86_64-pc-linux-gnu-library/4.4/starsdata/sentinel5p/S5P_NRTI_L2__NO2____20180717T120113_20180717T120613_03932_01_010002_20180717T125231.nc\":/PRODUCT/longitude"
## 
## $Y_BAND
## [1] "1"
## 
## $Y_DATASET
## [1] "NETCDF:\"/home/edzer/R/x86_64-pc-linux-gnu-library/4.4/starsdata/sentinel5p/S5P_NRTI_L2__NO2____20180717T120113_20180717T120613_03932_01_010002_20180717T125231.nc\":/PRODUCT/latitude"
## 
## attr(,"class")
## [1] "gdal_metadata"

which reveals where, in this dataset, the longitude and latitude arrays are kept.

nit.c = read_stars(subs[[6]]) 
## Warning in CPL_read_gdal(as.character(x), as.character(options),
## as.character(driver), : GDAL Message 1: The dataset has several variables that
## could be identified as vector fields, but not all share the same primary
## dimension. Consequently they will be ignored.
## Warning in CPL_read_gdal(as.character(x), as.character(options),
## as.character(driver), : GDAL Message 1: The dataset has several variables that
## could be identified as vector fields, but not all share the same primary
## dimension. Consequently they will be ignored.
## Warning in CPL_read_gdal(as.character(x), as.character(options),
## as.character(driver), : GDAL Message 1: The dataset has several variables that
## could be identified as vector fields, but not all share the same primary
## dimension. Consequently they will be ignored.
## Warning in CPL_read_gdal(as.character(x), as.character(options),
## as.character(driver), : GDAL Message 1: The dataset has several variables that
## could be identified as vector fields, but not all share the same primary
## dimension. Consequently they will be ignored.
## Warning in CPL_read_gdal(as.character(x), as.character(options),
## as.character(driver), : GDAL Message 1: The dataset has several variables that
## could be identified as vector fields, but not all share the same primary
## dimension. Consequently they will be ignored.
## Warning in CPL_read_gdal(as.character(x), as.character(options),
## as.character(driver), : GDAL Message 1: The dataset has several variables that
## could be identified as vector fields, but not all share the same primary
## dimension. Consequently they will be ignored.
## Warning in CPL_read_gdal(as.character(x), as.character(options),
## as.character(driver), : GDAL Message 1: The dataset has several variables that
## could be identified as vector fields, but not all share the same primary
## dimension. Consequently they will be ignored.
## Warning in CPL_read_gdal(as.character(x), as.character(options),
## as.character(driver), : GDAL Message 1: The dataset has several variables that
## could be identified as vector fields, but not all share the same primary
## dimension. Consequently they will be ignored.
## Warning in CPL_read_gdal(as.character(x), as.character(options),
## as.character(driver), : GDAL Message 1: The dataset has several variables that
## could be identified as vector fields, but not all share the same primary
## dimension. Consequently they will be ignored.
## Warning in CPL_read_gdal(as.character(x), as.character(options),
## as.character(driver), : GDAL Message 1: The dataset has several variables that
## could be identified as vector fields, but not all share the same primary
## dimension. Consequently they will be ignored.
threshold = units::set_units(9e+36, mol/m^2)
nit.c[[1]][nit.c[[1]] > threshold] = NA
nit.c
## stars object with 3 dimensions and 1 attribute
## attribute(s):
##                                                   Min.      1st Qu.
## nitrogendioxide_tropospheri... [mol/m^2] -3.301083e-05 1.868205e-05
##                                                Median         Mean      3rd Qu.
## nitrogendioxide_tropospheri... [mol/m^2] 2.622178e-05 2.898976e-05 3.629641e-05
##                                                  Max. NA's
## nitrogendioxide_tropospheri... [mol/m^2] 0.0003924858  330
## dimension(s):
##      from  to         offset         refsys                             values
## x       1 450             NA WGS 84 (CRS84) [450x278] -5.811 [°],...,30.95 [°]
## y       1 278             NA WGS 84 (CRS84)  [450x278] 28.36 [°],...,51.47 [°]
## time    1   1 2018-07-17 UTC        POSIXct                               NULL
##      x/y
## x    [x]
## y    [y]
## time    
## curvilinear grid

The curvilinear array has the actual arrays (raster layers, matrices) with longitude and latitude values read in its dimension table. We can plot this file:

plot(nit.c, breaks = "equal", reset = FALSE, axes = TRUE, as_points = TRUE, 
         pch = 16,  logz = TRUE, key.length = 1)
## Warning in NextMethod(): NaNs produced
## Warning in plot.sf(x, pal = col, ...): NaNs produced
maps::map('world', add = TRUE, col = 'red')

plot(nit.c, breaks = "equal", reset = FALSE, axes = TRUE, as_points = FALSE, 
         border = NA, logz = TRUE, key.length = 1)
## Warning in NextMethod(): NaNs produced
## Warning in plot.sf(x, pal = col, ...): NaNs produced
maps::map('world', add = TRUE, col = 'red')

We can downsample the data by

(nit.c_ds = stars:::st_downsample(nit.c, 8))
## stars object with 3 dimensions and 1 attribute
## attribute(s):
##                                                   Min.     1st Qu.       Median
## nitrogendioxide_tropospheri... [mol/m^2] -1.847503e-05 1.85778e-05 2.700901e-05
##                                                Mean      3rd Qu.         Max.
## nitrogendioxide_tropospheri... [mol/m^2] 2.9113e-05 3.642568e-05 0.0001363282
##                                          NA's
## nitrogendioxide_tropospheri... [mol/m^2]   32
## dimension(s):
##      from to         offset         refsys                           values x/y
## x       1 50             NA WGS 84 (CRS84) [50x31] -5.811 [°],...,30.14 [°] [x]
## y       1 31             NA WGS 84 (CRS84)  [50x31] 28.78 [°],...,51.47 [°] [y]
## time    1  1 2018-07-17 UTC        POSIXct                             NULL    
## curvilinear grid
plot(nit.c_ds, breaks = "equal", reset = FALSE, axes = TRUE, as_points = TRUE, 
         pch = 16, logz = TRUE, key.length = 1)
## Warning in NextMethod(): NaNs produced
## Warning in plot.sf(x, pal = col, ...): NaNs produced
maps::map('world', add = TRUE, col = 'red')

which doesn’t look nice, but plotting the cells as polygons looks better:

plot(nit.c_ds, breaks = "equal", reset = FALSE, axes = TRUE, as_points = FALSE, 
         border = NA, logz = TRUE, key.length = 1)
## Warning in NextMethod(): NaNs produced
## Warning in plot.sf(x, pal = col, ...): NaNs produced
maps::map('world', add = TRUE, col = 'red')

Another approach would be to warp the curvilinear grid to a regular grid, e.g. by

w = st_warp(nit.c, crs = 4326, cellsize = 0.25)
## Warning in transform_grid_grid(st_as_stars(src), st_dimensions(dest),
## threshold): using Euclidean distance measures on geodetic coordinates
## threshold set to 0.108545 : set a larger value if you see missing values where there shouldn't be
plot(w)

stars/inst/nc/0000755000176200001440000000000014644756215013001 5ustar liggesusersstars/inst/nc/timeseries.cdl0000644000176200001440000000415013777653456015647 0ustar liggesusersnetcdf timeseries { dimensions: station = 10 ; time = 20 ; variables: int num(station) ; num:long_name = "Station number" ; num:cf_role = "timeseries_id" ; int time(time) ; time:units = "days since 1970-01-01 00:00:00 UTC" ; time:long_name = "time" ; time:calendar = "gregorian" ; float pr(station, time) ; pr:units = "kg m-2 s-1" ; pr:_FillValue = -10.f ; pr:long_name = "Total precipitation flux" ; pr:coordinates = "lat lon alt num" ; pr:standard_name = "precipitation_flux" ; float lat(station) ; lat:units = "degrees_north" ; lat:long_name = "Station latitude" ; lat:standard_name = "latitude" ; float lon(station) ; lon:units = "degrees_east" ; lon:long_name = "Station longitude" ; lon:standard_name = "longitude" ; float alt(station) ; alt:units = "m" ; alt:long_name = "Vertical distance above the surface" ; alt:standard_name = "height" ; // global attributes: :featureType = "timeSeries" ; :Conventions = "CF-1.7" ; data: num = 1, 2, 3, 4, 5, 6, 7, 8, 9, 10 ; time = 10957, 11323, 11688, 12053, 12418, 12784, 13149, 13514, 13879, 14245, 14610, 14975, 15340, 15706, 16071, 16436, 16801, 17167, 17532, 17897 ; pr = 88, 54, 90, 35, 83, 19, 71, 38, 93, 65, 57, 16, 41, 42, 76, 77, 80, 52, 65, 12, 9, 37, 86, 82, 75, 67, 66, 75, 34, 27, 0, 84, 40, 75, 59, 48, 100, 55, 39, 44, 12, 56, 88, 69, 68, 36, 52, 75, 79, 44, 22, 43, 75, 23, 15, 16, 44, 75, 5, 21, 17, 58, 6, 28, 3, 39, 66, 9, 52, 94, 78, 99, 80, 45, 20, 9, 81, 42, 88, 92, 84, 25, 1, 9, 64, 50, 34, 56, 49, 58, 22, 31, 94, 32, 83, 28, 87, 94, 94, 60, 2, 69, 12, 89, 8, 50, 89, 22, 0, 47, 99, 57, 90, 24, 68, 87, 74, 33, 7, 46, 79, 39, 92, 44, 85, 40, 10, 82, 63, 10, 74, 60, 29, 73, 56, 88, 55, 98, 40, 30, 14, 10, 41, 30, 20, 98, 74, 46, 71, 69, 40, 26, 69, 69, 26, 62, 1, 58, 95, 86, 82, 55, 10, 51, 11, 98, 28, 19, 53, 33, 81, 8, 12, 74, 27, 71, 88, 8, 51, 56, 68, 24, 97, 87, 97, 80, 43, 81, 25, 40, 20, 81, 32, 80, 8, 7, 38, 52, 97, 87 ; lat = 68, 14, -20, -23, 12, 5, -77, 20, 46, -28 ; lon = -135, -102, 135, -63, -88, -5, 66, -165, 63, -168 ; alt = 0, 10, 500, 20, 75, -10, 54321, 63, 42, 100 ; } stars/inst/nc/bcsd_obs_1999.nc0000644000176200001440000077511413777653456015624 0ustar liggesusersCDF latitude! longitudeQtime CDIGClimate Data Interface version 1.5.6 (http://code.zmaw.de/projects/cdi) ConventionsCF-1.0historyMon Jan 7 18:59:08 2019: ncks -4 -L3 bcsd_obs_1999_two_var.nc bcsd_obs_1999_two_var.nc.comp Thu May 08 12:07:18 2014: cdo monsum gridded_obs/daily/gridded_obs.daily.Prcp.1950.nc gridded_obs/monthly/gridded_obs.monthly.pr.1950.ncCDOIClimate Data Operators version 1.5.6.1 (http://code.zmaw.de/projects/cdo)Metadata_ConventionsUnidata Dataset Discovery v1.0title+Monthly Gridded Meteorological ObservationssummaryThese are the monthly observational data used for BCSD downscaling. See: http://gdo-dcp.ucllnl.org/downscaled_cmip_projections/dcpInterface.html#About for more information.keywordsAtmospheric Temperature, Air Temperature Atmosphere, Precipitation, Rain, Maximum Daily Temperature, Minimum Daily Temperaturekeywords_vocabularyGCMD Science Keywordsidcida.usgs.gov/bcsd_obsnaming_authority cida.usgs.gov cdm_data_typeGrid date_created2014publisher_name$Center for Integrated Data Analytics publisher_urlhttps://www.cida.usgs.gov/publisher_emaildblodgett@usgs.gov institutionBVaries, see http://gdo-dcp.ucllnl.org/downscaled_cmip_projections/ date_issued 2015-11-01processing_level#Gridded meteorological observationsgeospatial_lon_minU<geospatial_lat_min@@geospatial_lon_maxRgeospatial_lat_max@Btime_coverage_start1950-01-15T00:00time_coverage_end1999-12-15T00:00time_coverage_resolutionP1MlicenseFreely availableacknowledgmentMaurer, E.P., A.W. Wood, J.C. Adam, D.P. Lettenmaier, and B. Nijssen, 2002, A Long-Term Hydrologically-Based Data Set of Land Surface Fluxes and States for the Conterminous United States, J. Climate 15(22), 3237-3251HistoryTranslated to CF-1.0 Conventions by Netcdf-Java CDM (CFGridWriter2) Original Dataset = bcsd_obs; Translation Date = 2019-01-03T20:03:59.756ZNCO`netCDF Operators version 4.7.6 (Homepage = http://nco.sf.net, Code = http://github.com/nco/nco) latitude  standard_namelatitude long_nameLatitudeunits degrees_northaxisYbounds latitude_bnds_CoordinateAxisTypeLat longitude  standard_name longitude long_name Longitudeunits degrees_eastaxisXboundslongitude_bnds_CoordinateAxisTypeLonDHpr  long_namemonthly_sum_prunitsmm/m _FillValue`xnamepr coordinatestime latitude longitude )tas  long_namemonthly_avg_tasunitsC _FillValue`xnametas missing_value`x coordinatestime latitude longitude )9Ptime  standard_nametimeunitsdays since 1950-01-01 00:00:00calendarstandard_CoordinateAxisTypeTimecB@BB@BB@BB@BB@BB @B B @B B @B B @B B @B B@BB@BB@BB@BB@BB@BB@©©©`© ¨¨¨`¨ §§§`§ ¦¦¦`¦ ¥¥¥`¥ ¤¤¤`¤ £££`£ ¢¢¢`¢ ¡¡¡`¡    `  ŸŸŸ`Ÿ žžž`ž ` œœœ`œ ›››`› ššš`š ™™™`™ ˜˜˜`˜ ———`— –––`– •C{CRCCCfCs3C=C$GC%C8RCCnCC*C+GC% C!C*O\C%=CuC޸CLCHC)C)C C C C!T{C!{C8RC {C C"C#CuCLCC:C \)C3CgC(C C%CCC ChC fBuCCC!O\CQCHCW C"pC)zC-nC$CCs3C&\C%HC@C=C!HCCCCC!qC&!HC$C(C#C&C*{C)LC$(CCC=qC C \C}qC )CC )C޸CBCC fC/CBuBaHCC =CW Cs3CECC!RCC}qCfC C*\C.T{C*C$qC!޸CCT{CzCC'C%5C=qC'HC%HC#C&=C'=C%fgC!)C{CC!)C&C'C%RCCCYCCRC)C)C .CLC CٚCC&fCCBChCCfgC\CLC'33C$C*qC.)C)CzCCC)C.pC0J=C8C8C.fC*(C%C'3C#:CHC!C)LC):C)cC,C!C C=CQC.CǮCCC=C3C C CCCCxRCJ=CYCGC&fC\C3CǮC)@C,\C*CǮCEC s3C)C,ffC/=C=C8RC0޸C*BC) C&{C))C$zC{CHC#QC.C1 C.pC* =C"Cs3CC}qCC33CfCC s3C CCC&C*=qC$0C C.C0CaHChC !HCCRC YCqC(uC6C#pCqCC#pCC'CnChC=qCxRCC(C)C%LCECC RC2&fC6C9C7=C.&fCC C%GC% >CCRC CCRCCC!C.C-5C*uCCaHCCcC 0C CCCC&fC8RC!aHC CqCCEC=qC)C CBuB)C aHCC#C\C\CT{C$0C5@C?CC@C<C<:C+C,C/ٙC-ǮC*CaHC}qCCǮCC#zC&C%{C-C2C.{C"CO\C+C{C)C C qC +C C>C T{C C CCCRCffCfCBBBaGBuB{C:CkC"C"QC"C+\C=C?\C>CC>C/C'C,3CRC:C\CC.CT{C O\C5)C:C,CC"{C/ C.C* =C!qCC uCC HCCCJ=CaHC:C{B33BBBuBƅBǮB8RBBB\)BuC }qC \)CC\CCs3CC'C.QC2LC7^C5C5 C8J=C;pCAW CC3CAaHC9HC8LC/.CCCCCC{C(3C?{CA&fC9hC2kC8ٚC7C7hC+=qCcCO\BBLC xRC C{CCCxRCC)B{BkB{BBB =B8RBW C{C C33C*3C0C)CCC$)C#O\C*)C2RC33C3J=C3fC<C?޸CGCH CEffC@ =C8C.T{C)C3CC C1{C CnC;=qCBCI =CECD!HCL CCkC.qC}qC8RBąBBC3C \CCfCs3C"\)C CHC BB{BBBBBBCCuC3CCCnCC".C%C/cC-J>C-C/C:HCA=qCHCMCJQCFCQCMC\+CYCfCeCSBCH{C#CC\C C kCs3C .CRCC4BC"3CqC{C (C5CBBBB{BBBffChC C3CCRCCpC CCGC LC%ffC5C<C?RCF{CI COCFffC5C33C\C^CfC T{CECHBBCCYC/CTCVC^C_CkCx\)CxCh\)CbCJ=qC2ٚC'C$BC(CCCuC1C< C0CfB\CO\CCcBuCs3BB)B.BCC =CC (C#C CpCECC J=C )C#C"C2C7C=CBCHuCNCFfC6pC"0C3CnCC\BGCQB۳3BpB=qC:CCDaGCZT{CQCa CqCluCqCz+CvC`YCQ+C9C0hC0 C")CCfCCBBB.BkC{CGC C3CCCC )CC:C CkCCRC CCzC =C \CC(}qCC^GCi>Cr5C)CRCC4{C0CnuCPqC9fC%kB)BB͔{B՞BLB33C+B8RBBC0C C C C\B{C)CffC.C=C CQCaHCqC\C2^C5EC2hC8GCCBBBЅB =B̀BfgBB.BCJ=C uC!GCCCnC :C.+C08RC-C)&fC+)C-8RC&C,RC833CC^CCCAC:uC%CCC YB\BfgB33BB>B3Cq C]0Cc{CtO\Ct >Ck.CeC)C CCChCaffCWEC;T{CTǮCC{CbYCJ#C޸C޸CBQB >BBBHCB=B8RBkBBڣB=qBBBCkC &fCC! 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ConventionsCF-1.6historypFri Jul 3 20:45:39 2020: ncks -d time,1,10 data.nc sub.nc 2020-06-30 07:07:13 GMT by grib_to_netcdf-2.16.0: /opt/ecmwf/eccodes/bin/grib_to_netcdf -S param -o /cache/data4/adaptor.mars.internal-1593500809.0007904-16187-13-88557124-d321-4eea-bab8-1367f56839fd.nc /cache/tmp/88557124-d321-4eea-bab8-1367f56839fd-adaptor.mars.internal-1593500809.0013933-16187-2-tmp.gribNCO`netCDF Operators version 4.9.2 (Homepage = http://nco.sf.net, Code = http://github.com/nco/nco) latitude units degrees_north long_namelatitude$level units millibars long_namepressure_level longitude units degrees_east long_name longitude$time units!hours since 1900-01-01 00:00:00.0 long_nametimecalendar gregorian(u  scale_factor?1D͸ add_offset@6|v _FillValue missing_valueunitsm s**-1 long_nameU component of wind standard_name eastward_wind (v  scale_factor?(А add_offset?1 _FillValue missing_valueunitsm s**-1 long_nameV component of wind standard_namenorthward_wind 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MB,@5Ǒ>{­ơɕԬf/L,6/]xɠpfį¯>I4a2_/|ą&G׷[şРZ۵:]gڱk.m?˶5 ;U (Ȅ̗φAUԾ_S_\W۲jԮBR߮,%Zo~lƷ:CXmXڼ EذVr=7vgmX:÷ǂˆ`NHιϳ>WO&c3˒°+\4˳= gO޶u[Z) >q,L?4ѺP/n+Ǿ̂3Ρ$A1c#ҕes٢Yѐ=Mܜ(Aޛomstars/inst/nc/ncdf_tidync.R0000644000176200001440000002061213777653456015422 0ustar liggesusers### The functions in this file are a work in progress and are ### ### not currently used by `stars` ### units_or_null <- function(u) { un = try(suppressWarnings(as_units(u)), silent = TRUE) if (inherits(un, "try-error")) { #warning(paste("ignoring unrecognized unit:", u), call. = FALSE) NULL } else un } unit_izer_ncmeta <- function(attribute, variable, ...) { unit_df <- attribute[attribute$name == "units", ] if (is.null(unit_df) || nrow(unit_df) < 1) return(NULL) unit <- lapply(unit_df$value, function(x) units_or_null(x[[1]])) unit_df$unit <- unit unit_df$unit_ok <- !unlist(lapply(unit, is.null)) variable$unit <- unit_df$unit[match(variable$name, unit_df$variable)] variable } # TODO # - allow stars_proxy to be pulled # - allow stars_proxy to apply select_var # - # examples # f <- system.file("nc/reduced.nc", package = "stars") # read_stars_tidync(f) # read_stars_tidync(f, select_var = "anom", proxy = FALSE) ## only works if proxy = FALSE # read_stars_tidync(f, lon = index <= 10, lat = index <= 12, time = index < 2) #' @importFrom units set_units read_stars_tidync = function(.x, ..., select_var = NULL, proxy = TRUE, make_time = TRUE, make_units = TRUE) { if (!requireNamespace("tidync", quietly = TRUE)) stop("package tidync required, please install it first") # nocov if (length(.x) > 1) { warning("only first source/file used") .x = .x[1L] } coord_var = ncmeta::nc_coord_var(.x) tnc = tidync::tidync(.x) %>% tidync::hyper_filter(...) if (!is.null(select_var)) tnc = tidync::activate(tnc, select_var[1]) ## FIXME: get this stuff from tidync object variable = unit_izer_ncmeta(ncmeta::nc_atts(.x), ncmeta::nc_vars(.x)) tt = tidync::hyper_transforms(tnc, all = FALSE) tt = lapply(tt, function(tab) tab[tab$selected, , drop = FALSE]) nms = names(tt) if (make_units) { for (idim in seq_along(tt)) { if (nms[idim] %in% variable$name) { uit <- variable$unit[match(names(tt)[idim], variable$name)][[1]] if (nms[idim] %in% coord_var$variable && !nms[idim] %in% coord_var[["T"]]) { ## we have a standard units thing if (!is.null(uit)) { tt[[idim]][[nms[idim]]] = units::as_units(tt[[idim]][[nms[idim]]], uit) } } }}} curvilinear = character(0) ## are we curvilinear? XY_curvi <- NULL if (all(c("X", "Y") %in% names(coord_var))) { cvar = coord_var[coord_var$variable == tidync::hyper_vars(tnc)$name[1], ] XY_curvi = unlist(cvar[1, c("X", "Y")]) if (all(!is.na(XY_curvi)) && all(tnc$variable$ndims[match(XY_curvi, tnc$variable$name)] == 2)) { ## AAND these are both 2d vars curvilinear = XY_curvi } else { XY_curvi <- character(0) } } curvi_coords = NULL if (length(curvilinear == 2)) { #grid = tnc$grid %>% tidyr::unnest() grid = tnc$grid %>% tidyr::unnest(cols = c(variables)) curvi_coords = tidync::hyper_array(tnc %>% ## FIXME ... tidync::activate((grid %>% dplyr::filter(variable == XY_curvi[1]) %>% dplyr::pull(.data$grid))[1L]), select_var = XY_curvi) names(curvi_coords)[1:2] <- curvilinear } ## can we create a raster? raster = NULL if (length(tt) > 1) { raster = get_raster(affine = c(0, 0), dimensions = names(tt)[1:2], curvilinear = length(curvilinear) == 2) } dims = vector("list", length(nms)) for (i in seq_along(dims)) { nm = nms[i] if (nrow(tt[[nm]]) > 1) { ## we are rectilinear, or degenerate rectilinear dims[[i]] = create_dimension(values = tt[[nm]][[nm]]) } else { ## we are simple offset uval = tt[[nm]][[nm]] ## so we can get an NA, whether units of not dims[[i]] = create_dimension(from = 1L, to = 1L, offset = uval, delta = uval[NA]) } } dims = create_dimensions(setNames(dims, nms), raster = raster) if (!is.null(curvi_coords)) { dims[[names(tt)[1]]]$values = curvi_coords[[1]] dims[[names(tt)[1]]]$offset = NA dims[[names(tt)[1]]]$delta = NA dims[[names(tt)[2]]]$values = curvi_coords[[2]] dims[[names(tt)[2]]]$offset = NA dims[[names(tt)[2]]]$delta = NA } if (make_time) { for (idim in seq_along(tt)) { if (nms[idim] %in% variable$name) { uit <- variable$unit[match(names(tt)[idim], variable$name)][[1]] if (nms[idim] %in% coord_var$variable && nms[idim] %in% coord_var[["T"]]) { ## we have a time thing dims[[idim]] = make_cal_time2(dims[[idim]], nms[idim], uit) } } } } if (!proxy) { x = tnc %>% tidync::hyper_array(select_var = select_var, drop = FALSE) ## always keep degenerate dims attr(x, "transforms") = NULL attr(x, "source") = NULL class(x) = "list" for (ivar in seq_along(x)) { if (names(x)[ivar] %in% variable$name) { uit <- variable$unit[match(names(x)[ivar], variable$name)][[1]] if (!is.null(uit)) x[[ivar]] <- units::as_units(x[[ivar]], uit) } } out = st_stars(x, dims) out = st_as_stars(out, curvilinear = curvi_coords) } else { ## if we are proxy, defer to GDAL subdatasets hvars = if (is.null(select_var)) tidync::hyper_vars(tnc)$name else select_var sds = unlist(sf::gdal_subdatasets(.x)) if (length(sds) > 0) { sds = unlist(lapply(sprintf(":%s", hvars), function(p) grep(p, sds, value = TRUE))) } else { sds = .x } out = stats::setNames(as.list(sds), hvars) out = st_stars_proxy(out, dims, NA_value = NA) #class(out) = c("ncdf_proxy", class(out)) } out } ## FIXME: this function should return unit-ed times, not modify dimensions ## it should also live in the units package (or RNetCDF, or PCICt or ...) make_cal_time <- function(dimensions, time_name, time_unit = NULL, cal = NULL) { td = which(names(dimensions) == time_name) if (length(td) == 1) { tm = get_dimension_values(dimensions[[time_name]], geotransform = NA) u = time_unit if (is.null(u) || inherits(u, "try-error")) { warning("ignoring units of time dimension, not able to interpret") return(dimensions) } if (! is.null(cal) && cal %in% c("360_day", "365_day", "noleap")) { if (!requireNamespace("PCICt", quietly = TRUE)) stop("package PCICt required, please install it first") # nocov t01 = set_units(0:1, u, mode = "standard") delta = if (grepl("months", u)) { if (cal == "360_day") set_units(30 * 24 * 3600, "s", mode = "standard") else set_units((365/12) * 24 * 3600, "s", mode = "standard") } else set_units(as_units(diff(as.POSIXct(t01))), "s", mode = "standard") origin = as.character(as.POSIXct(t01[1])) v.pcict = PCICt::as.PCICt(tm * as.numeric(delta), cal, origin) if (!is.null(dimensions[[td]]$values)) { v = dimensions[[td]]$values if (inherits(v, "intervals")) { start = PCICt::as.PCICt(v$start * as.numeric(delta), cal, origin) end = PCICt::as.PCICt(v$end * as.numeric(delta), cal, origin) dimensions[[td]]$values = make_intervals(start, end) } else dimensions[[td]]$values = v.pcict } else { dimensions[[td]]$offset = v.pcict[1] dimensions[[td]]$delta = diff(v.pcict[1:2]) } dimensions[[td]]$refsys = "PCICt" } else { # Gregorian/Julian, POSIXct: if (!is.null(dimensions[[td]]$values)) { v = dimensions[[td]]$values if (inherits(v, "intervals")) { start = as.POSIXct(units::set_units(v$start, u, mode = "standard")) # or: RNetCDF::utcal.nc(u, tm, "c") end = as.POSIXct(units::set_units(v$end, u, mode = "standard")) # or: RNetCDF::utcal.nc(u, tm, "c") dimensions[[td]]$values = make_intervals(start, end) } else dimensions[[td]]$values = as.POSIXct(units::set_units(tm, u, mode = "standard")) # or: RNetCDF::utcal.nc(u, tm, "c") } else { t0 = dimensions[[td]]$offset t1 = dimensions[[td]]$offset + dimensions[[td]]$delta t.posix = as.POSIXct(units::set_units(c(t0, t1), u, mode = "standard")) # or: utcal.nc(u, c(t0,t1), "c") dimensions[[td]]$offset = t.posix[1] dimensions[[td]]$delta = diff(t.posix) } dimensions[[td]]$refsys = "POSIXct" } } dimensions } stars/inst/nc/test_stageiv_xyt.nc0000644000176200001440000373676414214460066016747 0ustar liggesusersCDF timeyvxW # Originating_or_generating_CenterQUS National Weather Service, National Centres for Environmental Prediction (NCEP)#Originating_or_generating_SubcenterEnvironmental Modeling CenterGenerating_process_or_modelRRiver Forecast Center Quantitative Precipitation estimate mosaic generated by NCEP ConventionsCF-1.4historySat Jan 22 19:46:48 2022: ncks -x -v time_bounds test_stageiv_xyt_2.nc test_stageiv_xyt_2.nc Sat Jan 22 19:46:28 2022: ncatted -a bounds,time,d,, test_stageiv_xyt_2.nc Sat Jan 22 19:44:21 2022: ncks -x -v time_bounds test_stageiv_xyt.nc test_stageiv_xyt_2.nc Fri Feb 1 07:55:44 2019: ncpdq -a y,x test_stageiv.nc test_stageiv_2.nc Aggregated from NCEP Stage IV Analysis by dblodgett@usgs.gov 2016-01-01 featureTypeGRID_CoordSysBuilder#ucar.nc2.dataset.conv.CF1ConventionMetadata_ConventionsUnidata Dataset Discovery v1.0title9United States Stage IV Quantitative Precipitation ArchivesummaryMosaicked into a national product at NCEP, from the regional hourly multi-sensor (radar+gauges) precipitation analyses produced by the 12 River Forecast Centers over CONUS. Some manual QC done at the RFCs. The Stage II/IV job is run at 33min past the top of each hour. Hourly Stage IV is re-made hourly (if there is new input after valid time for the next 23 hours, then again at 1/3/5/7 days after valid time. Source: http://www.emc.ncep.noaa.gov/mmb/ylin/pcpanl/stage4/ Note: This dataset is not truely national. Some RFCs do not produce hourly precip estimates and others may not be present in the dataset. Please review the NCEP documentation for more information.keywordsUAtmosphere > Precipitation > Precipitation Amount Atmosphere > Precipitation > Rainkeywords_vocabularyGCMD Science Keywordsid stageivnaming_authority cida.usgs.gov cdm_data_typeGrid date_createdongoing institution,National Center for Environmental PredictionprojectVariousprocessing_levelPAggregated .grib archive from the NCEP. The original Hydrologic Rainfall Analysis Projection grid cell locations redefined by unprojecting grid cell locations using spherical earth projection algorithms. The cell lat/lon locations should be interpreted as being on a modern geotetic datum. This transformation is needed because the original grid cell location's lat/lon values were on a geodetic datum but were assumed to be on a spherical datum. See: https://pubs.usgs.gov/fs/2013/3035/pdf/fs2013-3035.pdf for more information. Note that this publication reverences the National Precipitation Verification Unit. The data represented here are aggregated from the same source as the National Precipitation Verification Unit used prior to being shut down. Data processing notes can be found here: http://www.emc.ncep.noaa.gov/mmb/ylin/pcpanl/acknowledgementyData originated at the National Center for Environmental Prediction: http://www.emc.ncep.noaa.gov/mmb/ylin/pcpanl/stage4/geospatial_lat_min24geospatial_lat_max53geospatial_lon_min-125geospatial_lon_max-66time_coverage_start2002-01-01T00:00:00Ztime_coverage_endpresentlicenseFreely availablecontributor_name$Center for Integrated Data Analyticscontributor_role#Aggregation and Serving via THREDDSgeospatial_lat_unitskmgeospatial_lat_resolution4geospatial_lon_unitskmgeospatial_lon_resolution4geospatial_vertical_unitskmNCO4.7.2 /Total_precipitation_surface_1_Hour_Accumulation long_nameCTotal precipitation (1_Hour Accumulation) @ Ground or water surface descriptionTotal precipitation missing_value coordinates time lat lonunitskg m^-2 _ChunkSizes<K standard_nameprecipitation_amount _FillValue cell_methodstime: sum (interval: 1 hr)iXlat units degrees_north 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={z=OOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOO==YOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOstars/inst/nc/d.nc0000644000176200001440000000000014344372470013526 0ustar liggesusersstars/inst/nc/test_stageiv_xyt_borked.nc0000644000176200001440000037347413777653456020311 0ustar liggesusersCDF timeyvxW time_bounds_1 $ Originating_or_generating_CenterQUS National Weather Service, National Centres for Environmental Prediction (NCEP)#Originating_or_generating_SubcenterEnvironmental Modeling CenterGenerating_process_or_modelRRiver Forecast Center Quantitative Precipitation estimate mosaic generated by NCEP ConventionsCF-1.4historyFri Aug 9 07:01:02 2019: ncks -d time,10 --3 test_stageiv_xyt_borked.nc test_stageiv_xyt_borked_small.nc Thu Apr 11 20:56:11 2019: ncks test_stageiv_xyt_coords_2.nc test_stageiv_xyt_data.nc Thu Apr 11 20:55:36 2019: ncpdq -a x,y test_stageiv_xyt_coords.nc test_stageiv_xyt_coords_2.nc Fri Feb 1 07:55:44 2019: ncpdq -a y,x test_stageiv.nc test_stageiv_2.nc Aggregated from NCEP Stage IV Analysis by dblodgett@usgs.gov 2016-01-01 featureTypeGRID_CoordSysBuilder#ucar.nc2.dataset.conv.CF1ConventionMetadata_ConventionsUnidata Dataset Discovery v1.0title9United States Stage IV Quantitative Precipitation ArchivesummaryMosaicked into a national product at NCEP, from the regional hourly multi-sensor (radar+gauges) precipitation analyses produced by the 12 River Forecast Centers over CONUS. Some manual QC done at the RFCs. The Stage II/IV job is run at 33min past the top of each hour. Hourly Stage IV is re-made hourly (if there is new input after valid time for the next 23 hours, then again at 1/3/5/7 days after valid time. Source: http://www.emc.ncep.noaa.gov/mmb/ylin/pcpanl/stage4/ Note: This dataset is not truely national. Some RFCs do not produce hourly precip estimates and others may not be present in the dataset. Please review the NCEP documentation for more information.keywordsUAtmosphere > Precipitation > Precipitation Amount Atmosphere > Precipitation > Rainkeywords_vocabularyGCMD Science Keywordsid stageivnaming_authority cida.usgs.gov cdm_data_typeGrid date_createdongoing institution,National Center for Environmental PredictionprojectVariousprocessing_levelPAggregated .grib archive from the NCEP. The original Hydrologic Rainfall Analysis Projection grid cell locations redefined by unprojecting grid cell locations using spherical earth projection algorithms. The cell lat/lon locations should be interpreted as being on a modern geotetic datum. This transformation is needed because the original grid cell location's lat/lon values were on a geodetic datum but were assumed to be on a spherical datum. See: https://pubs.usgs.gov/fs/2013/3035/pdf/fs2013-3035.pdf for more information. Note that this publication reverences the National Precipitation Verification Unit. The data represented here are aggregated from the same source as the National Precipitation Verification Unit used prior to being shut down. Data processing notes can be found here: http://www.emc.ncep.noaa.gov/mmb/ylin/pcpanl/acknowledgementyData originated at the National Center for Environmental Prediction: http://www.emc.ncep.noaa.gov/mmb/ylin/pcpanl/stage4/geospatial_lat_min24geospatial_lat_max53geospatial_lon_min-125geospatial_lon_max-66time_coverage_start2002-01-01T00:00:00Ztime_coverage_endpresentlicenseFreely availablecontributor_name$Center for Integrated Data Analyticscontributor_role#Aggregation and Serving via THREDDSgeospatial_lat_unitskmgeospatial_lat_resolution4geospatial_lon_unitskmgeospatial_lon_resolution4geospatial_vertical_unitskmNCO`netCDF Operators version 4.8.1 (Homepage = http://nco.sf.net, Code = http://github.com/nco/nco)history_of_appended_filesXThu Apr 11 20:56:11 2019: Appended file test_stageiv_xyt_coords_2.nc had following "history" attribute: Thu Apr 11 20:55:36 2019: ncpdq -a x,y test_stageiv_xyt_coords.nc test_stageiv_xyt_coords_2.nc Fri Feb 1 07:55:44 2019: ncpdq -a y,x test_stageiv.nc test_stageiv_2.nc Aggregated from NCEP Stage IV Analysis by dblodgett@usgs.gov 2016-01-01 /Total_precipitation_surface_1_Hour_Accumulation long_nameCTotal precipitation (1_Hour Accumulation) @ Ground or water surface descriptionTotal precipitation missing_value coordinates time lat lonunitskg m^-2 _ChunkSizes<K standard_nameprecipitation_amount _FillValue cell_methodstime: sum (interval: 1 hr)hlat units degrees_north _ChunkSizesK<_CoordinateAxisTypeLathTlon units degrees_east _ChunkSizesK<_CoordinateAxisTypeLonhVtime unitsHour since 2001-12-31T23:00:00Z standard_nametime long_name!GRIB forecast or observation timecalendarproleptic_gregorianbounds time_bounds_CoordinateAxisTypeTime _ChunkSizes$ time_bounds unitsHour since 2001-12-31T23:00:00Z long_namebounds for time _ChunkSizes,?@?@?aG??@Q@Q@???У?@?У?@@0@@`@XQ@XQ@@xQ@(@ @8Q@(Q@(Q@Q@@@Q@@@@Q@@??У?У?У?У?У?!G?aG???@@@???????@hQ@XQ@p@P@p@hQ@@@p@xQ@XQ@0@Q@ @@??@ @(Q@ @@Q@??????@?aG?aG?????@?????@Q@XQ@ @p@@`@(@@(@(@`@hQ@8Q@ @@@@@Q@Q@Q@@@8Q@ 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˜eL˜Z˜O˜D˜:1˜/a˜$˜˜˜——3—I—\—j—u—|——~—z—r—f—uV—jB—_+—T—H—=—2—'|—N——––m–+––͛–N–––M–––~)–r–gR–[–Pl–D–9u–-–"o–– X•š"š6šFšTš^šdšhšhšešx^šnTšdGšZ6šP"šF š;š1š'ššcš 8™ ™™™g™*™™™_™™™v™!™™yl™o ™d™ZD™O™Em™:™0™&™™™˜ ˜˜˜e˜˜8˜˜˜[˜˜ ˜]˜|˜q˜g>˜\˜Q˜F˜<7˜1l˜&˜˜˜—>—]—x—ڏ—ϣ—ij———————w—l—a—V—Ki—@K—5)—*———}–I–––ۓ–O–––k–––c––u–j8–^–S]–G–˜~˜s˜i.˜^v˜S˜H˜>;˜3u˜(˜˜ ˜9—`—————————— ——z—o—d—X—M—B—7—,—!f—?— –––z–?––ǽ–w–,–––5––x}–m–a–VK–J–?k–3–({––{–•k••K•׵š5šDšPšYš^šz`šp_šf[š\SšRHšH9š>'š4š)ššš šu™L™™™ػ™΄™J™ ™™™>™™™|P™q™g™]D™R™H™>™3™)?™™W™ ˜b˜˜^˜˜L˜ʾ˜,˜˜˜a˜˜˜v˜u˜k˜`j˜U˜J˜@=˜5|˜*˜˜#˜ T——————)—?—R—a—m—t—x—|x—qt—fl—[a—PR—E?—:(—/—#—— —}–O–––կ–r–1–––V–––{X–o–d–Y6–M–Bb–6–+}– –– •••p••K•òA0stars/inst/nc/reduced.R0000644000176200001440000000036713777653456014556 0ustar liggesuserslibrary(ncdf4) nc = nc_open("reduced.nc", write=TRUE) ncatt_put(nc, 'sst', 'units', 'degree_C') ncatt_put(nc, 'anom', 'units', 'degree_C') ncatt_put(nc, 'err', 'units', 'degree_C') ncatt_put(nc, 'ice', 'units', 'percent') nc_sync(nc) nc_close(nc) stars/inst/nc/lcc_km.nc0000644000176200001440000007546614236716405014567 0ustar liggesusersHDF  6{`OHDR " E*lambert_conformal_conicuprcptimex!y#PFRHP#( L?BTHDd(u&4ABTHD  d(u䛸FSHDPx(7%%9֝BTLFX 4&Jl3-KHX qdd;곁407uX Q.0-w/ZJ g\?wqLJB;mX 3AᎨBTLF 4JB;K?Q.ww4mX X X uX J JjQOHDR U  ?L h y FRHP  ( @:$BTHDd( BTHD  d( PFSHDPx(7BTLF`]XKX B:P,vPD\JPVvP`A[`מ`9 ǢGúe`X5BTLF `vPP`vPXGe`B9 :mOHDR o89k9k   || 8shuffledeflate?T9k 9p9FRHP_ ( <|LD` \"i?ļ^1_}~p cTc#3_BTLF TjLL\^1?BL\"~p 4 pBOHDR    || 8shuffledeflate` Q fj-FRHP'/ ( 88/BTHDd( UoBTHD  d( U8 FSHD'Px(7AbBTLFDc bGL 0 4 ;$0<,x/R9)2yF%_k4e >>i! xBTLF 0F%k4,0>9)bGD4 ! ROHDR kk   || 8shuffledeflate6 0CLASSDIMENSION_SCALE "NAMEx 4 _Netcdf4Dimid  "unitskm B long_namex coordinate of projection ?standard_nameprojection_x_coordinatejOHDR 99   || 8shuffledeflate5 0CLASSDIMENSION_SCALE "NAMEy 4 _Netcdf4Dimid  "unitskm B long_namey coordinate of projection ?standard_nameprojection_y_coordinate>?%/0"SdAA $AA DAAAx 8؁ X ^aValza5VJXa,%fggf&''&FGGFz{{z:;;:Z[[Zjkkj*++*JKKJ rssr2332RSSRT %))11!!>>..66&&::**22""<<,,44$$88((00 ёLvp077335511662" tuut4554TUUT~_>o^zO?zwnߺyW._xgN:yG:x /'+#-%)!.&*",$(@EYPsSTREElAL9kTREE*QTREE QkTREE'y9x^Sqnݮ7]BݺqvAGGːy:HM 'Id / ($,"*&.!)%-#+'i۶عk8xǎ8yz_op򕫆F׮߸y?xϞxc߾4337wppttrrvvqquussw  OHHLLJJNNIIMMKKO/((,,**..)AJKh -++/ohhlljjnniimmkko_X%ayH\Y!VW5 e}J3`  7 0@  4@2P@  6/ ?5)*.stars/inst/nc/3B42_Daily.19991231.7.test.nc0000644000176200001440000000326413777653456017141 0ustar liggesusersCDF lonlat BeginDate 1999-12-31 BeginTime 01:30:00.000ZEndDate 2000-01-01EndTime 01:29:59.999Z FileHeader[StartGranuleDateTime=1999-12-31T01:30:00.000Z; StopGranuleDateTime=2000-01-01T01:29:59.999Z InputPointer3B42.19991231.03.7.HDF;3B42.19991231.06.7.HDF;3B42.19991231.09.7.HDF;3B42.19991231.12.7.HDF;3B42.19991231.15.7.HDF;3B42.19991231.18.7.HDF;3B42.19991231.21.7.HDF;3B42.20000101.00.7A.HDFtitle9TRMM (TMPA) L3 Daily 0.25 x 0.25 degree (TRMM_3B42_Daily)ProductionTime2016-07-20T15:34:37.000Zhistory2019-08-01 19:35:54 GMT Hyrax-1.15.1 https://disc2.gesdisc.eosdis.nasa.gov:443/opendap/TRMM_L3/TRMM_3B42_Daily.7/1999/12/3B42_Daily.19991231.7.nc4.nc4?precipitation[381:589][0:267],lat[0:267],lon[381:589], Manually edited for testing in stars R package by dblodgett@usgs.gov  precipitation unitsmm long_nameaDaily accumulated precipitation (combined microwave-IR) estimate with gauge calibration over land coordinateslat lon _FillValue?origname precipitation fullnamepath/precipitationP@lat units degrees_north long_nameLatitudeorignamelat fullnamepath/latlon units degrees_east long_name Longitudeorignamelon fullnamepath/lon>=u=uGFEDC©@¨¨@§stars/inst/nc/plot_bcsd_obs_1999.R0000644000176200001440000000212013777653456016437 0ustar liggesusers## data-getting is described here ## https://github.com/r-spatial/stars/issues/94#issuecomment-459583295 f <- system.file("nc/test_stageiv_xyt.nc", package = "stars") library(stars) x <- read_ncdf(f, curvilinear = c("lon", "lat")) brks <- c(0.129999995231628, 0.879999995231628, 1.5, 2.12999987602234, 2.87999987602234, 3.5, 4.19000005722046, 5.05999994277954, 5.80999994277954, 6.75, 7.87999963760376, 8.88000011444092, 10.1300001144409, 11.25, 12.5, 13.75, 15, 16.25, 17.5, 18.75, 20, 21.25, 22.5, 23.75, 25, 26.3799991607666, 27.75, 29.1299991607666, 30.3799991607666, 32.25, 34, 35.6300010681152, 37.75, 39.629997253418, 42.129997253418, 45.25, 49.25, 58.75) qu_0_omit <- function(x, ..., n = 22) { x <- na.omit(x) c(0, quantile(x[x > 0], seq(0, 1, length.out = n))) } plot(stars::slice.stars(x, index = 17, along = "time"), border = NA, breaks = qu_0_omit(x[[1]][,,17]), reset = FALSE, col = viridis::viridis(22)) plot(sf::read_sf(system.file("gpkg/nc.gpkg", package = "sf"), "nc.gpkg"), add = TRUE, reset = FALSE, col = NA) stars/inst/nc/EURO-CORDEX_81_DOMAIN000_mask_int.nc.zip0000644000176200001440000014275213777653456021350 0ustar liggesusersPK6U O 3$EURO-CORDEX_81_DOMAIN000_mask_int.ncUT Q]Q]ux   yn%aM6wHՒZK]rҩ-fF$i6}-GJJe9+$$C$d ̂+̄$d N$f`ptL @b'׼uN{{ֿ{<~cWK} ?=x~쳯|ozۗso:zG~p÷?s>걧zzrsg_3/wk_x3?y{V}tїϼϼSŏ=~m^>O~O~O=?䋟~}n?۟'y_A?{{'>Уw\/S/W=]}3\㝫wo4n;دaЏ|P[k\|~{w>{Vx띫{/G?/~'zv5 '۟zf^zR]&}+?Sy?^/0 0 ?~>|So}on?_yroxO <܋zO23O=3yO?䋏r_=W^yǟG.S|~7/G/Yvgnòyṧ_Y .>a8>?_=/{g_[5éOx=<7/ʍq={Ǿëfؾbe}}c[=sO|ꃫ{a'{O<<ᷭ Sч|]/_{cps˽sx͓=ǟzvW.Vb_/ρg/q~rb>\9}0^~/ye]ݗ_?:|0}k>3 ^{q+/ozƱ~[wa{^Xsyaqx?/'1yNn?p>?6'>ԃ{ʇ>ѿvhԳAe]x}}'xzxվ^?}k^k^ &?Ͼp1?O߾7__u>rӫ˻/,p+p?mZ#= ­{cO8ui.?w_<_9{8\+:9q?rc1c1=}_c?K}<c1mRc{dž1c#1b1ئJ}c1)R+cmyNj1c;#1c1؆H}c1R7c+qc1XZy`1Ƹh=1^*c1XRc5%y`1ƸkI}c1R 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^a{.gw}o:<='-'?vrwONϞ//Փӿ;'?rr?ok'NN=9NN?trW{r'/=yr3_ӿNNo?9so:9O~|V'_WĿ? ڏ o?2G𞷆5|ƭn yk[1pLc5Xo |k8[9pNs5\o |k8[0&5ѭan cvk[0=hȁ?p-kr4\ hfGõ;p-kz4\ۣ hGõ?09q4̍a sh3G909u4̭a shsG;09y4ͣa shG=09}4a shG?Ѱ5q4a khX3G9Ѱ5u4a khXsG;Ѱ5y4ͣa khXG=Ѱ5}4a khXG?b p4Ć!F hGC8bKr4Ė! h9GC9bt4Ħ!F hYGC:bˎv4Ķ! hyGC;b x4ƣ!F hGC<bKz4֣! hC=b|bˏ~ýx<돇{p?x` 68x`v8`8#8#PK6U O 3$EURO-CORDEX_81_DOMAIN000_mask_int.ncUTQ]ux  PKjjstars/inst/nc/test-1.nc0000644000176200001440000000174413777653456014457 0ustar liggesusersCDF xyc3c4c5 a@Dxyc3c4c5?@@@@@@@ @"@$@&@(@*@,@.@0@1@2@3@4@5@6@7@8?񙙙@@@ffffff@ffffff@ffffff@ffffff@ 333333@"333333@$333333@&333333@(333333@*333333@,333333@.333333@0@1@2@3@4@5@6@7@8@@@@ @$@(@,@0@2@4@6@8@:@<@>@@@A@B@C@D@E@F@G@H@?@????stars/inst/nc/d0000644000176200001440000006403414344372455013153 0ustar liggesusersnetcdf sub { dimensions: latitude = 9 ; level = 2 ; longitude = 9 ; time = 10 ; variables: float latitude(latitude) ; latitude:units = "degrees_north" ; latitude:long_name = "latitude" ; int level(level) ; level:units = "millibars" ; level:long_name = "pressure_level" ; float longitude(longitude) ; longitude:units = "degrees_east" ; longitude:long_name = "longitude" ; int time(time) ; time:units = "hours since 1900-01-01 00:00:00.0" ; time:long_name = "time" ; time:calendar = "gregorian" ; short u(time, level, latitude, longitude) ; u:scale_factor = 0.000270934372177591 ; u:add_offset = 4.15255160556782 ; u:_FillValue = -32767s ; u:missing_value = -32767s ; u:units = "m s**-1" ; u:long_name = "U component of wind" ; u:standard_name = "eastward_wind" ; short v(time, level, latitude, longitude) ; v:scale_factor = 0.000187186943937716 ; v:add_offset = 1.28458200467256 ; v:_FillValue = -32767s ; v:missing_value = -32767s ; v:units = "m s**-1" ; v:long_name = "V component of wind" ; v:standard_name = "northward_wind" ; // global attributes: :Conventions = "CF-1.6" ; :history = "Fri Jul 3 20:45:39 2020: ncks -d time,1,10 data.nc sub.nc\n", "2020-06-30 07:07:13 GMT by grib_to_netcdf-2.16.0: /opt/ecmwf/eccodes/bin/grib_to_netcdf -S param -o /cache/data4/adaptor.mars.internal-1593500809.0007904-16187-13-88557124-d321-4eea-bab8-1367f56839fd.nc /cache/tmp/88557124-d321-4eea-bab8-1367f56839fd-adaptor.mars.internal-1593500809.0013933-16187-2-tmp.grib" ; :NCO = "netCDF Operators version 4.9.2 (Homepage = http://nco.sf.net, Code = http://github.com/nco/nco)" ; data: latitude = 52, 51.75, 51.5, 51.25, 51, 50.75, 50.5, 50.25, 50 ; level = 825, 850 ; longitude = 5, 5.25, 5.5, 5.75, 6, 6.25, 6.5, 6.75, 7 ; time = 1031161, 1031162, 1031163, 1031164, 1031165, 1031166, 1031167, 1031168, 1031169, 1031170 ; u = 31398, 31456, 30677, 29690, 28962, 28717, 28933, 29394, 29935, 30822, 30620, 29704, 28623, 27888, 27671, 27815, 28082, 28306, 30122, 29834, 28818, 27686, 26936, 26532, 26215, 26049, 26093, 27693, 27217, 26395, 25725, 25357, 24968, 24442, 24204, 24478, 23872, 23425, 23202, 23396, 23800, 23822, 23267, 22748, 22841, 21198, 20852, 21104, 22092, 23461, 24211, 23555, 22099, 21255, 20189, 20563, 21609, 23166, 24910, 25840, 24910, 22603, 20758, 22409, 23526, 24817, 25804, 26403, 26395, 25285, 23238, 21306, 26035, 27159, 27902, 27888, 27268, 26475, 25660, 24701, 23591, 27896, 27787, 27247, 26821, 26692, 26814, 27182, 27773, 28458, 27860, 27456, 26721, 26252, 26245, 26540, 26994, 27492, 27910, 28206, 27795, 26951, 26338, 26187, 26180, 26108, 26101, 26194, 27283, 27031, 26533, 26317, 26367, 26173, 25596, 25120, 25041, 24601, 24630, 24789, 25365, 26072, 26129, 25322, 24342, 23844, 22215, 22107, 22475, 23650, 25185, 25906, 25048, 23145, 21501, 20968, 21047, 21703, 23051, 24652, 25502, 24644, 22201, 19613, 21847, 22244, 22871, 23455, 23859, 23880, 23138, 21444, 19375, 23462, 23967, 24298, 24140, 23484, 22684, 22042, 21523, 20816, 29829, 31234, 32215, 32453, 31948, 31047, 30052, 29122, 28409, 30074, 31083, 31523, 31191, 30304, 29230, 28243, 27529, 27197, 29014, 29569, 29490, 28920, 28156, 27327, 26599, 26325, 26469, 27450, 27507, 27169, 26678, 26094, 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"Atmospheric Temperature, Air Temperature Atmosphere, Precipitation, Rain, Maximum Daily Temperature, Minimum Daily Temperature" ; } \keyword{datasets} stars/man/contour.stars.Rd0000644000176200001440000000145413777653456015327 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/plot.R \name{contour.stars} \alias{contour.stars} \title{plot contours of a stars object} \usage{ \method{contour}{stars}(x, ...) } \arguments{ \item{x}{object of class \code{stars}} \item{...}{other parameters passed on to \link[graphics]{contour}} } \description{ plot contours of a stars object } \details{ this uses the R internal contour algorithm, which (by default) plots contours; \link{st_contour} uses the GDAL contour algorithm that returns contours as simple features. } \examples{ d = st_dimensions(x = 1:ncol(volcano), y = 1:nrow(volcano)) r = st_as_stars(t(volcano)) r = st_set_dimensions(r, 1, offset = 0, delta = 1) r = st_set_dimensions(r, 2, offset = 0, delta = -1) plot(r, reset = FALSE) contour(r, add = TRUE) } stars/man/st_mosaic.Rd0000644000176200001440000000405614604601360014436 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/mosaic.R \name{st_mosaic} \alias{st_mosaic} \alias{st_mosaic.stars} \alias{st_mosaic.character} \alias{st_mosaic.stars_proxy} \title{build mosaic (composite) of several spatially disjoint stars objects} \usage{ st_mosaic(.x, ...) \method{st_mosaic}{stars}( .x, ..., dst = tempfile(fileext = file_ext), options = c("-vrtnodata", "-9999", "-srcnodata", "nan"), file_ext = ".tif" ) \method{st_mosaic}{character}( .x, ..., dst = tempfile(fileext = file_ext), options = c("-vrtnodata", "-9999"), file_ext = ".tif" ) \method{st_mosaic}{stars_proxy}( .x, ..., dst = tempfile(fileext = file_ext), options = c("-vrtnodata", "-9999"), file_ext = ".tif" ) } \arguments{ \item{.x}{object of class stars, or character vector with input dataset names} \item{...}{further input stars objects} \item{dst}{character; destination file name; this will be a VRT file with references to the source file(s), see details} \item{options}{character; options to the gdalbuildvrt command} \item{file_ext}{character; file extension, determining the format used to write to (".tif" implies GeoTIFF)} } \value{ the stars method returns a stars object with the composite of the input; the \code{character} method returns the file name of the file with the mosaic; see also the GDAL documentation of \code{gdalbuildvrt} } \description{ build mosaic (composite) of several spatially disjoint stars objects } \details{ the gdal function buildvrt builds a mosaic of input images; these input images can be multi-band, but not higher-dimensional data cubes or stars objects with multiple attributes; note that for the `stars` method, the `dst` file may contain references to temporary files that are going to be removed at termination of the R session. uses \link[sf]{gdal_utils} to internally call \code{buildvrt}; no executables external to R are called. } \examples{ x = read_stars(system.file("tif/L7_ETMs.tif", package = "stars")) x1 = x[,100:200,100:200,] x2 = x[,150:300,150:300,] plot(st_mosaic(x1, x2)) } stars/man/expand_dimensions.Rd0000644000176200001440000000137214576033423016171 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/dimensions.R \name{expand_dimensions} \alias{expand_dimensions} \alias{expand_dimensions.dimensions} \title{expand the dimension values into a list} \usage{ expand_dimensions(x, ...) \method{expand_dimensions}{dimensions}(x, ..., max = FALSE, center = NA) } \arguments{ \item{x}{object of class `stars` or `dimensions`} \item{...}{ignored} \item{max}{logical; if `TRUE` return the max (end) values of the dimensions intervals} \item{center}{logical; if `TRUE` return the center values of intervals, otherwise return offset (start) of intervals; if `NA` (default) return centers for x/y dimensions, offsets for all others} } \description{ expand the dimension values into a list } stars/man/st_coordinates.Rd0000644000176200001440000000222014600276652015474 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/stars.R, R/tidyverse.R \name{st_coordinates} \alias{st_coordinates} \alias{st_coordinates.stars} \alias{as.data.frame.stars} \alias{as_tibble.stars} \title{retrieve coordinates for raster or vector cube cells} \usage{ \method{st_coordinates}{stars}(x, ..., add_max = FALSE, center = TRUE) \method{as.data.frame}{stars}(x, ..., add_max = FALSE, center = NA, add_coordinates = TRUE) as_tibble.stars(.x, ..., add_max = FALSE, center = NA) } \arguments{ \item{x}{object of class \code{stars}} \item{...}{ignored} \item{add_max}{logical; if \code{TRUE}, dimensions are given with a min (x) and max (x_max) value} \item{center}{logical; (only if \code{add_max} is FALSE): should grid cell center coordinates be returned (TRUE) or offset values (FALSE)? \code{center} can be a named logical vector or list to specify values for each dimension.} \item{add_coordinates}{logical; if `TRUE`, columns with dimension values preceed the array values, otherwise they are omitted} \item{.x}{object to be converted to a tibble} } \description{ retrieve coordinates for raster or vector cube cells } stars/man/st_tile.Rd0000644000176200001440000000223114166576214014125 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/tile.R \name{st_tile} \alias{st_tile} \title{Specify parameters to load raster in blocks} \usage{ st_tile(img_rows, img_cols, x_window, y_window, overlap = 0) } \arguments{ \item{img_rows}{number of input raster rows (integer)} \item{img_cols}{number of input raster columns (integer)} \item{x_window}{number of rows in block (integer)} \item{y_window}{number of columns in block (integer)} \item{overlap}{number of overlapping pixels (integer)} } \value{ matrix with specified \code{nXOff}, \code{nYOff}, \code{nXsize}, and \code{nYSize} parameters for every block } \description{ Helper function for specifying the block parameters (\code{nXOff}, \code{nYOff}, \code{nXsize}, and \code{nYSize}) required by \code{RasterIO} argument in \link{read_stars} } \examples{ \dontrun{ tif = system.file("tif/L7_ETMs.tif", package = "stars") r = read_stars(tif, proxy = TRUE) tiles = st_tile(nrow(r), ncol(r), 256, 256) for (i in seq_len(nrow(tiles))) { tile = read_stars(tif, proxy = FALSE, RasterIO = tiles[i, ]) # write tiles to separate files write_stars(tile, dsn = paste0(i, ".tif")) } } } stars/man/plot.Rd0000644000176200001440000001742014576035610013441 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/ncproxy.R, R/plot.R, R/proxy.R \name{plot} \alias{plot} \alias{plot.nc_proxy} \alias{plot.stars} \alias{image.stars} \alias{plot.stars_proxy} \title{plot stars object, with subplots for each level of first non-spatial dimension} \usage{ \method{plot}{nc_proxy}(x, y, ..., downsample = get_downsample(dim(x)), max_times = 16) \method{plot}{stars}( x, y, ..., join_zlim = TRUE, main = make_label(x, 1), axes = FALSE, downsample = TRUE, nbreaks = 11, breaks = "quantile", col = grey(1:(nbreaks - 1)/nbreaks), key.pos = get_key_pos(x, ...), key.width = kw_dflt(x, key.pos), key.length = 0.618, key.lab = main, reset = TRUE, box_col = NA, center_time = FALSE, hook = NULL, mfrow = NULL, compact = TRUE ) \method{image}{stars}( x, ..., band = 1, attr = 1, asp = NULL, rgb = NULL, maxColorValue = ifelse(inherits(rgb, "data.frame"), 255, max(x[[attr]], na.rm = TRUE)), xlab = if (!axes) "" else names(d)[1], ylab = if (!axes) "" else names(d)[2], xlim = st_bbox(extent)$xlim, ylim = st_bbox(extent)$ylim, text_values = FALSE, text_color = "black", axes = FALSE, interpolate = FALSE, as_points = FALSE, key.pos = NULL, logz = FALSE, key.width = kw_dflt(x, key.pos), key.length = 0.618, add.geom = NULL, border = NA, useRaster = isTRUE(dev.capabilities()$rasterImage == "yes"), extent = x ) \method{plot}{stars_proxy}(x, y, ..., downsample = get_downsample(dim(x))) } \arguments{ \item{x}{object of class \code{stars}} \item{y}{ignored} \item{...}{further arguments: for \code{plot}, passed on to \code{image.stars}; for \code{image}, passed on to \code{image.default} or \code{rasterImage}.} \item{downsample}{logical or numeric; if \code{TRUE} will try to plot not many more pixels than actually are visible, if \code{FALSE}, no downsampling takes place, if numeric, the number of pixels/lines/bands etc that will be skipped; see Details.} \item{max_times}{integer; maximum number of time steps to attempt to plot.} \item{join_zlim}{logical; if \code{TRUE}, compute a single, joint zlim (color scale) for all subplots from \code{x}} \item{main}{character; subplot title prefix; use \code{""} to get only time, use \code{NULL} to suppress subplot titles} \item{axes}{logical; should axes and box be added to the plot?} \item{nbreaks}{number of color breaks; should be one more than number of colors. If missing and \code{col} is specified, it is derived from that.} \item{breaks}{numeric vector with actual color breaks, or a \code{style} name used in \link[classInt]{classIntervals}.} \item{col}{colors to use for grid cells, or color palette function} \item{key.pos}{numeric; side to plot a color key: 1 bottom, 2 left, 3 top, 4 right; set to \code{NULL} to omit key. Ignored if multiple columns are plotted in a single function call. Default depends on plot size, map aspect, and, if set, parameter \code{asp}. If it has length 2, the second value, ranging from 0 to 1, determines where the key is placed in the available space (default: 0.5, center).} \item{key.width}{amount of space reserved for width of the key (labels); relative or absolute (using lcm)} \item{key.length}{amount of space reserved for length of the key (labels); relative or absolute (using lcm)} \item{key.lab}{character; label for color key in case of multiple subplots, use \code{""} to suppress} \item{reset}{logical; if \code{FALSE}, keep the plot in a mode that allows adding further map elements; if \code{TRUE} restore original mode after plotting} \item{box_col}{color for box around sub-plots; use \code{NA} to suppress plotting of boxes around sub-plots.} \item{center_time}{logical; if \code{TRUE}, sub-plot titles will show the center of time intervals, otherwise their start} \item{hook}{NULL or function; hook function that will be called on every sub-plot; see examples.} \item{mfrow}{length-2 integer vector with nrows, ncolumns of a composite plot, to override the default layout} \item{compact}{logical; place facets compactly (TRUE), or spread over the plotting device area?} \item{band}{integer; which band (dimension) to plot} \item{attr}{integer; which attribute to plot} \item{asp}{numeric; aspect ratio of image} \item{rgb}{integer; specify three bands to form an rgb composite. Experimental: rgb color table; see Details.} \item{maxColorValue}{numeric; passed on to \link{rgb}} \item{xlab}{character; x axis label} \item{ylab}{character; y axis label} \item{xlim}{x axis limits} \item{ylim}{y axis limits} \item{text_values}{logical; print values as text on image?} \item{text_color}{character; color for printed text values} \item{interpolate}{logical; when using \link{rasterImage} (rgb), should pixels be interpolated?} \item{as_points}{logical; for curvilinear or sheared grids: parameter passed on to \link{st_as_sf}, determining whether raster cells will be plotted as symbols (fast, approximate) or small polygons (slow, exact)} \item{logz}{logical; if \code{TRUE}, use log10-scale for the attribute variable. In that case, \code{breaks} and \code{at} need to be given as log10-values; see examples.} \item{add.geom}{object of class \code{sfc}, or list with arguments to \code{plot}, that will be added to an image or sub-image} \item{border}{color used for cell borders (only in case \code{x} is a curvilinear or rotated/sheared grid)} \item{useRaster}{logical; use the rasterImage capabilities of the graphics device?} \item{extent}{object which has a \code{st_bbox} method; sets the plotting extent} } \description{ plot stars object, with subplots for each level of first non-spatial dimension, and customization of legend key } \details{ when plotting a subsetted \code{stars_proxy} object, the default value for argument \code{downsample} will not be computed correctly, and has to be set manually. Downsampling: a value for \code{downsample} of 0: no downsampling, 1: after every dimension value (pixel/line/band), one value is skipped (half of the original resolution), 2: after every dimension value, 2 values are skipped (one third of the original resolution), etc. If \code{downsample} is \code{TRUE} or a length 1 numeric vector, downsampling is only applied to the raster [x] and [y] dimensions. To remove unused classes in a categorical raster, use the \link[base]{droplevels} function. When bitmaps show visual artefacts (Moiré effects), make sure that device \link{png} is used rather than \code{ragg::agg_png} as the latter uses antialiasing for filled polygons which causes this; see also https://github.com/r-spatial/stars/issues/573 . use of an rgb color table is experimental; see https://github.com/r-spatial/mapview/issues/208 when plotting a subsetted \code{stars_proxy} object, the default value for argument \code{downsample} will not be computed correctly, and has to be set manually. } \examples{ st_bbox(L7_ETMs) |> st_as_sfc() |> st_centroid() |> st_coordinates() -> pt hook1 = function() { text(pt[,"X"], pt[,"Y"], "foo", col = 'orange', cex = 2) } plot(L7_ETMs, hook = hook1) x = st_set_dimensions(L7_ETMs, 3, paste0("B_", 1:6)) hook2 = function(..., row, col, nr, nrow, ncol, value, bbox) { str = paste0("row ", row, "/", nrow, ", col ", col, "/", ncol, "\nnr: ", nr, " value: ", value) bbox |> st_as_sfc() |> st_centroid() |> st_coordinates() -> pt text(pt[,"X"], pt[,"Y"], str, col = 'red', cex = 2) } plot(x, hook = hook2, col = grey(c(.2,.25,.3,.35))) if (isTRUE(dev.capabilities()$rasterImage == "yes")) { lc = read_stars(system.file("tif/lc.tif", package = "stars")) levels(lc[[1]]) = abbreviate(levels(lc[[1]]), 6) # so it's not only legend plot(lc, key.pos=4) } tif = system.file("tif/L7_ETMs.tif", package = "stars") x = read_stars(tif) image(x, col = grey((3:9)/10)) if (isTRUE(dev.capabilities()$rasterImage == "yes")) { image(x, rgb = c(1,3,5)) # false color composite } } stars/man/predict.stars.Rd0000644000176200001440000000166414576035610015253 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/proxy.R, R/stars.R \name{predict.stars} \alias{predict.stars} \alias{predict.stars_proxy} \title{Predict values, given a model object, for a stars or stars_proxy object} \usage{ \method{predict}{stars_proxy}(object, model, ...) \method{predict}{stars}(object, model, ..., drop_dimensions = FALSE) } \arguments{ \item{object}{object of class `stars`} \item{model}{model object of a class that has a predict method; check with `methods(class = class(object))`} \item{...}{arguments passed on to this predict method} \item{drop_dimensions}{logical; if `TRUE`, remove dimensions (coordinates etc) from `data.frame` with predictors} } \description{ Predict values, given a model object, for a stars or stars_proxy object } \details{ separate predictors in object need to be separate attributes in object; in case they are e.g. in a band dimension, use `split(object)` } stars/man/st_apply.Rd0000644000176200001440000000734714145541611014320 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/ops.R \name{st_apply} \alias{st_apply} \alias{st_apply.stars} \title{st_apply apply a function to one or more array dimensions} \usage{ \method{st_apply}{stars}( X, MARGIN, FUN, ..., CLUSTER = NULL, PROGRESS = FALSE, FUTURE = FALSE, rename = TRUE, .fname, single_arg = has_single_arg(FUN, list(...)) || can_single_arg(FUN), keep = FALSE ) } \arguments{ \item{X}{object of class \code{stars}} \item{MARGIN}{see \link[base]{apply}; index number(s) or name(s) of the dimensions over which \code{FUN} will be applied} \item{FUN}{see \link[base]{apply} and see Details.} \item{...}{arguments passed on to \code{FUN}} \item{CLUSTER}{cluster to use for parallel apply; see \link[parallel]{makeCluster}} \item{PROGRESS}{logical; if \code{TRUE}, use \code{pbapply::pbapply} to show progress bar} \item{FUTURE}{logical;if \code{TRUE}, use \code{future.apply::future_apply}} \item{rename}{logical; if \code{TRUE} and \code{X} has only one attribute and \code{FUN} is a simple function name, rename the attribute of the returned object to the function name} \item{.fname}{function name for the new attribute name (if one or more dimensions are reduced) or the new dimension (if a new dimension is created); if missing, the name of \code{FUN} is used} \item{single_arg}{logical; if \code{TRUE}, FUN takes a single argument (like \code{fn_ndvi1} below), if \code{FALSE} FUN takes multiple arguments (like \code{fn_ndvi2} below).} \item{keep}{logical; if \code{TRUE}, preserve dimension metadata (e.g. time stamps)} } \value{ object of class \code{stars} with accordingly reduced number of dimensions; in case \code{FUN} returns more than one value, a new dimension is created carrying the name of the function used; see the examples. Following the logic of \link[base]{apply}, This new dimension is put before the other dimensions; use \link{aperm} to rearrange this, see last example. } \description{ st_apply apply a function to array dimensions: aggregate over space, time, or something else } \details{ FUN is a function which either operates on a single object, which will be the data of each iteration step over dimensions MARGIN, or a function that has as many arguments as there are elements in such an object. See the NDVI examples below. The second form can be VERY much faster e.g. when a trivial function is not being called for every pixel, but only once (example). The heuristics for the default of \code{single_arg} work often, but not always; try setting this to the right value when \code{st_apply} gives an error. } \examples{ tif = system.file("tif/L7_ETMs.tif", package = "stars") x = read_stars(tif) st_apply(x, 1:2, mean) # mean band value for each pixel st_apply(x, c("x", "y"), mean) # equivalent to the above st_apply(x, 3, mean) # mean of all pixels for each band \dontrun{ st_apply(x, "band", mean) # equivalent to the above st_apply(x, 1:2, range) # min and max band value for each pixel fn_ndvi1 = function(x) (x[4]-x[3])/(x[4]+x[3]) # ONE argument: will be called for each pixel fn_ndvi2 = function(red,nir) (nir-red)/(nir+red) # n arguments: will be called only once ndvi1 = st_apply(x, 1:2, fn_ndvi1) # note that we can select bands 3 and 4 in the first argument: ndvi2 = st_apply(x[,,,3:4], 1:2, fn_ndvi2) all.equal(ndvi1, ndvi2) # compute the (spatial) variance of each band; https://github.com/r-spatial/stars/issues/430 st_apply(x, 3, function(x) var(as.vector(x))) # as.vector is required! # to get a progress bar also in non-interactive mode, specify: if (require(pbapply)) { # install it, if FALSE pboptions(type = "timer") } st_apply(x, 1:2, range) # dimension "range" is first; rearrange by: st_apply(x, 1:2, range) \%>\% aperm(c(2,3,1)) } } stars/man/st_intersects.stars.Rd0000644000176200001440000000231413777653456016523 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/geom.R \name{st_intersects.stars} \alias{st_intersects.stars} \title{spatial intersect predicate for stars and sfc object} \usage{ \method{st_intersects}{stars}(x, y, sparse = TRUE, ..., as_points = NA, transpose = FALSE) } \arguments{ \item{x}{object of class stars} \item{y}{object that has an `st_geometry` method: of class `sf` or `sfc`, or `stars` object with an `sfc` dimension} \item{sparse}{logical; if TRUE, return the a sparse logical matrix (object of class `sgbp`), if FALSE, return a logical matrix} \item{...}{ignored, or passed on to `st_intersects.sf` for curvilinear grids} \item{as_points}{logical, should grid cells be considered as points (TRUE) or polygons (FALSE)? Default: FALSE and warning emitted} \item{transpose}{logical; should the transpose of the `sgbp` object be returned?} } \value{ `sgbp` object if sparse = TRUE, logical matrix otherwise } \description{ spatial intersect predicate for stars and sfc object } \details{ curvilinear grids are always converted to polygons, so points on grid boundaries may intersect with two cells touched; for other grids each cell boundary or corner belongs only to one cell. } stars/man/merge.Rd0000644000176200001440000000200413777653456013572 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/stars.R \name{merge} \alias{merge} \alias{split.stars} \alias{split} \alias{merge.stars} \title{merge or split stars object} \usage{ \method{split}{stars}(x, f = length(dim(x)), drop = TRUE, ...) \method{merge}{stars}(x, y, ..., name = "attributes") } \arguments{ \item{x}{object of class \code{stars}} \item{f}{the name or index of the dimension to split; by default the last dimension} \item{drop}{ignored} \item{...}{if defined, the first unnamed argument is used for dimension values, if not defined, attribute names are used for dimension values} \item{y}{needs to be missing} \item{name}{name for the new dimension} } \value{ merge merges attributes of a stars object into a new dimension; split splits a dimension over attributes } \description{ merge attributes into a dimension, or split a dimension over attributes } \details{ split.stars works on the first attribute, and will give an error when more than one attribute is present } stars/man/read_ncdf.Rd0000644000176200001440000001015414576033423014365 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/ncdf.R \name{read_ncdf} \alias{read_ncdf} \title{Read NetCDF into stars object} \usage{ read_ncdf( .x, ..., var = NULL, ncsub = NULL, curvilinear = character(0), eps = sqrt(.Machine$double.eps), ignore_bounds = FALSE, make_time = TRUE, make_units = TRUE, proxy = NULL, downsample = 0 ) } \arguments{ \item{.x}{NetCDF file or source as a character vector or an nc_proxy object.} \item{...}{ignored} \item{var}{variable name or names (they must be on matching grids)} \item{ncsub}{matrix of start, count columns (see Details)} \item{curvilinear}{length two character named vector with names of variables holding longitude and latitude values for all raster cells. `stars` attempts to figure out appropriate curvilinear coordinates if they are not supplied.} \item{eps}{numeric; dimension value increases are considered identical when they differ less than \code{eps}} \item{ignore_bounds}{logical; should bounds values for dimensions, if present, be ignored?} \item{make_time}{if \code{TRUE} (the default), an attempt is made to provide a date-time class from the "time" variable} \item{make_units}{if \code{TRUE} (the default), an attempt is made to set the units property of each variable} \item{proxy}{logical; if \code{TRUE}, an object of class \code{stars_proxy} is read which contains array metadata only; if \code{FALSE} the full array data is read in memory. If not set, defaults to \code{TRUE} when the number of cells to be read is larger than \code{options(stars.n_proxy)}, or to 1e8 if that option was not set.} \item{downsample}{integer; number of cells to omit between samples along each dimension. e.g. \code{c(1,1,2)} would return every other cell in x and y and every third cell in the third dimension (z or t). If 0, no downsampling is applied. Note that this transformation is applied AFTER NetCDF data are read using st_downsample. As such, if proxy=TRUE, this option is ignored.} } \description{ Read data from a file (or source) using the NetCDF library directly. } \details{ The following logic is applied to coordinates. If any coordinate axes have regularly spaced coordinate variables they are reduced to the offset/delta form with 'affine = c(0, 0)', otherwise the values of the coordinates are stored and used to define a rectilinear grid. If the data has two or more dimensions and the first two are regular they are nominated as the 'raster' for plotting. If the \code{curvilinear} argument is used it specifies the 2D arrays containing coordinate values for the first two dimensions of the data read. It is currently assumed that the coordinates are 2D and that they relate to the first two dimensions in that order. If \code{var} is not set the first set of variables on a shared grid is used. \code{start} and \code{count} columns of ncsub must correspond to the variable dimension (nrows) and be valid index using \code{\link[RNetCDF]{var.get.nc}} convention (start is 1-based). If the count value is \code{NA} then all steps are included. Axis order must match that of the variable/s being read. } \examples{ f <- system.file("nc/reduced.nc", package = "stars") if (require(ncmeta, quietly = TRUE)) { read_ncdf(f) read_ncdf(f, var = c("anom")) read_ncdf(f, ncsub = cbind(start = c(1, 1, 1, 1), count = c(10, 12, 1, 1))) } if (require(ncmeta, quietly = TRUE)) { #' precipitation data in a curvilinear NetCDF prec_file = system.file("nc/test_stageiv_xyt.nc", package = "stars") prec = read_ncdf(prec_file, curvilinear = c("lon", "lat"), ignore_bounds = TRUE) } ##plot(prec) ## gives error about unique breaks ## remove NAs, zeros, and give a large number ## of breaks (used for validating in detail) qu_0_omit = function(x, ..., n = 22) { x = units::drop_units(na.omit(x)) c(0, quantile(x[x > 0], seq(0, 1, length.out = n))) } if (require(dplyr, quietly = TRUE)) { prec_slice = slice(prec, index = 17, along = "time") plot(prec_slice, border = NA, breaks = qu_0_omit(prec_slice[[1]]), reset = FALSE) nc = sf::read_sf(system.file("gpkg/nc.gpkg", package = "sf"), "nc.gpkg") plot(st_geometry(nc), add = TRUE, reset = FALSE, col = NA) } } stars/man/st_transform.Rd0000644000176200001440000000322214642501343015172 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/transform.R \name{st_transform} \alias{st_transform} \alias{st_transform.stars} \alias{st_transform_proj.stars} \title{transform geometries in stars objects to a new coordinate reference system, without warping} \usage{ \method{st_transform}{stars}(x, crs, ...) st_transform_proj.stars(x, crs, ...) } \arguments{ \item{x}{object of class \code{stars}, with either raster or simple feature geometries} \item{crs}{object of class \code{crs} with target crs} \item{...}{ignored} } \description{ transform geometries in stars objects to a new coordinate reference system, without warping } \details{ For simple feature dimensions, \link[sf]{st_transform} is called, leading to lossless transformation. For gridded spatial data, a curvilinear grid with transformed grid cell (centers) is returned, which is also lossless. To convert this to a regular grid in the new \code{CRS}, use \link{st_warp} (which is in general lossy). If array values contain geometries and an array as a whole is of class `sfc` and has a non-missing CRS, array geometries are also transformed. } \examples{ geomatrix = system.file("tif/geomatrix.tif", package = "stars") (x = read_stars(geomatrix)) new = st_crs('OGC:CRS84') y = st_transform(x, new) plot(st_transform(st_as_sfc(st_bbox(x)), new), col = NA, border = 'red') plot(st_as_sfc(y, as_points=FALSE), col = NA, border = 'green', axes = TRUE, add = TRUE) image(y, col = heat.colors(12), add = TRUE) plot(st_as_sfc(y, as_points=TRUE), pch=3, cex=.5, col = 'blue', add = TRUE) plot(st_transform(st_as_sfc(x, as_points=FALSE), new), add = TRUE) } \seealso{ \link{st_warp} } stars/man/st_rotate.Rd0000644000176200001440000000372014576033423014465 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/rotate.R \name{st_rotate} \alias{st_rotate} \alias{st_rotate.stars} \alias{st_rotate.sfc} \alias{st_rotate.sf} \title{Transform rotated pole long/lat regular grid to unrotated curvilinear grid} \usage{ \method{st_rotate}{stars}(.x, lon0, lat0, north = TRUE, ...) \method{st_rotate}{sfc}(.x, lon0, lat0, north = TRUE, ...) \method{st_rotate}{sf}(.x, lon0, lat0, north = TRUE, ...) } \arguments{ \item{.x}{object of class \code{stars}} \item{lon0}{longitude of the rotated pole in degrees} \item{lat0}{latitude of the rotated pole in degrees} \item{north}{logical; if \code{TRUE} the pole refers to the North pole, otherwise the South pole} \item{...}{ignored} } \value{ curvilinear stars object with coordinates in regular long/lat (North pole at lat=90) } \description{ Transform rotated long/lat regular grid to unrotated curvilinear grid } \examples{ if (require("starsdata") && require("maps")) { # data downloaded from https://esgf-data.dkrz.de/search/cosmo-rea/ nc = "netcdf/ts_EUR-6km_ECMWF-ERAINT_REA6_r1i1p1f1_COSMO_v1_mon_201801-201812.nc" f = system.file(nc, package = "starsdata") m = read_mdim(f, "ts") print(m) # NOTE this function is obsolete when reading m as # m = read_mdim(f, "ts", curvilinear = c("longitude", "latitude")) if (require(RNetCDF)) { x = open.nc(f) lon = att.get.nc(x, "rotated_latitude_longitude", "grid_north_pole_longitude") lat = att.get.nc(x, "rotated_latitude_longitude", "grid_north_pole_latitude") close.nc(x) print(c(lon = lon, lat = lat)) } else { lon = -162 lat = 39.25 } m1 = st_rotate(m, lon, lat) print(m1) h = function() maps::map(add = TRUE) plot(m1, downsample = c(10, 10, 5), axes = TRUE, hook = h, mfrow = c(1, 2)) # curvilinear grid: downsample for plotting speed m2 = st_warp(m1, crs = st_crs("OGC:CRS84"), threshold = .1) plot(m2, hook = h, mfrow = c(3, 4)) # regular grid: plots fast } } stars/man/st_xy2sfc.Rd0000644000176200001440000000166013777653456014426 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/sf.R \name{st_xy2sfc} \alias{st_xy2sfc} \title{replace x y raster dimensions with simple feature geometry list (points, or polygons = rasterize)} \usage{ st_xy2sfc(x, as_points, ..., na.rm = TRUE) } \arguments{ \item{x}{object of class \code{stars}} \item{as_points}{logical; if \code{TRUE}, generate points at cell centers, else generate polygons} \item{...}{arguments passed on to \code{st_as_sfc}} \item{na.rm}{logical; omit (remove) cells which are entirely missing valued (across other dimensions)?} } \value{ object of class \code{stars} with x and y raster dimensions replaced by a single sfc geometry list column containing either points, or polygons. Adjacent cells with identical values are not merged; see \code{st_rasterize} for this. } \description{ replace x y raster dimensions with simple feature geometry list (points, or polygons = rasterize) } stars/man/st_dimensions.Rd0000644000176200001440000001141514451263410015330 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/dimensions.R \name{st_dimensions} \alias{st_dimensions} \alias{st_dimensions.stars} \alias{st_dimensions<-} \alias{st_dimensions<-.stars} \alias{st_dimensions<-.stars_proxy} \alias{st_dimensions<-.list} \alias{st_dimensions.array} \alias{st_dimensions.default} \alias{st_set_dimensions} \alias{st_get_dimension_values} \title{get dimensions from stars object} \usage{ st_dimensions(.x, ...) \method{st_dimensions}{stars}(.x, ...) st_dimensions(x) <- value \method{st_dimensions}{stars}(x) <- value \method{st_dimensions}{stars_proxy}(x) <- value \method{st_dimensions}{list}(x) <- value \method{st_dimensions}{array}(.x, ...) \method{st_dimensions}{default}( .x, ..., .raster, affine = c(0, 0), cell_midpoints = FALSE, point = FALSE ) st_set_dimensions( .x, which, values = NULL, point = NULL, names = NULL, xy, ... ) st_get_dimension_values(.x, which, ..., where = NA, max = FALSE, center = NA) } \arguments{ \item{.x}{object to retrieve dimensions information from} \item{...}{further arguments} \item{x}{object of class \code{dimensions}} \item{value}{new object of class \code{dimensions}, with matching dimensions} \item{.raster}{length 2 character array with names (if any) of the raster dimensions} \item{affine}{numeric; specify parameters of the affine transformation} \item{cell_midpoints}{logical; if \code{TRUE} AND the dimension values are strictly regular, the values are interpreted as the cell midpoint values rather than the cell offset values when calculating offset (i.e., the half-cell-size correction is applied); can have a value for each dimension, or else is recycled} \item{point}{logical; does the pixel value (measure) refer to a point (location) value or to an pixel (area) summary value?} \item{which}{integer or character; index or name of the dimension to be changed} \item{values}{values for this dimension (e.g. \code{sfc} list-column), or length-1 \code{dimensions} object; setting special value \code{NULL} removes dimension values, for instance to remove curvilinear raster coordinates} \item{names}{character; vector with new names for all dimensions, or with the single new name for the dimension indicated by \code{which}} \item{xy}{length-2 character vector; (new) names for the \code{x} and \code{y} raster dimensions} \item{where}{character, one of 'start', 'center' or 'end'. Set to NA (default) to ignore and use \code{max} and \code{center} explictly. This argument provides a convenient alternative to setting \code{max} and \code{center}.} \item{max}{logical; if \code{TRUE} return the end, rather than the beginning of an interval} \item{center}{logical; if \code{TRUE} return the center of an interval; if \code{NA} return the center for raster dimensions, and the start of intervals in other cases} } \value{ the \code{dimensions} attribute of \code{x}, of class \code{dimensions} } \description{ get dimensions from stars object } \details{ dimensions can be specified in two ways. The simplest is to pass a vector with numeric values for a numeric dimension, or character values for a categorical dimension. Parameter \code{cell_midpoints} is used to specify whether numeric values refer to the offset (start) of a dimension interval (default), or to the center; the center case is only available for regular dimensions. For rectilinear numeric dimensions, one can specify either a vector with cell borders (start values), or a data.frame with two columns named "start" and "end", with the respective interval start and end values. In the first case, the end values are computed from the start values by assuming the last two intervals have equal width. } \examples{ x = read_stars(system.file("tif/L7_ETMs.tif", package = "stars")) # Landsat 7 ETM+ band semantics: https://landsat.gsfc.nasa.gov/the-enhanced-thematic-mapper-plus/ # set bands to values 1,2,3,4,5,7: (x1 = st_set_dimensions(x, "band", values = c(1,2,3,4,5,7), names = "band_number", point = TRUE)) # set band values as bandwidth rbind(c(0.45,0.515), c(0.525,0.605), c(0.63,0.69), c(0.775,0.90), c(1.55,1.75), c(2.08,2.35)) \%>\% units::set_units("um") -> bw # or: units::set_units(µm) -> bw # set bandwidth midpoint: (x2 = st_set_dimensions(x, "band", values = 0.5 * (bw[,1]+bw[,2]), names = "bandwidth_midpoint", point = TRUE)) # set bandwidth intervals: (x3 = st_set_dimensions(x, "band", values = make_intervals(bw), names = "bandwidth")) m = matrix(1:20, nrow = 5, ncol = 4) dim(m) = c(x = 5, y = 4) # named dim (s = st_as_stars(m)) st_get_dimension_values(s, 'x', where = "start") st_get_dimension_values(s, 'x', center = FALSE) st_get_dimension_values(s, 'x', where = "center") st_get_dimension_values(s, 'x', center = TRUE) st_get_dimension_values(s, 'x', where = "end") st_get_dimension_values(s, 'x', max = TRUE) } stars/man/st_join.stars.Rd0000644000176200001440000000253713777653456015306 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/geom.R \name{st_join.stars} \alias{st_join.stars} \title{Spatially join a stars and an `sf` object} \usage{ \method{st_join}{stars}( x, y, join = st_intersects, ..., what = "left1", as_points = NA, warn = TRUE ) } \arguments{ \item{x}{object of class stars} \item{y}{object of class sf, or one that can be coerced into that by \link{st_as_sf}} \item{join}{the join function, which should return an sgbp object; see details} \item{...}{arguments that will be passed on to the join function} \item{what}{"left1", "right" or "inner"; see details} \item{as_points}{logical; controls whether grid cells in \code{x} will be treated as points, or as cell areas; the \link{st_intersects.stars} method by default will derive this from \code{x}'s metadata, or else assume areas.} \item{warn}{logical; if TRUE, warn on 1-to-many matches when \code{what} is \code{"left1"}} } \value{ If what is "left1", an object of class stars with the (first) value of y at spatial instances of x } \description{ Spatially join a stars and an `sf` object } \details{ When there is more than one match to a single x value, the first matching record from y is taken (and if \code{warn} is TRUE a warning is raised). If what is "inner", an object of class \code{sf} with all matching records of x and y. } stars/man/L7_ETMs.Rd0000644000176200001440000000127714144045432013632 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/datasets.R \docType{data} \name{L7_ETMs} \alias{L7_ETMs} \title{Landsat-7 bands for a selected region around Olinda, BR} \format{ An object of class \code{stars_proxy} (inherits from \code{stars}) of dimension 349 x 352 x 6. } \usage{ L7_ETMs } \description{ Probably containing the six 30 m bands: \itemize{ \item Band 1 Visible (0.45 - 0.52 µm) 30 m \item Band 2 Visible (0.52 - 0.60 µm) 30 m \item Band 3 Visible (0.63 - 0.69 µm) 30 m \item Band 4 Near-Infrared (0.77 - 0.90 µm) 30 m \item Band 5 Short-wave Infrared (1.55 - 1.75 µm) 30 m \item Band 7 Mid-Infrared (2.08 - 2.35 µm) 30 m } } \keyword{datasets} stars/DESCRIPTION0000644000176200001440000000557414645437762013151 0ustar liggesusersPackage: stars Title: Spatiotemporal Arrays, Raster and Vector Data Cubes Version: 0.6-6 Authors@R: c(person(given = "Edzer", family = "Pebesma", role = c("aut", "cre"), email = "edzer.pebesma@uni-muenster.de", comment = c(ORCID = "0000-0001-8049-7069")), person(given = "Michael", family = "Sumner", role = "ctb", comment=c(ORCID = "0000-0002-2471-7511")), person(given = "Etienne", family = "Racine", role = "ctb"), person(given = "Adriano", family = "Fantini", role = "ctb"), person(given = "David", family = "Blodgett", role = "ctb"), person(given = "Krzysztof", family = "Dyba", role = "ctb", comment=c(ORCID = "0000-0002-8614-3816"))) Description: Reading, manipulating, writing and plotting spatiotemporal arrays (raster and vector data cubes) in 'R', using 'GDAL' bindings provided by 'sf', and 'NetCDF' bindings by 'ncmeta' and 'RNetCDF'. License: Apache License URL: https://r-spatial.github.io/stars/, https://github.com/r-spatial/stars/ BugReports: https://github.com/r-spatial/stars/issues/ Additional_repositories: https://cran.uni-muenster.de/pebesma/ LazyData: true Depends: R (>= 3.3.0), abind, sf (>= 1.0-15) Imports: methods, parallel, classInt (>= 0.4-1), rlang, units Suggests: Cairo, OpenStreetMap, PCICt, RNetCDF (>= 1.8-2), clue, covr, cubble (>= 0.3.0), cubelyr, digest, dplyr (>= 0.7-0), exactextractr, FNN, future.apply, ggforce, ggplot2, ggthemes, gstat, httr, jsonlite, knitr, lwgeom, maps, mapdata, ncdfgeom, ncmeta (>= 0.0.3), pbapply, plm, randomForest, raster, rmarkdown, sp, spacetime, spatstat (>= 2.0-1), spatstat.geom, starsdata, terra (>= 1.4-22), testthat, tibble, tidyr, tsibble, viridis, xts, zoo VignetteBuilder: knitr Encoding: UTF-8 RoxygenNote: 7.3.1 Collate: 'aggregate.R' 'cubble.R' 'datasets.R' 'dimensions.R' 'extract.R' 'factors.R' 'geom.R' 'gt.R' 'init.R' 'intervals.R' 'mdim.R' 'mosaic.R' 'ncdf.R' 'ncproxy.R' 'OpenStreetMap.R' 'ops.R' 'plot.R' 'prcomp.R' 'proxy.R' 'rasterize.R' 'raster.R' 'read.R' 'rotate.R' 'sample.R' 'sf.R' 'spacetime.R' 'spatstat.R' 'sp.R' 'stars.R' 'subset.R' 'tidyverse.R' 'tile.R' 'transform.R' 'values.R' 'warp.R' 'write.R' 'xts.R' NeedsCompilation: no Packaged: 2024-07-14 14:12:14 UTC; edzer Author: Edzer Pebesma [aut, cre] (), Michael Sumner [ctb] (), Etienne Racine [ctb], Adriano Fantini [ctb], David Blodgett [ctb], Krzysztof Dyba [ctb] () Maintainer: Edzer Pebesma Repository: CRAN Date/Publication: 2024-07-16 09:40:02 UTC

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apt-get -y update \ && apt-get install -y \ libcurl4-openssl-dev \ qpdf \ pandoc \ make \ wget \ git \ libudunits2-dev \ libgdal-dev \ libgeos-dev \ libproj-dev \ r-base-dev \ gdb \ valgrind \ vim RUN apt-get install -y texinfo \ texlive-base \ texlive-extra-utils \ texlive-fonts-extra \ texlive-fonts-recommended \ texlive-generic-recommended \ texlive-latex-base \ texlive-latex-extra \ texlive-latex-recommended # stuff for the tmaptools/rmapshaper/geojsonio etc stack: RUN apt-get install -y libv8-3.14-dev libprotobuf-dev protobuf-compiler libcairo2-dev pandoc pandoc-citeproc RUN add-apt-repository -y ppa:opencpu/jq RUN apt-get update RUN apt-get install -y libjq-dev ## Check out & build R-devel: RUN apt-get install -y subversion RUN cd /tmp && svn co https://svn.r-project.org/R/trunk R-devel RUN /tmp/R-devel/tools/rsync-recommended ## Build and install according the standard 'recipe' I emailed/posted years ago RUN apt-get install -y libreadline-dev libbz2-dev RUN cd /tmp/R-devel \ && R_PAPERSIZE=letter \ R_BATCHSAVE="--no-save --no-restore" \ R_BROWSER=xdg-open \ PAGER=/usr/bin/pager \ PERL=/usr/bin/perl \ R_UNZIPCMD=/usr/bin/unzip \ R_ZIPCMD=/usr/bin/zip \ R_PRINTCMD=/usr/bin/lpr \ LIBnn=lib \ AWK=/usr/bin/awk \ CFLAGS=$(R CMD config CFLAGS) \ CXXFLAGS=$(R CMD config CXXFLAGS) \ ./configure --enable-R-shlib \ --without-blas \ --without-lapack \ --with-readline \ --with-recommended-packages \ --program-suffix=dev \ --with-x=no \ && make \ && make install \ && rm -rf /tmp/R-devel ## Set default CRAN repo RUN echo 'options(repos = c(CRAN = "https://cran.rstudio.com/"), download.file.method = "libcurl")' >> /usr/local/lib/R/etc/Rprofile.site RUN Rscript -e 'install.packages(c("sf", "lwgeom", "covr", "raster", "rgdal", "Rcpp", "magrittr", "abind", "units", "ggplot2", "ggthemes", "viridis", "testthat", "knitr", "covr", "rmarkdown", "PCICt", "ggforce", "gstat", "pbapply", "plm", "spacetime", "xts"), dependencies = TRUE, repos = "https://cloud.r-project.org")' RUN Rscript -e 'install.packages("starsdata", repos = "http://pebesma.staff.ifgi.de/")' RUN git clone https://github.com/r-spatial/stars.git RUN R CMD build --no-manual stars #RUN R CMD check --no-build-vignettes --no-manual --as-cran --run-dontrun sf_*tar.gz RUN R CMD check --no-manual stars_*tar.gz CMD ["/bin/bash"] stars/inst/docker/ubuntu/0000755000176200001440000000000014576033423015163 5ustar liggesusersstars/inst/docker/ubuntu/Dockerfile0000644000176200001440000000231314576033423017154 0ustar liggesusersFROM ubuntu:16.04 # minimal docker file to get sp and sf running on ubunty 16.04 image, # using gdal/geos/proj from ppa:ubuntugis/ubuntugis-unstable MAINTAINER "edzerpebesma" edzer.pebesma@uni-muenster.de RUN apt-get update && apt-get install -y software-properties-common RUN add-apt-repository ppa:ubuntugis/ubuntugis-unstable RUN echo "deb https://cran.rstudio.com/bin/linux/ubuntu xenial/ " >> /etc/apt/sources.list RUN apt-key adv --keyserver keyserver.ubuntu.com --recv-keys E084DAB9 RUN apt-get update RUN apt-get upgrade -y RUN export DEBIAN_FRONTEND=noninteractive; apt-get -y update \ && apt-get install -y \ libcurl4-openssl-dev \ qpdf \ pandoc \ make \ wget \ git \ libudunits2-dev \ libgdal-dev \ libgeos-dev \ libproj-dev \ r-base-dev \ gdb \ valgrind \ vim RUN Rscript -e 'install.packages(c("Rcpp", "magrittr", "abind", "units", "sf", "ggplot2", "ggthemes", "viridis", "testthat", "knitr", "covr", "rmarkdown"), repos = "https://cloud.r-project.org")' RUN git clone https://github.com/r-spatial/stars.git RUN R CMD build --no-manual stars #RUN R CMD check --no-build-vignettes --no-manual --as-cran --run-dontrun sf_*tar.gz RUN R CMD check --no-manual stars_*tar.gz CMD ["/bin/bash"] stars/inst/docker/fedora/0000755000176200001440000000000013777653456015121 5ustar liggesusersstars/inst/docker/fedora/Dockerfile0000644000176200001440000000464413777653456017123 0ustar liggesusersFROM fedora:latest # minimal docker file to get sp and sf running on ubunty 16.04 image, # using gdal/geos/proj from ppa:ubuntugis/ubuntugis-unstable MAINTAINER "edzerpebesma" edzer.pebesma@uni-muenster.de RUN yum install -y gdal-devel geos-devel udunits2-devel RUN yum install -y proj-devel proj-epsg proj-nad RUN yum install -y netcdf-devel RUN yum install -y pandoc pandoc-citeproc RUN yum install -y readline-devel curl-devel wget clang RUN yum install -y R-devel # needed by R packages: RUN yum install -y libxml2-devel openssl-devel cairo-devel postgresql-devel unixODBC-devel # install R-devel from source, with clang: RUN wget https://stat.ethz.ch/R/daily/R-devel.tar.gz RUN tar zxvf R-devel.tar.gz # Get https://www.stats.ox.ac.uk/pub/bdr/Rconfig/r-devel-linux-x86_64-fedora-clang into ./config.site: RUN echo $'CC=clang \n\ OBJC=clang \n\ CXX=clang++ \n\ FC=gfortran \n\ F77=gfortran \n\ CFLAGS="-g -O3 -Wall -pedantic -mtune=native" \n\ FFLAGS="-g -O2 -mtune=native -Wall -pedantic" \n\ FCFLAGS="-g -O2 -mtune=native -Wall -pedantic" \n\ CXXFLAGS="-g -O3 -Wall -pedantic -mtune=native -frtti" \n\ CPPFLAGS="-I/usr/local/clang/include -I/usr/local/include" \n\ JAVA_HOME=/usr/lib/jvm/jre-11 \n\ LDFLAGS="-L/usr/local/clang/lib64 -L/usr/local/lib64"' > R-devel/config.site RUN (cd R-devel; ./configure --with-x=no; make; make install) RUN /usr/local/bin/Rscript -e 'install.packages(c("XML", "Rcpp", "units", "DBI", "classInt", "magrittr", "lwgeom", "tibble", "knitr", "sp", "maps", "markdown", "testthat", "maptools", "dplyr", "rgeos", "rgdal", "tidyr", "stars", "rmarkdown", "covr", "ggplot2", "mapview", "microbenchmark", "odbc", "pool", "raster", "rmarkdown", "RPostgres", "RPostgreSQL", "RSQLite", "spatstat", "tmap", "ncdf4", "ncmeta"), repos = "https://cloud.r-project.org")' RUN /usr/local/bin/Rscript -e 'install.packages(c("ggthemes", "gstat", "pbapply", "plm", "spacetime", "zoo", "xts"), repos = "https://cloud.r-project.org")' # get & check CRAN version of stars: RUN cd .. RUN wget https://cran.r-project.org/src/contrib/stars_0.2-0.tar.gz #RUN _R_CHECK_FORCE_SUGGESTS_=false /usr/local/bin/R CMD check --as-cran stars*gz RUN yum install -y git RUN git clone https://github.com/r-spatial/stars.git RUN /usr/local/bin/Rscript -e 'install.packages(c("PCICt"), repos = "https://cloud.r-project.org")' RUN /usr/local/bin/R CMD build stars #RUN _R_CHECK_FORCE_SUGGESTS_=false /usr/local/bin/R CMD check --as-cran stars*gz CMD ["/bin/bash"] stars/inst/docker/trusty/0000755000176200001440000000000014576033423015213 5ustar liggesusersstars/inst/docker/trusty/Dockerfile0000644000176200001440000000314014576033423017203 0ustar liggesusersFROM ubuntu:14.04 # minimal docker file to get sp and sf running on trusty images # using gdal/geos/proj from ppa:ubuntugis/ubuntugis-unstable MAINTAINER "edzerpebesma" edzer.pebesma@uni-muenster.de RUN apt-get update && apt-get install -y software-properties-common RUN add-apt-repository ppa:ubuntugis/ubuntugis-unstable RUN echo "deb https://cran.rstudio.com/bin/linux/ubuntu trusty/ " >> /etc/apt/sources.list RUN apt-key adv --keyserver keyserver.ubuntu.com --recv-keys E084DAB9 RUN apt-get update RUN apt-get upgrade -y RUN export DEBIAN_FRONTEND=noninteractive; apt-get -y update \ && apt-get install -y \ libcurl4-openssl-dev \ qpdf \ pandoc \ make \ wget \ git \ libudunits2-dev \ libgdal-dev \ libgeos-dev \ libproj-dev \ r-base-dev \ gdb \ valgrind \ vim RUN Rscript -e 'install.packages(c("Rcpp", "magrittr", "abind", "units", "sf", "ggplot2", "ggthemes", "viridis", "testthat", "knitr", "covr", "rmarkdown"), repos = "https://cloud.r-project.org")' RUN git clone https://github.com/r-spatial/stars.git RUN export DEBIAN_FRONTEND=noninteractive; apt-get install -y pandoc-citeproc RUN Rscript -e 'install.packages(c("dplyr"), repos = "https://cloud.r-project.org")' RUN R CMD build --no-build-vignettes --no-manual stars RUN Rscript -e 'install.packages(c("PCICt","RNetCDF","ggforce","gstat","lwgeom","maps","ncmeta","pbapply","plm","raster","sp","spacetime","zoo","xts"), repos = "https://cloud.r-project.org")' #RUN R CMD check --no-build-vignettes --no-manual --as-cran --run-dontrun sf_*tar.gz RUN _R_CHECK_FORCE_SUGGESTS_=false R CMD check --no-manual stars_*tar.gz CMD ["/bin/bash"] stars/inst/gpkg/0000755000176200001440000000000013777653456013342 5ustar liggesusersstars/inst/gpkg/s2.gpkg0000644000176200001440000030000013777653456014531 0ustar liggesusersSQLite format 3@ 'GPKG .?   h\=mUndefined geographic SRSNONEundefinedundefined geographic coordinate reference system[;kUndefined cartesian SRSNONEundefinedundefined cartesian coordinate reference systemf +iWGS 84 geodeticEPSGGEOGCS["WGS 84",DATUM["WGS_1984",SPHEROID["WGS 84",6378137,298.257223563,AUTHORITY["EPSG","7030"]],AUTHORITY["EPSG","6326"]],PRIMEM["Greenwich",0,AUTHORITY["EPSG","8901"]],UNIT["degree",0.0174532925199433,AUTHORITY["EPSG","9122"]],AXIS["Latitude",NORTH],AXIS["Longitude",EAST],AUTHORITY["EPSG","4326"]]longitude/latitude coordinates in decimal degrees on the WGS 84 spheroid Q  =clfeaturescl2021-01-01T21:03:53.542Z@@J|R@o6W5@JM  cl  cl cl  cl clgeomPOLYGON   clgeom  cl      |  h k~ w8MJQ-!triggergpkg_tile_matrix_zoom_level_updategpkg_tile_matrixCREATE TRIGGER 'gpkg_tile_matrix_zoom_level_update' BEFORE UPDATE of zoom_level ON 'gpkg_tile_matrix' FOR EACH ROW BEGIN SELECT RAISE(ABORT, 'update on table ''gpkg_tile_matrix'' violates constraint: zoom_level cannot be less than 0') WHERE (NEW.zoom_level < 0); END<Q-triggergpkg_tile_matrix_zoom_level_insertgpkg_tile_matrixCREATE TRIGGER 'gpkg_tile_matrix_zoom_level_insert' BEFORE INSERT ON 'gpkg_tile_matrix' FOR EACH ROW BEGIN SELECT RAISE(ABORT, 'insert on table ''gpkg_tile_matrix'' violates constraint: zoom_level cannot be less than 0') WHERE (NEW.zoom_level < 0); END? S-indexsqlite_autoindex_gpkg_tile_matrix_1gpkg_tile_matrixC --9tablegpkg_tile_matrixgpkg_tile_matrix CREATE TABLE gpkg_tile_matrix (table_name TEXT NOT NULL,zoom_level INTEGER NOT NULL,matrix_width INTEGER NOT NULL,matrix_height INTEGER NOT NULL,tile_width INTEGER NOT NULL,tile_height INTEGER NOT NULL,pixel_x_size DOUBLE NOT NULL,pixel_y_size DOUBLE NOT NULL,CONSTRAINT pk_ttm PRIMARY KEY (table_name, zoom_level),CONSTRAINT fk_tmm_table_name FOREIGN KEY (table_name) REFERENCES gpkg_contents(table_name)) 55_tablegpkg_tile_matrix_setgpkg_tile_matrix_set CREATE TABLE gpkg_tile_matrix_set (table_name TEXT NOT NULL PRIMARY KEY,srs_id INTEGER NOT NULL,min_x DOUBLE NOT NULL,min_y DOUBLE NOT NULL,max_x DOUBLE NOT NULL,max_y DOUBLE NOT NULL,CONSTRAINT fk_gtms_table_name FOREIGN KEY (table_name) REFERENCES gpkg_contents(table_name),CONSTRAINT fk_gtms_srs FOREIGN KEY (srs_id) REFERENCES gpkg_spatial_ref_sys (srs_id))G [5indexsqlite_autoindex_gpkg_tile_matrix_set_1gpkg_tile_matrix_set 77tablegpkg_geometry_columnsgpkg_geometry_columnsCREATE TABLE gpkg_geometry_columns (table_name TEXT NOT NULL,column_name TEXT NOT NULL,geometry_type_name TEXT NOT NULL,srs_id INTEGER NOT NULL,z TINYINT NOT NULL,m TINYINT NOT NULL,CONSTRAINT pk_geom_cols PRIMARY KEY (table_name, column_name),CONSTRAINT uk_gc_table_name UNIQUE (table_name),CONSTRAINT fk_gc_tn FOREIGN KEY (table_name) REFERENCES gpkg_contents(table_name),CONSTRAINT fk_gc_srs FOREIGN KEY (srs_id) REFERENCES gpkg_spatial_ref_sys (srs_id))I ]7indexsqlite_autoindex_gpkg_geometry_columns_2gpkg_geometry_columns I]7indexsqlite_autoindex_gpkg_geometry_columns_1gpkg_geometry_columns //[tablegpkg_ogr_contentsgpkg_ogr_contentsCREATE TABLE gpkg_ogr_contents(table_name TEXT NOT NULL PRIMARY KEY,feature_count INTEGER DEFAULT NULL)AU/indexsqlite_autoindex_gpkg_ogr_contents_1gpkg_ogr_contents''wtablegpkg_contentsgpkg_contentsCREATE TABLE gpkg_contents (table_name TEXT NOT NULL PRIMARY KEY,data_type TEXT NOT NULL,identifier TEXT UNIQUE,description TEXT DEFAULT '',last_change DATETIME NOT NULL DEFAULT (strftime('%Y-%m-%dT%H:%M:%fZ','now')),min_x DOUBLE, min_y DOUBLE,max_x DOUBLE, max_y DOUBLE,srs_id INTEGER,CONSTRAINT fk_gc_r_srs_id FOREIGN KEY (srs_id) REFERENCES gpkg_spatial_ref_sys(srs_id))9M'indexsqlite_autoindex_gpkg_contents_2gpkg_contents9M'indexsqlite_autoindex_gpkg_contents_1gpkg_contents55)tablegpkg_spatial_ref_sysgpkg_spatial_ref_sysCREATE TABLE gpkg_spatial_ref_sys (srs_name TEXT NOT NULL,srs_id INTEGER NOT NULL PRIMARY KEY,organization TEXT NOT NULL,organization_coordsys_id INTEGER NOT NULL,definition TEXT NOT NULL,description TEXT)   bu)% 3 < 9 4 urOtableclclCREATE TABLE "cl" ( "fid" INTEGER PRIMARY KEY AUTOINCREMENT NOT NULL, "geom" POLYGON, "use" TEXT)YU-;triggergpkg_tile_matrix_pixel_y_size_updategpkg_tile_matrixCREATE TRIGGER 'gpkg_tile_matrix_pixel_y_size_update' BEFORE UPDATE OF pixel_y_size ON 'gpkg_tile_matrix' FOR EACH ROW BEGIN SELECT RAISE(ABORT, 'update on table ''gpkg_tile_matrix'' violates constraint: pixel_y_size must be greater than 0') WHERE NOT (NEW.pixel_y_size > 0); ENDIU-triggergpkg_tile_matrix_pixel_y_size_insertgpkg_tile_matrixCREATE TRIGGER 'gpkg_tile_matrix_pixel_y_size_insert' BEFORE INSERT ON 'gpkg_tile_matrix' FOR EACH ROW BEGIN SELECT RAISE(ABORT, 'insert on table ''gpkg_tile_matrix'' violates constraint: pixel_y_size must be greater than 0') WHERE NOT (NEW.pixel_y_size > 0); ENDYU-;triggergpkg_tile_matrix_pixel_x_size_updategpkg_tile_matrixCREATE TRIGGER 'gpkg_tile_matrix_pixel_x_size_update' BEFORE UPDATE OF pixel_x_size ON 'gpkg_tile_matrix' FOR EACH ROW BEGIN SELECT RAISE(ABORT, 'update on table ''gpkg_tile_matrix'' violates constraint: pixel_x_size must be greater than 0') WHERE NOT (NEW.pixel_x_size > 0); ENDIU-triggergpkg_tile_matrix_pixel_x_size_insertgpkg_tile_matrixCREATE TRIGGER 'gpkg_tile_matrix_pixel_x_size_insert' BEFORE INSERT ON 'gpkg_tile_matrix' FOR EACH ROW BEGIN SELECT RAISE(ABORT, 'insert on table ''gpkg_tile_matrix'' violates constraint: pixel_x_size must be greater than 0') WHERE NOT (NEW.pixel_x_size > 0); ENDYW-9triggergpkg_tile_matrix_matrix_height_updategpkg_tile_matrixCREATE TRIGGER 'gpkg_tile_matrix_matrix_height_update' BEFORE UPDATE OF matrix_height ON 'gpkg_tile_matrix' FOR EACH ROW BEGIN SELECT RAISE(ABORT, 'update on table ''gpkg_tile_matrix'' violates constraint: matrix_height cannot be less than 1') WHERE (NEW.matrix_height < 1); ENDHW-triggergpkg_tile_matrix_matrix_height_insertgpkg_tile_matrixCREATE TRIGGER 'gpkg_tile_matrix_matrix_height_insert' BEFORE INSERT ON 'gpkg_tile_matrix' FOR EACH ROW BEGIN SELECT RAISE(ABORT, 'insert on table ''gpkg_tile_matrix'' violates constraint: matrix_height cannot be less than 1') WHERE (NEW.matrix_height < 1); ENDFTU-1triggergpkg_tile_matrix_matrix_width_updategpkg_tile_matrixCREATE TRIGGER 'gpkg_tile_matrix_matrix_width_update' BEFORE UPDATE OF matrix_width ON 'gpkg_tile_matrix' FOR EACH ROW BEGIN SELECT RAISE(ABORT, 'update on table ''gpkg_tile_matrix'' violates constraint: matrix_width cannot be less than 1') WHERE (NEW.matrix_width < 1); ENDDU-triggergpkg_tile_matrix_matrix_width_insertgpkg_tile_matrixCREATE TRIGGER 'gpkg_tile_matrix_matrix_width_insert' BEFORE INSERT ON 'gpkg_tile_matrix' FOR EACH ROW BEGIN SELECT RAISE(ABORT, 'insert on table ''gpkg_tile_matrix'' violates constraint: matrix_width cannot be less than 1') WHERE (NEW.matrix_width < 1); END  ? %6#GP6u^+e@Kᾣ@RƵ|J@@"JJ@ o@NWNJ@6u^+e@RƵ|J@H@ϰ>J@Kᾣ@HNݤJ@8L^H @@"JJ@ o@NWNJ@clouds_land4V!GPѧ@kϭ}=@\.J@XP0J@ wo@J@ѧ@=J@m#5@ G}J@ L@\.J@kϭ}=@+#J@ʂ@XP0J@ wo@J@clouds_sea=vGPKrߝ@6W6o@Xo/J@MJ@g@@J@b}D@T%5J@Krߝ@yP#J@S@Xo/J@?L$5:@J@6W6o@iZJ@x4@MJ@g@@J@sea>vGPS@/q@]XJ@OԞJ@ @lhG@0m8rJ@S@kJ@6N\"@+ J@D3{@]XJ@(!@ƄwJ@/q@ʱJ@yy}st@OԞJ@ @lhG@0m8rJ@land cl X-q!clgeomgpkg_rtree_indexhttp://www.geopackage.org/spec120/#extension_rtreewrite-only - clgeomgpkg_rtree_index      - -P$@/@TBUBVt@@yBW{BW@̨@BVBW @)Y@uBU;BUW   3 E ;E3'5+triggerrtree_cl_geom_deleteclCREATE TRIGGER "rtree_cl_geom_delete" AFTER DELETE ON "cl" WHEN old."geom" NOT NULL BEGIN DELETE FROM "rtree_cl_geom" WHERE id = OLD."fid"; ENDx&73triggerrtree_cl_geom_update4clCREATE TRIGGER "rtree_cl_geom_update4" AFTER UPDATE ON "cl" WHEN OLD."fid" != NEW."fid" AND (NEW."geom" ISNULL OR ST_IsEmpty(NEW."geom")) BEGIN DELETE FROM "rtree_cl_geom" WHERE id IN (OLD."fid", NEW."fid"); END}%7=triggerrtree_cl_geom_update3clCREATE TRIGGER "rtree_cl_geom_update3" AFTER UPDATE ON "cl" WHEN OLD."fid" != NEW."fid" AND (NEW."geom" NOTNULL AND NOT ST_IsEmpty(NEW."geom")) BEGIN DELETE FROM "rtree_cl_geom" WHERE id = OLD."fid"; INSERT OR REPLACE INTO "rtree_cl_geom" VALUES (NEW."fid",ST_MinX(NEW."geom"), ST_MaxX(NEW."geom"),ST_MinY(NEW."geom"), ST_MaxY(NEW."geom")); ENDs$7)triggerrtree_cl_geom_update2clCREATE TRIGGER "rtree_cl_geom_update2" AFTER UPDATE OF "geom" ON "cl" WHEN OLD."fid" = NEW."fid" AND (NEW."geom" ISNULL OR ST_IsEmpty(NEW."geom")) BEGIN DELETE FROM "rtree_cl_geom" WHERE id = OLD."fid"; ENDT#7ktriggerrtree_cl_geom_update1clCREATE TRIGGER "rtree_cl_geom_update1" AFTER UPDATE OF "geom" ON "cl" WHEN OLD."fid" = NEW."fid" AND (NEW."geom" NOTNULL AND NOT ST_IsEmpty(NEW."geom")) BEGIN INSERT OR REPLACE INTO "rtree_cl_geom" VALUES (NEW."fid",ST_MinX(NEW."geom"), ST_MaxX(NEW."geom"),ST_MinY(NEW."geom"), ST_MaxY(NEW."geom")); END/"5#triggerrtree_cl_geom_insertclCREATE TRIGGER "rtree_cl_geom_insert" AFTER INSERT ON "cl" WHEN (new."geom" NOT NULL AND NOT ST_IsEmpty(NEW."geom")) BEGIN INSERT OR REPLACE INTO "rtree_cl_geom" VALUES (NEW."fid",ST_MinX(NEW."geom"), ST_MaxX(NEW."geom"),ST_MinY(NEW."geom"), ST_MaxY(NEW."geom")); END!55!tablertree_cl_geom_parentrtree_cl_geom_parentCREATE TABLE "rtree_cl_geom_parent"(nodeno INTEGER PRIMARY KEY,parentnode)s 11tablertree_cl_geom_nodertree_cl_geom_nodeCREATE TABLE "rtree_cl_geom_node"(nodeno INTEGER PRIMARY KEY,data)w33tablertree_cl_geom_rowidrtree_cl_geom_rowidCREATE TABLE "rtree_cl_geom_rowid"(rowid INTEGER PRIMARY KEY,nodeno)r''%tablertree_cl_geomrtree_cl_geomCREATE VIRTUAL TABLE "rtree_cl_geom" USING rtree(id, minx, maxx, miny, maxy)=Q+indexsqlite_autoindex_gpkg_extensions_1gpkg_extensionsw++%tablegpkg_extensionsgpkg_extensionsCREATE TABLE gpkg_extensions (table_name TEXT,column_name TEXT,extension_name TEXT NOT NULL,definition TEXT NOT NULL,scope TEXT NOT NULL,CONSTRAINT ge_tce UNIQUE (table_name, column_name, extension_name))dKwtriggertrigger_delete_feature_count_clclCREATE TRIGGER "trigger_delete_feature_count_cl" AFTER DELETE ON "cl" BEGIN UPDATE gpkg_ogr_contents SET feature_count = feature_count - 1 WHERE lower(table_name) = lower('cl'); ENDdKwtriggertrigger_insert_feature_count_clclCREATE TRIGGER "trigger_insert_feature_count_cl" AFTER INSERT ON "cl" BEGIN UPDATE gpkg_ogr_contents SET feature_count = feature_count + 1 WHERE lower(table_name) = lower('cl'); ENDP++Ytablesqlite_sequencesqlite_sequenceCREATE TABLE sqlite_sequence(name,seq)stars/build/0000755000176200001440000000000014644756275012531 5ustar liggesusersstars/build/vignette.rds0000644000176200001440000000064214644756275015072 0ustar liggesusersO0LфDO.6.!&&^V-LnٞhI~?}v/MBs GMTDuHC8dQN}vTK-CEjy@ )> ^Yٽ zŤb0=hGD5DD,6[A y-LB(m VCنOL(2$tZ{l5\ixLY!zngftPҽ`?h*σ&^⡁:\% st_set_crs('OGC:CRS84') \%>\% st_warp(st_as_stars(st_bbox(), dx = 2)) -> s plot(r, axes = TRUE) # no CRS set, so no degree symbols in labels plot(s, axes = TRUE) # downsample raster (90 to 270 m) r = read_stars(system.file("tif/olinda_dem_utm25s.tif", package = "stars")) r270 = st_as_stars(st_bbox(r), dx = 270) r270 = st_warp(r, r270) } stars/man/st_contour.Rd0000644000176200001440000000171313777653456014700 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/sf.R \name{st_contour} \alias{st_contour} \title{Compute or plot contour lines or sets} \usage{ st_contour( x, na.rm = TRUE, contour_lines = FALSE, breaks = classInt::classIntervals(na.omit(as.vector(x[[1]])))$brks ) } \arguments{ \item{x}{object of class \code{stars}} \item{na.rm}{logical; should missing valued cells be removed, or also be converted to features?} \item{contour_lines}{logical; if \code{FALSE}, polygons are returned (contour sets), otherwise contour lines} \item{breaks}{numerical; values at which to "draw" contour levels} } \description{ Compute contour lines or sets } \details{ this function requires GDAL >= 2.4.0 } \seealso{ for polygonizing rasters following grid boundaries, see \link{st_as_sf} with arguments \code{as_points=FALSE} and \code{merge=TRUE}; \link{contour} plots contour lines using R's native algorithm (which also plots contour levels) } stars/man/stars_subset.Rd0000644000176200001440000001046314451263410015175 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/proxy.R, R/subset.R \name{stars_subset} \alias{stars_subset} \alias{[<-.stars_proxy} \alias{[.stars} \alias{[<-.stars} \alias{st_flip} \title{subset stars objects} \usage{ \method{[}{stars_proxy}(x, i, downsample = 0) <- value \method{[}{stars}(x, i = TRUE, ..., drop = FALSE, crop = !is_curvilinear(x)) \method{[}{stars}(x, i) <- value st_flip(x, which = 1) } \arguments{ \item{x}{object of class \code{stars}} \item{i}{first selector: integer, logical or character vector indicating attributes to select, or object of class \code{sf}, \code{sfc}, \code{bbox}, or \code{stars} used as spatial selector; see details} \item{downsample}{downsampling rate used in case \code{i} is a \code{stars_proxy} object} \item{value}{array of dimensions equal to those in \code{x}, or a vector or value that will be recycled to such an array} \item{...}{further (logical or integer vector) selectors, matched by order, to select on individual dimensions} \item{drop}{logical; if \code{TRUE}, degenerate dimensions (with only one value) are dropped} \item{crop}{logical; if \code{TRUE} and parameter \code{i} is a spatial geometry (\code{sf} or \code{sfc}) object, the extent (bounding box) of the result is cropped to match the extent of \code{i} using \link{st_crop}. Cropping curvilinear grids is not supported.} \item{which}{character or integer; dimension(s) to be flipped} } \value{ \code{st_flip} flips (reverts) the array values along the chosen dimension without(s) changing the dimension properties } \description{ subset stars objects } \details{ If \code{i} is an object of class \code{sf}, \code{sfc} or \code{bbox}, the spatial subset covering this geometry is selected, possibly followed by cropping the extent. Array values for which the cell centre is not inside the geometry are assigned \code{NA}. If \code{i} is of class \code{stars}, and attributes of \code{i} are \code{logical}, cells in \code{x} corresponding to \code{NA} or \code{FALSE} cells in \code{i} are assigned an \code{NA}. Dimension ranges containing negative values or \code{NA} may be partially supported. in an assignment (or replacement form, \code{[<-}), argument \code{i} needs to be either (i) a \code{stars} object with logical attribute(s) that has dimensions matching (possibly after recycling) those of \code{x}, in which case the \code{TRUE} cells will be replaced and \code{i} and/or \code{value} will be recycled to the dimensions of the arrays in \code{x}, or (ii) a length-one integer or character vector indicating which array to replace, in which case \code{value} may be stars object or a vector or array (that will be recycled). } \examples{ tif = system.file("tif/L7_ETMs.tif", package = "stars") x = read_stars(tif) x[,,,1:3] # select bands x[,1:100,100:200,] # select x and y by range x["L7_ETMs.tif"] # select attribute xy = structure(list(x = c(293253.999046018, 296400.196497684), y = c(9113801.64775462, 9111328.49619133)), .Names = c("x", "y")) pts = st_as_sf(data.frame(do.call(cbind, xy)), coords = c("x", "y"), crs = st_crs(x)) image(x, axes = TRUE) plot(st_as_sfc(st_bbox(pts)), col = NA, add = TRUE) bb = st_bbox(pts) (xx = x[bb]) image(xx) plot(st_as_sfc(bb), add = TRUE, col = NA) image(x) pt = st_point(c(x = 290462.103109179, y = 9114202.32594085)) buf = st_buffer(st_sfc(pt, crs = st_crs(x)), 1500) plot(buf, add = TRUE) buf = st_sfc(st_polygon(list(st_buffer(pt, 1500)[[1]], st_buffer(pt, 1000)[[1]])), crs = st_crs(x)) image(x[buf]) plot(buf, add = TRUE, col = NA) image(x[buf, crop=FALSE]) plot(buf, add = TRUE, col = NA) # with i of class stars: x[x > 75] # generates lots of NA's; pattern for each band x[x[,,,1] > 75] # recycles a single band template for all bands x = read_stars(tif) # replace, using a logical stars selector: cuts all values above 90 to 90 x[x > 90] = 90 # replace a single attribute when there are more than one: s = split(x) names(s) = paste0("band", 1:6) # rescale only band 1: s[1] = s[1] * 0.75 # rescale only attribute named "band2": s["band2"] = s["band2"] * 0.85 # create a new attribute from a numeric vector: s["rnorm"] = rnorm(prod(dim(s))) s lc = read_stars(system.file("tif/lc.tif", package = "stars")) x = c(orig = lc, flip_x = st_flip(lc, "x"), flip_y = st_flip(lc, "y"), flip_xy = st_flip(lc, c("x", "y")), along = 3) plot(x) } stars/man/st_as_stars.Rd0000644000176200001440000002030714576035610015006 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/cubble.R, R/ncdf.R, R/OpenStreetMap.R, % R/proxy.R, R/rasterize.R, R/raster.R, R/sf.R, R/stars.R, R/xts.R \name{st_as_stars} \alias{st_as_stars} \alias{st_as_stars.cubble_df} \alias{st_as_stars.ncdfgeom} \alias{st_as_stars.OpenStreetMap} \alias{st_as_stars.stars_proxy} \alias{st_as_stars.data.frame} \alias{st_as_stars.Raster} \alias{st_as_stars.SpatRaster} \alias{st_as_stars.sf} \alias{st_as_stars.list} \alias{st_as_stars.default} \alias{st_as_stars.stars} \alias{st_as_stars.bbox} \alias{st_as_stars.xts} \title{convert objects into a stars object} \usage{ \method{st_as_stars}{cubble_df}(.x, ..., check_times = FALSE) \method{st_as_stars}{ncdfgeom}(.x, ..., sf_geometry = NA) \method{st_as_stars}{OpenStreetMap}(.x, ..., as_col = FALSE) \method{st_as_stars}{stars_proxy}( .x, ..., downsample = 0, url = attr(.x, "url"), envir = parent.frame() ) \method{st_as_stars}{data.frame}(.x, ..., dims = coords, xy, y_decreasing = TRUE, coords = 1:2) \method{st_as_stars}{Raster}(.x, ..., att = 1, ignore_file = FALSE) \method{st_as_stars}{SpatRaster}( .x, ..., ignore_file = FALSE, as_attributes = all(terra::is.factor(.x)) ) \method{st_as_stars}{sf}(.x, ..., dims = attr(.x, "sf_column")) st_as_stars(.x, ...) \method{st_as_stars}{list}(.x, ..., dimensions = NULL) \method{st_as_stars}{default}(.x = NULL, ..., raster = NULL) \method{st_as_stars}{stars}(.x, ..., curvilinear = NULL, crs = st_crs("OGC:CRS84")) \method{st_as_stars}{bbox}( .x, ..., nx, ny, dx = dy, dy = dx, xlim = .x[c("xmin", "xmax")], ylim = .x[c("ymin", "ymax")], values = 0, n = 64800, pretty = FALSE, inside = FALSE, nz, proxy = FALSE ) \method{st_as_stars}{xts}(.x, ..., dimensions, name = "attr") } \arguments{ \item{.x}{object to convert} \item{...}{in case \code{.x} is of class \code{bbox}, arguments passed on to \link{pretty}. In case \code{.x} is of class \code{nc_proxy}, arguments passed on to \code{\link{read_ncdf}}.} \item{check_times}{logical; should we check that the time stamps of all time series are identical?} \item{sf_geometry}{sf data.frame with geometry and attributes to be added to stars object. Must have same number of rows as timeseries instances.} \item{as_col}{logical; return rgb numbers (FALSE) or (character) color values (TRUE)?} \item{downsample}{integer: if larger than 0, downsample with this rate (number of pixels to skip in every row/column); if length 2, specifies downsampling rate in x and y.} \item{url}{character; URL of the stars endpoint where the data reside} \item{envir}{environment to resolve objects in} \item{dims}{the column names or indices that form the cube dimensions} \item{xy}{the x and y raster dimension names or indices; only takes effect after \code{dims} has been specified, see details} \item{y_decreasing}{logical; if TRUE, (numeric) y values get a negative delta (decrease with increasing index)} \item{coords}{same as dims, for symmetry with \link[sf]{st_as_sf}} \item{att}{see \link[raster:factor]{factorValues}; column in the RasterLayer's attribute table} \item{ignore_file}{logical; if \code{TRUE}, ignore the SpatRaster object file name} \item{as_attributes}{logical; if \code{TRUE} and \code{.x} has more than one layer, load these as separate attributes rather than as a band or time dimension (only implemented for the case where \code{ignore_file} is \code{TRUE})} \item{dimensions}{object of class dimensions} \item{raster}{character; the names of the dimensions that denote raster dimensions} \item{curvilinear}{only for creating curvilinear grids: named length 2 list holding longitude and latitude matrices or stars arrays, or the names of the corresponding attributes in \code{.x}; the names of this vector should correspond to raster dimensions the matrices are associated with; see Details.} \item{crs}{object of class \code{crs} with the coordinate reference system of the values in \code{curvilinear}; see details} \item{nx}{integer; number of cells in x direction; see details} \item{ny}{integer; number of cells in y direction; see details} \item{dx}{numeric or object of class units; cell size in x direction; see details} \item{dy}{numeric or object of class units; cell size in y direction; see details} \item{xlim}{length 2 numeric vector with extent (min, max) in x direction} \item{ylim}{length 2 numeric vector with extent (min, max) in y direction} \item{values}{value(s) to populate the raster values with} \item{n}{the (approximate) target number of grid cells} \item{pretty}{logical; should cell coordinates have \link{pretty} values?} \item{inside}{logical; should all cells entirely fall inside the bbox, potentially not covering it completely (\code{TRUE}), or always cover the bbox (\code{FALSE}), or find a good approximation (\code{NA}, default)?} \item{nz}{integer; number of cells in z direction; if missing no z-dimension is created.} \item{proxy}{logical; should a \code{stars_proxy} object be created? (requires gdal_create binary when sf < 1.0-6)} \item{name}{character; attribute name for array from an \code{xts} object} } \description{ convert objects into a stars object } \details{ For the \code{ncdfgeom} method: objects are point-timeseries with optional line or polygon geometry for each timeseries specified with the \code{sf_geometry} parameter. See \pkg{ncdfgeom} for more about this NetCDF-based format for geometry and timeseries. If \code{xy} is not specified and the first two dimensions in \code{dims} are both numeric, then it is set to these two dimensions. The \code{st_as_stars} method for \code{sf} objects without any additional arguments returns a one-dimensional data cube with a dimension for the simple features geometries, and all remaining attributes as data cube attributes. When used with further arguments, the method for \code{data.frame}s is called. if \code{curvilinear} is a list with \code{stars} objects with longitude and latitude values, its coordinate reference system is typically not that of the latitude and longitude values. If \code{curvilinear} contains the names of two arrays in \code{.x}, then these are removed from the returned object. For the \code{bbox} method: if \code{pretty} is \code{TRUE}, raster cells may extend the coordinate range of \code{.x} on all sides. If in addition to \code{nx} and \code{ny}, \code{dx} and \code{dy} are also missing, these are set to a single value computed as \code{sqrt(diff(xlim)*diff(ylim)/n)}. If \code{nx} and \code{ny} are missing and \code{values} is a matrix, the number of columns and rows of the matrix are taken. Otherwise, if \code{nx} and \code{ny} are missing, they are computed as the (ceiling, floor, or rounded to integer value) of the ratio of the (x or y) range divided by (dx or dy), depending on the value of \code{inside}. Positive \code{dy} will be made negative. Further named arguments (\code{...}) are passed on to \code{pretty}. If \code{dx} or \code{dy} are \code{units} objects, their value is converted to the units of \code{st_crs(.x)} (only when sf >= 1.0-7). for the \code{xts} methods, if \code{dimensions} are provided, time has to be the first dimension. } \examples{ if (require(plm, quietly = TRUE)) { data(Produc, package = "plm") st_as_stars(Produc) } if (require(dplyr, quietly = TRUE)) { # https://stackoverflow.com/questions/77368957/ spatial_dim <- st_sf( ID = 1:3, geometry = list( st_polygon(list( cbind(c(0, 1, 1, 0, 0), c(0, 0, 1, 1, 0)) )), st_polygon(list( cbind(c(1, 2, 2, 1, 1), c(0, 0, 1, 1, 0)) )), st_polygon(list( cbind(c(2, 3, 3, 2, 2), c(0, 0, 1, 1, 0)) )) ) ) weekdays_dim <- data.frame(weekdays = c("Monday", "Tuesday", "Wednesday", "Thursday", "Friday", "Saturday", "Sunday")) hours_dim <- data.frame(hours = c("8am", "11am", "4pm", "11pm")) sf_dta <- spatial_dim |> cross_join(weekdays_dim)|> cross_join(hours_dim) |> mutate(population = rnorm(n(), mean = 1000, sd = 200)) |> select(everything(), geometry) st_as_stars(sf_dta, dims = c("weekdays", "hours", "geometry")) } demo(nc, echo=FALSE,ask=FALSE) st_as_stars(nc) st_as_stars(st_drop_geometry(nc), dims = "NAME") data.frame(expand.grid(x=1:5, y = 1:5), z = rnorm(25)) |> st_as_stars() nc = st_read(system.file("gpkg/nc.gpkg", package="sf")) st_as_stars(nc) } stars/man/prcomp.Rd0000644000176200001440000000205314600276522013754 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/prcomp.R \name{prcomp} \alias{prcomp} \alias{prcomp.stars_proxy} \alias{prcomp.stars} \title{Principle components of stars object} \usage{ \method{prcomp}{stars_proxy}(x, ..., downsample = 0) \method{prcomp}{stars}(x, ..., quiet = FALSE) } \arguments{ \item{x}{object of class `stars` or `stars_proxy`} \item{...}{see \link[stats]{prcomp}} \item{downsample}{see \link{st_as_stars}} \item{quiet}{logical; if `TRUE`, suppress message that PCs will be computed on last dimension; see details} } \value{ object of class `prcomp`, see \link[stats]{prcomp} } \description{ Compute principle components of stars object } \details{ if `x` has only one attribute, principle components will be computed in the space of the last dimension of `x` to predict PC scores into a `stars` object, use \link{predict.stars}; see example below } \examples{ l7 = split(st_as_stars(L7_ETMs), 3) # use bands as features l7 |> prcomp() |> plot() l7 |> prcomp() |> predict(l7, model = _) |> merge() |> plot() } stars/man/st_extract.Rd0000644000176200001440000000456714603253272014650 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/extract.R \name{st_extract} \alias{st_extract} \alias{st_extract.stars} \title{Extract cell values at point locations} \usage{ st_extract(x, ...) \method{st_extract}{stars}( x, at, ..., bilinear = FALSE, time_column = attr(at, "time_column") \%||\% attr(at, "time_col"), interpolate_time = bilinear, FUN = mean ) } \arguments{ \item{x}{object of class \code{stars} or \code{stars_proxy}} \item{...}{passed on to \link{aggregate.stars} when geometries are not exclusively POINT geometries} \item{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}} \item{bilinear}{logical; use bilinear interpolation rather than nearest neighbour?} \item{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 .} \item{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.} \item{FUN}{function used to aggregate pixel values when geometries of \code{at} intersect with more than one pixel} } \value{ if \code{at} is of class \code{matrix}, a matrix with extracted values is returned; otherwise: if \code{x} has more dimensions than only x and y (raster), an object of class \code{stars} with POINT geometries replacing x and y raster dimensions, if this is not the case, an object of \code{sf} with extracted values. } \description{ Extract cell values at point locations } \details{ points outside the raster are returned as \code{NA} values. For large sets of points for which extraction is needed, passing a matrix as to \code{at} may be much faster than passing an \code{sf} or \code{sfc} object. } \examples{ tif = system.file("tif/L7_ETMs.tif", package = "stars") r = read_stars(tif) pnt = st_sample(st_as_sfc(st_bbox(r)), 10) st_extract(r, pnt) st_extract(r, pnt) \%>\% st_as_sf() st_extract(r[,,,1], pnt) st_extract(r, st_coordinates(pnt)) # "at" is a matrix: return a matrix } stars/man/in-methods.Rd0000644000176200001440000000061314050270337014520 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/ops.R \docType{methods} \name{\%in\%,stars-method} \alias{\%in\%,stars-method} \title{evaluate whether cube values are in a given set} \usage{ \S4method{\%in\%}{stars}(x, table) } \arguments{ \item{x}{data cube value} \item{table}{values of the set} } \description{ evaluate whether cube values are in a given set } stars/man/make_intervals.Rd0000644000176200001440000000072014414346723015463 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/intervals.R \name{make_intervals} \alias{make_intervals} \title{create an intervals object} \usage{ make_intervals(start, end) } \arguments{ \item{start}{vector with start values, or 2-column matrix with start and end values in column 1 and 2, respectively} \item{end}{vector with end values} } \description{ create an intervals object, assuming left-closed and right-open intervals } stars/man/st_cells.Rd0000644000176200001440000000152514621476642014277 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/stars.R \name{st_cells} \alias{st_cells} \title{return the cell index corresponding to the location of a set of points} \usage{ st_cells(x, sf) } \arguments{ \item{x}{object of class \code{stars}} \item{sf}{object of class \code{sf} or \code{sfc}} } \description{ If the object has been cropped without normalization, then the indices return are relative to the original uncropped extent. See \code{\link{st_crop}} } \examples{ set.seed(1345) st_bbox(L7_ETMs) |> st_as_sfc() |> st_sample(10) -> pts (x <- st_cells(L7_ETMs, pts)) # get the pixel values (first band only): st_as_stars(L7_ETMs)[[1]][x] # get pixel values for all bands: st_as_stars(L7_ETMs) |> split() |> sapply(`[`, x) # compare with st_extract(): st_as_stars(L7_ETMs) |> split() |> st_extract(pts) } stars/man/print_stars.Rd0000644000176200001440000000221514576035610015027 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/dimensions.R, R/stars.R \name{print_stars} \alias{print_stars} \alias{as.data.frame.dimensions} \alias{print.dimensions} \alias{print.stars} \title{print stars or dimensions object} \usage{ \method{as.data.frame}{dimensions}( x, ..., digits = max(3, getOption("digits") - 3), usetz = TRUE, stars_crs = getOption("stars.crs") \%||\% 28, all = FALSE ) \method{print}{dimensions}(x, ...) \method{print}{stars}(x, ..., n = 1e+05, abbrev = 30) } \arguments{ \item{x}{object of class stars or of class dimensions} \item{...}{passed on to \code{as.data.frame.dimensions}} \item{digits}{number of digits to print numbers} \item{usetz}{logical; used to format \code{PCICt} or \code{POSIXct} values} \item{stars_crs}{maximum width of string for CRS objects} \item{all}{logical; if \code{TRUE} print also fields entirely filled with \code{NA} or \code{NULL}} \item{n}{when prod(dim(x)) > 10 * n, the first n cells are used for attribute summary statistics} \item{abbrev}{number of characters to abbreviate attribute names to} } \description{ print stars or dimensions object } stars/man/st_downsample.Rd0000644000176200001440000000423114464700544015337 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/sample.R \name{st_downsample} \alias{st_downsample} \alias{st_downsample.stars} \alias{st_downsample.stars_proxy} \title{downsample stars or stars_proxy objects} \usage{ st_downsample(x, n, ...) \method{st_downsample}{stars}(x, n, ..., offset = 0, FUN) \method{st_downsample}{stars_proxy}(x, n, ...) } \arguments{ \item{x}{object of class stars or stars_proxy} \item{n}{integer; for each dimension the number of pixels/lines/bands etc that will be skipped; see Details.} \item{...}{arguments passed on to \code{FUN} (e.g., \code{na.rm = TRUE} to ignore missing values if FUN is \code{mean})} \item{offset}{integer; offset(s) for downsampling, in pixels, starting at the offset of each dimension; should be smaller or equal to \code{n}} \item{FUN}{function; if given, downsampling will apply FUN to each of the the subtiles} } \description{ downsample a stars or stars_proxy object either by skipping rows, columns and bands, or by computing a single value (e.g. the mean) from the sub-tiles involved } \details{ If all n == 0, no downsampling takes place; if it is 1, every second row/column/band is skipped, if it is 2, every second+third row/column/band are skipped, etc. Downsampling a \code{stars_proxy} object returns a \code{stars} object, is equivalent to calling \code{st_as_stars(x, downsample = 2)}, and only downsamples the first two (x and y) dimensions. Downsampled regular rasters keep their dimension offsets, have a cell size (delta) that is n[i]+1 times larger, and may result in a (slightly) different extent. Note that terra's \link[terra]{aggregate} with \code{fact=2} corresponds to \code{st_downsample(x, n = 1, FUN = mean)}: \code{fact} is one larger than \code{n}. } \examples{ (m = matrix(1:121, 11, 11)) (s = st_as_stars(m)) st_downsample(s, 1) st_downsample(s, 1)[[1]] st_downsample(s, 1, offset = 1) st_downsample(s, 1, offset = 1)[[1]] st_downsample(s, 1, offset = c(0,1)) st_downsample(s, 1, offset = c(0,1))[[1]] st_downsample(s, 1, FUN = mean) st_downsample(s, 1, FUN = mean)[[1]] st_downsample(s, 1, offset = 1, FUN = mean) st_downsample(s, 1, offset = c(0,1), FUN = mean)[[1]] } stars/man/coerce-methods.Rd0000644000176200001440000000201714144045432015352 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/raster.R \name{as} \alias{as} \alias{coerce,stars,Raster-method} \alias{coerce,stars_proxy,Raster-method} \alias{coerce,stars,Terra-method} \alias{coerce,stars_proxy,Terra-method} \title{Coerce stars object into a Raster raster or brick} \arguments{ \item{from}{object to coerce} } \value{ RasterLayer or RasterBrick SpatRaster } \description{ Coerce stars object into a Raster raster or brick Coerce stars object into a terra SpatRaster } \details{ If the stars object has more than three dimensions, all dimensions higher than the third will be collapsed into the third dimensions. If the stars object has only an x/y raster but multiple attributes, these are merged first, then put in a raster brick. If the stars object has more than three dimensions, all dimensions higher than the third will be collapsed into the third dimensions. If the stars object has only an x/y raster but multiple attributes, these are merged first, then put in a SpatRaster. } stars/man/ops_stars.Rd0000644000176200001440000000243414046025022014463 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/ops.R \name{ops_stars} \alias{ops_stars} \alias{Ops.stars} \alias{Math.stars} \alias{Ops.stars_proxy} \alias{Math.stars_proxy} \title{S3 Ops Group Generic Functions for stars objects} \usage{ \method{Ops}{stars}(e1, e2) \method{Math}{stars}(x, ...) \method{Ops}{stars_proxy}(e1, e2) \method{Math}{stars_proxy}(x, ...) } \arguments{ \item{e1}{object of class \code{stars}} \item{e2}{object of class \code{stars}} \item{x}{object of class stars} \item{...}{parameters passed on to the Math functions} } \value{ object of class \code{stars} } \description{ Ops functions for stars objects, including comparison, product and divide, add, subtract } \details{ if \code{e1} or \code{e2} is is a numeric vector, or \code{e2} has less or smaller dimensions than \code{e1}, then \code{e2} is recycled such that it fits \code{e1}, using usual R array recycling rules. The user needs to make sure this is sensible; it may be needed to use \code{aperm} to permutate dimensions first. } \examples{ tif = system.file("tif/L7_ETMs.tif", package = "stars") x = read_stars(tif) x * x x / x x + x x + 10 all.equal(x * 10, 10 * x) tif = system.file("tif/L7_ETMs.tif", package = "stars") x = read_stars(tif) a = sqrt(x) b = log(x, base = 10) } stars/man/mdim.Rd0000644000176200001440000000611714576033423013412 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/mdim.R \name{mdim} \alias{mdim} \alias{read_mdim} \alias{write_mdim} \title{Read or write data using GDAL's multidimensional array API} \usage{ read_mdim( filename, variable = character(0), ..., options = character(0), raster = NULL, offset = integer(0), count = integer(0), step = integer(0), proxy = FALSE, debug = FALSE, bounds = TRUE, curvilinear = NA ) write_mdim( x, filename, driver = detect.driver(filename), ..., root_group_options = character(0), options = character(0), as_float = TRUE ) } \arguments{ \item{filename}{name of the source or destination file or data source} \item{variable}{name of the array to be read; if `"?"`, a list of array names is returned, with group name as list element names.} \item{...}{ignored} \item{options}{character; driver specific options regarding the opening (read_mdim) or creation (write_mdim) of the dataset} \item{raster}{names of the raster variables (default: first two dimensions)} \item{offset}{integer; offset for each dimension (pixels) of sub-array to read, defaults to 0 for each dimension(requires sf >= 1.0-9)} \item{count}{integer; size for each dimension (pixels) of sub-array to read (default: read all); a value of NA will read the corresponding dimension entirely; counts are relative to the step size (requires sf >= 1.0-9)} \item{step}{integer; step size for each dimension (pixels) of sub-array to read; defaults to 1 for each dimension (requires sf >= 1.0-9)} \item{proxy}{logical; return proxy object? (not functional yet)} \item{debug}{logical; print debug info?} \item{bounds}{logical or character: if \code{TRUE} tries to infer from "bounds" attribute; if character, named vector of the form \code{c(longitude="lon_bnds", latitude="lat_bnds")} with names dimension names} \item{curvilinear}{control reading curvilinear (geolocation) coordinate arrays; if \code{NA} try reading the x/y dimension names; if character, defines the arrays to read; if \code{FALSE} do not try; see also \link{read_stars}} \item{x}{stars object} \item{driver}{character; driver name} \item{root_group_options}{character; driver specific options regarding the creation of the root group} \item{as_float}{logical; if \code{TRUE} write 4-byte floating point numbers, if \code{FALSE} write 8-byte doubles} } \description{ Read or write data using GDAL's multidimensional array API } \details{ it is assumed that the first two dimensions are easting and northing } \examples{ set.seed(135) m = matrix(runif(10), 2, 5) names(dim(m)) = c("stations", "time") times = as.Date("2022-05-01") + 1:5 pts = st_as_sfc(c("POINT(0 1)", "POINT(3 5)")) s = st_as_stars(list(Precipitation = m)) |> st_set_dimensions(1, values = pts) |> st_set_dimensions(2, values = times) nc = tempfile(fileext=".nc") if (compareVersion(sf_extSoftVersion()["GDAL"], "3.4.0") > -1) { write_mdim(s, nc) # try ncdump on the generated file print(read_mdim(nc)) } } \seealso{ \link[sf]{gdal_utils}, in particular util \code{mdiminfo} to query properties of a file or data source containing arrays } stars/man/st_set_bbox.Rd0000644000176200001440000000062413777653456015014 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/stars.R \name{st_set_bbox} \alias{st_set_bbox} \title{set bounding box parameters of regular grid} \usage{ st_set_bbox(x, value, ...) } \arguments{ \item{x}{object of class dimensions, stars or stars_proxy} \item{value}{object of class bbox} \item{...}{ignored} } \description{ set bounding box parameters of regular grid } stars/man/st_geotransform.Rd0000644000176200001440000000317314576033423015677 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/gt.R \name{st_geotransform} \alias{st_geotransform} \alias{st_geotransform<-} \alias{st_geotransform<-.stars} \title{get or set the geotransform, or rotation matrix} \usage{ st_geotransform(x, ...) st_geotransform(x) <- value \method{st_geotransform}{stars}(x) <- value } \arguments{ \item{x}{object of class stars or dimensions} \item{...}{ignored} \item{value}{length 6 numeric vector, or 2 x 2 (scaled) rotation matrix} } \description{ get or set the geotransform, or rotation matrix } \examples{ # using the "classical" rotation matrix, see https://en.wikipedia.org/wiki/Rotation_matrix : rot = function(theta, dxdy = c(1., -1.)) { th = theta / 180 * pi matrix(c(cos(th), sin(th), -sin(th), cos(th)), 2, 2) \%*\% matrix(c(dxdy[2], 0, 0, dxdy[1]), 2, 2) } l = st_downsample(st_as_stars(L7_ETMs), 9) # save time in plotting st_geotransform(l) = rot(20, c(28.5, 28.5)) # clockwise, 20 degrees, scale by cell size plot(l[,,,1]) m = rot(20, c(1, 2)) g = expand.grid(x = 0:4, y = 0:4) plot(g[1:2], asp = 1) text(g[,1], g[,2], labels = seq_along(g[,1]), pos = 4) g = t(m \%*\% t(as.matrix(g))) points(g, col = 'red') text(g[,1], g[,2], labels = seq_along(g[,1]), pos = 4, col = 'red') m = matrix(1:20, 4) s0 = st_as_stars(m) s = s0 # dy > 0, clockwise rotation: st_geotransform(s) = rot(10, c(1,1)) plot(s0, reset = FALSE) plot(s, add = TRUE) # dy < 0, counter clockwise rotation, + expansion in x-direction: layout(1) s0 = st_as_stars(st_bbox(s0), dx = 1) s0$values = 1:20 s0 plot(s0, reset = FALSE) s = s0 st_geotransform(s) = rot(10, c(2,1)) plot(s, add = TRUE) } stars/man/st_raster_type.Rd0000644000176200001440000000211214146737530015525 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/stars.R \name{st_raster_type} \alias{st_raster_type} \title{get the raster type (if any) of a stars object} \usage{ st_raster_type(x, dimension = character(0)) } \arguments{ \item{x}{object of class \code{stars}} \item{dimension}{optional: numbers or names of dimension(s) to get per-dimension type} } \value{ if \code{dimension} is not specified, return the spatial raster type: one of \code{NA} (if the object does not have raster dimensions), \code{"curvilinear"}, \code{"rectilinear"}, \code{"affine"}, or \code{"regular"}. In case dimension(s) are specified, return one of \code{"regular"}, \code{"rectilinear"} (irregular but numeric), or \code{"discrete"} (anything else). } \description{ get the raster type (if any) of a stars object } \details{ categories \code{"curvilinear"} and \code{"affine"} only refer to the relationship between a pair of spatial (raster) dimensions. } \examples{ tif = system.file("tif/L7_ETMs.tif", package = "stars") x = read_stars(tif) st_raster_type(x) st_raster_type(x, 1:3) } stars/man/cut_stars.Rd0000644000176200001440000000220413777653456014504 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/factors.R \name{cut_stars} \alias{cut_stars} \alias{cut.array} \alias{cut.matrix} \alias{cut.stars} \title{cut methods for stars objects} \usage{ \method{cut}{array}(x, breaks, ...) \method{cut}{matrix}(x, breaks, ...) \method{cut}{stars}(x, breaks, ...) } \arguments{ \item{x}{see \link[base]{cut}} \item{breaks}{see \link[base]{cut}} \item{...}{see \link[base]{cut}} } \value{ an array or matrix with a \code{levels} attribute; see details } \description{ cut methods for stars objects } \details{ R's \code{factor} only works for vectors, not for arrays or matrices. This is a work-around (or hack?) to keep the factor levels generated by \code{cut} and use them in plots. } \examples{ tif = system.file("tif/L7_ETMs.tif", package = "stars") x = read_stars(tif) cut(x, c(0, 50, 100, 255)) cut(x[,,,1], c(0, 50, 100, 255)) plot(cut(x[,,,1], c(0, 50, 100, 255))) tif = system.file("tif/L7_ETMs.tif", package = "stars") x1 = read_stars(tif) (x1_cut = cut(x1, breaks = c(0, 50, 100, Inf))) # shows factor in summary plot(x1_cut[,,,c(3,6)]) # propagates through [ and plot } stars/man/c.stars.Rd0000644000176200001440000000364114576035610014040 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/proxy.R, R/stars.R \name{c.stars} \alias{c.stars} \alias{c.stars_proxy} \title{combine multiple stars objects, or combine multiple attributes in a single stars object into a single array} \usage{ \method{c}{stars_proxy}( ..., along = NA_integer_, along_crs = FALSE, try_hard = FALSE, nms = names(list(...)), tolerance = sqrt(.Machine$double.eps) ) \method{c}{stars}( ..., along = NA_integer_, try_hard = FALSE, nms = names(list(...)), tolerance = sqrt(.Machine$double.eps) ) } \arguments{ \item{...}{object(s) of class \code{star}: in case of multiple arguments, these are combined into a single stars object, in case of a single argument, its attributes are combined into a single attribute. In case of multiple objects, all objects should have the same dimensionality.} \item{along}{integer; see \link{read_stars}} \item{along_crs}{logical; if \code{TRUE}, combine arrays along a CRS dimension} \item{try_hard}{logical; if \code{TRUE} and some arrays have different dimensions, combine those that dimensions matching to the first array} \item{nms}{character; vector with array names} \item{tolerance}{numeric; values used in \link{all.equal} to compare dimension values combine those that dimensions matching to the first array} } \value{ a single \code{stars} object with merged (binded) arrays. } \description{ combine multiple stars objects, or combine multiple attributes in a single stars object into a single array } \details{ An error is raised when attempting to combine arrays with different measurement units into a single array. If this was intentded, \code{drop_units} can be used to remove units of a \code{stars} object before merging. } \examples{ tif = system.file("tif/L7_ETMs.tif", package = "stars") x = read_stars(tif) (new = c(x, x)) c(new) # collapses two arrays into one with an additional dimension c(x, x, along = 3) } stars/man/dplyr.Rd0000644000176200001440000000371214263276064013617 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/tidyverse.R \name{dplyr} \alias{dplyr} \alias{filter.stars} \alias{filter.stars_proxy} \alias{mutate.stars} \alias{mutate.stars_proxy} \alias{transmute.stars} \alias{transmute.stars_proxy} \alias{select.stars} \alias{select.stars_proxy} \alias{rename.stars} \alias{rename.stars_proxy} \alias{pull.stars} \alias{pull.stars_proxy} \alias{as.tbl_cube.stars} \alias{slice.stars} \alias{slice.stars_proxy} \alias{replace_na.stars} \alias{replace_na.stars_proxy} \title{dplyr verbs for stars objects} \usage{ filter.stars(.data, ...) filter.stars_proxy(.data, ...) mutate.stars(.data, ...) mutate.stars_proxy(.data, ...) transmute.stars(.data, ...) transmute.stars_proxy(.data, ...) select.stars(.data, ...) select.stars_proxy(.data, ...) rename.stars(.data, ...) rename.stars_proxy(.data, ...) pull.stars(.data, var = -1) pull.stars_proxy(.data, ...) as.tbl_cube.stars(x, ...) slice.stars(.data, along, index, ..., drop = length(index) == 1) slice.stars_proxy(.data, along, index, ...) replace_na.stars(data, replace, ...) replace_na.stars_proxy(data, ...) } \arguments{ \item{.data}{object of class \code{stars}} \item{...}{see \link[dplyr]{filter}} \item{var}{see \link[dplyr]{pull}} \item{x}{object of class \code{stars}} \item{along}{name or index of dimension to which the slice should be applied} \item{index}{integer value(s) for this index} \item{drop}{logical; drop dimensions that only have a single index?} \item{data}{data set to work on} \item{replace}{see \link[tidyr]{replace_na}: list with variable=value pairs, where value is the replacement value for NA's} } \description{ dplyr verbs for stars objects; package dplyr needs to be loaded before these methods can be used for stars objects. } \examples{ tif = system.file("tif/L7_ETMs.tif", package = "stars") x1 = read_stars(tif) if (require(dplyr, quietly = TRUE)) { x1 \%>\% slice("band", 2:3) x1 \%>\% slice("x", 50:100) } } stars/man/st_dim_to_attr.Rd0000644000176200001440000000124513777653456015514 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/stars.R \name{st_dim_to_attr} \alias{st_dim_to_attr} \title{create an array with dimension values} \usage{ st_dim_to_attr(x, which = seq_along(dim(x))) } \arguments{ \item{x}{object of class \code{stars}} \item{which}{integer; indices of the dimensions to address (default: all)} } \value{ \code{stars} object with dimension values as attributes } \description{ create an array with dimension values } \examples{ tif = system.file("tif/L7_ETMs.tif", package = "stars") x1 = read_stars(tif) (x = st_dim_to_attr(x1)) plot(x) (x = st_dim_to_attr(x1, 2:3)) plot(x) (x= st_dim_to_attr(x1, 3)) plot(x) } stars/man/st_res.Rd0000644000176200001440000000221314353574021013751 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/stars.R \name{st_res} \alias{st_res} \title{obtain (spatial) resolution of a stars object} \usage{ st_res(x, all = FALSE, absolute = !all) } \arguments{ \item{x}{an object of class \code{stars}} \item{all}{logical; if FALSE return a vector with the x/y raster resolution} \item{absolute}{logical; only works when \code{all = FALSE}; if TRUE return absolute resolution values, if FALSE return \code{delta} values} } \value{ if \code{all = FALSE} a vector with x/y raster resolutions, otherwise a list with delta values } \description{ obtain resolution(s) of a stars object: by default only the (absolute) x/y raster dimensions, optionally all \code{delta} dimension parameters } \examples{ st_res(L7_ETMs) st_res(L7_ETMs, absolute = FALSE) st_res(L7_ETMs, all = TRUE) if (require(starsdata)) { paste0("netcdf/", c("avhrr-only-v2.19810901.nc", "avhrr-only-v2.19810902.nc", "avhrr-only-v2.19810903.nc", "avhrr-only-v2.19810904.nc")) |> system.file(package = "starsdata") |> read_stars(quiet = TRUE) -> x st_res(x) |> print() st_res(x, all = TRUE) |> print() } } stars/man/write_stars.Rd0000644000176200001440000000541114353574021015023 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/write.R \name{write_stars} \alias{write_stars} \alias{write_stars.stars} \alias{write_stars.stars_proxy} \alias{detect.driver} \title{write stars object to gdal dataset (typically: to file)} \usage{ write_stars(obj, dsn, layer, ...) \method{write_stars}{stars}( obj, dsn, layer = 1, ..., driver = detect.driver(dsn), options = character(0), type = if (is.factor(obj[[1]]) && length(levels(obj[[1]])) < 256) "Byte" else "Float32", NA_value = NA_real_, update = FALSE, normalize_path = TRUE, scale_offset = c(1, 0) ) \method{write_stars}{stars_proxy}( obj, dsn, layer = 1, ..., driver = detect.driver(dsn), options = character(0), scale_offset = c(1, 0), type = "Float32", NA_value = NA_real_, chunk_size = c(dim(obj)[1], floor(2.5e+07/dim(obj)[1])), progress = TRUE ) detect.driver(filename) } \arguments{ \item{obj}{object of class \code{stars}} \item{dsn}{gdal dataset (file) name} \item{layer}{attribute name; if missing, the first attribute is written} \item{...}{passed on to \link[sf:gdal]{gdal_write}} \item{driver}{driver driver name; see \link[sf]{st_drivers}} \item{options}{character vector with dataset creation options, passed on to GDAL} \item{type}{character; output binary type, one of: \code{Byte} for eight bit unsigned integer, \code{UInt16} for sixteen bit unsigned integer, \code{Int16} for sixteen bit signed integer, \code{UInt32} for thirty two bit unsigned integer, \code{Int32} for thirty two bit signed integer, \code{Float32} for thirty two bit floating point, \code{Float64} for sixty four bit floating point.} \item{NA_value}{non-NA value that should represent R's \code{NA} value in the target raster file; if set to \code{NA}, it will be ignored.} \item{update}{logical; if \code{TRUE}, an existing file is being updated} \item{normalize_path}{logical; see \link{read_stars}} \item{scale_offset}{length 2 numeric vector with scale, offset values: raw values computed by raw = (value - offset) / scale are written to dsn; scale and offset values are written to dsn or else a warning is raised} \item{chunk_size}{length two integer vector with the number of pixels (x, y) used in the read/write loop; see details.} \item{progress}{logical; if \code{TRUE}, a progress bar is shown} \item{filename}{character; used for guessing driver short name based on file extension; see examples} } \description{ write stars object to gdal dataset (typically: to file) } \details{ \code{write_stars} first creates the target file, then updates it sequentially by writing blocks of \code{chunk_size}. in case \code{obj} is a multi-file \code{stars_proxy} object, all files are written as layers into the output file \code{dsn} } \examples{ detect.driver("L7_ETMs.tif") } stars/man/st_as_sf.Rd0000644000176200001440000000504514011050736014252 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/sf.R \name{st_as_sf} \alias{st_as_sf} \alias{st_as_sfc.stars} \alias{st_as_sf.stars} \alias{st_as_sf.stars_proxy} \title{Convert stars object into an sf object} \usage{ \method{st_as_sfc}{stars}(x, ..., as_points, which = seq_len(prod(dim(x)[1:2]))) \method{st_as_sf}{stars}( x, ..., as_points = FALSE, merge = FALSE, na.rm = TRUE, use_integer = is.logical(x[[1]]) || is.integer(x[[1]]), long = FALSE, connect8 = FALSE ) \method{st_as_sf}{stars_proxy}(x, ..., downsample = 0) } \arguments{ \item{x}{object of class \code{stars}} \item{...}{ignored} \item{as_points}{logical; should cells be converted to points or to polygons? See details.} \item{which}{linear index of cells to keep (this argument is not recommended to be used)} \item{merge}{logical; if \code{TRUE}, cells with identical values are merged (using \code{GDAL_Polygonize} or \code{GDAL_FPolygonize}); if \code{FALSE}, a polygon for each raster cell is returned; see details} \item{na.rm}{logical; should missing valued cells be removed, or also be converted to features?} \item{use_integer}{(relevant only if \code{merge} is \code{TRUE}): if \code{TRUE}, before polygonizing values are rounded to 32-bits signed integer values (GDALPolygonize), otherwise they are converted to 32-bit floating point values (GDALFPolygonize).} \item{long}{logical; if \code{TRUE}, return a long table form \code{sf}, with geometries and other dimensions recycled} \item{connect8}{logical; if \code{TRUE}, use 8 connectedness. Otherwise the 4 connectedness algorithm will be applied.} \item{downsample}{see \link{st_as_stars}} } \description{ Convert stars object into an sf object } \details{ If \code{merge} is \code{TRUE}, only the first attribute is converted into an \code{sf} object. If \code{na.rm} is \code{FALSE}, areas with \code{NA} values are also written out as polygons. Note that the resulting polygons are typically invalid, and use \link[sf:valid]{st_make_valid} to create valid polygons out of them. } \examples{ tif = system.file("tif/L7_ETMs.tif", package = "stars") x = read_stars(tif) x = x[,1:100,1:100,6] # subset of a band with lower values in it x[[1]][x[[1]] < 30] = NA # set lower values to NA x[[1]] = x[[1]] < 100 # make the rest binary x (p = st_as_sf(x)) # removes NA areas (p = st_as_sf(x[,,,1], merge = TRUE)) # glues polygons together all(st_is_valid(p)) # not all valid, see details plot(p, axes = TRUE) (p = st_as_sf(x, na.rm = FALSE, merge = TRUE)) # includes polygons with NA values plot(p, axes = TRUE) } stars/man/read_stars.Rd0000644000176200001440000001316514376173527014624 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/read.R \name{read_stars} \alias{read_stars} \title{read raster/array dataset from file or connection} \usage{ read_stars( .x, sub = TRUE, ..., options = character(0), driver = character(0), quiet = FALSE, NA_value = NA_real_, along = NA_integer_, RasterIO = list(), proxy = getOption("stars.n_proxy") \%||\% 1e+08, curvilinear = character(0), normalize_path = TRUE, RAT = character(0), tolerance = 1e-10, exclude = "", shorten = TRUE ) } \arguments{ \item{.x}{character vector with name(s) of file(s) or data source(s) to be read, or a function that returns such a vector} \item{sub}{character, integer or logical; name, index or indicator of sub-dataset(s) to be read} \item{...}{passed on to \link{st_as_stars} if \code{curvilinear} was set} \item{options}{character; opening options} \item{driver}{character; driver to use for opening file. To override fixing for subdatasets and autodetect them as well, use \code{NULL}.} \item{quiet}{logical; print progress output?} \item{NA_value}{numeric value to be used for conversion into NA values; by default this is read from the input file} \item{along}{length-one character or integer, or list; determines how several arrays are combined, see Details.} \item{RasterIO}{list with named parameters for GDAL's RasterIO, to further control the extent, resolution and bands to be read from the data source; see details.} \item{proxy}{logical; if \code{TRUE}, an object of class \code{stars_proxy} is read which contains array metadata only; if \code{FALSE} the full array data is read in memory. Always \code{FALSE} for curvilinear girds. If set to a number, defaults to \code{TRUE} when the number of cells to be read is larger than that number.} \item{curvilinear}{length two character vector with names of subdatasets holding longitude and latitude values for all raster cells, or named length 2 list holding longitude and latitude matrices; the names of this list should correspond to raster dimensions referred to} \item{normalize_path}{logical; if \code{FALSE}, suppress a call to \link{normalizePath} on \code{.x}} \item{RAT}{character; raster attribute table column name to use as factor levels} \item{tolerance}{numeric; passed on to \link{all.equal} for comparing dimension parameters.} \item{exclude}{character; vector with category value(s) to exclude} \item{shorten}{logical or character; if \code{TRUE} and \code{length(.x) > 1}, remove common start and end parts of array names; if character a new prefix} } \value{ object of class \code{stars} } \description{ read raster/array dataset from file or connection } \details{ In case \code{.x} contains multiple files, they will all be read and combined with \link{c.stars}. Along which dimension, or how should objects be merged? If \code{along} is set to \code{NA} it will merge arrays as new attributes if all objects have identical dimensions, or else try to merge along time if a dimension called \code{time} indicates different time stamps. A single name (or positive value) for \code{along} will merge along that dimension, or create a new one if it does not already exist. If the arrays should be arranged along one of more dimensions with values (e.g. time stamps), a named list can passed to \code{along} to specify them; see example. \code{RasterIO} is a list with zero or more of the following named arguments: \code{nXOff}, \code{nYOff} (both 1-based: the first row/col has offset value 1), \code{nXSize}, \code{nYSize}, \code{nBufXSize}, \code{nBufYSize}, \code{bands}, \code{resample}. See \url{https://gdal.org/doxygen/classGDALDataset.html} for their meaning; \code{bands} is an integer vector containing the band numbers to be read (1-based: first band is 1). Note that if \code{nBufXSize} or \code{nBufYSize} are specified for downsampling an image, resulting in an adjusted geotransform. \code{resample} reflects the resampling method and has to be one of: "nearest_neighbour" (the default), "bilinear", "cubic", "cubic_spline", "lanczos", "average", "mode", or "Gauss". Data that are read into memory (\code{proxy=FALSE}) are read into a numeric (double) array, except for categorical variables which are read into an numeric (integer) array of class \code{factor}. } \examples{ tif = system.file("tif/L7_ETMs.tif", package = "stars") (x1 = read_stars(tif)) (x2 = read_stars(c(tif, tif))) (x3 = read_stars(c(tif, tif), along = "band")) (x4 = read_stars(c(tif, tif), along = "new_dimensions")) # create 4-dimensional array x1o = read_stars(tif, options = "OVERVIEW_LEVEL=1") t1 = as.Date("2018-07-31") # along is a named list indicating two dimensions: read_stars(c(tif, tif, tif, tif), along = list(foo = c("bar1", "bar2"), time = c(t1, t1+2))) m = matrix(1:120, nrow = 12, ncol = 10) dim(m) = c(x = 10, y = 12) # named dim st = st_as_stars(m) attr(st, "dimensions")$y$delta = -1 attr(st, "dimensions")$y$offset = 12 st tmp = tempfile(fileext = ".tif") write_stars(st, tmp) (red <- read_stars(tmp)) read_stars(tmp, RasterIO = list(nXOff = 1, nYOff = 1, nXSize = 10, nYSize = 12, nBufXSize = 2, nBufYSize = 2))[[1]] (red <- read_stars(tmp, RasterIO = list(nXOff = 1, nYOff = 1, nXSize = 10, nYSize = 12, nBufXSize = 2, nBufYSize = 2))) red[[1]] # cell values of subsample grid: \dontrun{ plot(st, reset = FALSE, axes = TRUE, ylim = c(-.1,12.1), xlim = c(-.1,10.1), main = "nBufXSize & nBufYSize demo", text_values = TRUE) plot(st_as_sfc(red, as_points = TRUE), add = TRUE, col = 'red', pch = 16) plot(st_as_sfc(st_as_stars(st), as_points = FALSE), add = TRUE, border = 'grey') plot(st_as_sfc(red, as_points = FALSE), add = TRUE, border = 'green', lwd = 2) } file.remove(tmp) } stars/man/geom_stars.Rd0000644000176200001440000000432614353574021014624 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/tidyverse.R \name{geom_stars} \alias{geom_stars} \alias{theme_stars} \title{ggplot geom for stars objects} \usage{ geom_stars( mapping = NULL, data = NULL, ..., downsample = 0, sf = FALSE, na.action = na.pass ) theme_stars(...) } \arguments{ \item{mapping}{see \link[ggplot2:geom_tile]{geom_raster}} \item{data}{see \link[ggplot2:geom_tile]{geom_raster}} \item{...}{see \link[ggplot2:geom_tile]{geom_raster}} \item{downsample}{downsampling rate: e.g. 3 keeps rows and cols 1, 4, 7, 10 etc.; a value of 0 does not downsample; can be specified for each dimension, e.g. \code{c(5,5,0)} to downsample the first two dimensions but not the third.} \item{sf}{logical; if \code{TRUE} rasters will be converted to polygons and plotted using \link[ggplot2:ggsf]{geom_sf}.} \item{na.action}{function; if \code{NA} values need to be removed before plotting use the value \code{na.omit} here (only applies to objects with raster dimensions)} } \description{ ggplot geom for stars objects } \details{ \code{geom_stars} returns (a call to) either \link[ggplot2:geom_tile]{geom_raster}, \link[ggplot2]{geom_tile}, or \link[ggplot2:ggsf]{geom_sf}, depending on the raster or vector geometry; for the first to, an \link[ggplot2]{aes} call is constructed with the raster dimension names and the first array as fill variable. Further calls to \link[ggplot2:coord_fixed]{coord_equal} and \link[ggplot2]{facet_wrap} are needed to control aspect ratio and the layers to be plotted; see examples. If a \code{stars} array contains hex color values, and no \code{fill} parameter is given, the color values are used as fill color; see the example below. If visual artefacts occur (Moiré-Effekt), then see the details section of \link{plot.stars} } \examples{ system.file("tif/L7_ETMs.tif", package = "stars") \%>\% read_stars() -> x if (require(ggplot2, quietly = TRUE)) { ggplot() + geom_stars(data = x) + coord_equal() + facet_wrap(~band) + theme_void() + scale_x_discrete(expand=c(0,0))+ scale_y_discrete(expand=c(0,0)) # plot rgb composite: st_as_stars(L7_ETMs)[,,,1:3] |> st_rgb() -> x # x contains colors as pixel values ggplot() + geom_stars(data = x) } } stars/man/st_rasterize.Rd0000644000176200001440000000437614576033423015207 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/rasterize.R \name{st_rasterize} \alias{st_rasterize} \title{rasterize simple feature geometries} \usage{ st_rasterize( sf, template = guess_raster(sf, ...) \%||\% st_as_stars(st_bbox(sf), values = NA_real_, ...), file = tempfile(), driver = "GTiff", options = character(0), align = FALSE, proxy = FALSE, ... ) } \arguments{ \item{sf}{object of class \code{sf}} \item{template}{optional; stars object with desired target geometry, or target geometry alignment if \code{align=TRUE}; see details} \item{file}{temporary file name} \item{driver}{driver for temporary file} \item{options}{character; options vector for \code{GDALRasterize}} \item{align}{logical; if \code{TRUE}, \code{template} is only used for the geometry _alignment_, informing target resolution and offset} \item{proxy}{logical; should a proxy object be returned?} \item{...}{arguments passed on to \link{st_as_stars}} } \description{ rasterize simple feature geometries } \details{ if `template` is a `stars` object, non-NA cells that are not covered by `sf` receive the value in `template`; see also argument `align`. } \examples{ demo(nc, echo = FALSE, ask = FALSE) (x = st_rasterize(nc)) # default grid: plot(x, axes = TRUE) # a bit more customized grid: (x = st_rasterize(nc, st_as_stars(st_bbox(nc), nx = 100, ny = 50, values = NA_real_))) plot(x, axes = TRUE) (ls = st_sf(a = 1:2, st_sfc(st_linestring(rbind(c(0.1, 0), c(1.1, 1))), st_linestring(rbind(c(0, 0.05), c(1, 0.05)))))) (grd = st_as_stars(st_bbox(ls), nx = 10, ny = 10, xlim = c(0, 1.0), ylim = c(0, 1), values = NA_real_)) # Only the left-top corner is part of the grid cell: sf_extSoftVersion()["GDAL"] plot(st_rasterize(ls, grd), axes = TRUE, reset = FALSE) # ALL_TOUCHED=FALSE; plot(ls, add = TRUE, col = "red") plot(st_rasterize(ls, grd, options = "ALL_TOUCHED=TRUE"), axes = TRUE, reset = FALSE) plot(ls, add = TRUE, col = "red") # add lines to existing 0 values, summing values in case of multiple lines: (grd = st_as_stars(st_bbox(ls), nx = 10, ny = 10, xlim = c(0, 1.0), ylim = c(0, 1), values = 0)) r = st_rasterize(ls, grd, options = c("MERGE_ALG=ADD", "ALL_TOUCHED=TRUE")) plot(r, axes = TRUE, reset = FALSE) plot(ls, add = TRUE, col = "red") } stars/man/redimension.Rd0000644000176200001440000000172414576035610014777 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/proxy.R, R/stars.R \name{redimension} \alias{redimension} \alias{st_redimension.stars_proxy} \alias{st_redimension} \alias{st_redimension.stars} \title{redimension array, or collapse attributes into a new dimension} \usage{ \method{st_redimension}{stars_proxy}( x, new_dims = st_dimensions(x), along = list(new_dim = names(x)), ... ) st_redimension(x, new_dims, along, ...) \method{st_redimension}{stars}( x, new_dims = st_dimensions(x), along = setNames(list(names(x)), name), ..., name = "new_dim" ) } \arguments{ \item{x}{object of class \code{stars}} \item{new_dims}{target dimensions: either a `dimensions` object or an integer vector with the dimensions' sizes} \item{along}{named list with new dimension name and values} \item{...}{ignored} \item{name}{character name of the new dimension} } \description{ redimension array, or collapse attributes into a new dimension } stars/man/stars_sentinel2.Rd0000644000176200001440000000077214576033423015604 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/datasets.R \docType{data} \name{stars_sentinel2} \alias{stars_sentinel2} \title{Sentinel-2 sample tile} \format{ An object of class \code{stars_proxy} (inherits from \code{stars}) of dimension 10980 x 10980 x 4. } \usage{ stars_sentinel2 } \description{ Sentinel-2 sample tile, downloaded from reads the four 10-m bands: B2 (490 nm), B3 (560 nm), B4 (665 nm) and B8 (842 nm) } \keyword{datasets} stars/man/st_sfc2xy.Rd0000644000176200001440000000114213777653456014421 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/rasterize.R \name{st_sfc2xy} \alias{st_sfc2xy} \title{replace POINT simple feature geometry list with an x y raster} \usage{ st_sfc2xy(x, ...) } \arguments{ \item{x}{object of class \code{stars}, or of class \code{sf}} \item{...}{passed on to \link{as.data.frame.stars}} } \value{ object of class \code{stars} with a POINT list replaced by x and y raster dimensions. This only works when the points are distributed over a regular or rectilinear grid. } \description{ replace POINT simple feature geometry list with an x y raster } stars/man/aggregate.stars.Rd0000644000176200001440000001016514644747141015550 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/aggregate.R \name{aggregate.stars} \alias{aggregate.stars} \alias{aggregate} \title{spatially or temporally aggregate stars object} \usage{ \method{aggregate}{stars}( x, by, FUN, ..., drop = FALSE, join = st_intersects, as_points = any(st_dimension(by) == 2, na.rm = TRUE), rightmost.closed = FALSE, left.open = FALSE, exact = FALSE ) } \arguments{ \item{x}{object of class \code{stars} with information to be aggregated} \item{by}{object of class \code{sf} or \code{sfc} for spatial aggregation, for temporal aggregation a vector with time values (\code{Date}, \code{POSIXct}, or \code{PCICt}) that is interpreted as a sequence of left-closed, right-open time intervals or a string like "months", "5 days" or the like (see \link{cut.POSIXt}), or a function that cuts time into intervals; if by is an object of class \code{stars}, it is converted to sfc by \code{st_as_sfc(by, as_points = FALSE)} thus ignoring its time component. Note: each pixel is assigned to only a single group (in the order the groups occur) so non-overlapping spatial features and temporal windows are recommended.} \item{FUN}{aggregation function, such as \code{mean}} \item{...}{arguments passed on to \code{FUN}, such as \code{na.rm=TRUE}} \item{drop}{logical; ignored} \item{join}{function; function used to find matches of \code{x} to \code{by}} \item{as_points}{see \link[stars]{st_as_sf}: shall raster pixels be taken as points, or small square polygons?} \item{rightmost.closed}{see \link{findInterval}} \item{left.open}{logical; used for time intervals, see \link{findInterval} and \link{cut.POSIXt}} \item{exact}{logical; if \code{TRUE}, use \link[exactextractr]{coverage_fraction} to compute exact overlap fractions of polygons with raster cells} } \description{ spatially or temporally aggregate stars object, returning a data cube with lower spatial or temporal resolution } \examples{ # aggregate time dimension in format Date tif = system.file("tif/L7_ETMs.tif", package = "stars") t1 = as.Date("2018-07-31") x = read_stars(c(tif, tif, tif, tif), along = list(time = c(t1, t1+1, t1+2, t1+3)))[,1:30,1:30] st_get_dimension_values(x, "time") x_agg_time = aggregate(x, by = t1 + c(0, 2, 4), FUN = max) # aggregate time dimension in format Date - interval by_t = "2 days" x_agg_time2 = aggregate(x, by = by_t, FUN = max) st_get_dimension_values(x_agg_time2, "time") #TBD: #x_agg_time - x_agg_time2 # aggregate time dimension in format POSIXct x = st_set_dimensions(x, 4, values = as.POSIXct(c("2018-07-31", "2018-08-01", "2018-08-02", "2018-08-03")), names = "time") by_t = as.POSIXct(c("2018-07-31", "2018-08-02")) x_agg_posix = aggregate(x, by = by_t, FUN = max) st_get_dimension_values(x_agg_posix, "time") #TBD: # x_agg_time - x_agg_posix aggregate(x, "2 days", mean) if (require(ncmeta, quietly = TRUE)) { # Spatial aggregation, see https://github.com/r-spatial/stars/issues/299 prec_file = system.file("nc/test_stageiv_xyt.nc", package = "stars") prec = read_ncdf(prec_file, curvilinear = c("lon", "lat")) prec_slice = dplyr::slice(prec, index = 17, along = "time") nc = sf::read_sf(system.file("gpkg/nc.gpkg", package = "sf"), "nc.gpkg") nc = st_transform(nc, st_crs(prec_slice)) agg = aggregate(prec_slice, st_geometry(nc), mean) plot(agg) } # example of using a function for "by": aggregate by month-of-year d = c(10, 10, 150) a = array(rnorm(prod(d)), d) # pure noise times = Sys.Date() + seq(1, 2000, length.out = d[3]) m = as.numeric(format(times, "\%m")) signal = rep(sin(m / 12 * pi), each = prod(d[1:2])) # yearly period s = (st_as_stars(a) + signal) \%>\% st_set_dimensions(3, values = times) f = function(x, format = "\%B") { months = format(as.Date(paste0("01-", 1:12, "-1970")), format) factor(format(x, format), levels = months) } agg = aggregate(s, f, mean) plot(agg) } \seealso{ \link[sf]{aggregate}, \link[sf]{st_interpolate_aw}, \link{st_extract}, https://github.com/r-spatial/stars/issues/317 } stars/man/st_crop.Rd0000644000176200001440000000710214644747141014135 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/proxy.R, R/subset.R \name{st_crop} \alias{st_crop} \alias{st_crop.stars_proxy} \alias{st_crop.stars} \title{crop a stars object} \usage{ \method{st_crop}{stars_proxy}( x, y, ..., crop = TRUE, epsilon = sqrt(.Machine$double.eps), collect = TRUE ) \method{st_crop}{stars}( x, y, ..., crop = TRUE, epsilon = sqrt(.Machine$double.eps), as_points = all(st_dimension(y) == 2, na.rm = TRUE), normalize = FALSE ) } \arguments{ \item{x}{object of class \code{stars}} \item{y}{object of class \code{sf}, \code{sfc} or \code{bbox}; see Details below.} \item{...}{ignored} \item{crop}{logical; if \code{TRUE}, the spatial extent of the returned object is cropped to still cover \code{obj}, if \code{FALSE}, the extent remains the same but cells outside \code{y} are given \code{NA} values.} \item{epsilon}{numeric; factor to shrink the bounding box of \code{y} towards its center before cropping.} \item{collect}{logical; if \code{TRUE}, repeat cropping on \code{stars} object, i.e. after data has been read} \item{as_points}{logical; only relevant if \code{y} is of class \code{sf} or \code{sfc}: if \code{FALSE}, treat \code{x} as a set of points, else as a set of small polygons. Default: \code{TRUE} if \code{y} is two-dimensional, else \code{FALSE}; see Details} \item{normalize}{logical; if \code{TRUE} then pass the cropped object to \code{\link[sf]{st_normalize}} before returning.} } \description{ crop a stars object } \details{ for raster \code{x}, \code{st_crop} selects cells that intersect with \code{y}. For intersection, are raster cells interpreted as points or as small polygons? If \code{y} is of class \code{stars}, \code{x} raster cells are interpreted as points; if \code{y} is of class \code{bbox}, \code{x} cells are interpreted as cells (small polygons). Otherwise, if \code{as_points} is not given, cells are interpreted as points if \code{y} has a two-dimensional geometry. } \examples{ l7 = read_stars(system.file("tif/L7_ETMs.tif", package = "stars")) d = st_dimensions(l7) # area around cells 3:10 (x) and 4:11 (y): offset = c(d[["x"]]$offset, d[["y"]]$offset) res = c(d[["x"]]$delta, d[["y"]]$delta) bb = st_bbox(c(xmin = offset[1] + 2 * res[1], ymin = offset[2] + 11 * res[2], xmax = offset[1] + 10 * res[1], ymax = offset[2] + 3 * res[2]), crs = st_crs(l7)) l7[bb] # equivalent: st_crop(l7, bb) plot(l7[,1:13,1:13,1], reset = FALSE) image(l7[bb,,,1], add = TRUE, col = sf.colors()) plot(st_as_sfc(bb), add = TRUE, border = 'green', lwd = 2) # slightly smaller bbox: bb = st_bbox(c(xmin = offset[1] + 2.1 * res[1], ymin = offset[2] + 10.9 * res[2], xmax = offset[1] + 9.9 * res[1], ymax = offset[2] + 3.1 * res[2]), crs = st_crs(l7)) l7[bb] plot(l7[,1:13,1:13,1], reset = FALSE) image(l7[bb,,,1], add = TRUE, col = sf.colors()) plot(st_as_sfc(bb), add = TRUE, border = 'green', lwd = 2) # slightly larger bbox: bb = st_bbox(c(xmin = offset[1] + 1.9 * res[1], ymin = offset[2] + 11.1 * res[2], xmax = offset[1] + 10.1 * res[1], ymax = offset[2] + 2.9 * res[2]), crs = st_crs(l7)) l7[bb] plot(l7[,1:13,1:13,1], reset = FALSE) image(l7[bb,,,1], add = TRUE, col = sf.colors()) plot(st_as_sfc(bb), add = TRUE, border = 'green', lwd = 2) # half a cell size larger bbox: bb = st_bbox(c(xmin = offset[1] + 1.49 * res[1], ymin = offset[2] + 11.51 * res[2], xmax = offset[1] + 10.51 * res[1], ymax = offset[2] + 2.49 * res[2]), crs = st_crs(l7)) l7[bb] plot(l7[,1:13,1:13,1], reset = FALSE) image(l7[bb,,,1], add = TRUE, col = sf.colors()) plot(st_as_sfc(bb), add = TRUE, border = 'green', lwd = 2) } stars/man/st_rgb.Rd0000644000176200001440000000351614467372755013761 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/plot.R \name{st_rgb} \alias{st_rgb} \title{reduce dimension to rgb (alpha) hex values} \usage{ st_rgb( x, dimension = 3, use_alpha = dim(x)[dimension] == 4, maxColorValue = 255L, probs = c(0, 1), stretch = NULL ) } \arguments{ \item{x}{object of class \code{stars}} \item{dimension}{dimension name or number to reduce} \item{use_alpha}{logical; if TRUE, the fourth band will be used as alpha values} \item{maxColorValue}{integer; maximum value for colors} \item{probs}{probability values for quantiles used for stretching by "percent".} \item{stretch}{logical or character; if \code{TRUE} or \code{"percent"}, each band is stretched to 0 ... maxColorValue by "percent clip" method using probs values. If \code{"histogram"}, a "histogram equalization" is performed (\code{probs} values are ignored). If stretch is \code{NULL} or \code{FALSE}, no stretching is performed. Other character values are interpreted as "percent" and a message will be printed.} } \description{ reduce dimension to rgb (alpha) hex values } \details{ the dimension's bands are mapped to red, green, blue, alpha; if a different ordering is wanted, use \link{[.stars} to reorder a dimension, see examples. 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