stars/0000755000175000017500000000000014157522342011545 5ustar nileshnileshstars/demo/0000755000175000017500000000000013777653456012513 5ustar nileshnileshstars/demo/00Index0000644000175000017500000000016213777653456013644 0ustar nileshnileshspacetime examples from the spacetime package, redone with stars udf_demo demo scripts for (possible) openEO udfs stars/demo/udf_demo.R0000644000175000017500000000406213777653456014422 0ustar nileshnileshlibrary(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/spacetime.R0000644000175000017500000000227713777653456014620 0ustar nileshnileshlibrary(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 = 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*vignettes/stars4.Rmd cc00d3438b845ce0ed7529a39b4dba49 *vignettes/stars5.Rmd 0af96f2cea2f6c25582404e661e40c65 *vignettes/stars6.Rmd 08e507bbd03192bc1e41cac8a80411dd *vignettes/stars7.Rmd stars/NEWS.md0000644000175000017500000003347314157127445012662 0ustar nileshnilesh# version 0.5-5 * `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 accross 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/mtennekes/tmap/issues/536 * add `st_bbox()`, `st_crs()` and `st_as_stars()` methods for terra's `SpatRaster` objects; https://github.com/mtennekes/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/mtennekes/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/mtennekes/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/mtennekes/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/mtennekes/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 favour 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/DESCRIPTION0000644000175000017500000000503314157522342013254 0ustar nileshnileshPackage: stars Title: Spatiotemporal Arrays, Raster and Vector Data Cubes Version: 0.5-5 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")) 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: http://gis-bigdata.uni-muenster.de/pebesma/ LazyData: true Depends: R (>= 3.3.0), abind, sf (>= 1.0.3) Imports: methods, parallel, classInt (>= 0.4-1), lwgeom, rlang, units Suggests: PCICt, RNetCDF (>= 1.8-2), clue, covr, cubelyr, digest, dplyr (>= 0.7-0), exactextractr, future.apply, ggforce, ggplot2, ggthemes, gstat, httr, jsonlite, knitr, maps, mapdata, ncdfgeom, ncmeta (>= 0.0.3), pbapply, plm, randomForest, raster, rgdal, rmarkdown, sp, spacetime, spatstat (>= 2.0-1), spatstat.geom, starsdata, terra (>= 1.4-22), testthat, tidyr, viridis, xts, zoo VignetteBuilder: knitr Encoding: UTF-8 RoxygenNote: 7.1.2 Collate: 'init.R' 'stars.R' 'read.R' 'sf.R' 'dimensions.R' 'values.R' 'plot.R' 'tidyverse.R' 'transform.R' 'ops.R' 'write.R' 'raster.R' 'sp.R' 'spacetime.R' 'ncdf.R' 'proxy.R' 'factors.R' 'rasterize.R' 'subset.R' 'warp.R' 'aggregate.R' 'xts.R' 'intervals.R' 'geom.R' 'mosaic.R' 'spatstat.R' 'OpenStreetMap.R' 'sample.R' 'extract.R' 'datasets.R' NeedsCompilation: no Packaged: 2021-12-18 11:04:04 UTC; edzer Author: Edzer Pebesma [aut, cre] (), Michael Sumner [ctb] (), Etienne Racine [ctb], Adriano Fantini [ctb], David Blodgett [ctb] Maintainer: Edzer Pebesma Repository: CRAN Date/Publication: 2021-12-19 03:20:02 UTC stars/man/0000755000175000017500000000000014157127445012325 5ustar nileshnileshstars/man/c.stars.Rd0000644000175000017500000000354714145400526014171 0ustar nileshnilesh% Generated by roxygen2: do not edit by hand % Please edit documentation in R/stars.R, R/proxy.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}( ..., along = NA_integer_, try_hard = FALSE, nms = names(list(...)), tolerance = sqrt(.Machine$double.eps) ) \method{c}{stars_proxy}( ..., along = NA_integer_, along_crs = FALSE, try_hard = FALSE, nms = names(list(...)), tolerance = sqrt(.Machine$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{try_hard}{logical; if \code{TRUE} and some arrays have different dimensions,} \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} \item{along_crs}{logical; if \code{TRUE}, combine arrays along a CRS dimension} } \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.Rd0000644000175000017500000000364114144045432013741 0ustar nileshnilesh% 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, ...) 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) library(dplyr) x1 \%>\% slice("band", 2:3) x1 \%>\% slice("x", 50:100) } stars/man/st_extract.Rd0000644000175000017500000000440314144045432014764 0ustar nileshnilesh% 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 points in rows, indicating where to extract 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 dimension "time" in \code{x}, after which this dimension is reduced. This is useful to extract data cube values along a trajectory; see https://github.com/r-spatial/stars/issues/352 .} \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/plot.Rd0000644000175000017500000001324614144045432013567 0ustar nileshnilesh% Generated by roxygen2: do not edit by hand % Please edit documentation in R/plot.R, R/proxy.R \name{plot} \alias{plot} \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}{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 = lcm(1.8), key.length = 0.618, reset = TRUE, box_col = grey(0.8), center_time = FALSE, hook = NULL, mfrow = NULL ) \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 = lcm(1.8), key.length = 0.618, add.geom = NULL, border = NA, useRaster = isTRUE(dev.capabilities("rasterImage")$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{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{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{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}{actual color breaks, or a method name used for \link[classInt]{classIntervals}.} \item{col}{colors to use for grid cells} \item{key.pos}{integer; 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}.} \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{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{0} 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.} \item{mfrow}{length-2 integer vector with nrows, ncolumns of a composite plot, to override the default layout} \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{ 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. 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{ tif = system.file("tif/L7_ETMs.tif", package = "stars") x = read_stars(tif) image(x, col = grey((3:9)/10)) image(x, rgb = c(1,3,5)) # rgb composite } stars/man/write_stars.Rd0000644000175000017500000000477714030675422015172 0ustar nileshnilesh% 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 ) \method{write_stars}{stars_proxy}( obj, dsn, layer = 1, ..., driver = detect.driver(dsn), options = character(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{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_apply.Rd0000644000175000017500000000734714145541611014452 0ustar nileshnilesh% 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/contour.stars.Rd0000644000175000017500000000145413777653456015461 0ustar nileshnilesh% 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_contour.Rd0000644000175000017500000000171313777653456015032 0ustar nileshnilesh% 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/coerce-methods.Rd0000644000175000017500000000201714144045432015504 0ustar nileshnilesh% 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/cut_stars.Rd0000644000175000017500000000220413777653456014636 0ustar nileshnilesh% 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/st_transform.Rd0000644000175000017500000000300413777653456015347 0ustar nileshnilesh% 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, ...) \method{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). } \examples{ 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, 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_sfc2xy.Rd0000644000175000017500000000114213777653456014553 0ustar nileshnilesh% 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/in-methods.Rd0000644000175000017500000000061314050270337014652 0ustar nileshnilesh% 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/geom_stars.Rd0000644000175000017500000000315314025220153014741 0ustar nileshnilesh% 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) 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}.} } \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. } \examples{ system.file("tif/L7_ETMs.tif", package = "stars") \%>\% read_stars() -> x library(ggplot2) 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)) } stars/man/reexports.Rd0000644000175000017500000000063513777653456014670 0ustar nileshnilesh% Generated by roxygen2: do not edit by hand % Please edit documentation in R/init.R \docType{import} \name{reexports} \alias{reexports} \alias{st_transform_proj} \title{Objects exported from other packages} \keyword{internal} \description{ These objects are imported from other packages. Follow the links below to see their documentation. \describe{ \item{lwgeom}{\code{\link[lwgeom]{st_transform_proj}}} }} stars/man/st_raster_type.Rd0000644000175000017500000000211214146737530015657 0ustar nileshnilesh% 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/st_downsample.Rd0000644000175000017500000000225314144045432015464 0ustar nileshnilesh% 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 object by skipping rows, columns and bands} \usage{ st_downsample(x, n, ...) \method{st_downsample}{stars}(x, n, ...) \method{st_downsample}{stars_proxy}(x, n, ...) } \arguments{ \item{x}{object of class stars or stars_proxy} \item{n}{numeric; the number of pixels/lines/bands etc that will be skipped; see Details.} \item{...}{ignored} } \description{ downsample stars or stars_proxy object by skipping rows, columns and bands } \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. } stars/man/st_rgb.Rd0000644000175000017500000000333714144045432014071 0ustar nileshnilesh% 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 } \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) } \seealso{ \link{st_apply}, \link[grDevices]{rgb} } stars/man/read_ncdf.Rd0000644000175000017500000000631113777653456014537 0ustar nileshnilesh% 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 = 1e-12, ignore_bounds = FALSE, make_time = TRUE, make_units = TRUE ) } \arguments{ \item{.x}{NetCDF file or source} \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 atttempt 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} } \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 dimemsion (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") 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))) #' 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))) } library(dplyr) 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_rasterize.Rd0000644000175000017500000000400114157127445015325 0ustar nileshnilesh% 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, ... ) } \arguments{ \item{sf}{object of class \code{sf}} \item{template}{stars object with desired target geometry, or target geometry alignment if \code{align=TRUE}} \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} contain the geometry alignment, informing target resolution and offset only.} \item{...}{arguments passed on to \link{st_as_stars}} } \description{ rasterize simple feature geometries } \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/st_warp.Rd0000644000175000017500000000713414144045432014267 0ustar nileshnilesh% Generated by roxygen2: do not edit by hand % Please edit documentation in R/warp.R \name{st_warp} \alias{st_warp} \title{Warp (resample) grids in stars objects to a new grid, possibly in an new coordinate reference system} \usage{ st_warp( 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" ) } \arguments{ \item{src}{object of class \code{stars} with source raster} \item{dest}{object of class \code{stars} with target raster geometry} \item{...}{ignored} \item{crs}{coordinate reference system for destination grid, only used when \code{dest} is missing} \item{cellsize}{length 1 or 2 numeric; cellsize in target coordinate reference system units} \item{segments}{(total) number of segments for segmentizing the bounding box before transforming to the new crs} \item{use_gdal}{logical; if \code{TRUE}, use gdalwarp, through \link[sf]{gdal_utils}} \item{options}{character vector with options, passed on to gdalwarp} \item{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} \item{debug}{logical; if \code{TRUE}, do not remove the temporary gdalwarp destination file, and print its name} \item{method}{character; see details for options; methods other than \code{near} only work when \code{use_gdal=TRUE}} } \description{ Warp (resample) grids in stars objects to a new grid, possibly in an new coordinate reference system } \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 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} (currently only with \code{method='ngb'}). 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. } \examples{ geomatrix = system.file("tif/geomatrix.tif", package = "stars") (x = read_stars(geomatrix)) new_crs = st_crs(4326) 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(4326) \%>\% 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_set_bbox.Rd0000644000175000017500000000062413777653456015146 0ustar nileshnilesh% 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/ops_stars.Rd0000644000175000017500000000243414046025022014615 0ustar nileshnilesh% 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/st_xy2sfc.Rd0000644000175000017500000000166013777653456014560 0ustar nileshnilesh% 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/aggregate.stars.Rd0000644000175000017500000000735114046033007015666 0ustar nileshnilesh% 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}); 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.} \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") 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") x_agg_time - x_agg_posix aggregate(x, "2 days", mean) # 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) } stars/man/st_as_stars.Rd0000644000175000017500000001256414157127445015151 0ustar nileshnilesh% Generated by roxygen2: do not edit by hand % Please edit documentation in R/stars.R, R/sf.R, R/raster.R, R/ncdf.R, % R/proxy.R, R/rasterize.R, R/xts.R, R/OpenStreetMap.R \name{st_as_stars} \alias{st_as_stars} \alias{st_as_stars.list} \alias{st_as_stars.default} \alias{st_as_stars.stars} \alias{st_as_stars.bbox} \alias{st_as_stars.sf} \alias{st_as_stars.Raster} \alias{st_as_stars.SpatRaster} \alias{st_as_stars.ncdfgeom} \alias{st_as_stars.stars_proxy} \alias{st_as_stars.data.frame} \alias{st_as_stars.xts} \alias{st_as_stars.OpenStreetMap} \title{convert objects into a stars object} \usage{ 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(4326)) \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 ) \method{st_as_stars}{sf}(.x, ..., name = attr(.x, "sf_column")) \method{st_as_stars}{Raster}(.x, ..., att = 1, ignore_file = FALSE) \method{st_as_stars}{SpatRaster}(.x, ..., ignore_file = FALSE) \method{st_as_stars}{ncdfgeom}(.x, ..., sf_geometry = NA) \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 = dims[1:2], y_decreasing = TRUE, coords = 1:2 ) \method{st_as_stars}{xts}(.x, ..., dimensions, name = "attr") \method{st_as_stars}{OpenStreetMap}(.x, ..., as_col = FALSE) } \arguments{ \item{.x}{object to convert} \item{...}{in case \code{.x} is of class \code{bbox}, arguments passed on to \link{pretty}} \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; the names of this list should correspond to raster dimensions referred to} \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; cell size in x direction; see details} \item{dy}{numeric; 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?} \item{nz}{integer; number of cells in z direction; if missing no z-dimension is created.} \item{name}{character; attribute name for array from an \code{xts} object} \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{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{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 dims has been specified} \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{as_col}{logical; return rgb numbers (FALSE) or (character) color values (TRUE)?} } \description{ convert objects into a stars object } \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. 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, they are computed as the ceiling of the ratio of the (x or y) range divided by (dx or dy), unless \code{inside} is \code{TRUE}, in which case ceiling is replaced by floor. Positive \code{dy} will be made negative. Further named arguments (\code{...}) are passed on to \code{pretty}. 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. for the \code{xts} methods, if \code{dimensions} are provided, time has to be the first dimension. } \examples{ data(Produc, package = "plm") st_as_stars(Produc, y_decreasing = FALSE) } stars/man/st_join.stars.Rd0000644000175000017500000000253713777653456015440 0ustar nileshnilesh% 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/st_mosaic.Rd0000644000175000017500000000351013777653456014611 0ustar nileshnilesh% 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} \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 imput images can be multi-band, but not higher-dimensional data cubes or stars objects with multiple attributes 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/st_crop.Rd0000644000175000017500000000666114151135652014267 0ustar nileshnilesh% 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) ) } \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} } \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/predict.stars.Rd0000644000175000017500000000166414144045432015377 0ustar nileshnilesh% Generated by roxygen2: do not edit by hand % Please edit documentation in R/stars.R, R/proxy.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}(object, model, ..., drop_dimensions = FALSE) \method{predict}{stars_proxy}(object, model, ...) } \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/L7_ETMs.Rd0000644000175000017500000000127714144045432013764 0ustar nileshnilesh% 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/man/read_stars.Rd0000644000175000017500000001312114144045432014730 0ustar nileshnilesh% Generated by roxygen2: do not edit by hand % Please edit documentation in R/read.R \name{read_stars} \alias{read_stars} \alias{is_big} \title{read raster/array dataset from file or connection} \usage{ read_stars( .x, ..., options = character(0), driver = character(0), sub = TRUE, quiet = FALSE, NA_value = NA_real_, along = NA_integer_, RasterIO = list(), proxy = is_functions(.x) || (!length(curvilinear) && is_big(.x, sub = sub, driver = driver, normalize_path = normalize_path, ...)), curvilinear = character(0), normalize_path = TRUE, RAT = character(0), tolerance = 1e-10 ) is_big(x, ..., sub = sub, n_proxy = options("stars.n_proxy")[[1]] \%||\% 1e+08) } \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{...}{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{sub}{character, integer or logical; name, index or indicator of sub-dataset(s) to be read} \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 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{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{x}{object to be read with \link{read_stars}} \item{n_proxy}{integer; number of cells above which .x will be read as stars proxy object, i.e. not as in-memory arrays but left on disk} } \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". } \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/make_intervals.Rd0000644000175000017500000000072013777653456015634 0ustar nileshnilesh% 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_intersects.stars.Rd0000644000175000017500000000231413777653456016655 0ustar nileshnilesh% 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/redimension.Rd0000644000175000017500000000161013777653456015143 0ustar nileshnilesh% Generated by roxygen2: do not edit by hand % Please edit documentation in R/stars.R, R/proxy.R \name{redimension} \alias{redimension} \alias{st_redimension} \alias{st_redimension.stars} \alias{st_redimension.stars_proxy} \title{redimension array, or collapse attributes into a new dimension} \usage{ st_redimension(x, new_dims, along, ...) \method{st_redimension}{stars}( x, new_dims = st_dimensions(x), along = list(new_dim = names(x)), ... ) \method{st_redimension}{stars_proxy}( x, new_dims = st_dimensions(x), along = list(new_dim = names(x)), ... ) } \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} } \description{ redimension array, or collapse attributes into a new dimension } stars/man/merge.Rd0000644000175000017500000000200413777653456013724 0ustar nileshnilesh% 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/stars_subset.Rd0000644000175000017500000000611314144045432015325 0ustar nileshnilesh% Generated by roxygen2: do not edit by hand % Please edit documentation in R/subset.R \name{stars_subset} \alias{stars_subset} \alias{[.stars} \alias{[<-.stars} \alias{st_flip} \title{subset stars objects} \usage{ \method{[}{stars}(x, i = TRUE, ..., drop = FALSE, crop = !is_curvilinear(x)) \method{[}{stars}(x, i, downsample = 0) <- 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} or \code{sfc} used as spatial selector; see details} \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{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{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}. in an assignment (or replacement form, \code{[<-}), argument \code{i} needs to be a \code{stars} object with dimensions identical to \code{x}, and \code{value} will be recycled to the dimensions of the arrays in \code{x}. } \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) 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/figures/0000755000175000017500000000000014144045432013760 5ustar nileshnileshstars/man/figures/README-plot2-1.png0000644000175000017500000003065614076314023016630 0ustar nileshnileshPNG  IHDRMR/PLTE  !!!"""###$$$%%%&&&'''((()))***+++,,,---...///000111222333444555666777888999:::;;;<<<===>>>???@@@AAABBBCCCDDDEEEFFFGGGHHHIIIJJJKKKLLLMMMNNNOOOPPPQQQRRRSSSTTTUUUVVVWWWXXXYYYZZZ[[[\\\]]]^^^___```aaabbbcccdddeeefffggghhhiiijjjkkklllmmmnnnooopppqqqrrrssstttuuuvvvwwwxxxyyyzzz{{{|||}}}~~~]} pHYsod IDATx|Ugf7l*i$!! 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Ia]: !CB< !CB< !CB< !CB< !CB< !CB< !CB< !CB< !CB< !CB< !CB< !CB< !CB< !CB< !CB<$v LˮpgϼDl\E9n҉$Gُpn82V>PT8g~8jBvbٮ}ll6~C~ˡe:}gz웧 xq˲ ۅL&#$lrٜYe浴?onnv!䥗^zg^|lr~馦lrN3466fs/>s/B AkB!l!PB!l!P>` 5VڪZkZ]x=R/,,v! _(_aG!G!6xB(aG!6xB(aG!6xB(aG:.ic555۷/}z_j|Βߒs=eee/-9[m,Ot:sծACOw޶D"xl6[wwLMMM, dƕ{=q\W<O(P( ~wZZZhv\**lv__<Μ|EkqСo|wqD"6m}7/I¾lxp8|/xzRP(\@ (J$I$P9>s`puT+]se6xI\wu~; o~s׮]ZNMM566l~ \.xƍEEEVu2,g~pw.]<Μ|EknС>!y;vK*eyg2,ֶ¯kVu֥-\-$;v'?ۯ#>w^&Lr`0.RF^xp.tM555555ׯ߿.WܙurkkСК~+_Yezz!LVVV>wu]W;44T__nf;qD._qKr{]:l:sծAOw}7f3'o|cӦMOdNcrښosq{omm-lv[ooo]]}---.w\Z}\,B!B!#By\% 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_dim_to_attr.Rd0000644000175000017500000000124513777653456015646 0ustar nileshnilesh% 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_coordinates.Rd0000644000175000017500000000176713777653456015664 0ustar nileshnilesh% 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) 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{.x}{object to be converted to a tibble} } \description{ retrieve coordinates for raster or vector cube cells } stars/vignettes/0000755000175000017500000000000014157340043013551 5ustar nileshnileshstars/vignettes/stars5.Rmd0000644000175000017500000001205214157337763015455 0ustar nileshnilesh--- title: "5. vector-raster conversions, reprojection, warping" author: "Edzer Pebesma" output: rmarkdown::html_vignette vignette: > %\VignetteIndexEntry{5. vector-raster and raster-vector conversion} %\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) knitr::opts_chunk$set(collapse = TRUE) knitr::opts_chunk$set(fig.height = 4.5) knitr::opts_chunk$set(fig.width = 6) suppressPackageStartupMessages(library(sf)) ev = sf_extSoftVersion()["GDAL"] >= "2.4.0" ``` This vignette shows how `stars` object can be moved from vector and raster representations. # Rasterizing an `sf` vector object ```{r} library(stars) system.file("gpkg/nc.gpkg", package = "sf") %>% read_sf() %>% st_transform(32119) -> nc nc$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/stars6.Rmd0000644000175000017500000002007514157337763015462 0ustar nileshnilesh--- title: "6. How `raster` functions map to `stars` functions" author: "Sébastien Rochette, Adriano Fantini, Edzer Pebesma" output: rmarkdown::html_vignette vignette: > %\VignetteIndexEntry{6-raster-stars-mapping} %\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, comment = "#>") knitr::opts_chunk$set(fig.height = 4.5) knitr::opts_chunk$set(fig.width = 6) suppressPackageStartupMessages(library(dplyr)) ``` 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", "[ ] <-", "", "cut", "cut", "", "subs", "", "", "reclassify", "mutate with case_when", "or forcats::fct_recode ?", "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_dimensions, look for delta", "may not be a constant in case of rectilinear or curvilinear grids", "yres", "st_dimensions, look for delta", "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", "", "", "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/stars1.Rmd0000644000175000017500000003017514157337763015457 0ustar nileshnilesh--- 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$set(echo = TRUE) knitr::opts_chunk$set(collapse = TRUE) knitr::opts_chunk$set(fig.height = 4.5) knitr::opts_chunk$set(fig.width = 6) ev = suppressWarnings(require(starsdata, quietly = TRUE)) ``` 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 offset 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 generalisation 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 eval=FALSE} 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=FALSE} st %>% as.tbl_cube() ``` The following demonstrates how `dplyr` can filter bike travel, and compute mean bike traffic by hour of day: ```{r eval=FALSE} 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)) ``` stars/vignettes/stars2.Rmd0000644000175000017500000003206714157337763015462 0ustar nileshnilesh--- 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) knitr::opts_chunk$set(collapse = TRUE) knitr::opts_chunk$set(fig.height = 4.5) knitr::opts_chunk$set(fig.width = 6) set.seed(13579) EVAL = x = suppressWarnings(require(starsdata, quietly = TRUE)) && sf::sf_extSoftVersion()["GDAL"] >= "2.2.0" ``` 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", 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]) 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$file.sep, "img6.tif") write_stars(s4, fn4) write_stars(s5, fn5) write_stars(s6, fn6) (r2 = read_stars(c(fn4, fn5, fn6), proxy = TRUE)) st_as_stars(r2) %>% merge() %>% plot(breaks = "equal", text_values = TRUE, text_color = 'orange', axes = TRUE) st_as_stars(r2[,2:4,2:4]) %>% merge() %>% plot(breaks = "equal", text_values = TRUE, text_color = 'orange', axes = TRUE) ``` Finally, an example where the first raster has the higher resolution: ```{r eval=TRUE} (r3 = read_stars(c(fn6, fn5, fn4), proxy = TRUE)) st_as_stars(r3) %>% merge() %>% plot(breaks = "equal", text_values = TRUE, text_color = 'orange', axes = TRUE) st_as_stars(r3[,2:6,3:6]) %>% merge() %>% plot(breaks = "equal", text_values = TRUE, text_color = 'orange', axes = TRUE) ``` stars/vignettes/stars3.Rmd0000644000175000017500000000534614157337763015463 0ustar nileshnilesh--- title: "3. stars tidyverse methods" author: "Edzer Pebesma" output: rmarkdown::html_vignette vignette: > %\VignetteIndexEntry{3. stars tidyverse methods} %\VignetteEngine{knitr::rmarkdown} %\VignetteEncoding{UTF-8} --- **For a better version of the stars vignettes see** https://r-spatial.github.io/stars/articles/ ```{r setup, include=FALSE} knitr::opts_chunk$set(echo = TRUE) knitr::opts_chunk$set(collapse = TRUE) knitr::opts_chunk$set(fig.height = 4.5) knitr::opts_chunk$set(fig.width = 6) #ev = FALSE ev = TRUE ``` 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/vignettes/stars7.Rmd0000644000175000017500000000055114157337763015460 0ustar nileshnilesh--- 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.Rmd0000644000175000017500000002556414157337763015470 0ustar nileshnilesh--- 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) knitr::opts_chunk$set(collapse = TRUE) knitr::opts_chunk$set(fig.height = 4.5) knitr::opts_chunk$set(fig.width = 6) set.seed(13579) ``` 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 * an attribute called `dimensions` with a `dimensions` object carrying 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`, * 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) Clearly, `offset` and `delta` only apply to _regularly_ discretized dimensions, and are `NA` if this is not the case. `from` and `to` will usually be 1 and the dimension size, but `from` may be larger than 1 in case a regular sub-grid got cut out or was cropped. Rectilinear and curvilinear grids need grid values in `values`; this can be irregularly _spaced_ coordinate values, or coordinate _intervals_ of irregular width, or spatial geometries encoded in an `sfc` vector ("list-column"), or a matrix with grid cell centre values (longitude or latitude) for curvilinear grids. ## 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 origing $(0,0)$ 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, one with the latitudes and one with the longitudes for every grid cell. Using the GDAL interface, we need to compose a `stars` object from the array and a long and lat matrix. The matrices with longitude and latitude values are stored in the `values` field of their respective dimensions. 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 `st_as_stars` on three arrays, with data, longitude and latitudes: ```{r eval=EVAL} 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")) { threshold = units::set_units(9e+36, mol/m^2) } else { threshold = 9e+36 } nit.c[[1]][nit.c[[1]] > threshold] = NA st_crs(nit.c) = 4326 nit.c ``` ```{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 = stars:::st_downsample(nit.c, 8)) 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') ``` which doesn't look nice, but plotting the cells as polygons looks better: ```{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') ``` stars/build/0000755000175000017500000000000014157340043012640 5ustar nileshnileshstars/build/vignette.rds0000644000175000017500000000056314157340043015203 0ustar nileshnileshMO YWf&KJ|(Qe\3%tiOg=z]]@ްڄGbx1ˁxEkʏnrKw[!v M"[LS(qcY8;a"~͖;stars/tests/0000755000175000017500000000000014145275656012721 5ustar nileshnileshstars/tests/sf.Rout.save0000644000175000017500000001453014144045432015126 0ustar nileshnilesh R version 4.1.1 (2021-08-10) -- "Kick Things" Copyright (C) 2021 The R Foundation for Statistical Computing Platform: x86_64-pc-linux-gnu (64-bit) 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 values x/y x 1 20 288776 28.5 SIRGAS 2000 / UTM zone 25S FALSE NULL [x] y 1 20 9120761 -28.5 SIRGAS 2000 / UTM zone 25S FALSE NULL [y] band 1 6 NA NA NA NA NULL > # 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 offset delta refsys point values geom 1 3 NA NA NA TRUE POINT (0 1), POINT (1 2), 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 offset delta refsys point values geom 1 3 NA NA NA TRUE POINT (0 1), POINT (1 2), POINT (2 3) new_dim 1 3 NA NA NA 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 offset delta refsys point values geom 1 3 NA NA NA TRUE POINT (0 1), POINT (1 2), 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 geom 1 3 NA NA NA TRUE time 1 3 2017-11-27 1 days Date NA values geom POINT (0 1), POINT (1 2), POINT (2 3) time 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 offset delta refsys point values geom 1 6 NA NA NA 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 refsys point values x/y x 1 10 0 0.2 NA NA NULL [x] y 1 10 3 -0.2 NA NA NULL [y] > > proc.time() user system elapsed 1.232 0.036 1.261 stars/tests/dimensions.Rout.save0000644000175000017500000000465614032622214016671 0ustar nileshnilesh R version 4.0.5 (2021-03-31) -- "Shake and Throw" Copyright (C) 2021 The R Foundation for Statistical Computing Platform: x86_64-pc-linux-gnu (64-bit) 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 point values x/y time 1 12 NA NA POSIXct NA 1999-01-31,...,1999-12-31 x 1 81 -85 0.125 NA NA NULL [x] y 1 33 37.125 -0.125 NA 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 point values x/y time 1 12 NA NA POSIXct NA 1999-01-31,...,1999-12-31 x 1 81 -85 0.125 NA NA NULL [x] y 1 33 37.125 -0.125 NA NA NULL [y] > > proc.time() user system elapsed 1.607 0.031 1.631 stars/tests/nc.Rout.save0000644000175000017500000002702514144045432015121 0ustar nileshnilesh R version 4.1.0 (2021-05-18) -- "Camp Pontanezen" Copyright (C) 2021 The R Foundation for Statistical Computing Platform: x86_64-pc-linux-gnu (64-bit) 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 offset delta refsys point values county 1 100 NA NA NA NA Ashe,...,Brunswick var 1 3 NA NA NA NA BIR , SID , NWBIR year 1 2 NA NA NA NA 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 offset delta refsys point geometries 1 100 NA NA NAD27 FALSE var 1 3 NA NA NA NA year 1 2 NA NA NA NA values geometries MULTIPOLYGON (((-81.47276 3...,...,MULTIPOLYGON (((-78.65572 3... 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 offset delta refsys point county 1 100 NA NA NAD27 FALSE var 1 3 NA NA NA NA year 1 2 NA NA NA NA values county MULTIPOLYGON (((-81.47276 3...,...,MULTIPOLYGON (((-78.65572 3... 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: pop.BIR.1974 pop.SID.1974 pop.NWBIR.1974 pop.BIR.1979 pop.SID.1979 Ashe 1091 1 10 1364 0 Alleghany 487 0 10 542 3 Surry 3188 5 208 3616 6 Currituck 508 1 123 830 2 Northampton 1421 9 1066 1606 3 Hertford 1452 7 954 1838 5 Camden 286 0 115 350 2 Gates 420 0 254 594 2 Warren 968 4 748 1190 2 Stokes 1612 1 160 2038 5 pop.NWBIR.1979 county Ashe 19 MULTIPOLYGON (((-81.47276 3... Alleghany 12 MULTIPOLYGON (((-81.23989 3... Surry 260 MULTIPOLYGON (((-80.45634 3... Currituck 145 MULTIPOLYGON (((-76.00897 3... Northampton 1197 MULTIPOLYGON (((-77.21767 3... Hertford 1237 MULTIPOLYGON (((-76.74506 3... Camden 139 MULTIPOLYGON (((-76.00897 3... Gates 371 MULTIPOLYGON (((-76.56251 3... Warren 844 MULTIPOLYGON (((-78.30876 3... Stokes 176 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 offset delta refsys point county 1 100 NA NA NAD27 FALSE year 1 2 NA NA NA NA values county MULTIPOLYGON (((-81.47276 3...,...,MULTIPOLYGON (((-78.65572 3... 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 offset delta refsys point frac2 1 2 NA NA NA NA county 1 100 NA NA NAD27 FALSE year 1 2 NA NA NA NA values frac2 sidsr.SID , nwbr.NWBIR county MULTIPOLYGON (((-81.47276 3...,...,MULTIPOLYGON (((-78.65572 3... 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 offset delta refsys point frac2an 1 2 NA NA NA NA county 1 100 NA NA NAD27 FALSE year 1 2 NA NA NA NA values frac2an SID , NWBIR county MULTIPOLYGON (((-81.47276 3...,...,MULTIPOLYGON (((-78.65572 3... 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 offset delta refsys point county 1 100 NA NA NAD27 FALSE year 1 2 NA NA NA NA frac2 1 2 NA NA NA NA values county MULTIPOLYGON (((-81.47276 3...,...,MULTIPOLYGON (((-78.65572 3... year 1974, 1979 frac2 sidsr.SID , nwbr.NWBIR > 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 offset delta refsys point county 1 100 NA NA NAD27 FALSE year 1 2 NA NA NA NA values county MULTIPOLYGON (((-81.47276 3...,...,MULTIPOLYGON (((-78.65572 3... 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 offset delta refsys point county 1 100 NA NA NAD27 FALSE var 1 3 NA NA NA NA values county MULTIPOLYGON (((-81.47276 3...,...,MULTIPOLYGON (((-78.65572 3... 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 offset delta refsys point county 1 100 NA NA NAD27 FALSE var 1 3 NA NA NA NA year 1 2 NA NA NA NA values county MULTIPOLYGON (((-81.47276 3...,...,MULTIPOLYGON (((-78.65572 3... var BIR , SID , NWBIR year 1974, 1979 > > proc.time() user system elapsed 1.042 0.060 1.092 stars/tests/transform.Rout.save0000644000175000017500000000712414032622155016531 0ustar nileshnilesh R version 4.0.5 (2021-03-31) -- "Shake and Throw" Copyright (C) 2021 The R Foundation for Statistical Computing Platform: x86_64-pc-linux-gnu (64-bit) 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 values x/y x 1 20 1841002 1.5 WGS 84 / UTM zone 11N TRUE NULL [x] y 1 20 1144003 -1.5 WGS 84 / UTM zone 11N TRUE NULL [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)) > x__ = st_transform_proj(x, st_crs(4326)$proj4string) > st_crs(x_) = st_crs(x__) > all.equal(x_,x__) [1] TRUE > > # 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))) stars object with 2 dimensions and 1 attribute attribute(s): Min. 1st Qu. Median Mean 3rd Qu. Max. L7_ETMs.tif 25 43 51 50.72 60 74 dimension(s): from to offset delta refsys point geometry 1 100 NA NA WGS 84 FALSE band 1 3 NA NA NA NA values geometry POLYGON ((-34.91617 -7.9498...,...,POLYGON ((-34.91385 -7.9521... band NULL > (x__ = st_transform_proj(x, st_crs(4326))) stars object with 2 dimensions and 1 attribute attribute(s): Min. 1st Qu. Median Mean 3rd Qu. Max. L7_ETMs.tif 25 43 51 50.72 60 74 dimension(s): from to offset delta refsys point geometry 1 100 NA NA WGS 84 FALSE band 1 3 NA NA NA NA values geometry POLYGON ((-34.91617 -7.9498...,...,POLYGON ((-34.91385 -7.9521... band NULL > all.equal(x_,x__,check.attributes = FALSE) [1] TRUE > > # nothing to transform: now errs: > try(st_transform(st_as_stars(list(matrix(1,10,10))), st_crs(4326))) Error in st_transform.stars(st_as_stars(list(matrix(1, 10, 10))), st_crs(4326)) : !is.na(st_crs(x)) is not TRUE > try(st_transform_proj(st_as_stars(list(matrix(1,10,10))), st_crs(4326))) Error in st_transform_proj.stars(st_as_stars(list(matrix(1, 10, 10))), : !is.na(st_crs(x)) is not TRUE > > proc.time() user system elapsed 1.014 0.041 1.051 stars/tests/datasets.R0000644000175000017500000000016414144045432014637 0ustar nileshnileshsuppressPackageStartupMessages(library(stars)) bcsd_obs L7_ETMs if (require(starsdata)) { print(stars_sentinel2) } stars/tests/curvilinear.Rout.save0000644000175000017500000001025314144045432017037 0ustar nileshnilesh R version 4.1.0 (2021-05-18) -- "Camp Pontanezen" Copyright (C) 2021 The R Foundation for Statistical Computing Platform: x86_64-pc-linux-gnu (64-bit) 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 (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() + } + + } /PRODUCT/longitude, /PRODUCT/latitude, /PRODUCT/SUPPORT_DATA/DETAILED_RESULTS/nitrogendioxide_summed_total_column, stars object with 3 dimensions and 1 attribute attribute(s): Min. 1st Qu. Median nitrogendioxide_summed_total_c... 3.650813e-05 7.451281e-05 8.296968e-05 Mean 3rd Qu. Max. NA's nitrogendioxide_summed_total_c... 8.581399e-05 9.397442e-05 0.0004535302 330 dimension(s): from to offset delta refsys point values x 1 450 NA NA WGS 84 NA [450x278] -5.81066 [°],...,30.9468 [°] y 1 278 NA NA WGS 84 NA [450x278] 28.3605 [°],...,51.4686 [°] time 1 1 NA NA NA NA NULL x/y x [x] y [y] time curvilinear grid stars object with 3 dimensions and 1 attribute attribute(s): Min. 1st Qu. Median nitrogendioxide_summed_total_c... 4.383528e-05 7.480038e-05 8.344652e-05 Mean 3rd Qu. Max. NA's nitrogendioxide_summed_total_c... 8.607374e-05 9.398567e-05 0.000192237 32 dimension(s): from to offset delta refsys point values x/y x 1 50 NA NA WGS 84 NA [50x31] -5.81066 [°],...,30.1405 [°] [x] y 1 31 NA NA WGS 84 NA [50x31] 28.7828 [°],...,51.4686 [°] [y] time 1 1 NA NA NA NA NULL curvilinear grid null device 1 There were 15 warnings (use warnings() to see them) > > proc.time() user system elapsed 7.037 0.135 7.166 stars/tests/plot.R0000644000175000017500000000142013777653456014027 0ustar nileshnileshlibrary(spacetime) suppressPackageStartupMessages(library(stars)) 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/stars.Rout.save0000644000175000017500000005413714144045432015661 0ustar nileshnilesh R version 4.1.1 (2021-08-10) -- "Kick Things" Copyright (C) 2021 The R Foundation for Statistical Computing Platform: x86_64-pc-linux-gnu (64-bit) 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 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 1 6 NA NA NA NA NULL > (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 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 1 6 NA NA NA NA NULL > 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 values x/y a 1 349 288776 28.5 SIRGAS 2000 / UTM zone 25S FALSE NULL [x] b 1 352 9120761 -28.5 SIRGAS 2000 / UTM zone 25S FALSE NULL [y] c 1 6 NA NA NA NA NULL > 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 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 1 6 NA NA NA NA NULL > 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 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 1 6 NA NA NA NA NULL > 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 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 1 3 NA NA NA NA NULL > 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 values x/y x 1 100 288776 28.5 SIRGAS 2000 / UTM zone 25S FALSE NULL [x] y 100 200 9120761 -28.5 SIRGAS 2000 / UTM zone 25S FALSE NULL [y] band 1 6 NA NA NA NA NULL > 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 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 1 6 NA NA NA NA NULL > 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 offset delta refsys point values band 1 6 NA NA NA NA NULL > 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 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] > 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 values x/y range 1 2 NA NA NA NA NULL 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] > > geomatrix = system.file("tif/geomatrix.tif", package = "stars") > x = read_stars(geomatrix) > y = st_transform(x, st_crs(4326)) > st_coordinates(x)[1:10,] x y 1 1841000 1144000 2 1841002 1143995 3 1841003 1143990 4 1841004 1143985 5 1841006 1143980 6 1841008 1143975 7 1841009 1143970 8 1841010 1143965 9 1841012 1143960 10 1841014 1143955 > > nc = system.file("nc/tos_O1_2001-2002.nc", package = "stars") > (x = read_stars(nc)) stars object with 3 dimensions and 1 attribute attribute(s): Min. 1st Qu. Median Mean 3rd Qu. Max. tos_O1_2001-2002.nc [K] 271.1709 275.0928 285.4899 286.6974 299.0914 305.5038 NA's tos_O1_2001-2002.nc [K] 228240 dimension(s): from to offset delta refsys point values x/y x 1 180 0 2 NA NA NULL [x] y 1 170 90 -1 NA NA NULL [y] time 1 24 2001-01-16 30 days PCICt NA NULL > st_bbox(x) xmin ymin xmax ymax 0 -80 360 90 > s = st_as_stars(st_bbox(x)) > st_bbox(s) xmin ymin xmax ymax 0.00000 -80.06943 360.54719 90.00000 > s = st_as_stars(st_bbox(x), inside = TRUE) > st_bbox(s) xmin ymin xmax ymax 0.0000 -79.0976 359.5754 90.0000 > (s = st_as_stars(st_bbox(x), dy = 1)) 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 point values x/y x 1 360 0 1 NA NA NULL [x] y 1 170 90 -1 NA NA NULL [y] > st_bbox(s) xmin ymin xmax ymax 0 -80 360 90 > identical(st_as_stars(st_bbox(x), dx = 1), st_as_stars(st_bbox(x), dy = 1)) [1] TRUE > s = st_as_stars(st_bbox(x), dx = 10) > st_bbox(s) xmin ymin xmax ymax 0 -80 360 90 > s = st_as_stars(st_bbox(x), dx = 20) > st_bbox(s) xmin ymin xmax ymax 0 -90 360 90 > > df = as.data.frame(x) > units::drop_units(x) stars object with 3 dimensions and 1 attribute attribute(s): Min. 1st Qu. Median Mean 3rd Qu. Max. tos_O1_2001-2002.nc 271.1709 275.0928 285.4899 286.6974 299.0914 305.5038 NA's tos_O1_2001-2002.nc 228240 dimension(s): from to offset delta refsys point values x/y x 1 180 0 2 NA NA NULL [x] y 1 170 90 -1 NA NA NULL [y] time 1 24 2001-01-16 30 days PCICt NA NULL > > dimnames(x) [1] "x" "y" "time" > dimnames(x) <- letters[1:3] > dimnames(x) [1] "a" "b" "c" > 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 point values x/y x 1 360 -180 1 WGS 84 NA NULL [x] y 1 180 90 -1 WGS 84 NA NULL [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 point values x/y x 1 180 -1 2 NA NA NULL [x] y 1 90 90 -2 NA NA NULL [y] zlev 1 1 0 [m] NA NA NA NULL time 1 1 1981-12-31 UTC NA POSIXct NA NULL > 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. X 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 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 1 6 NA NA NA NA NULL > 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 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 1 6 NA NA NA NA NULL sensor 1 2 NA NA NA NA NULL > 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 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 1 6 NA NA NA NA NULL new_dim 1 2 NA NA NA NA NULL > 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 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 1 12 NA NA NA NA NULL > 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 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 1 12 NA NA NA NA NULL > > # 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 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 1 6 NA NA NA NA NULL > 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 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] > 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 (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) + } sst, anom, sst, anom, sst, anom, sst, anom, sst, anom, sst, anom, sst, anom, sst, anom, sst, anom, stars object with 4 dimensions and 2 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 dimension(s): from to offset delta refsys point values x/y x 1 1440 0 0.25 NA NA NULL [x] y 1 720 90 -0.25 NA NA NULL [y] zlev 1 1 0 [m] NA NA NA NULL time 1 9 1981-09-01 UTC 1 days POSIXct NA NULL stars object with 3 dimensions and 2 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 dimension(s): from to offset delta refsys point values x/y x 1 1440 0 0.25 NA NA NULL [x] y 1 720 90 -0.25 NA NA NULL [y] times 1 3 1981-09-01 1 days Date NA NULL > > st_dimensions(list(matrix(1, 4, 4))) # st_dimensions.default from to offset delta refsys point X1 1 1 NA NA NA FALSE values X1 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1 > > if (FALSE && require("starsdata")) { + # 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 6.106 0.329 6.427 stars/tests/subset.Rout.save0000644000175000017500000001017414144045432016023 0ustar nileshnilesh R version 4.1.1 (2021-08-10) -- "Kick Things" Copyright (C) 2021 The R Foundation for Statistical Computing Platform: x86_64-pc-linux-gnu (64-bit) 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 1.043 0.049 1.084 stars/tests/rectilinear.Rout.save0000644000175000017500000000562114144045432017020 0ustar nileshnilesh R version 4.1.1 (2021-08-10) -- "Kick Things" Copyright (C) 2021 The R Foundation for Statistical Computing Platform: x86_64-pc-linux-gnu (64-bit) 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 offset delta refsys point x 1 348 NA NA SIRGAS 2000 / UTM zone 25S FALSE y 1 351 NA NA SIRGAS 2000 / UTM zone 25S FALSE band 1 6 NA NA 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 refsys point values x/y x 1 4 1 1 NA FALSE NULL [x] y 1 4 2 1 NA FALSE NULL [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 offset delta refsys point values x/y x 1 4 NA NA NA FALSE [1,2),...,[4,6) [x] y 1 4 NA NA NA 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 1.098 0.064 1.163 stars/tests/rectilinear.R0000644000175000017500000000142613777653456015360 0ustar nileshnileshsuppressPackageStartupMessages(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/testthat/0000755000175000017500000000000014157522342014547 5ustar nileshnileshstars/tests/testthat/test_gdal.R0000644000175000017500000000240413777653456016662 0ustar nileshnileshlibrary(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") 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: points = system.file("gpkg/nc.gpkg", package="sf") expect_true(gdal_utils("grid", points, tf)) expect_true(gdal_utils("buildvrt", sd2, tf3)) # expect_true(gdal_utils("demprocessing", sd2, tf, processing = "hillshade")) # breaks on fedora-clang }) # 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_ncdf.R0000644000175000017500000001532513777653456016673 0ustar nileshnileshcontext("read ncdf") f <- system.file("nc/reduced.nc", package = "stars") test_that("basic reduced comes back as expected", { 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$values), 1) }) test_that("variable subsetting", { nc <- read_ncdf(f, var = c("anom")) expect_equal(names(nc), "anom") }) test_that("domain subsetting", { nc <- read_ncdf(f, ncsub = cbind(start = c(1, 1, 1, 1), count = c(10, 12, 1, 1))) st_dim <- st_dimensions(nc) expect_equal(st_dim$lon$to - st_dim$lon$from, 9) expect_equal(st_dim$lat$to - st_dim$lat$from, 11) expect_error(nc <- read_ncdf(f, ncsub = cbind(start = c(1, 1, 1, 1), count = c(200, 12, 1, 1))), "start or count out of bounds") expect_error(nc <- read_ncdf(f, ncsub = cbind(start = c(1, 1, 1), count = c(200, 12, 1))), "input ncsub doesn't match available dims") # Leaving this here -- NA or -1 counts should return all but this causes other errors. # nc <- read_ncdf(f, ncsub = cbind(start = c(1, 1, 1, 1), # count = c(NA, NA, 1, 1))) # st_dim <- st_dimensions(nc) # expect_equal(st_dim$lon$to - st_dim$lon$from, c("lon" = 179)) # expect_equal(st_dim$lat$to - st_dim$lat$from, c("lat" = 89)) }) test_that("normal bcsd", { nc <- read_ncdf(system.file("nc/bcsd_obs_1999.nc", package = "stars")) expect_equal(names(nc), c("pr", "tas")) st_dim <- st_dimensions(nc) expect_equal(names(st_dim), c("longitude", "latitude", "time")) expect_equal(st_dim$longitude$delta, 0.125) }) test_that("non canonical axis order is handled right", { expect_warning(nc <- read_ncdf(system.file("nc/3B42_Daily.19991231.7.test.nc", package = "stars")), "Non-canonical axis order found, attempting to correct.") expect_equal(names(st_dimensions(nc)), c("lon", "lat")) expect_equal(st_dimensions(nc)[[1]]$to, 4L) expect_equal(st_dimensions(nc)[[2]]$to, 5L) }) test_that("euro cordex extra dimvars", { f <- unzip(zipfile = system.file("nc/EURO-CORDEX_81_DOMAIN000_mask_int.nc.zip", package = "stars"), exdir = tempdir()) expect_warning(out <- read_ncdf(f, var = "mask"), "Didn't find a longitude of prime meridian for datum, assuming 0.") expect_equal(names(out), c("mask")) #expect_true(sf::st_crs(out) == #sf::st_crs("+proj=lcc +lat_1=30 +lat_2=65 +lat_0=48 +lon_0=9.75 +x_0=-6000 +y_0=-6000 +a=6371229 +b=6371229 +units=m +no_defs")) expect_error(out <- suppressWarnings(read_ncdf(f, var = "mask", curvilinear = c("X", "Y"))), "Curvilinear variables not found in file.") out1 <- suppressWarnings(read_ncdf(f, var = "mask", curvilinear = c("xlat", "xlon"))) out2 <- suppressWarnings(read_ncdf(f, var = "mask", curvilinear = c("xlon", "xlat"))) expect_true(attr(st_dimensions(out1), "raster")$curvilinear) expect_true(attr(st_dimensions(out2), "raster")$curvilinear) expect_equal(st_dimensions(out1), st_dimensions(out2)) }) test_that("curvilinear", { f <- system.file("nc/test_stageiv_xyt.nc", package = "stars") warn <- capture_warnings(out <-read_ncdf(f, curvilinear = c(X = "lon", Y = "lat"))) expect_match(warn[1], "bounds for time seem to be reversed; reverting them") st_dim <- st_dimensions(out) expect_true(all(st_dim$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$values), setNames(c(87, 118), c("x", "y"))) # Should also find the curvilinear grid. suppressWarnings(out <- read_ncdf(f, var = "Total_precipitation_surface_1_Hour_Accumulation")) expect_true(attr(st_dimensions(out), "raster")$curvilinear) }) test_that("curvilinear broked", { 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_warning(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"))) }) test_that("high-dim from rasterwise", { 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_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", { 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", { f <- system.file("nc/test_adaptor.cams_regional_fc.nc", package = "stars") 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", { 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) }) stars/tests/testthat/test_rectilinear.R0000644000175000017500000000613113777653456020255 0ustar nileshnileshcontext("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", { 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_st_as_stars.R0000644000175000017500000000413713777653456020305 0ustar nileshnileshcontext("st_as_stars tests") options(rgdal_show_exportToProj4_warnings = "none") test_that("basic st_as_stars", { skip_on_os("solaris") 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", { 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/spacetime.R0000644000175000017500000000106413777653456015027 0ustar nileshnileshsuppressPackageStartupMessages(library(stars)) library(spacetime) 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/testthat.Rout.save0000644000175000017500000000151013777653456016376 0ustar nileshnilesh R version 4.0.3 (2020-10-10) -- "Bunny-Wunnies Freak Out" Copyright (C) 2020 The R Foundation for Statistical Computing Platform: x86_64-pc-linux-gnu (64-bit) 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. > ## load dependencies > library(testthat) > suppressPackageStartupMessages(library(stars)) > > ## test package > test_check("stars") > > proc.time() user system elapsed 7.462 0.137 7.531 stars/tests/extract.R0000644000175000017500000000641614144045432014507 0ustar nileshnilesh# 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$s2 = s2[[1]] names(s1)[c(1,3)] = c("nearest", "bilinear") print(s1[sample(10000, 5),]) } } stars/tests/extract.Rout.save0000644000175000017500000001100214144045432016157 0ustar nileshnilesh R version 4.1.1 (2021-08-10) -- "Kick Things" Copyright (C) 2021 The R Foundation for Statistical Computing Platform: x86_64-pc-linux-gnu (64-bit) R is free software and comes with ABSOLUTELY NO WARRANTY. You are welcome to redistribute it under certain conditions. Type 'license()' or 'licence()' for distribution details. R is a collaborative project with many contributors. Type 'contributors()' for more information and 'citation()' on how to cite R or R packages in publications. Type 'demo()' for some demos, 'help()' for on-line help, or 'help.start()' for an HTML browser interface to help. Type 'q()' to quit R. > # Create 'stars' object > set.seed(1331) > suppressPackageStartupMessages(library(stars)) > volcano = rbind(volcano, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA) # add NA rows > d = st_dimensions(x = 1:ncol(volcano), y = 1:nrow(volcano)) > (r = st_as_stars(t(volcano))) stars object with 2 dimensions and 1 attribute attribute(s): Min. 1st Qu. Median Mean 3rd Qu. Max. NA's A1 94 108 124 130.1879 150 195 732 dimension(s): from to offset delta refsys point values x/y X1 1 61 0 1 NA FALSE NULL [x] X2 1 99 0 1 NA FALSE NULL [y] > r = st_set_dimensions(r, 1, offset = 0, delta = 1) > r = st_set_dimensions(r, 2, offset = nrow(volcano), delta = -1) > > # Create points > pnt = st_sample(st_as_sfc(st_bbox(r)), 100) > pnt = st_as_sf(pnt) > > # Extract - 'st_join' > x = st_join(pnt, st_as_sf(r)) > > # Extract - 'st_extract' > y = st_extract(r, pnt) > > # check there are NA's: > any(is.na(x)) [1] TRUE > # Compare > all.equal(x$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 0.718 0.044 0.753 stars/tests/sf.R0000644000175000017500000000257613777653456013476 0ustar nileshnileshsuppressPackageStartupMessages(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/predict.Rout.save0000644000175000017500000000637714144045432016162 0ustar nileshnilesh R version 4.1.1 (2021-08-10) -- "Kick Things" Copyright (C) 2021 The R Foundation for Statistical Computing Platform: x86_64-pc-linux-gnu (64-bit) 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 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 1 6 NA NA NA NA NULL > 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 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 1 6 NA NA NA NA NULL > (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 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 1 6 NA NA NA NA NULL call_list: [[1]] predict(object = object, model = model) attr(,".Environment") > 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") > 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 2.548 0.104 2.643 stars/tests/proxy.Rout.save0000644000175000017500000003601314144045432015677 0ustar nileshnilesh R version 4.1.1 (2021-08-10) -- "Kick Things" Copyright (C) 2021 The R Foundation for Statistical Computing Platform: x86_64-pc-linux-gnu (64-bit) 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 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 1 6 NA NA NA NA NULL > 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 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 1 6 NA NA NA NA NULL call_list: [[1]] x[i = i, , , 2:3, drop = drop, crop = crop] attr(,".Environment") > 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 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 1 6 NA NA NA NA NULL call_list: [[1]] e1 + e2 attr(,".Environment") > 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 values x/y x 1 117 288776 85.0128 SIRGAS 2000 / UTM zone 25S FALSE NULL [x] y 1 118 9120761 -85.0169 SIRGAS 2000 / UTM zone 25S FALSE NULL [y] band 1 6 NA NA NA NA NULL > 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 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 1 6 NA NA NA NA NULL call_list: [[1]] aperm(a = a, perm = perm) attr(,".Environment") > 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 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 1 6 NA NA NA NA NULL > 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 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 1 6 NA NA NA NA NULL > (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 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 1 6 NA NA NA NA NULL call_list: [[1]] st_apply(X = X, MARGIN = MARGIN, FUN = FUN, CLUSTER = CLUSTER, PROGRESS = PROGRESS, FUTURE = FUTURE, rename = rename, .fname = .fname) attr(,".Environment") > (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 values x/y x 1 175 288776 56.8371 SIRGAS 2000 / UTM zone 25S FALSE NULL [x] y 1 176 9120761 -57 SIRGAS 2000 / UTM zone 25S FALSE NULL [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 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 1 6 NA NA NA NA NULL 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") > (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 values x/y x 1 175 288776 56.8371 SIRGAS 2000 / UTM zone 25S FALSE NULL [x] y 1 176 9120761 -57 SIRGAS 2000 / UTM zone 25S FALSE NULL [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 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 1 6 NA NA NA NA NULL 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") > (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 values x 1 175 288776 56.8371 SIRGAS 2000 / UTM zone 25S FALSE NULL band 1 6 NA NA NA NA NULL 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 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 1 6 NA NA NA NA NULL > 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 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 1 6 NA NA NA NA NULL > 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 (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) + } sst, anom, sst, anom, sst, anom, sst, anom, sst, anom, sst, anom, sst, anom, sst, anom, sst, anom, stars_proxy object with 2 attributes in 18 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" $anom [1] "[...]/avhrr-only-v2.19810901.nc:anom" [2] "[...]/avhrr-only-v2.19810902.nc:anom" [3] "[...]/avhrr-only-v2.19810903.nc:anom" [4] "[...]/avhrr-only-v2.19810904.nc:anom" [5] "[...]/avhrr-only-v2.19810905.nc:anom" [6] "[...]/avhrr-only-v2.19810906.nc:anom" [7] "[...]/avhrr-only-v2.19810907.nc:anom" [8] "[...]/avhrr-only-v2.19810908.nc:anom" [9] "[...]/avhrr-only-v2.19810909.nc:anom" dimension(s): from to offset delta refsys point values x/y x 1 1440 0 0.25 NA NA NULL [x] y 1 720 90 -0.25 NA NA NULL [y] zlev 1 1 0 [m] NA NA NA NULL time 1 9 1981-09-01 UTC 1 days POSIXct NA NULL sst, anom, sst, anom, sst, anom, sst, anom, sst, anom, sst, anom, sst, anom, sst, anom, sst, anom, stars object with 4 dimensions and 2 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 dimension(s): from to offset delta refsys point values x/y x 1 1440 0 0.25 NA NA NULL [x] y 1 720 90 -0.25 NA NA NULL [y] zlev 1 1 0 [m] NA NA NA NULL time 1 9 1981-09-01 UTC 1 days POSIXct NA NULL stars object with 5 dimensions and 1 attribute attribute(s), summary of first 1e+05 cells: Min. 1st Qu. Median Mean 3rd Qu. Max. NA's sst.anom -1.8 -1.19 -1.05 -0.320167 -0.2 9.36 13360 dimension(s): from to offset delta refsys point values x/y x 1 1440 0 0.25 NA NA NULL [x] y 1 720 90 -0.25 NA NA NULL [y] zlev 1 1 0 [m] NA NA NA NULL time 1 9 1981-09-01 UTC 1 days POSIXct NA NULL new_dim 1 2 NA NA NA NA sst , anom Error in c.stars_proxy(l[[1]], l[[2]], l[[3]], along = list(times = as.Date("1981-09-01") + : for proxy objects, along argument as list is not implemented stars_proxy object with 1 attribute in 2 file(s): $sst.anom [1] "[...]/avhrr-only-v2.19810901.nc:sst" [2] "[...]/avhrr-only-v2.19810901.nc:anom" dimension(s): from to offset delta refsys point values x/y x 1 1440 0 0.25 NA NA NULL [x] y 1 720 90 -0.25 NA NA NULL [y] zlev 1 1 0 [m] NA NA NA NULL time 1 1 1981-09-01 UTC NA POSIXct NA NULL times 1 2 1981-09-01 1 days Date NA NULL > > # demonstrate that environments work: > x = read_stars(tif, proxy = TRUE) > f = function(x) { + g = function(xx) { + ndvi = function(z) (z[2]-z[1])/(z[2]+z[1]) + 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 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 1 6 NA NA NA NA NULL call_list: [[1]] st_apply(X = X, MARGIN = MARGIN, FUN = FUN, CLUSTER = CLUSTER, PROGRESS = PROGRESS, FUTURE = FUTURE, rename = rename, .fname = .fname) attr(,".Environment") > 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 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] > > proc.time() user system elapsed 5.060 0.363 5.417 stars/tests/curvilinear.R0000644000175000017500000000340413777653456015400 0ustar nileshnileshsuppressPackageStartupMessages(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 (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/stars.R0000644000175000017500000001022213777653456014205 0ustar nileshnileshSys.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)[1:10,] nc = system.file("nc/tos_O1_2001-2002.nc", package = "stars") (x = read_stars(nc)) st_bbox(x) s = st_as_stars(st_bbox(x)) st_bbox(s) s = st_as_stars(st_bbox(x), inside = TRUE) st_bbox(s) (s = st_as_stars(st_bbox(x), dy = 1)) st_bbox(s) 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) st_bbox(s) s = st_as_stars(st_bbox(x), dx = 20) st_bbox(s) df = as.data.frame(x) units::drop_units(x) dimnames(x) dimnames(x) <- letters[1:3] dimnames(x) 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 (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")) { # 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/aggregate.Rout.save0000644000175000017500000001400014144045432016434 0ustar nileshnilesh R version 4.1.0 (2021-05-18) -- "Camp Pontanezen" Copyright (C) 2021 The R Foundation for Statistical Computing Platform: x86_64-pc-linux-gnu (64-bit) 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. > library(stars) Loading required package: abind Loading required package: sf Linking to GEOS 3.9.0, GDAL 3.2.1, PROJ 7.2.1 > > library(spacetime) > data(air) # this loads several datasets in .GlobalEnv > dim(air) space time 70 4383 > 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)) > > (aq = st_as_stars(list(PM10 = air), dimensions = d)) 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 > (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 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 47.26986...,...,POLYGON ((11.98261 52.46097... 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 <- read_stars(tmp, RasterIO = list(nXOff = 1, nYOff = 1, nXsize = 10, nYSize = 12, + nBufXSize = 2, nBufYSize = 2))) stars object with 3 dimensions and 1 attribute attribute(s): Min. 1st Qu. Median Mean 3rd Qu. Max. file46ef83309cd.tif 33 199.25 365.5 365.5 531.75 698 dimension(s): from to offset delta refsys point values x/y x 1 2 0 5 WGS 84 FALSE NULL [x] y 1 2 12 -6 WGS 84 FALSE NULL [y] band 1 6 NA NA NA NA NULL > > sfc = st_set_crs(st_as_sfc(red, as_points = FALSE), st_crs(st)) > (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 > a[[1]] [,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 > sum(a[[1]])*30 == sum(1:720) [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 values x/y time 1 10 2019-02-18 1 days Date NA NULL 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 11,...,...,POLYGON ((9 1, 10 1, 10 0, ... t NULL > > proc.time() user system elapsed 1.389 0.053 1.442 stars/tests/dimensions.R0000644000175000017500000000113713777653456015226 0ustar nileshnilesh# 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/sp.Rout.save0000644000175000017500000001634713777653456015176 0ustar nileshnilesh R version 4.0.2 (2020-06-22) -- "Taking Off Again" Copyright (C) 2020 The R Foundation for Statistical Computing Platform: x86_64-pc-linux-gnu (64-bit) 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") > library(sp) > demo(meuse) demo(meuse) ---- ~~~~~ > require(sp) > crs = CRS("+init=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 > # 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)) stars object with 1 dimensions and 12 attributes attribute(s): cadmium copper lead zinc Min. : 0.200 Min. : 14.00 Min. : 37.0 Min. : 113.0 1st Qu.: 0.800 1st Qu.: 23.00 1st Qu.: 72.5 1st Qu.: 198.0 Median : 2.100 Median : 31.00 Median :123.0 Median : 326.0 Mean : 3.246 Mean : 40.32 Mean :153.4 Mean : 469.7 3rd Qu.: 3.850 3rd Qu.: 49.50 3rd Qu.:207.0 3rd Qu.: 674.5 Max. :18.100 Max. :128.00 Max. :654.0 Max. :1839.0 elev dist om ffreq soil lime Min. : 5.180 Min. :0.00000 Min. : 1.000 1:84 1:97 0:111 1st Qu.: 7.546 1st Qu.:0.07569 1st Qu.: 5.300 2:48 2:46 1: 44 Median : 8.180 Median :0.21184 Median : 6.900 3:23 3:12 Mean : 8.165 Mean :0.24002 Mean : 7.478 3rd Qu.: 8.955 3rd Qu.:0.36407 3rd Qu.: 9.000 Max. :10.520 Max. :0.88039 Max. :17.000 NA's :2 landuse dist.m W :50 Min. : 10.0 Ah :39 1st Qu.: 80.0 Am :22 Median : 270.0 Fw :10 Mean : 290.3 Ab : 8 3rd Qu.: 450.0 (Other):25 Max. :1000.0 NA's : 1 dimension(s): from to offset delta refsys point geometry 1 155 NA NA +proj=sterea +lat_0=52.15... TRUE values geometry POINT (181072 333611),...,POINT (180627 330190) > (gr = st_as_stars(meuse.grid)) stars object with 2 dimensions and 5 attributes attribute(s): part.a part.b dist soil ffreq Min. :0.000 Min. :0.000 Min. :0.000 1 :1665 1 : 779 1st Qu.:0.000 1st Qu.:0.000 1st Qu.:0.119 2 :1084 2 :1335 Median :0.000 Median :1.000 Median :0.272 3 : 354 3 : 989 Mean :0.399 Mean :0.601 Mean :0.297 NA's:5009 NA's:5009 3rd Qu.:1.000 3rd Qu.:1.000 3rd Qu.:0.440 Max. :1.000 Max. :1.000 Max. :0.993 NA's :5009 NA's :5009 NA's :5009 dimension(s): from to offset delta refsys point values x/y x 1 78 178440 40 +proj=sterea +lat_0=52.15... NA NULL [x] y 1 104 333760 -40 +proj=sterea +lat_0=52.15... NA NULL [y] > > m2 = as(m, "Spatial") > summary(m2) Object of class SpatialPointsDataFrame Coordinates: min max coords.x1 178605 181390 coords.x2 329714 333611 Is projected: TRUE proj4string : [+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] Number of points: 155 Data attributes: cadmium copper lead zinc Min. : 0.200 Min. : 14.00 Min. : 37.0 Min. : 113.0 1st Qu.: 0.800 1st Qu.: 23.00 1st Qu.: 72.5 1st Qu.: 198.0 Median : 2.100 Median : 31.00 Median :123.0 Median : 326.0 Mean : 3.246 Mean : 40.32 Mean :153.4 Mean : 469.7 3rd Qu.: 3.850 3rd Qu.: 49.50 3rd Qu.:207.0 3rd Qu.: 674.5 Max. :18.100 Max. :128.00 Max. :654.0 Max. :1839.0 elev dist om ffreq soil lime Min. : 5.180 Min. :0.00000 Min. : 1.000 1:84 1:97 0:111 1st Qu.: 7.546 1st Qu.:0.07569 1st Qu.: 5.300 2:48 2:46 1: 44 Median : 8.180 Median :0.21184 Median : 6.900 3:23 3:12 Mean : 8.165 Mean :0.24002 Mean : 7.478 3rd Qu.: 8.955 3rd Qu.:0.36407 3rd Qu.: 9.000 Max. :10.520 Max. :0.88039 Max. :17.000 NA's :2 landuse dist.m W :50 Min. : 10.0 Ah :39 1st Qu.: 80.0 Am :22 Median : 270.0 Fw :10 Mean : 290.3 Ab : 8 3rd Qu.: 450.0 (Other):25 Max. :1000.0 NA's : 1 > gr2 = as(gr, "Spatial") > summary(gr2) Object of class SpatialGridDataFrame Coordinates: min max x 178440 181560 y 329600 333760 Is projected: TRUE proj4string : [+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] Grid attributes: cellcentre.offset cellsize cells.dim x 178460 40 78 y 329620 40 104 Data attributes: part.a part.b dist soil ffreq Min. :0.000 Min. :0.000 Min. :0.000 1 :1665 1 : 779 1st Qu.:0.000 1st Qu.:0.000 1st Qu.:0.119 2 :1084 2 :1335 Median :0.000 Median :1.000 Median :0.272 3 : 354 3 : 989 Mean :0.399 Mean :0.601 Mean :0.297 NA's:5009 NA's:5009 3rd Qu.:1.000 3rd Qu.:1.000 3rd Qu.:0.440 Max. :1.000 Max. :1.000 Max. :0.993 NA's :5009 NA's :5009 NA's :5009 > > all.equal(meuse, m2, check.attributes = FALSE) [1] TRUE > fullgrid(meuse.grid) = TRUE > all.equal(meuse.grid, gr2, check.attributes = FALSE) [1] TRUE > > #all.equal(meuse, m2) > #all.equal(meuse.grid, gr2) > > proc.time() user system elapsed 0.970 0.081 1.043 stars/tests/rasterize.R0000644000175000017500000000237714144045432015047 0ustar nileshnileshsuppressPackageStartupMessages(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/aggregate.R0000644000175000017500000000241413777653456015003 0ustar nileshnileshlibrary(stars) library(spacetime) 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)) (aq = st_as_stars(list(PM10 = air), dimensions = d)) (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 <- read_stars(tmp, RasterIO = list(nXOff = 1, nYOff = 1, nXsize = 10, nYSize = 12, nBufXSize = 2, nBufYSize = 2))) sfc = st_set_crs(st_as_sfc(red, as_points = FALSE), st_crs(st)) (a = aggregate(st, sfc, mean)) a[[1]] sum(a[[1]])*30 == sum(1:720) tm0 = as.Date("2019-02-19") + -1:8 (a = aggregate(st, tm0, 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/redimension.R0000644000175000017500000000052614010071511015332 0ustar nileshnileshsuppressPackageStartupMessages(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"))))) stars/tests/gridtypes.Rout.save0000644000175000017500000002613014032621024016520 0ustar nileshnilesh R version 4.0.5 (2021-03-31) -- "Shake and Throw" Copyright (C) 2021 The R Foundation for Statistical Computing Platform: x86_64-pc-linux-gnu (64-bit) 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 refsys point values a 1 3 1 1 NA FALSE NULL b 1 3 1 1 NA FALSE NULL band 1 2 NA 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 refsys point values x/y x 1 3 1 1 NA FALSE NULL [x] y 1 3 1 1 NA FALSE NULL [y] band 1 2 NA 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 refsys point values x/y x 1 3 NA NA NA FALSE [1,2), [2,4), [4,6) [x] y 1 3 1 1 NA FALSE NULL [y] band 1 2 NA 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 refsys point values x/y x 1 3 NA NA NA FALSE [0.5,1.5), [1.5,3.0), [3.0,5.0) [x] y 1 3 0.5 1 NA FALSE NULL [y] band 1 2 NA 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 refsys point values x/y x 1 3 NA NA NA TRUE 1, 2, 4 [x] y 1 3 0.5 1 NA TRUE NULL [y] band 1 2 NA 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 refsys point values x/y x 1 3 NA NA NA FALSE [1,2), [2,4), [4,8) [x] y 1 3 1 1 NA FALSE NULL [y] band 1 2 NA 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 refsys point values x 1 3 NA NA NA TRUE POINT (0 1), POINT (2 1), POINT (4 3) y 1 3 1 1 NA FALSE NULL band 1 2 NA 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 refsys point values x/y x 1 3 1 1 NA FALSE NULL [x] y 1 3 1 1 NA FALSE NULL [y] band 1 2 NA 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 offset delta refsys point values x/y lon 1 3 NA NA WGS 84 FALSE [3x3] 0.03,...,0.89 [x] lat 1 3 NA NA WGS 84 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 (2 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 offset delta refsys point values X1 1 4 NA NA Date FALSE 2019-01-20,...,2019-01-30 X2 1 3 NA NA NA TRUE POINT (0 1), POINT (2 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 0.731 0.053 0.776 stars/tests/testthat.R0000644000175000017500000000017313777653456014715 0ustar nileshnilesh## load dependencies library(testthat) suppressPackageStartupMessages(library(stars)) ## test package test_check("stars") stars/tests/proxy.R0000644000175000017500000000427213777653456014242 0ustar nileshnileshSys.setenv(TZ="UTC") suppressPackageStartupMessages(library(sf)) suppressPackageStartupMessages(library(stars)) tif = system.file("tif/L7_ETMs.tif", package = "stars") (r = read_stars(tif, proxy = TRUE)) plot(r) dim(r) r[,,,2:3] r+r st_as_stars(sin(r), downsample = 2) aperm(r, c(3,2,1)) 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 (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(z) (z[2]-z[1])/(z[2]+z[1]) st_apply(xx, 1:2, ndvi) } g(x) } (x = f(x)) st_as_stars(x) stars/tests/spacetime.Rout.save0000644000175000017500000002014714144045432016471 0ustar nileshnilesh R version 4.1.0 (2021-05-18) -- "Camp Pontanezen" Copyright (C) 2021 The R Foundation for Statistical Computing Platform: x86_64-pc-linux-gnu (64-bit) 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(spacetime) > set.seed(1331) > Sys.setenv(TZ="UTC") > example(STFDF) 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 > > x = st_as_stars(stfdf) > xx = as(x, "STFDF") > > # as.xts.stars: > library(xts) Loading required package: zoo Attaching package: 'zoo' The following objects are masked from 'package:base': as.Date, as.Date.numeric > xts = as.xts(x) Warning message: In as.xts.stars(x) : all but first attribute will be ignored > st_as_stars(xts) stars object with 2 dimensions and 1 attribute attribute(s): Min. 1st Qu. Median Mean 3rd Qu. Max. attr 9.38 11.225 20.8 20.38167 29.5 31.2 dimension(s): from to offset delta refsys point values time 1 4 2010-08-05 10:00:00 UTC 1 hours POSIXct NA NULL others 1 3 NA NA NA NA NULL > st_as_stars(xts, dimensions = st_dimensions(x)) stars object with 2 dimensions and 1 attribute attribute(s): Min. 1st Qu. Median Mean 3rd Qu. Max. attr 9.38 11.225 20.8 20.38167 29.5 31.2 dimension(s): from to offset delta refsys point sfc 1 3 NA NA NA TRUE time 1 4 2010-08-05 10:00:00 UTC 1 hours POSIXct NA values sfc POINT (0 0), POINT (0 1), POINT (1 1) time NULL > > 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")) > > > proc.time() user system elapsed 1.701 0.093 1.786 stars/tests/write.R0000644000175000017500000000046013777653456014206 0ustar nileshnileshsuppressPackageStartupMessages(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.R0000644000175000017500000000132414144045432014326 0ustar nileshnileshoptions(rgdal_show_exportToProj4_warnings = "none") suppressPackageStartupMessages(library(stars)) 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)) ## 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)) stars/tests/warp.R0000644000175000017500000000253014144045432013777 0ustar nileshnileshsuppressPackageStartupMessages(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) plot(x2, breaks = "equal", axes=TRUE) plot(y, breaks = "equal", axes=TRUE) names(x2) = names(y) all.equal(x2, y) # yes? if (FALSE) { # FIXME: to be removed when sf >= 0.7-4 is on CRAN # does gdal reproduce with stars template object? (x2 = st_warp(x, y, use_gdal = TRUE)) # does gdal reproduce what stars does, default cell size? (x2 = st_warp(x, crs = st_crs(x), use_gdal = FALSE)) (y = st_warp(x, x2, use_gdal = TRUE, debug = FALSE)) # try with multiple bands: tif = system.file("tif/L7_ETMs.tif", package = "stars") (x1 = read_stars(tif)) (x1p = read_stars(tif, proxy = TRUE)) (x1a = st_warp(x1, crs = st_crs(4326))) (x1b = st_warp(x1, x1p, use_gdal = TRUE)) # 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] plot(x2, breaks = "equal", axes=TRUE, reset = FALSE) plot(st_as_sfc(st_bbox(x2)), add = TRUE, col = NA, border = 'red') ### doesn't work: FIXME: check with more recent GDAL: #(y = st_warp(x, x2, use_gdal = TRUE, debug = FALSE)) #plot(y, breaks = "equal") #names(x2) = names(y) #all.equal(x2, y) } stars/tests/tidy.R0000644000175000017500000000120213777653456014020 0ustar nileshnileshsuppressPackageStartupMessages(library(stars)) suppressPackageStartupMessages(library(dplyr)) 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) stars/tests/crop.Rout.save0000644000175000017500000000430114144045432015454 0ustar nileshnilesh R version 4.1.1 (2021-08-10) -- "Kick Things" Copyright (C) 2021 The R Foundation for Statistical Computing Platform: x86_64-pc-linux-gnu (64-bit) 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 values x/y x 158 268 288776 28.5 SIRGAS 2000 / UTM zone 25S FALSE NULL [x] y 245 331 9120761 -28.5 SIRGAS 2000 / UTM zone 25S FALSE NULL [y] band 1 6 NA NA NA NA NULL > 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) > > proc.time() user system elapsed 2.352 0.072 2.417 stars/tests/predict.R0000644000175000017500000000074013777653456014507 0ustar nileshnileshsuppressPackageStartupMessages(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/area.R0000644000175000017500000000173413777653456013771 0ustar nileshnileshsuppressPackageStartupMessages(library(stars)) tif = system.file("tif/L7_ETMs.tif", package = "stars") x = read_stars(tif) (a = st_area(x)) 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))) plot(a, axes = TRUE, border = NA) } stars/tests/raster.Rout.save0000644000175000017500000002046614145275656016041 0ustar nileshnilesh R version 4.1.2 (2021-11-01) -- "Bird Hippie" Copyright (C) 2021 The R Foundation for Statistical Computing Platform: x86_64-pc-linux-gnu (64-bit) 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)) > > 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 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 1 6 NA NA NA NA NULL > (r = as(x, "Raster")) class : RasterBrick dimensions : 352, 349, 122848, 6 (nrow, ncol, ncell, nlayers) resolution : 28.5, 28.5 (x, y) extent : 288776.3, 298722.8, 9110729, 9120761 (xmin, xmax, ymin, ymax) crs : +proj=utm +zone=25 +south +ellps=GRS80 +towgs84=0,0,0,0,0,0,0 +units=m +no_defs source : memory names : layer.1, layer.2, layer.3, layer.4, layer.5, layer.6 min values : 47, 32, 21, 9, 1, 1 max values : 255, 255, 255, 255, 255, 255 time : 1, 2, 3, 4, 5, 6 > (y = st_as_stars(r)) stars object with 3 dimensions and 1 attribute attribute(s): Min. 1st Qu. Median Mean 3rd Qu. Max. layer.1 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 NA NULL [x] y 1 352 9120761 -28.5 SIRGAS 2000 / UTM zone 25S NA NULL [y] band 1 6 1 1 NA NA NULL > > # single band: > x = adrop(x[,,,1]) > r = as(x, "Raster") > (y = st_as_stars(r)) stars object with 2 dimensions and 1 attribute attribute(s): Min. 1st Qu. Median Mean 3rd Qu. Max. layer 47 67 78 79.14772 89 255 dimension(s): from to offset delta refsys point values x/y x 1 349 288776 28.5 SIRGAS 2000 / UTM zone 25S NA NULL [x] y 1 352 9120761 -28.5 SIRGAS 2000 / UTM zone 25S NA NULL [y] > > # proxy: > (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 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 1 6 NA NA NA NA NULL > (r = as(x, "Raster")) class : RasterBrick dimensions : 352, 349, 122848, 6 (nrow, ncol, ncell, nlayers) resolution : 28.5, 28.5 (x, y) extent : 288776.3, 298722.8, 9110729, 9120761 (xmin, xmax, ymin, ymax) crs : +proj=utm +zone=25 +south +ellps=GRS80 +units=m +no_defs source : L7_ETMs.tif names : L7_ETMs.1, L7_ETMs.2, L7_ETMs.3, L7_ETMs.4, L7_ETMs.5, L7_ETMs.6 min values : 0, 0, 0, 0, 0, 0 max values : 255, 255, 255, 255, 255, 255 > (y = 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 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 1 6 NA NA NA NA NULL > (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 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 1 6 NA NA NA NA NULL > > ## terra ------------- > (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 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 1 6 NA NA NA NA NULL > (r = as(x, "SpatRaster")) class : SpatRaster dimensions : 352, 349, 6 (nrow, ncol, nlyr) resolution : 28.5, 28.5 (x, y) extent : 288776.3, 298722.8, 9110729, 9120761 (xmin, xmax, ymin, ymax) coord. ref. : SIRGAS 2000 / UTM zone 25S (EPSG:31985) source : memory names : band1, band2, band3, band4, band5, band6 min values : 47, 32, 21, 9, 1, 1 max values : 255, 255, 255, 255, 255, 255 > (y = st_as_stars(r)) stars object with 3 dimensions and 1 attribute attribute(s): Min. 1st Qu. Median Mean 3rd Qu. Max. band1 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 NA NULL y 1 352 9120761 -28.5 SIRGAS 2000 / UTM zone 25S NA NULL band 1 6 NA NA NA NA band1,...,band6 x/y x [x] y [y] band > > # single band: > x = adrop(x[,,,1]) > r = as(x, "SpatRaster") > (y = st_as_stars(r)) stars object with 2 dimensions and 1 attribute attribute(s): Min. 1st Qu. Median Mean 3rd Qu. Max. lyr.1 47 67 78 79.14772 89 255 dimension(s): from to offset delta refsys point values x/y x 1 349 288776 28.5 SIRGAS 2000 / UTM zone 25S NA NULL [x] y 1 352 9120761 -28.5 SIRGAS 2000 / UTM zone 25S NA NULL [y] > > # proxy: > (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 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 1 6 NA NA NA NA NULL > (r = as(x, "SpatRaster")) class : SpatRaster dimensions : 352, 349, 6 (nrow, ncol, nlyr) resolution : 28.5, 28.5 (x, y) extent : 288776.3, 298722.8, 9110729, 9120761 (xmin, xmax, ymin, ymax) coord. ref. : SIRGAS 2000 / UTM zone 25S (EPSG:31985) source : L7_ETMs.tif names : L7__1, L7__2, L7__3, L7__4, L7__5, L7__6 > (y = 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 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 1 6 NA NA NA NA NULL > (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 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 1 6 NA NA NA NA NULL > > proc.time() user system elapsed 5.904 0.183 6.079 stars/tests/sp.R0000644000175000017500000000140613777653456013477 0ustar nileshnileshoptions(rgdal_show_exportToProj4_warnings = "none") library(sp) 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/subset.R0000644000175000017500000000115313777653456014361 0ustar nileshnileshsuppressPackageStartupMessages(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/warp.Rout.save0000644000175000017500000000414014144045432015463 0ustar nileshnilesh R version 4.1.1 (2021-08-10) -- "Kick Things" Copyright (C) 2021 The R Foundation for Statistical Computing Platform: x86_64-pc-linux-gnu (64-bit) 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) > plot(x2, breaks = "equal", axes=TRUE) > plot(y, breaks = "equal", axes=TRUE) > names(x2) = names(y) > all.equal(x2, y) # yes? [1] TRUE > > if (FALSE) { # FIXME: to be removed when sf >= 0.7-4 is on CRAN + # does gdal reproduce with stars template object? + (x2 = st_warp(x, y, use_gdal = TRUE)) + + # does gdal reproduce what stars does, default cell size? + (x2 = st_warp(x, crs = st_crs(x), use_gdal = FALSE)) + (y = st_warp(x, x2, use_gdal = TRUE, debug = FALSE)) + + # try with multiple bands: + tif = system.file("tif/L7_ETMs.tif", package = "stars") + (x1 = read_stars(tif)) + (x1p = read_stars(tif, proxy = TRUE)) + (x1a = st_warp(x1, crs = st_crs(4326))) + (x1b = st_warp(x1, x1p, use_gdal = TRUE)) + + # 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] + plot(x2, breaks = "equal", axes=TRUE, reset = FALSE) + plot(st_as_sfc(st_bbox(x2)), add = TRUE, col = NA, border = 'red') + ### doesn't work: FIXME: check with more recent GDAL: + #(y = st_warp(x, x2, use_gdal = TRUE, debug = FALSE)) + #plot(y, breaks = "equal") + #names(x2) = names(y) + #all.equal(x2, y) + } > > proc.time() user system elapsed 0.841 0.057 0.890 stars/tests/transform.R0000644000175000017500000000224013777653456015065 0ustar nileshnileshsuppressPackageStartupMessages(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)) x__ = st_transform_proj(x, st_crs(4326)$proj4string) st_crs(x_) = st_crs(x__) 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))) all.equal(x_,x__,check.attributes = FALSE) # nothing to transform: now errs: try(st_transform(st_as_stars(list(matrix(1,10,10))), st_crs(4326))) try(st_transform_proj(st_as_stars(list(matrix(1,10,10))), st_crs(4326))) stars/tests/nc.R0000644000175000017500000000233413777653456013456 0ustar nileshnilesh# 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/datasets.Rout.save0000644000175000017500000000405514145275656016345 0ustar nileshnilesh R version 4.1.2 (2021-11-01) -- "Bird Hippie" Copyright (C) 2021 The R Foundation for Statistical Computing Platform: x86_64-pc-linux-gnu (64-bit) 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 point values x/y x 1 81 -85 0.125 NA NA NULL [x] y 1 33 37.125 -0.125 NA NA NULL [y] time 1 12 NA NA POSIXct NA 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 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 1 6 NA NA NA NA NULL > if (require(starsdata)) { + print(stars_sentinel2) + } Loading required package: starsdata stars_proxy object with 1 attribute in 1 file(s): $`MTD_MSIL1C.xml:10m:EPSG_32632` [1] "[...]/MTD_MSIL1C.xml:10m:EPSG_32632" dimension(s): from to offset delta refsys point values x/y x 1 10980 3e+05 10 WGS 84 / UTM zone 32N NA NULL [x] y 1 10980 6e+06 -10 WGS 84 / UTM zone 32N NA NULL [y] band 1 4 NA NA NA NA B4,...,B8 > > proc.time() user system elapsed 0.911 0.048 0.950 stars/tests/area.Rout.save0000644000175000017500000000670114144045432015427 0ustar nileshnilesh R version 4.1.1 (2021-08-10) -- "Kick Things" Copyright (C) 2021 The R Foundation for Statistical Computing Platform: x86_64-pc-linux-gnu (64-bit) 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)) 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 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] > > 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 offset delta refsys point values x/y x 1 5 NA NA NA FALSE [0,0.5),...,[4,5) [x] y 1 4 NA NA NA 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 offset delta refsys point values x/y x 1 5 NA NA NA FALSE [0,0.5),...,[4,5) [x] y 1 4 NA NA NA 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))) + plot(a, axes = TRUE, border = NA) + } /PRODUCT/longitude, /PRODUCT/latitude, /PRODUCT/SUPPORT_DATA/DETAILED_RESULTS/nitrogendioxide_summed_total_column, stars object with 2 dimensions and 1 attribute attribute(s): Min. 1st Qu. Median Mean 3rd Qu. Max. area [m^2] 25983110 28008871 35690854 42912281 54562137 100943458 dimension(s): from to offset delta refsys point values x/y x 1 450 NA NA WGS 84 NA [450x278] -5.81066 [°],...,30.9468 [°] [x] y 1 278 NA NA WGS 84 NA [450x278] 28.3605 [°],...,51.4686 [°] [y] curvilinear grid There were 15 warnings (use warnings() to see them) > > proc.time() user system elapsed 8.324 0.076 8.391 stars/tests/crop.R0000644000175000017500000000163713777653456014026 0ustar nileshnileshsuppressPackageStartupMessages(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) stars/tests/plot.Rout.save0000644000175000017500000000373214144045432015476 0ustar nileshnilesh R version 4.1.0 (2021-05-18) -- "Camp Pontanezen" Copyright (C) 2021 The R Foundation for Statistical Computing Platform: x86_64-pc-linux-gnu (64-bit) 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. > library(spacetime) > suppressPackageStartupMessages(library(stars)) > data(air) # this loads several datasets in .GlobalEnv > dim(air) space time 70 4383 > 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 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 2.558 0.068 2.618 stars/tests/spatstat.Rout.save0000644000175000017500000000201114017260103016341 0ustar nileshnilesh R version 3.6.2 (2019-12-12) -- "Dark and Stormy Night" Copyright (C) 2019 The R Foundation for Statistical Computing Platform: x86_64-pc-linux-gnu (64-bit) 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(spatstat.geom)) > suppressPackageStartupMessages(library(sf)) > > library(stars) Loading required package: abind > tif = system.file("tif/L7_ETMs.tif", package = "stars") > s = adrop(read_stars(tif)[,,,1]) > 70 > plot(s) > m = as.owin(s) > plot(m) > table(m$m) FALSE TRUE 39494 83354 > > proc.time() user system elapsed 1.461 0.080 1.541 stars/tests/spatstat.R0000644000175000017500000000037314017260103014665 0ustar nileshnileshsuppressPackageStartupMessages(library(spatstat.geom)) suppressPackageStartupMessages(library(sf)) library(stars) tif = system.file("tif/L7_ETMs.tif", package = "stars") s = adrop(read_stars(tif)[,,,1]) > 70 plot(s) m = as.owin(s) plot(m) table(m$m) stars/tests/tidy.Rout.save0000644000175000017500000001026414144045432015467 0ustar nileshnilesh R version 4.1.1 (2021-08-10) -- "Kick Things" Copyright (C) 2021 The R Foundation for Statistical Computing Platform: x86_64-pc-linux-gnu (64-bit) 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(dplyr)) > 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 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 1 6 NA NA NA NA NULL > > (y <- x %>% select(L7_ETMs.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 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 1 6 NA NA NA NA NULL > (y <- x %>% filter(band > 2)) stars object with 3 dimensions and 1 attribute attribute(s): Min. 1st Qu. Median Mean 3rd Qu. Max. L7_ETMs.tif 1 47 67 66.68804 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 1 4 3 1 NA NA NULL > (z <- x %>% mutate(foo = 2* L7_ETMs.tif)) 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 foo 2 108 138 137.82484 172 510 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 1 6 NA NA NA NA NULL > x %>% pull(L7_ETMs.tif) %>% class() [1] "array" > x %>% filter(x > 289900) stars object with 3 dimensions and 1 attribute attribute(s): Min. 1st Qu. Median Mean 3rd Qu. Max. L7_ETMs.tif 1 55 70 69.3936 87 255 dimension(s): from to offset delta refsys point values x/y x 1 310 289888 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 1 6 1 1 NA NA NULL > x %>% slice("x", 50:100) stars object with 3 dimensions and 1 attribute attribute(s): Min. 1st Qu. Median Mean 3rd Qu. Max. L7_ETMs.tif 11 56 68 70.50099 82 252 dimension(s): from to offset delta refsys point values x/y x 50 100 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 1 6 NA NA NA NA NULL > > 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) > > proc.time() user system elapsed 4.224 0.149 4.365 stars/tests/redimension.Rout.save0000644000175000017500000000572614144045432017041 0ustar nileshnilesh R version 4.1.1 (2021-08-10) -- "Kick Things" Copyright (C) 2021 The R Foundation for Statistical Computing Platform: x86_64-pc-linux-gnu (64-bit) 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 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 1 6 NA NA NA NA NULL foo 1 4 1 1 NA NA NULL > (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 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 1 6 NA NA NA NA NULL foo 1 4 1 1 NA NA NULL > st_as_stars(rr) stars object with 4 dimensions and 2 attributes attribute(s), summary of first 1e+05 cells: Min. 1st Qu. Median Mean 3rd Qu. Max. a.L7_ETMs.tif.a.L7_ETMs.tif.b.... 47 65 76 77.3419 87 255 a.L7_ETMs.tif.a.L7_ETMs.tif.b.... 47 65 76 77.3419 87 255 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 1 6 NA NA NA NA NULL foo 1 4 1 1 NA NA NULL > #(rrr = st_redimension(rr, along = list(bar = as.Date(c("2001-01-01", "2002-01-01"))))) > > proc.time() user system elapsed 1.424 0.107 1.523 stars/tests/write.Rout.save0000644000175000017500000000353314144045432015651 0ustar nileshnilesh R version 4.1.1 (2021-08-10) -- "Kick Things" Copyright (C) 2021 The R Foundation for Statistical Computing Platform: x86_64-pc-linux-gnu (64-bit) 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 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 1 6 NA NA NA NA NULL > > # 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 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 1 6 NA NA NA NA NULL > write_stars(x, tempfile(), driver = "GTiff") > > proc.time() user system elapsed 0.859 0.044 0.895 stars/tests/gridtypes.R0000644000175000017500000000576413777653456015102 0ustar nileshnileshsuppressPackageStartupMessages(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/rasterize.Rout.save0000644000175000017500000001311214144045432016521 0ustar nileshnilesh R version 4.1.1 (2021-08-10) -- "Kick Things" Copyright (C) 2021 The R Foundation for Statistical Computing Platform: x86_64-pc-linux-gnu (64-bit) 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 values x/y x 1 461 -84.3239 0.0192484 NAD27 FALSE NULL [x] y 1 141 36.5896 -0.0192484 NAD27 FALSE NULL [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 values x/y x 1 100 -84.3239 0.0886687 NAD27 FALSE NULL [x] y 1 50 36.5896 -0.0541531 NAD27 FALSE NULL [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 refsys point values x/y x 1 10 0 0.1 NA NA NULL [x] y 1 10 1 -0.1 NA NA NULL [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 refsys point values x/y x 1 10 0 0.1 NA NA NULL [x] y 1 10 1 -0.1 NA NA NULL [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 0.869 0.072 0.934 stars/R/0000755000175000017500000000000014157334513011747 5ustar nileshnileshstars/R/geom.R0000644000175000017500000001217014044725166013025 0ustar nileshnilesh#' 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: 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.R0000644000175000017500000000201014144045432013666 0ustar nileshnilesh#' 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: http://gdo-dcp.ucllnl.org/downscaled_cmip_projections/dcpInterface.html#About 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 https://scihub.copernicus.eu/ #' reads the four 10-m bands: B2 (490 nm), B3 (560 nm), B4 (665 nm) and B8 (842 nm) "stars_sentinel2" stars/R/xts.R0000644000175000017500000000303114144045432012700 0ustar nileshnileshas.xts.stars <- function(x,...) { if (!requireNamespace("xts", quietly = TRUE)) stop("xts required: install that first") # nocov if (length(x) > 1) warning("all but first attribute will be ignored") 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 = st_upfront(x[1], time) 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) } 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(list(as.matrix(.x))) .x = st_set_dimensions(.x, 1, time) if (!is.null(cn)) .x = st_set_dimensions(.x, 2, cn) st_set_dimensions(setNames(.x, name), names = c("time", "others")) } } stars/R/plot.R0000644000175000017500000005436414145275656013075 0ustar nileshnileshmake_label = function(x, i = 1) { dims = dim(x) if ((length(dims) <= 2 || dims[3] == 1) && inherits(x[[i]], "units")) make_unit_label(paste0("'", names(x)[i], "'"), units(x[[i]])) else names(x)[i] } #' 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 actual color breaks, or a method name used for \link[classInt]{classIntervals}. #' @param col colors to use for grid cells #' @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 integer; 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}. #' @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 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{0} 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. #' @param mfrow length-2 integer vector with nrows, ncolumns of a composite plot, to override the default layout #' @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. #' @export 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 = lcm(1.8), key.length = 0.618, reset = TRUE, box_col = grey(.8), center_time = FALSE, hook = NULL, mfrow = NULL) { flatten = function(x, i) { # collapse all non-x/y dims into one, and select "layer" i d = st_dimensions(x) dxy = attr(d, "raster")$dimensions dims = dim(x) nms = names(x) x = x[[1]] aux = setdiff(names(dims), dxy) newdims = c(dims[dxy], prod(dims[aux])) dim(x) = newdims st_as_stars(setNames(list(x[,,i]), nms[1]), dimensions = d[dxy]) } x = st_normalize(x) if (is.character(x[[1]])) # rgb values key.pos = NULL if (missing(col) && is.factor(x[[1]])) col = attr(x[[1]], "colors") %||% sf.colors(length(levels(x[[1]])), categorical = TRUE) key.pos.missing = missing(key.pos) breaks.missing = missing(breaks) if (missing(nbreaks) && !missing(col)) nbreaks = length(col) + 1 opar = par() dots = list(...) #if (any(dim(x) == 1)) # x = adrop(x) if (is.factor(x[[1]]) && any(is.na(levels(x[[1]])))) x = droplevels(x) # https://github.com/r-spatial/stars/issues/339 if (join_zlim && !is.character(x[[1]])) { breaks = get_breaks(x, breaks, nbreaks, dots$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$logz) && !((has_raster(x) && (is_curvilinear(x) || has_rotate_or_shear(x))) || has_sfc(x))) x = log10(x) # otherwise, defer log-transforming to sf::plot.sf if (!missing(y)) stop("y argument should be missing") if (has_raster(x)) { dxy = attr(st_dimensions(x), "raster")$dimensions loop = setdiff(names(dim(x)), dxy) # dimension (name) over which we loop, if any x = aperm(x, c(dxy, loop)) zlim = if (join_zlim) range(unclass(x[[1]]), na.rm = TRUE) else rep(NA_real_, 2) dims = dim(x) x = if (isTRUE(downsample)) { n = dims * 0 # keep names n[dxy] = get_downsample(dims, rgb = is.numeric(dots$rgb)) st_downsample(x, n) } else if (is.numeric(downsample)) st_downsample(x, downsample) dims = dim(x) # may have changed by st_downsample if (length(dims) == 2 || dims[3] == 1 || (!is.null(dots$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.factor(values) || length(unique(na.omit(values))) > 1) && length(col) > 1 && is.null(dots$rgb) && !is_curvilinear(x)) { # plot key? switch(key.pos, layout(matrix(c(2,1), nrow = 2, ncol = 1), widths = 1, heights = c(1, key.width)), # 1 bottom layout(matrix(c(1,2), nrow = 1, ncol = 2), widths = c(key.width, 1), heights = 1), # 2 left layout(matrix(c(1,2), nrow = 2, ncol = 1), widths = 1, heights = c(key.width, 1)), # 3 top layout(matrix(c(2,1), nrow = 1, ncol = 2), widths = c(1, key.width), heights = 1) # 4 right ) if (is.factor(values)) .image_scale_factor(levels(values), col, key.pos = key.pos, key.width = key.width, key.length = key.length, axes = axes, ...) else .image_scale(values, col, breaks = breaks, key.pos = key.pos, key.width = key.width, key.length = key.length, axes = axes, ...) } # map panel: mar = c(axes * 2.1, axes * 2.1, 1 * !is.null(main), 0) if (!is.null(key.pos) && key.pos %in% 1:4) mar[key.pos] = mar[key.pos] + .5 par(mar = mar) # plot the map: image(x, ..., axes = axes, breaks = breaks, col = col, key.pos = key.pos, key.width = key.width, key.length = key.length, main = NULL) if (!is.null(main)) title(main) } else { ## MULTIPLE IMAGES -- now loop over dimensions 3: draw.key = !is.null(key.pos) && join_zlim if (! draw.key) key.pos = NULL lt = sf::.get_layout(st_bbox(x), dims[3], par("din"), if (join_zlim && key.pos.missing) -1 else key.pos, key.width, mfrow = mfrow) title_size = if (is.null(main)) 0 else 1.2 layout(lt$m, widths = lt$widths, heights = lt$heights, respect = FALSE) 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$logz) } else br = breaks image(im, xlab = "", ylab = "", axes = axes, zlim = zlim, breaks = br, col = col, key.pos = NULL, main = NULL, ...) if (!is.null(main)) { if (length(main) == dims[3]) title(main[i]) else if (identical(main, names(x)[1])) # default value: omit on multi title(paste(format(labels[i]))) else # user-defined title(paste(main, format(labels[i]))) } if (!is.null(hook) && is.function(hook)) hook() box(col = box_col) } for (i in seq_len(prod(lt$mfrow) - dims[3])) # empty panels: plot.new() if (draw.key) { values = structure(x[[1]], dim = NULL) if (is.factor(values)) .image_scale_factor(levels(values), col, key.pos = lt$key.pos, key.width = key.width, key.length = key.length, axes = axes,...) else .image_scale(values, col, breaks = breaks, key.pos = lt$key.pos, key.width = key.width, key.length = key.length, axes = axes,...) } } } else if (has_sfc(x)) { # if (key.pos.missing) # key.pos = -1 plot(st_as_sf(x), ..., 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) { layout(matrix(1)) # reset desel = which(names(opar) %in% c("cin", "cra", "csi", "cxy", "din", "page", "pin")) par(opar[-desel]) } } 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)) #' image(x, rgb = c(1,3,5)) # rgb 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)$ylim, text_values = FALSE, text_color = 'black', axes = FALSE, interpolate = FALSE, as_points = FALSE, key.pos = NULL, logz = FALSE, key.width = lcm(1.8), key.length = 0.618, add.geom = NULL, border = NA, useRaster = isTRUE(dev.capabilities("rasterImage")$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) force(xlim) force(ylim) d = st_dimensions(x) dimxy = attr(d, "raster")$dimensions dimx = dimxy[1] dimy = dimxy[2] dimxn = match(dimx, names(d)) dimyn = match(dimy, names(d)) if (has_sfc(x) && length(dim(x)) == 2) { # time series of features: ed = lapply(expand_dimensions(x), function(x) if (inherits(x, "sfc")) seq_along(x) else x) names(ed) = c("x", "y") ed$z = x[[attr]] axes = TRUE return(image(ed, ..., xlab = xlab, ylab = ylab)) } if (! is_curvilinear(x)) { dims = expand_dimensions.stars(x, center = FALSE, max = FALSE) d_max = expand_dimensions.stars(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))) { bb = st_bbox(x) 1 / cos((mean(bb[c(2,4)]) * pi)/180) } 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))) 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(x), ...) } 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 (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 #' @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 (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]])) } } 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/ncdf.R0000644000175000017500000006704014042247226013010 0ustar nileshnilesh #' 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") #' 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 #' @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 atttempt 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 #' @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 dimemsion (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 #' #' 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))) #' } #' library(dplyr) #' 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 = 1e-12, ignore_bounds = FALSE, make_time = TRUE, make_units = TRUE) { 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 # 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 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) nc_prj <- .get_nc_projection(meta$attribute, rep_var, all_coord_var) coord_var <- .clean_coord_var(all_coord_var, rep_var, meta, nc_prj, curvilinear) 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) # 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) # 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) # Get all the data from the nc file out_data <- .get_data(nc, var, dims, dimid_matcher) out_data <- setNames(out_data, var) out_data <- .set_nc_units(out_data, meta$attribute, make_units) # Create stars dimensions object axis_matcher <- match(dims$axis[1:sum(!is.na(dims$axis))], c("X", "Y", "Z", "T")) if(length(dims$axis) > 4) { axis_matcher <- c(axis_matcher, 5:length(dims$axis)) } dimensions <- create_dimensions(setNames(dim(out_data[[1]]), dims$name[axis_matcher]), 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$attribute) dimensions <- .get_nc_time(dimensions, make_time, coord_var, rep_var, meta) # Make initial response data ret = st_stars(out_data, dimensions) st_crs(ret) <- nc_prj # Add curvilinear and return if (length(curvilinear) == 2) { curvi_coords = .get_curvilinear_coords(curvilinear, dimensions, nc, dims) return(st_as_stars(ret, curvilinear = curvi_coords)) } else return(ret) } .fix_meta <- function(meta) { nas <- is.na(meta$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]]$delta = NA } } d } .get_vars <- function(var, meta) { if (is.null(var)) { ix <- 1 if (meta$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_nc_projection <- function(atts, rep_var, coord_var) { if(!is.null(atts)) { nc_grid_mapping <- suppressWarnings(ncmeta::nc_grid_mapping_atts(atts, rep_var)) } else { nc_grid_mapping <- list() } if(length(nc_grid_mapping) == 0) { rep_coord_var <- coord_var[coord_var$variable == rep_var, ] if(nrow(rep_coord_var) == 0) rep_coord_var[1, ] <- NA if(.is_degrees(atts, rep_coord_var$X)) { message(paste("No projection information found in nc file. \n", "Coordinate variable units found to be degrees, \n", "assuming WGS84 Lat/Lon.")) return(st_crs(4326)) } else { warning("No projection information found in nc file.") return(st_crs(NULL)) } } else { ret <- try(st_crs(ncmeta::nc_gm_to_prj(nc_grid_mapping))) if(class(ret) == "try-error") { return(st_crs(NULL)) } else { return(ret) } } } .is_degrees <- function(atts, var) { units <- .get_attributes(atts, "units", var)$value[[1]] !is.null(units) && grepl("degrees", units, ignore.case = TRUE) } .clean_coord_var <- function(c_v, var, meta, prj, curvilinear) { c_v <- c_v[c_v$variable == var, ] if(nrow(c_v) == 0) c_v[1, ] <- NA not_na <- apply(c_v, 1, function(x) sum(!is.na(x))) c_v <- c_v[which(not_na == max(not_na)), ] check_curvi <- .check_curvilinear(c_v, var, meta$variable, curvilinear) if(length(curvilinear) > 0) { if(length(check_curvi) == 0) stop("Curvilinear coordinate variables provided by not found in file.") c_v <- c_v[c_v$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$Y & !is.na(prj)) { c_v <- c_v[c_v$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$Y)) { warning("Specified curvilinear coordinate variables not found as X/Y coordinate variables.") return(stats::setNames(curvilinear, c("X", "Y"))) } else { if(!curvilinear[1] %in% xy_coords$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$name)] == 2)) { return(XY_curvi) } else { return(character(0)) } } else { return(character(0)) } } #' Gets dimension info ensuring they are in an order suited to coordinate #' variables that need to be subset appropriately. #' @noRd .get_dims <- function(meta, var, c_v, axis) { dims <-meta$dimension[match(meta$axis$dimension[meta$axis$variable == var], meta$dimension$id), ] canon_order <- c("X", "Y", "Z", "T") dims$coord_var <- "" dims$axis <- canon_order[1:nrow(dims)] 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, ]$coord_var <- x_cv$variable dims[dims$id == y_cv$dimension, ]$coord_var <- y_cv$variable } 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$Z)) { z_axis <- axis[axis$variable == c_v$Z, ]$dimension 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_axis <- axis[axis$variable == c_v$T, ]$dimension if(length(z_axis) > 1) { z_axis <- z_axis[z_axis != t_axis] } dim_matcher <- c(dim_matcher, z_axis, t_axis) } if(all(!is.na(dim_matcher))) { if(length(dim_matcher) != length(dims$id)) { dim_matcher <- c(dim_matcher, dims$id[!dims$id %in% dim_matcher]) dim_matcher <- unique(dim_matcher) } dims <- dims[match(dims$id, dim_matcher), ] dims$axis <- canon_order[1:nrow(dims)] } 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)$dimids matcher <- match(coordvar_dimids, var_dimids) if(!all(diff(matcher[1:2]) == 1)) { warning("Non-canonical axis order found, attempting to correct.") } } matcher }, c_v = coord_var, nc = nc), var) } .get_data <- function(nc, var, dims, dimid_matcher) { out_data <- lapply(var, function(.v) { dm <- match(RNetCDF::var.inq.nc(nc, .v)$dimids, dims[, "id", drop = TRUE]) if(is.null(dm)) dm <- c(1:nrow(dims)) ret <- RNetCDF::var.get.nc(nc, variable = .v, start = dims[, "start", drop = TRUE][dm], count = dims[, "count", drop = TRUE][dm], collapse = FALSE, ## keep 1-dims unpack = TRUE, ## offset and scale applied internally rawchar = TRUE) ## needed for NC_CHAR, as per 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 lapply(out_data, function(.v) { if (mode(.v) == "raw") { array(unlist(lapply(.v, rawToChar)), dims$length) } else .v }) } .get_attributes <- function(attribute, att, var = NULL) { if(!is.null(var)) attribute <- attribute[attribute$variable == var, ] if(att %in% attribute$name) attribute[attribute$name == att, ] else { NULL } } .add_ncsub <- function(dims, ncsub) { 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$count <- ncsub[, "count"] } return(dims) } .set_nc_units <- function(data_list, nc_atts, make_units) { # Get all the units nc_units <- .get_attributes(nc_atts, "units") if (!is.null(nc_units) && nrow(nc_units) > 0 && make_units) { for (i in names(data_list)) { if (is.numeric(data_list[[i]]) && i %in% nc_units$variable) { uval <- unlist(nc_units$value[nc_units$variable == i]) units(data_list[[i]]) <- 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) 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$name[ic])[subidx] } else { coords[[ic]] <- subidx } } return(coords) } .get_nc_raster <- function(coords) { ## can we create a raster? raster = NULL ## which coords are regular if (length(coords) > 1) { raster = get_raster(affine = c(0, 0), dimensions = names(coords)[1:2], curvilinear = FALSE) } return(raster) } .get_nc_dimensions <- function(dimensions, coord_var, coords, nc, dims, var_names, curvilinear, eps, ignore_bounds, atts) { to_rectilinear = FALSE ## hack longitudes for #277 if (any(lon_coord <- grepl("lon", coord_var$X, ignore.case = TRUE))) { lon_coord <- coord_var$X[lon_coord][1] lons <- coords[[lon_coord]] if (!is.null(lons) && !regular_intervals(lons, epsilon = eps) && .is_degrees(atts, lon_coord) && lons[1L] > 180 && min(lons) > 0) { coords[[lon_coord]] <- ((coords[[lon_coord]] + 180) %% 360) - 180 } } regular <- .is_regular(coords, eps) for (i in seq_along(coords)) { if (names(coords)[i] %in% var_names && !ignore_bounds && length(bounds <- coord_var[coord_var$variable == names(coords)[i], ]$bounds) > 0 && bounds %in% var_names) { bounds = RNetCDF::var.get.nc(nc, bounds) if (!is.matrix(bounds)) # single instance, returns a vector bounds = matrix(bounds, nrow = 2) is_reg = ncol(bounds) > 1 && length(u <- .unique_fuzz(apply(bounds, 2, diff), eps)) == 1 && length(v <- .unique_fuzz(diff(bounds[1,]), eps)) == 1 if (is_reg && abs(u + v) < eps) { warning(paste("bounds for", names(coords)[i], "seem to be reversed; reverting them")) bounds = apply(bounds, 2, sort) # should not be needed according to CF, but see #133 u = v } if (is_reg && abs(v - u) < eps) { dimensions[[i]]$offset = bounds[1,1] dimensions[[i]]$delta = v } else { dimensions[[i]]$values = make_intervals(bounds[1,], bounds[2,]) dimensions[[i]]$point = FALSE if (i %in% 1:2 && length(curvilinear) < 1) # FIXME: ? hard-coding here that lon lat are in the first two dimensions: to_rectilinear = TRUE } } else if (regular[i]) { mdc = mean(diff(coords[[i]])) coord_name = names(coords)[i] t_dim = coord_var[coord_var$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]][dims$start[i]] else dimensions[[i]]$offset[1L] = coords[[i]][dims$start[i]] - mdc/2 ## NaN for singleton dims, but that seems ok unless we have explicit interval? dimensions[[i]]$delta[1L] = mdc } else { dimensions[[i]]$values = coords[[i]] ## offset/delta for fall-back index (and for NA test ) ## https://github.com/r-spatial/stars/blob/master/R/dimensions.R#L294-L303 dimensions[[i]]$offset[1L] = NA_real_ dimensions[[i]]$delta[1L] = NA_real_ } } if (to_rectilinear) dimensions = .as_rectilinear(dimensions) return(dimensions) } .get_nc_time <- function(dimensions, make_time, coord_var, var, meta) { # sort out time -> POSIXct: if (make_time) { if (all("T" %in% names(coord_var))) { tvar = coord_var[coord_var$variable == var, ] TIME_name = as.character(na.omit(unlist( coord_var[coord_var$variable == var, ][c("T")]))) if (!is.na(TIME_name) && length(TIME_name) == 1L && meta$variable$ndims[match(TIME_name, meta$variable$name)] == 1) { atts = meta$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] if (TIME_name %in% names(dimensions)) { tdim <- NULL if (is.null(tunit) || inherits(tunit, "try-error")) { warning("ignoring units of time dimension, not able to interpret") } else { tdim = make_cal_time2(dimensions[[TIME_name]], time_unit = tunit, cal = calendar) } if (!is.null(tdim)) dimensions[[TIME_name]] <- 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(dimension, time_name, time_unit = NULL, cal = NULL) { tm = get_dimension_values(dimension, geotransform = NA) 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$source) } else { NA } } #' @param sf_geometry sf data.frame with geometry and attributes to be added to stars object. #' Must have same number of rows as timeseries instances. #' @details For the \code{ncdfgeom} method: objects are point-timeseries with optional line or polygon geometry for each timeseries specified with the \code{sf_geometry} parameter. See \pkg{ncdfgeom} for more about this NetCDF-based format for geometry and timeseries. #' @name st_as_stars #' @export #' st_as_stars.ncdfgeom <- function(.x, ..., sf_geometry = NA) { crs <- sf::st_crs(4326) if(length(.x$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("sf" %in% class(sf_geometry)) { 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/spacetime.R0000644000175000017500000000605414144300304014034 0ustar nileshnilesh#' @export st_as_stars.STSDF = function(.x, ...) st_as_stars(as(.x, "STFDF"), ...) #' @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)) } 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/extract.R0000644000175000017500000001465714144300304013544 0ustar nileshnilesh#' 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 points in rows, indicating where to extract x #' @param bilinear logical; use bilinear interpolation rather than nearest neighbour? #' @param time_column character or integer; name or index of a column with time or date values that will be matched to values of the dimension "time" in \code{x}, after which this dimension is reduced. This is useful to extract data cube values along a trajectory; see https://github.com/r-spatial/stars/issues/352 . #' @param 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) 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)) 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 = "factor") } 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)) { tm = match("time", names(st_dimensions(x))) # FIXME: select based on refsys in time classes if (is.na(tm)) stop("cannot match times: x does not have a dimension called 'time'") tm_cube = st_dimensions(x)$time$values %||% st_get_dimension_values(x, "time") tm_ix = match_time(tm_pts, tm_cube, !st_dimensions(x)$time$point, interpolate_time) if (!interpolate_time) m = lapply(m, function(p) p[cbind(seq_along(at), tm_ix)]) else { interpolate = function(x, ix) { i = floor(ix) di = ix - i if (is.na(ix) || di == 0) x[i] else (1 - di) * x[i] + di * x[i+1] } # each time series is reduced to a _single_ time step, interpolated: m = lapply(m, function(n) mapply(interpolate, asplit(n, 1), tm_ix)) } } if (NCOL(m[[1]]) > 1) { # multi-band: if (inherits(at, "matrix")) do.call(cbind, m) else { # return stars: for (i in seq_along(x)) dim(m[[i]]) = c(length(at), dim(x)[-(1:2)]) d = structure(st_dimensions(x), raster = get_raster(dimensions = rep(NA_character_,2))) d[[1]] = create_dimension(values = at) d[[2]] = NULL names(d)[1] = sf_column setNames(st_as_stars(m, dimensions = d), names(x)) } } else { df = setNames(as.data.frame(lapply(m, function(i) structure(i, dim = NULL))), names(x)) if (inherits(at, "matrix")) df else { # return sf: df[[sf_column]] = st_geometry(at) if (!is.null(time_column)) { # add time columns of both cube and at: if (inherits(tm_cube, "intervals")) tm_cube = as.list(tm_cube) df$time = tm_cube[tm_ix] df[[time_column]] = tm_pts } st_as_sf(df) } } } # 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/sf.R0000644000175000017500000002307014144300304012467 0ustar nileshnilesh# 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 = get_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) { 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")$dimensions xy_pos = match(dxy, names(d)) stopifnot(all(xy_pos == 1:2)) # find which records are NA for all attributes: a = abind(x, along = length(dim(x)) + 1) keep = if (na.rm) as.vector(apply(a, c(1,2), function(x) !all(is.na(x)))) else rep(TRUE, prod(dim(x)[1:2])) # flatten two dims x,y to one dim sfc (replacing "x") sfc = st_as_sfc(x, as_points = as_points, ..., which = which(keep)) # overwrite raster-x with sfc: d[[ dxy[1] ]] = create_dimension(from = 1, to = length(sfc), values = sfc) # rename raster-x to sfc: names(d)[names(d) == dxy[1] ] = "geometry" # remove y: d[[ dxy[2] ]] = NULL attr(d, "raster") = get_raster(dimensions = rep(NA_character_, 2)) # flatten arrays: x = unclass(x) # to omit using [[<-.stars method: for (i in seq_along(x)) dim(x[[i]]) = c(geometry = length(keep), olddim[-xy_pos]) # reduce arrays to non-NA cells: if (na.rm) { args = rep(list(rlang::missing_arg()), length(dim(x[[1]]))) args[[1]] = which(keep) args[["drop"]] = FALSE for (i in seq_along(x)) x[[i]] = structure(eval(rlang::expr(x[[i]][ !!!args ])), levels = attr(x[[i]], "levels")) } st_stars(x, dimensions = d) } st_as_sf.dimensions = function(x) { ix = which_sfc(x)[1] st_sf(setNames(list(x[[ ix ]]$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(get_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 = get_geotransform(x), use_contours = FALSE, connect8 = connect8, ...) # factor levels? if (!is.null(lev <- attr(x[[1]], "levels"))) ret[[1]] = structure(ret[[1]], class = "factor", levels = lev) 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 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 (!is.null(other_values) && length(other_values) == ncol(df)) names(df) = other_values 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 = get_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 #' @param name character; name for the geometry dimensions #' @export st_as_stars.sf = function(.x, ..., name = attr(.x, "sf_column")) { geom = st_geometry(.x) if (length(list(...))) stop("... arguments ignored") dimensions = create_dimensions(setNames(list(create_dimension(1, length(geom), refsys = st_crs(geom), values = geom)), name)) lst = lapply(st_set_geometry(.x, NULL), function(x) { dim(x) = length(geom); x }) st_as_stars(lst, dimensions = dimensions) } stars/R/stars.R0000644000175000017500000010526414157127445013242 0ustar nileshnileshsplit_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") } #' 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} 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)) { 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]]$point = FALSE } } } } st_stars(.x, dimensions %||% create_dimensions(dim(.x[[1]]))) } st_stars = function(x, dimensions) { # sanity checks: stopifnot(is.list(x)) stopifnot(inherits(dimensions, "dimensions")) stopifnot(!is.null(attr(dimensions, "raster"))) # for (i in seq_along(x)) # names(dim(x[[i]])) = names(dimensions) structure(x, dimensions = dimensions, class = "stars") } #' @name st_as_stars #' @export #' @param raster character; the names of the dimensions that denote raster dimensions st_as_stars.default = function(.x = NULL, ..., raster = NULL) { args = if (is.null(.x)) list(...) else append(list(.x), list(...)) if (length(args) == 0) return(st_as_stars(st_bbox())) isdim = sapply(args, inherits, what = "dimensions") dimensions = if (! any(isdim)) { if (is.array(args[[1]]) && !is.null(dimnames(args[[1]]))) st_dimensions(args[[1]]) else do.call(st_dimensions, lapply(dim(args[[1]]), function(x) seq_len(x) - 1)) } else { d = args[[ which(isdim)[1] ]] if (is.null(raster)) raster = attr(d, "raster") d } if (is.null(raster) && !has_sfc(dimensions)) { w = which(sapply(dimensions, function(x) is.null(x$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)) st_as_stars.list(args, dimensions = dimensions) } #' @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. #' @export #' @name st_as_stars st_as_stars.stars = function(.x, ..., curvilinear = NULL, crs = st_crs(4326)) { 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)), 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; cell size in x direction; see details #' @param dy numeric; 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? #' @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, they are computed as the ceiling of the ratio of the (x or y) range divided by (dx or dy), unless \code{inside} is \code{TRUE}, in which case ceiling is replaced by floor. Positive \code{dy} will be made negative. Further named arguments (\code{...}) are passed on to \code{pretty}. #' @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) { 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 (missing(dx) && missing(dy)) { if (missing(nx)) dx = dy = sqrt(adx * ady / n) else { dx = diff(xlim)/nx dy = -diff(ylim)/ny } } if (missing(nx)) nx = ifelse(inside, floor(diff(xlim) / dx), ceiling(diff(xlim) / dx)) if (missing(ny)) { if (dy > 0) dy = -dy ny = ifelse(inside, floor(-diff(ylim) / dy), ceiling(-diff(ylim) / dy)) } 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: st_as_stars(values = array(values, c(x = nx[[1L]], y = ny[[1L]])), # [[1]] unnames dims = create_dimensions(list(x = x, y = y), get_raster())) else { z = create_dimension(from = 1, to = nz[[1]]) st_as_stars(values = array(values, c(x = nx[[1L]], y = ny[[1L]], z = nz[[1]])), # [[1]] unnames 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 = get_geotransform(obj) if (isTRUE(st_is_longlat(st_crs(obj)))) { bb = st_bbox(obj) sign = ifelse(x[,1] < bb["xmin"], 1., ifelse(x[,1] > bb["xmax"], -1., 0.)) x[,1] = x[,1] + sign * 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") } 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$refsys %in% c("POSIXct", "Date", "PCICt")))) } has_sfc = function(x) { length(which_sfc(x)) > 0 } #' retrieve coordinates for raster or vector cube cells #' #' retrieve coordinates for raster or vector cube cells #' @param x object of class \code{stars} #' @param add_max logical; if \code{TRUE}, dimensions are given with a min (x) and max (x_max) value #' @param center logical; (only if \code{add_max} is FALSE): should grid cell center coordinates be returned (TRUE) or offset values (FALSE)? \code{center} can be a named logical vector or list to specify values for each dimension. #' @name st_coordinates #' @param ... ignored #' @export st_coordinates.stars = function(x, ..., add_max = FALSE, center = TRUE) { dims = st_dimensions(x) xy = attr(dims, "raster")$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, get_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 as.data.frame.stars = function(x, ..., add_max = FALSE, center = NA) { data.frame(st_coordinates(x, add_max = add_max, center = center), lapply(x, function(y) structure(y, dim = NULL))) } #' @export print.stars = function(x, ..., n = 1e5, abbrev = 30) { add_units = function(x) { f = function(obj) if (inherits(obj, "units")) paste0("[", enc2utf8(as.character(units(obj))), "]") else "" paste(names(x), sapply(x, f)) } shorten = function(s) { if (nchar(s) > abbrev) paste0(substr(s, 1, abbrev), "...") 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 } #' 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, #' @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, function(x) inherits(x, "stars")))) stop("all arguments to c() should be stars objects") if (any(sapply(dots, function(x) inherits(x, "stars_proxy")))) stop("convert stars_proxy objects to stars first using st_as_stars()") if (length(dots) == 1) { if (!missing(along)) warning("along argument ignored; maybe you wanted to use st_redimension?") dots[[1]] } else if (identical(along, NA_integer_)) { # 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)) { ret = st_as_stars(do.call(c, lapply(dots, unclass)), dimensions = st_dimensions(dots[[1]])) if (!missing(nms)) { if (length(nms) != length(ret)) stop("length of argument nms must equal the number of attributes") names(ret) = nms } ret } 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")) setNames(st_as_stars(do.call(c, lapply(dots[ident], unclass)), dimensions = st_dimensions(dots[[1]])), nms[ident]) } } } 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 = 4326) 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 = get_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] ]]$refsys = value } # set crs of sfc's, if any: for (j in which_sfc(x)) x[[ j ]]$refsys = 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) ]]$values } #' @name merge #' @aliases split #' @param f the name or index of the dimension to split; by default the last dimension #' @param drop ignored #' @details split.stars works on the first attribute, and will give an error when more than one attribute is present #' @export split.stars = function(x, f = length(dim(x)), drop = TRUE, ...) { stopifnot(length(x) == 1) d = st_dimensions(x) if (is.character(f)) f = which(names(d) == f) ret = lapply(seq_len(dim(x)[f]), function(y) asub(x[[1]], y, f, drop = TRUE)) spl = st_as_stars(ret, dimensions = d[-f]) if (is.null(names(spl))) names(spl) = if (!is.null(d[[f]]$values)) d[[f]]$values else make.names(seq_along(spl)) spl } #' merge or split stars object #' #' merge attributes into a dimension, or split a dimension over attributes #' @param x object of class \code{stars} #' @param y needs to be missing #' @param name name for the new dimension #' @param ... if defined, the first unnamed argument is used for dimension values, if not defined, attribute names are used for dimension values #' @returns merge merges attributes of a stars object into a new dimension; split splits a dimension over attributes #' @name merge #' @export merge.stars = function(x, y, ..., name = "attributes") { dots = list(...) if (!missing(y)) stop("argument y needs to be missing: merging attributes of x") old_dim = st_dimensions(x) out = do.call(abind, st_redimension(x)) if (is.factor(x[[1]]) && is.character(out)) out = structure(factor(as.vector(out), levels = levels(x[[1]])), dim = dim(out)) new_dim = if (length(dots)) create_dimension(values = dots[[1]]) else create_dimension(values = names(x)) dims = setNames(c(old_dim, list(new_dim)), make.unique(c(names(old_dim), name))) d = create_dimensions(dims, raster = attr(old_dim, "raster")) if (!is.null(names(dots))) names(d)[length(d)] = names(dots) st_as_stars(out, dimensions = d) } sort_out_along = function(ret) { d1 = st_dimensions(ret[[1]]) d2 = st_dimensions(ret[[2]]) if ("time" %in% names(d1) && (isTRUE(d1$time$offset != d2$time$offset) || !any(d1$time$values %in% d2$time$values))) "time" else NA_integer_ } #' @export is.na.stars = function(x, ...) { st_as_stars(lapply(x, is.na), dimensions = st_dimensions(x)) } #' redimension array, or collapse attributes into a new dimension #' #' redimension array, or collapse attributes into a new dimension #' @name redimension #' @export st_redimension = function(x, new_dims, along, ...) UseMethod("st_redimension") #' @export #' @name redimension #' @param x object of class \code{stars} #' @param new_dims target dimensions: either a `dimensions` object or an integer vector with the dimensions' sizes #' @param along named list with new dimension name and values #' @param ... ignored st_redimension.stars = function(x, new_dims = st_dimensions(x), along = list(new_dim = names(x)), ...) { d = st_dimensions(x) if (inherits(new_dims, "dimensions")) { di = dim(new_dims) } else { di = new_dims new_dims = create_dimensions(di) } if (!isTRUE(all.equal(di, dim(x), check.attributes = FALSE))) { if (prod(dim(x)) != prod(di)) stop("product of dim(new_dim) does not match that of x") for (i in seq_len(min(length(di), length(dim(x))))) if (di[i] == dim(x)[i]) new_dims[[i]] = d[[i]] x = unclass(x) for (i in seq_along(x)) dim(x[[i]]) = di st_stars(x, dimensions = new_dims) } else { # collapse attributes into dimension if (length(x) == 1) # only one attribute: do nothing x else { new_dim = create_dimension(values = along[[1]]) dims = create_dimensions(c(d, new_dim = list(new_dim)), attr(d, "raster")) if (length(names(along)) == 1) names(dims)[names(dims) == "new_dim"] = names(along) ret = list(attr = do.call(abind, c(unclass(x), along = length(dim(x)) + 1))) st_stars(setNames(ret, paste(names(x), collapse = ".")), dimensions = dims) } } } #' @export "$<-.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")) 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) { st_stars(lapply(x, 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) 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)) { dimension_type = function(d) { if (!any(is.na(c(d$offset, d$delta)))) "regular" 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" } stars/R/values.R0000644000175000017500000000725513777653456013423 0ustar nileshnilesh## $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) } 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/dimensions.R0000644000175000017500000005746014157127414014256 0ustar nileshnilesh#' 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") NextMethod() } #' @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 #' @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 (! is.null(values)) { if (is.na(which)) stop("which should be a name or index of an existing dimensions") if (inherits(values, "dimensions")) { if (is.null(names)) names <- names(values) values <- values[[1]]$values } if (!inherits(values, "intervals") && dim(.x)[which] != length(values)) { if (dim(.x)[which] == length(values) - 1) # create intervals: values = as_intervals(values) else stop(paste("length of values (", length(values), ") does not match length of dimension", which, "(", dim(.x)[which], ")")) } d[[which]] = create_dimension(values = values, point = point %||% d[[which]]$point, ...) if (! is.null(names) && length(names) == 1) base::names(d)[which] = names r = attr(d, "raster") if (isTRUE(r$curvilinear)) { # FIXME: there's much more that should be checked for curvilinear grids... # https://github.com/r-spatial/stars/issues/460 if (which %in% r$dimensions && !is.matrix(values)) attr(d, "raster")$curvilinear = FALSE } # else if (inherits(values, "sfc")) # base::names(d)[which] = "sfc" } else if (!is.null(point) && is.logical(point)) { d[[which]]$point = point } else 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 } else if (! missing(xy)) { stopifnot(length(xy) == 2) r = attr(d, "raster") r$dimensions = as.character(xy) attr(d, "raster") = r } else d[[which]] = create_dimension(from = 1, to = dim(.x)[which], ...) if (inherits(.x, "stars_proxy")) structure(.x, dimensions = d) else st_as_stars(unclass(.x), dimensions = d) } #' @name st_dimensions #' @param where character, one of 'start', 'center' or 'end'. Set to NA (default) to ignore and use \code{max} and \code{center} explictly. This argument provides a convenient alternative to setting \code{max} and \code{center}. #' @param max logical; if \code{TRUE} return the end, rather than the beginning of an interval #' @param center logical; if \code{TRUE} return the center of an interval; if \code{NA} return the center for raster dimensions, and the start of intervals in other cases #' @export #' @examples #' m = matrix(1:20, nrow = 5, ncol = 4) #' dim(m) = c(x = 5, y = 4) # named dim #' (s = st_as_stars(m)) #' st_get_dimension_values(s, 'x', where = "start") #' st_get_dimension_values(s, 'x', center = FALSE) #' st_get_dimension_values(s, 'x', where = "center") #' st_get_dimension_values(s, 'x', center = TRUE) #' st_get_dimension_values(s, 'x', where = "end") #' st_get_dimension_values(s, 'x', max = TRUE) st_get_dimension_values = function(.x, which, ..., where = NA, max = FALSE, center = NA) { if ((!is.numeric(which) && !is.character(which)) || length(which) != 1) stop("argument which should be a length 1 dimension index or name") # nocov if (!is.na(where)){ w = tolower(where[1]) if (w == 'center'){ max = FALSE center = TRUE } else if (w == 'start'){ max = FALSE center = FALSE } else if (w == 'end'){ max = TRUE center = NA } else { stop("where, if not NA, must be 'start', 'center' or 'end': ", where) } } expand_dimensions(.x, ..., max = max, center = center)[[which]] } #' @export "[.dimensions" = function(x, i, j,..., drop = FALSE) { raster = attr(x, "raster") ret = unclass(x)[i] if (isTRUE(all(raster$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")))) 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$start # refsys: if (inherits(example, "POSIXct")) refsys = "POSIXct" else if (inherits(example, "Date")) refsys = "Date" else if (inherits(example, "PCICt")) refsys = "PCICt" else if (inherits(example, "units")) refsys = "udunits" if (inherits(values, "sfc")) { point = inherits(values, "sfc_POINT") if (!is.na(st_crs(values)) && is.na(refsys)) # inherit: refsys = st_crs(values) } if (is.numeric(values) && (is.na(point) || !point)) { values = if (is_raster) set_dimension_values(centers = values) else set_dimension_values(start = values) } } structure(list(from = unname(from), to = unname(to), offset = unname(offset), delta = unname(delta), refsys = refsys, point = unname(point), values = values), class = "dimension") } create_dimensions = function(lst, raster = NULL) { if (is.numeric(lst)) # when called with a dim(array) argument: lst = setNames(lapply(seq_along(lst), function(i) create_dimension(from = 1, to = lst[i])), names(lst)) if (is.null(names(lst))) names(lst) = make.names(seq_along(lst)) if (any(names(lst) == "")) { sel = which(names(lst) == "") names(lst)[sel] = make.names(seq_along(sel)) } if (is.null(raster)) raster = get_raster(dimensions = c(NA_character_, NA_character_)) structure(lst, raster = raster, class = "dimensions") } get_crs = function(pr) { if (!is.null(pr$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 # set up raster: raster = get_raster(affine = pr$geotransform[c(3,5)], dimensions = c("x", "y"), curvilinear = FALSE) create_dimensions(lst, raster) } get_raster = function(affine = rep(0, 2), dimensions = c("x", "y"), curvilinear = FALSE) { if (any(is.na(affine))) { # warning("setting NA affine values to zero") affine = c(0, 0) } structure(list(affine = affine, dimensions = dimensions, curvilinear = curvilinear), class = "stars_raster") } get_geotransform = function(x) { if (inherits(x, "stars")) x = st_dimensions(x) stopifnot(inherits(x, "dimensions")) r = attr(x, "raster") if (is.null(r)) rep(NA_real_, 6) else { xd = x[[ r$dimensions[1] ]] yd = x[[ r$dimensions[2] ]] c(xd$offset, xd$delta, r$affine[1], yd$offset, r$affine[2], yd$delta) } } #' @export print.stars_raster = function(x, ...) { if (any(is.na(x$affine))) cat(paste("affine parameters:", x$affine[1], x$affine[2], "\n")) else if (any(x$affine != 0.0)) cat(paste("sheared raster with parameters:", x$affine[1], 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_ } 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$dim_extra[[v]] = as.numeric(rhs) # nocov } cal = get_val(paste0(v, "#calendar"), meta) u = get_val(paste0(v, "#units"), meta) if (! is.na(u)) { if (v == "time" && !is.na(cal) && cal %in% c("360_day", "365_day", "noleap")) { origin = 0:1 units(origin) = try_as_units(u) delta = if (grepl("months", u)) { if (cal == "360_day") set_units(30 * 24 * 3600, "s", mode = "standard") else set_units((365/12) * 24 * 3600, "s", mode = "standard") } else set_units(as_units(diff(as.POSIXct(origin))), "s", mode = "standard") origin_txt = as.character(as.POSIXct(origin[1])) if (!requireNamespace("PCICt", quietly = TRUE)) stop("package PCICt required, please install it first") # nocov pr$dim_extra[[v]] = PCICt::as.PCICt(pr$dim_extra[[v]] * as.numeric(delta), cal, origin_txt) } else { units(pr$dim_extra[[v]]) = try_as_units(u) if (v == "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$point = switch(point, Area=FALSE, Point=TRUE, NA) properties } expand_dimensions = function(x, ...) UseMethod("expand_dimensions") expand_dimensions.stars = function(x, ...) { expand_dimensions(st_dimensions(x), ...) } # returns, in case of numeric dimensions: # center = TRUE: return center values for x and y coordinates, interval start values otherwise # center = FALSE: return start values # center = NA: return centers for x/y raster, otherwise start values # add_max = TRUE: add in addition to x and y start values an x_max and y_max end values expand_dimensions.dimensions = function(x, ..., max = FALSE, center = NA) { if (length(center) == 1) center = setNames(rep(center, length(x)), names(x)) if (length(max) == 1) max = setNames(rep(max, length(x)), names(x)) if (is.list(max)) max = unlist(max) if (is.list(center)) center = unlist(center) if (any(max & center, na.rm = TRUE)) stop("only one of max and center can be TRUE, not both") where = setNames(rep(0.0, length(x)), names(x)) # offset for (i in seq_along(x)) { if (max[i]) where[i] = 1.0 if (isTRUE(center[i])) where[i] = 0.5 } dimensions = x xy = attr(x, "raster")$dimensions gt = get_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]) # where_xy = if (!max && (is.na(center) || center)) # 0.5 # else # where 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$from + 1 } ), names(x)) else lengths(expand_dimensions(x)) # FIXME: optimise? } #' @export as.data.frame.dimensions = function(x, ..., digits = 6, usetz = TRUE, stars_crs = getOption("stars.crs") %||% 28) { lst = lapply(x, function(y) { if (length(y$values) > 3) { y$values = if (is.array(y$values)) paste0("[", paste(dim(y$values), collapse = "x"), "] ", format(min(y$values), digits = digits), ",...,", format(max(y$values), digits = digits)) else if (inherits(y$values[[1]], "crs")) paste0(format(y$values[[1]]), ",...,", format(y$values[[length(y$values)]])) else paste0(format(head(y$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 } ) mformat = function(x, ..., digits) { if (inherits(x, c("PCICt", "POSIXct"))) format(x, ..., usetz = usetz) else format(x, digits = digits, ...) } lst = lapply(lst, function(x) sapply(x, mformat, digits = digits)) ret = data.frame(do.call(rbind, lst), stringsAsFactors = FALSE) 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) = c(names(lst[[1]]), "x/y") } ret } #' @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_ } if (! isTRUE(all.equal(d1, di, tolerance = tolerance))) return(FALSE) } } TRUE } 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$to # valid range if (max(i) > -1 && min(i) < 0) stop("cannot mix positive and negative indexes") if (is.logical(i)) i = which(i) else if (all(i < 0)) i = setdiff(rang, abs(i)) # subtract if (all(diff(i) == 1)) { if (max(i) > length(rang)) stop("invalid range selected") sel = rang[i] x$from = min(sel) x$to = max(sel) } else { # invalidate offset & delta x$delta = x$offset = NA x$from = 1 x$to = length(i) } } } x } #' @export `[<-.dimensions` = function(x, i, value) { # stopifnot(length(i) == length(value)) if (!is.null(value)) create_dimensions(NextMethod(), raster = attr(x, "raster")) else NextMethod() } #' @export dimnames.stars = function(x) { names(st_dimensions(x)) } #' @export `dimnames<-.stars` = function(x, value) { for (i in seq_along(x)) names(dim(x[[i]])) = value names(attr(x, "dimensions")) = value x } stars/R/rasterize.R0000644000175000017500000001721714157334513014112 0ustar nileshnilesh# align bb with the first two dimensions of d st_align = function(bb, d) { up1 = function(x) ifelse(sign(x) < 0, -ceiling(abs(x)), floor(x)) up2 = function(x) ifelse(sign(x) > 0, ceiling(abs(x)), -floor(abs(x))) dx = d[[1]]$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 stars object with desired target geometry, or target geometry alignment if \code{align=TRUE} #' @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} contain the geometry alignment, #' informing target resolution and offset only. #' @param ... arguments passed on to \link{st_as_stars} #' @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, ...) { 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]]$delta template = st_as_stars(bb, values = NA_real_, dx = dx, dy = dy, nx = round(diff(bb[c("xmin", "xmax")])/dx), ny = round(diff(bb[c("ymin", "ymax")])/dx)) } else template = st_normalize(template) is_numeric = function(x) is.numeric(x) || is.factor(x) isn = sapply(sf, is_numeric) sf = if (!any(isn)) { sf$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, get_geotransform(template), file, driver, options) ret = read_stars(file, driver = driver) 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 = "factor", levels = levels(attrs[[i]])) } setNames(ret, names(attrs)) } guess_raster = function(x, ...) { if (length(list(...))) return(NULL) if (all(st_dimension(x) == 0)) { # POINT cc = st_coordinates(x) ux = sort(unique(cc[,1])) uy = sort(unique(cc[,2])) dux = unique(diff(ux)) if (length(dux) > 1) { if (var(dux)/mean(dux) < 1e-8) dux = mean(dux) else return(NULL) } duy = unique(diff(uy)) if (length(duy) > 1) { if (var(duy)/mean(duy) < 1e-8) duy = mean(dux) else return(NULL) } if (length(ux) * length(uy) <= 2 * nrow(cc)) { 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 dims has been specified #' @param y_decreasing logical; if TRUE, (numeric) y values get a negative delta (decrease with increasing index) #' @name st_as_stars #' @examples #' data(Produc, package = "plm") #' st_as_stars(Produc, y_decreasing = FALSE) st_as_stars.data.frame = function(.x, ..., dims = coords, xy = dims[1:2], 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 (is.character(xy)) xy = match(xy, names(.x)) if (any(is.na(xy))) stop("xy coordinates not found in data") index = NULL dimensions = list() if (length(dims) >= 2) { 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(v, digest::digest) uv = unique(dig) # no need to sort ix = match(dig, uv) # but look up "hash collision" } else { suv = 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) 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 } ) } else { l = lapply(.x, as.array) dimensions[[1]] = if (length(dims) == 0 || dims < 1) create_dimension(values = row.names(.x)) else { l[[dims]] = NULL create_dimension(values = .x[[dims]]) } names(dimensions) = "rows" d = create_dimensions(dimensions) } 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/aggregate.R0000644000175000017500000002315414157127414014025 0ustar nileshnilesh#' 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}); 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. #' @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 #' @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") #' 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") #' x_agg_time - x_agg_posix #' aggregate(x, "2 days", mean) #' # 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) { if (inherits(by, "stars")) { by = st_as_sfc(by, as_points = FALSE) # and if not, then use st_normalize(by) } classes = c("sf", "sfc", "POSIXct", "Date", "PCICt", "character", "function") if (!is.function(by) && !inherits(by, classes)) stop(paste("currently, only `by' arguments of class", paste(classes, collapse= ", "), "supported")) if (missing(FUN)) stop("missing FUN argument") if (exact && inherits(by, c("sf", "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(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, mean)) see https://github.com/r-spatial/stars/issues/289 if (!identical(FUN, sum)) { 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): for (i in seq_along(x)) { if (inherits(x[[i]], "units")) agg[[i]] = units::set_units(agg[[i]], units(x[[i]]), mode = "standard") } ret = st_as_stars(agg, dimensions = create_dimensions(append(list(sfc = create_dimension(values = by)), st_dimensions(x)[-(1:2)]))) return(ret) } geom = "geometry" 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)) } if (inherits(by, "sf")) { geom = attr(by, "sf_column") by = st_geometry(by) } # 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 (inherits(by, "function")) { 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, ...) { do.call(rbind, lapply(uq, function(i) { sel <- which(grps == i) if (!isTRUE(any(sel))) rep(NA_real_, ncol(x)) else apply(x[sel, , drop = FALSE], 2, FUN, ...) } )) } # 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]] = units::set_units(agr_grps(a, grps, seq_along(by), FUN, ...), u, mode = "standard") } # reconstruct dimensions table: d[[1]] = create_dimension(values = by) names(d)[1] = if (inherits(by, c("POSIXct", "Date", "PCICt", "function"))) "time" else geom if (drop_y) d = d[-2] # y newdim = c(sfc = length(by), dims[-(1:ndims)]) 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, "stars")) by = st_as_sfc(by, as_points = FALSE) if (!inherits(by, c("sf", "sfc", "sfg"))) stop("aggregate.stars_proxy only implemented for spatial `by' arguments") by = st_geometry(by) # 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, ...)) do.call(c, c(l, along = list(which_sfc(l[[1]])))) } stars/R/proxy.R0000644000175000017500000005011614157127414013256 0ustar nileshnileshshorten_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$file.sep, clean) else clean } #' @export print.stars_proxy = function(x, ..., n = 1e5, nfiles = 10, simplify = TRUE) { if (!is.null(attr(x, "resolutions"))) cat("multi-resolution ") cat("stars_proxy object with", length(x), if (length(x) > 1) "attributes" else "attribute", "in", sum(lengths(x)), "file(s)") if (length(x[[1]]) > nfiles) cat("; showing the first", min(length(x[[1]]), nfiles), "filenames\n") else cat(":\n") names = lapply(x, function(nm) if (is.function(nm)) nm() else nm) if (simplify) print(lapply(names, shorten_names, n = nfiles)) else print(lapply(names, head, n = nfiles)) if (!is.null(attr(x, "NA_value")) && !is.na(attr(x, "NA_value"))) cat("NA_value: ", attr(x, "NA_value"), "\n") cat("dimension(s):\n") print(st_dimensions(x), ...) if (!is.null(attr(x, "call_list"))) { cat("call_list:\n") print(unlist(attr(x, "call_list"))) } } #' @export dim.stars_proxy = function(x) { dim(st_dimensions(x)) } #' @export as.data.frame.stars_proxy = function(x, ...) { as.data.frame(st_as_stars(x), ...) } #' @name plot #' @export #' @details when plotting a subsetted \code{stars_proxy} object, the default value for argument \code{downsample} will not be computed correctly, and has to be set manually. plot.stars_proxy = function(x, y, ..., downsample = get_downsample(dim(x))) { plot(st_as_stars(x, downsample = downsample, ...), ..., downsample = 0) } st_stars_proxy = function(x, dimensions, ..., NA_value, resolutions, RasterIO = list()) { stopifnot(!missing(NA_value)) stopifnot(!missing(resolutions)) stopifnot(length(list(...)) == 0) stopifnot(is.list(x)) stopifnot(inherits(dimensions, "dimensions")) if (length(RasterIO) == 0) RasterIO = NULL structure(x, dimensions = dimensions, NA_value = NA_value, resolutions = resolutions, RasterIO = RasterIO, class = c("stars_proxy", "stars")) } add_resolution = function(lst) { n = length(lst) resolutions = data.frame(x = numeric(n), y = numeric(n)) for (i in seq_along(lst)) { d = st_dimensions(lst[[i]]) xy = attr(d, "raster")$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(...) if (!all(sapply(dots, function(x) inherits(x, "stars_proxy")))) stop("all arguments to c() should be stars_proxy objects") # 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) # 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(do.call(c, lapply(dots, unclass)), dimensions = st_dimensions(dots[[1]]), NA_value = attr(dots[[1]], "NA_value"), resolutions = NULL) else if (identical_dimensions(dots, ignore_resolution = TRUE, tolerance = tolerance)) { dots = add_resolution(dots) st_stars_proxy(do.call(c, lapply(dots, unclass)), dimensions = st_dimensions(dots[[1]]), resolutions = attr(dots, "resolutions"), NA_value = attr(dots[[1]], "NA_value")) } 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")) setNames(st_stars_proxy(do.call(c, lapply(dots[ident], unclass)), dimensions = st_dimensions(dots[[1]]), NA_value = attr(dots[[1]], "NA_value"), resolutions = NULL), nms[ident]) } } } else { 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) } } } 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$to) } # do it: ret = vector("list", length(x)) res <- attr(x, "resolutions") for (i in seq_along(ret)) { if (!is.null(res) && any(res[1,] != res[i,])) { mult = c(res[i,1] / res[1,1], res[i,2] / res[1,2]) rasterio$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$from - 1, mult[2]))) } else offset = c(0,0) file_name = unclass(x)[[i]] if (is.function(file_name)) # realise: file_name = file_name() ret[[i]] = read_stars(file_name, RasterIO = rasterio, NA_value = attr(x, "NA_value") %||% NA_real_, normalize_path = FALSE, proxy = FALSE, ...) if (i == 1) dm1 = dim(ret[[1]]) else { xrange = seq_len(dm1[1]) + offset[1] yrange = seq_len(dm1[2]) + offset[2] ret[[i]] = ret[[i]] [ , xrange, yrange ] st_dimensions(ret[[i]]) = st_dimensions(ret[[1]]) } } along = if (length(dim(x)) > 3) setNames(list(st_get_dimension_values(x, 4)), tail(names(st_dimensions(x)), 1)) else list(new_dim = names(x)) ret = if (length(ret) == 1) st_redimension(ret[[1]], along = along) else do.call(c, lapply(ret, st_redimension, along = along)) new_dim = st_dimensions(ret) # for (dm in setdiff(names(d), xy)) # copy over non x/y dimension values, if present: # if (dm %in% names(new_dim)) # new_dim[[dm]] = d[[dm]] if (length(d) > 2) for (dm in 3:length(d)) { new_dim[[dm]] = d[[dm]] # copy all fields - what if this was downsampled? names(new_dim)[dm] = names(d)[dm] } ret = unclass(ret) for (i in seq_along(ret)) dim(ret[[i]]) = dim(new_dim) adrop(st_set_crs(st_stars(setNames(ret, names(x)), new_dim), st_crs(x))) } check_xy_warn = function(call, dimensions) { if (as.character(as.list(call)[[1]]) == "st_apply") { # check dims MARGIN = as.list(call)$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 ALLWAYS 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()) } #' @export "[<-.stars_proxy" = function(x, i, downsample = 0, value) { collect(x, match.call(), "[<-", c("x", "i", "value", "downsample"), env = environment()) } #' @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 } mc = match.call() lst = as.list(mc) cl = attr(x, "call_list") if (length(lst) < 3) return(x) # if (missing(i)) # 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) lst[["i"]] = TRUE # this one has been handled now } for (ix in 1:3) { # FIXME: process further dimensions than 3? if (length(lst) >= 4 && !is.null(r <- get_range(lst[[4]]))) { attr(x, "dimensions")[[ix]]$from = r[1] attr(x, "dimensions")[[ix]]$to = r[2] lst[[4]] = NULL # remove } } } else if (crop && inherits(i, c("sf", "sfc", "stars", "bbox"))) { x = st_crop(x, i, ..., collect = FALSE) # does bounding box cropping only if (inherits(i, c("stars", "bbox"))) lst[["i"]] = TRUE # this one has been handled now } # return: if (length(lst) == 3 && isTRUE(lst[["i"]]) && is.null(cl)) # all is done x else # still processing the geometries inside the bbox: collect(x, as.call(lst), "[", c("x", "i", "drop", "crop"), env = environment()) # postpone every arguments > 3 to after reading cells } # shrink bbox with e * width in each direction bb_shrink = function(bb, e) { dx = diff(bb[c("xmin", "xmax")]) dy = diff(bb[c("ymin", "ymax")]) st_bbox(setNames(c(bb["xmin"] + e * dx, bb["ymin"] + e * dy, bb["xmax"] - e * dx, bb["ymax"] - e * dy), c("xmin", "ymin", "xmax", "ymax"))) } #' @name st_crop #' @param collect logical; if \code{TRUE}, repeat cropping on \code{stars} object, i.e. after data has been read #' @export st_crop.stars_proxy = function(x, y, ..., crop = TRUE, epsilon = sqrt(.Machine$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) # crop y: if (dm[[ yd ]]$delta < 0) cr[1:2, 2] = cr[2:1, 2] dm[[ yd ]]$from = max(1, cr[1, 2], na.rm = TRUE) dm[[ yd ]]$to = min(d_max[yd], cr[2, 2], na.rm = TRUE) } x = st_stars_proxy(x, dm, NA_value = attr(x, "NA_value"), resolutions = attr(x, "resolutions")) # crop to bb if (collect) collect(x, match.call(), "st_crop", c("x", "y", "crop", "epsilon"), env = environment(), ...) # crops further when realised else x } #' @export st_apply.stars_proxy = function(X, MARGIN, FUN, ..., CLUSTER = NULL, PROGRESS = FALSE, FUTURE = FALSE, rename = TRUE, .fname) { mc = match.call() if (missing(.fname)) .fname = as.character(mc[["FUN"]])[1] collect(X, mc, "st_apply", c("X", "MARGIN", "FUN", "CLUSTER", "PROGRESS", "FUTURE", "rename", ".fname"), env = environment(), ...) } #' @export #' @name predict.stars predict.stars_proxy = function(object, model, ...) { collect(object, match.call(), "predict", c("object", "model"), env = environment(), ...) } #' @export "[[<-.stars_proxy" = function(x, i, value) { y = unclass(x) y[[i]] = value structure(y, class = class(x)) } #nocov start get_data_url = function(url, expr = NULL) { if (!requireNamespace("httr", quietly = TRUE)) # GET, POST, PUT stop("package httr required, please install it first") # nocov if (!requireNamespace("jsonlite", quietly = TRUE)) # base64_dec, base64_enc, toJSON, fromJSON stop("package jsonlite required, please install it first") # nocov if (is.null(expr)) jsonlite::fromJSON( httr::content(httr::GET(url), "text", encoding = "UTF-8")) else { js = jsonlite::fromJSON( httr::content(httr::POST(url, body = list(expr = expr), encode = "json"), "text", encoding = "UTF-8")) if (is.list(js) && !is.null(js$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/write.R0000644000175000017500000001535014030675413013225 0ustar nileshnilesh# 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) } 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} #' @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) { if (missing(layer) && length(obj) > 1) warning("all but first attribute are ignored") 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 = get_geotransform(obj), update = update) 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), 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, 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 = get_geotransform(structure(obj, dimensions = d)) sf::gdal_write(structure(obj, dimensions = d), ..., file = dsn, driver = driver, options = options, type = type, 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, 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', asc = 'ascii', nc = , cdf = , ncdf = 'netcdf', 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.R0000644000175000017500000002553414157205702013400 0ustar nileshnilesh#' @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)) }) #' @name st_as_stars #' @param ignore_file logical; if \code{TRUE}, ignore the SpatRaster object file name #' @export st_as_stars.SpatRaster = function(.x, ..., ignore_file = FALSE) { 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 (attr_name == "") attr_name = "values" if (!ignore_file && all(src$source != "")) { # there can be multiple files, but only the first one is used here. # perhaps a warning should be given; better would be to iterate over "sid" # but you might have a situation where some sources are filenames and others are not lst = vector("list", length(unique(src$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$sid == i]) r = try(read_stars(file, RasterIO = RasterIO, ...), silent = TRUE) if (! inherits(r, "try-error")) { if (is.na(st_crs(r))) r = st_set_crs(r, st_crs(terra::crs(.x))) r = fix_dims(r, e) if (length(unique(src$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$bands, collapse = " "))) lst[[i]] = r } ret = if (length(lst) > 1) merge(setNames(do.call(c, lst), names(.x))) else r if (!all(is.na(terra::time(.x)))) ret = st_set_dimensions(ret, 3, values = terra::time(.x), names = "time") return(setNames(ret, attr_name)) } v = terra::values(.x, mat = FALSE) dim(v) = dim(.x)[c(2,1,3)] if (all(terra::is.factor(.x))) { l = terra::levels(.x)[[1]] colors = try(rgb(terra::coltab(.x)[[1]], maxColorValue = 255)) if (inherits(colors, "try-error") || length(colors) == 0) colors = NULL else colors = colors[-length(colors)] v = structure(v + 1, class = "factor", levels = l, colors = colors) # FIXME: should we handle levels for all layers here, or break on multiple different ones? } dimensions = list( x = create_dimension(from = 1, to = dim(v)[1], offset = e[1], delta = (e[2]-e[1])/dim(v)[1], refsys = st_crs(terra::crs(.x))), y = create_dimension(from = 1, to = dim(v)[2], offset = e[4], delta = (e[3]-e[4])/dim(v)[2], refsys = st_crs(terra::crs(.x)))) dimensions$band = create_dimension(values = names(.x)) # l = if (length(names) > 1) # setNames(list(v), deparse(substitute(.x), 50)) # else # setNames(list(v), names(.x)[1]) ret = st_as_stars(list(v), 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") setNames(ret, attr_name) } #' 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$from + 1) } attr(r, "dimensions")[[dxy[1]]] = dimx attr(r, "dimensions")[[dxy[2]]] = dimy r } st_as_raster = function(x, class, ...) { stopifnot(inherits(x, "stars")) x_crs = st_crs(x) x = st_upfront(x) # x/y dimensions first if (length(dim(x)) > 3) { warning("folding all higher dimensions into the third dimension") # nocov x = st_apply(x, 1:2, as.vector) # fortunes::fortune("side effect") # nocov } if (length(dim(x)) == 2 && length(x) > 1) x = merge(x) d = st_dimensions(x) if (d[[2]]$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) values = if (is.factor(x[[1]])) { structure(x[[1]], dim = NULL) } else { as.vector(x[[1]]) # would convert factor into character } 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$wkt) terra::values(b) = values if (all(vapply(x, is.factor, FUN.VALUE = logical(1)))) { terra::coltab(b) = lapply(x, function(x) t(col2rgb(attr(x, "colors"), alpha = TRUE))) } if (length(dim(x)) != 2){ z = seq(d[[third]]) if (!any(is.na(z))) { if (is.character(z)) { names(b) = z } else { names(b) = paste0(third, z) } } } } else { if (length(dim(x)) == 2) { b = raster::raster(nrows=dim(x)[ dxy[2] ], ncols=dim(x)[ dxy[1] ], xmn = bb[1], xmx = bb[3], ymn = bb[2], ymx = bb[4], crs = as(x_crs, "CRS"), vals = values) } else { third = setdiff(names(d), dxy) b = raster::brick(nrows=dim(x)[ dxy[2] ], ncols=dim(x)[ dxy[1] ], xmn = bb[1], xmx = bb[3], ymn = bb[2], ymx = bb[4], nl = dim(x)[third], crs = as(x_crs, "CRS")) raster::values(b) = values z = seq(d[[third]]) if (!any(is.na(z))) { if (is.character(z)) names(b) = z else b = raster::setZ(b, z) } } } return(b) } stars/R/warp.R0000644000175000017500000002437514144045432013051 0ustar nileshnileshhas_global_longitude = function(x) { st_is_longlat(x) && isTRUE(all.equal(as.numeric(st_bbox(x)$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/mtennekes/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)), get_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")$dimensions = dimsxy structure(x, dimensions = dx) } } # transform grid x to dimensions target # x is a stars object, target is a dimensions object transform_grid_grid = function(x, target) { stopifnot(inherits(x, "stars"), inherits(target, "dimensions")) x = rename_xy_dimensions(x, target) # so we can match by name xy_names = attr(target, "raster")$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 = 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 gdalwarp, 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 ... 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(4326) #' 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(4326) %>% 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} (currently only with \code{method='ngb'}). 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") { if (!inherits(src, "stars_proxy")) src = st_normalize(src) if (!is.na(crs)) crs = st_crs(crs) if (!missing(dest) && inherits(dest, "crs")) stop("target crs should be specified with crs = ..., not as argument dest") ret = if (use_gdal) { 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 (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)) { if (is.na(crs)) stop("either dest or crs should be specified") dest = default_target_grid(src, crs = crs, cellsize = cellsize, segments = segments) } else 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)) } # 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/intervals.R0000644000175000017500000000526614157127414014112 0ustar nileshnilesh#' 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)) } length.intervals = function(x) length(x$start) head.intervals = function(x, n) make_intervals(head(x$start, n), head(x$end, n)) tail.intervals = function(x, n) make_intervals(tail(x$start, n), tail(x$end, n)) c.intervals = function(...) { dots = list(...) start = do.call(c, lapply(dots, function(x) x$start)) end = do.call(c, lapply(dots, function(x) x$end)) make_intervals(start, end) } `[.intervals` = function(x, i, ...) { make_intervals(x$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")) { 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$end, SIMPLIFY = FALSE), class = "intervals_list") } #' @export format.intervals_list = function(x, ...) { sapply(x, format, ...) } #' @export `[.intervals_list` = function(x, i, ...) { ret = NextMethod() is_null = sapply(ret, is.null) ret[is_null] = list(make_intervals(NA_real_, NA_real_)) structure(ret, class = "intervals_list") } stars/R/OpenStreetMap.R0000644000175000017500000000227114026145251014614 0ustar nileshnilesh#' @name st_as_stars #' @param as_col logical; return rgb numbers (FALSE) or (character) color values (TRUE)? #' @export st_as_stars.OpenStreetMap = function(.x, ..., as_col = FALSE) { ncols = .x$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/sp.R0000644000175000017500000000427114026141730012510 0ustar nileshnileshstars_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[1], 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/R/init.R0000644000175000017500000000154614144045432013036 0ustar nileshnilesh#' @importFrom graphics image.default image par plot title box text axis plot.new plot.window rasterImage layout lcm contour hist #' @importFrom grDevices dev.capabilities dev.size grey rgb col2rgb #' @importFrom utils head tail setTxtProgressBar txtProgressBar packageVersion #' @importFrom stats na.omit runif aggregate setNames predict quantile var #' @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 # re-export: #' @importFrom lwgeom st_transform_proj #' @export lwgeom::st_transform_proj setOldClass("stars") setOldClass("stars_proxy") .onLoad = function(libname, pkgname) { register_all_s3_methods() # dynamically registers non-imported pkgs (tidyverse) # nocov } stars/R/subset.R0000644000175000017500000003000514157127414013375 0ustar nileshnilesh#' 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} or \code{sfc} 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}. #' @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) "[.stars" = function(x, i = TRUE, ..., drop = FALSE, crop = !is_curvilinear(x)) { missing.i = missing(i) # special case: 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) } 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")) # 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] ]]$values[!!!args_xy])) } # dimensions: #mc0 = mc[1:3] # "[", x, first dim for (i in seq_along(d)) { # one-at-a-time: name_i = names(d)[i] argi = args[i] if (! (is_curvilinear(d) && name_i %in% xy) && # as that was handled above all(argi[[1]] != rlang::missing_arg()) && is.numeric(eval(argi[[1]])) && ! all(diff(eval(argi[[1]])) == 1)) d[[i]]$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 a \code{stars} object with dimensions identical to \code{x}, and \code{value} will be recycled to the dimensions of the arrays in \code{x}. "[<-.stars" = function(x, i, downsample = 0, value) { if (inherits(i, "stars_proxy")) i = st_as_stars(i, downsample = downsample) if (!inherits(i, "stars")) stop("selector should be a stars object") fun = function(x, y, value) { x[y] = value; x } st_as_stars(mapply(fun, x, i, value = value, SIMPLIFY = FALSE), dimensions = st_dimensions(x)) } #' 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. #' @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)) { 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 (crop && (is_regular_grid(x) || has_rotate_or_shear(x))) { rastxy = attr(dm, "raster")$dimensions xd = rastxy[1] yd = rastxy[2] bb = if (!inherits(y, "bbox")) st_bbox(y) else y if (any(is.na(as.numeric(bb)))) # as.numeric() can go after sf 0.7-5 stop("NA values in bounding box of y") if (epsilon != 0) bb = bb_shrink(bb, epsilon) cr = colrow_from_xy(matrix(bb, 2, byrow = TRUE), dm, NA_outside = FALSE) # FALSE: https://github.com/r-spatial/stars/issues/455 cr[,1] = cr[,1] - dm[[xd]]$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") if (!inherits(y, "bbox")) { # post-process: burn in geometry mask dxy = attr(dm, "raster")$dimensions xy_grd = if (is_curvilinear(x) || !as_points) # FIXME: for curvilinear as_points should work too! st_as_sfc(st_dimensions(x)[dxy], as_points = as_points, geotransform = get_geotransform(x)) else st_as_sf(do.call(expand.grid, expand_dimensions.stars(x)[dxy]), coords = dxy, crs = st_crs(x)) inside = st_intersects(st_union(y), xy_grd)[[1]] d = dim(x) # cropped x mask = rep(TRUE, prod(d[dxy])) mask[inside] = FALSE mask = array(mask, d) # replicates over secondary dims for (i in seq_along(x)) x[[i]][mask] = NA } x } #' @export st_normalize.stars = function(x, domain = c(0, 0, 1, 1), ...) { stopifnot(all(domain == c(0,0,1,1))) if (has_raster(x)) { x = st_upfront(x) d = st_dimensions(x) if (d[[1]]$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]]$from = 1 } st_stars(x, dimensions = d) } else x } #' @name stars_subset #' @param which character or integer; dimension(s) to be flipped #' @export #' @return \code{st_flip} flips (reverts) the array values along the chosen dimension #' without(s) changing the dimension properties #' @examples #' lc = read_stars(system.file("tif/lc.tif", package = "stars")) #' x = c(orig = lc, #' flip_x = st_flip(lc, "x"), #' flip_y = st_flip(lc, "y"), #' flip_xy = st_flip(lc, c("x", "y")), #' along = 3) #' plot(x) st_flip = function(x, which = 1) { if (is.character(which)) which = match(which, names(dim(x))) stopifnot(all(which %in% seq_along(dim(x)))) dims = lapply(dim(x), seq_len) for (i in which) dims[[ i ]] = rev(dims[[ i ]]) for (i in seq_along(x)) x[[i]] = do.call(`[`, c(list(x[[i]]), dims)) x } stars/R/transform.R0000644000175000017500000001020714026145251014077 0ustar nileshnileshto_curvilinear = function(x) { if (! is_curvilinear(x)) { xy = attr(st_dimensions(x), "raster")$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, get_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(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, 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). #' @export st_transform.stars = function(x, crs, ...) { stopifnot(!is.na(crs), !is.na(st_crs(x))) 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]]$refsys = crs } structure(x, dimensions = d) } else { if (! has_raster(x)) { warning("no spatial coordinates present: st_transform does nothing") x } else transform_curvilinear(to_curvilinear(x), crs) } } #' @name st_transform #' @export st_transform_proj.stars = function(x, crs, ...) { stopifnot(!is.na(crs), !is.na(st_crs(x))) if (has_sfc(x)) { if (!requireNamespace("lwgeom", quietly = TRUE)) stop("package lwgeom required, please install it first") # nocov try_proj = function(x, crs) { ret = try(st_transform(x, crs), silent = TRUE) if (inherits(ret, "try-error")) { if (inherits(crs, "crs")) crs = crs$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/read.R0000644000175000017500000003464714145275656013034 0ustar nileshnileshmaybe_normalizePath = function(.x, np = FALSE) { prefixes = c("NETCDF:", "HDF5:", "HDF4:", "HDF4_EOS:", "SENTINEL2_L1", "SENTINEL2_L2", "GPKG:") 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 normalizePath(.x, mustWork = FALSE) } 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)) } #' 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 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 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 #' @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". #' @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, ..., options = character(0), driver = character(0), sub = TRUE, quiet = FALSE, NA_value = NA_real_, along = NA_integer_, RasterIO = list(), proxy = is_functions(.x) || (!length(curvilinear) && is_big(.x, sub = sub, driver=driver, normalize_path = normalize_path, ...)), curvilinear = character(0), normalize_path = TRUE, RAT = character(0), tolerance = 1e-10) { 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 (length(curvilinear) == 2 && is.character(curvilinear)) { 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(x) > 1) { # loop over data sources and RETURNS: 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_) return(do.call(c, append(ret, list(along = along, tolerance = tolerance)))) } # else: data = sf::gdal_read(get_names(x), options = options, driver = driver, read_data = !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$driver[1] ret = lapply(sub_datasets, .read_stars, options = options, driver = driver, quiet = quiet, proxy = proxy, curvilinear = curvilinear) if (! quiet) cat("\n") # return: if (length(ret) == 1) ret[[1]] else { ret = do.call(c, append(ret, list(try_hard = TRUE, nms = nms))) if (length(nms) == length(ret)) # lost my ability to solve this here... setNames(ret, nms) else ret } } else { # we have one single array: if (!isTRUE(sub)) { sub_required = if (is.null(list(...)$sub_required)) FALSE else list(...)$sub_required if (sub_required) stop(paste("only one array present in", .x, ": cannot resolve subdataset", sub)) else warning("only one array present: argument 'sub' will be ignored") } meta_data = structure(data, data = NULL) # take meta_data only data = if (proxy) .x else get_data_units(attr(data, "data")) # extract data array; sets units if present if (meta_data$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$driver[1] == "AIG" && identical(names(at[[1]]), c("VALUE", "COUNT"))) at = list() # skip it: https://github.com/r-spatial/stars/issues/435 # FIXME: how to handle multiple color, category or attribute tables? if (!proxy && (any(lengths(ct) > 0) || any(lengths(at) > 0))) { min_value = if (!is.null(meta_data$ranges) && meta_data$ranges[1,2] == 1) meta_data$ranges[1,1] else min(data, na.rm = TRUE) data[data < min_value] = NA if (min_value < 0) stop("categorical values should have minimum value >= 0") if (any(lengths(ct) > 0)) { ct = ct[[ which(length(ct) > 0)[1] ]] co = apply(ct, 1, function(x) rgb(x[1], x[2], x[3], x[4], maxColorValue = 255)) if (min_value > 0) co = co[-seq_len(min_value)] # removes [0,...,(min_value-1)] f = factor(as.vector(data), levels = seq(min_value, length.out = length(co))) data = structure(f, dim = dim(data), colors = co) } if (any(lengths(at) > 0)) { which.at = which(lengths(at) > 0)[1] which.column = if (length(RAT)) RAT else which(sapply(at[[which.at]], class) == "character")[1] at = at[[ which.at ]][[ which.column ]] if (min_value > 0) at = at[-seq_len(min_value)] if (!is.factor(data) && min_value == 0) { data = data + 1 warning("categorical data values starting at 0 are shifted with one to start at 1") } attr(data, "levels") = at } data = structure(data, 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$long_name else if (is.function(.x)) names(.x()) %||% .x() else x name_x = tail(strsplit(name_x, '[\\\\/]+')[[1]], 1) ret = if (proxy) { # no data present, subclass of "stars": st_stars_proxy(setNames(list(x), names(.x) %||% name_x), create_dimensions_from_gdal_meta(dims, meta_data), NA_value = NA_value, resolutions = NULL, RasterIO = RasterIO) } else st_stars(setNames(list(data), names(.x) %||% name_x), create_dimensions_from_gdal_meta(dim(data), meta_data)) if (is.list(curvilinear)) st_as_stars(ret, curvilinear = curvilinear, ...) else ret } } #' @export #' @name read_stars #' @param x object to be read with \link{read_stars} #' @param n_proxy integer; number of cells above which .x will be read as stars #' proxy object, i.e. not as in-memory arrays but left on disk is_big = function(x, ..., sub = sub, n_proxy = options("stars.n_proxy")[[1]] %||% 1.e8) { prod(dim(read_stars(x, ..., sub = sub, proxy = TRUE, quiet = TRUE))) > n_proxy } get_data_units = function(data) { units = unique(attr(data, "units")) # will fail parsing in as_units() when more than one if (length(units) > 1) { warning(paste("more than one unit available for array: ignoring all")) # nocov structure(data, units = NULL) } else { if (!is.null(units) && nzchar(units)) { if (units == "arc-second") # datum grids may have this units = "arcsecond" units = try_as_units(units) } if (inherits(units, "units")) units::set_units(structure(data, units = NULL), units, mode = "standard") else structure(data, units = NULL) } } read_mdim = function(x, variable = character(0), ..., options = character(0), raster = NULL) { ret = gdal_read_mdim(x, variable, options) create_units = function(x) { u <- attr(x, "units") if (is.null(u) || u == "") x else { u = units::set_units(x, u, mode = "standard") p = try(as.POSIXct(u), silent = TRUE) if (inherits(p, "POSIXct")) p else u } } l = lapply(ret$dimensions, function(x) create_units(x$values[[1]])) d = rev(lapply(l, function(x) create_dimension(values = x))) if (is.null(raster)) raster = get_raster(dimensions = names(d)[1:2]) lst = lapply(ret$array_list, function(x) structure(x, dim = rev(dim(x)))) st_set_crs(st_stars(lst, dimensions = structure(d, raster = raster, class = "dimensions")), ret$srs) } write_mdim = function(x, filename, ...) { stopifnot(inherits(x, "stars")) to_units = function(x) if (inherits(x, c("POSIXct", "Date"))) units::as_units(x) else x dimension_values = rev(lapply(expand_dimensions(x), to_units)) units = sapply(dimension_values, function(x) if(inherits(x, "units")) as.character(units(x)) else "") gdal_write_mdim(st_as_stars(x), filename, dimension_values, units) invisible(x) } stars/R/sample.R0000644000175000017500000000524714144045432013356 0ustar nileshnilesh# 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 object by skipping rows, columns and bands #' #' @param x object of class stars or stars_proxy #' @param n numeric; the number of pixels/lines/bands etc that will be skipped; see Details. #' @param ... ignored #' @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. #' @name st_downsample #' @export st_downsample.stars = function(x, n, ...) { n = as.integer(n) stopifnot(all(n >= 0)) if (!all(n == 0)) { d = dim(x) n = rep(n, length.out = length(d)) dims = st_dimensions(x) regular = is_regular_grid(x) args = rep(list(rlang::missing_arg()), length(d)+1) for (i in seq_along(d)) { if (n[i] > 0) { sq = seq(1, d[i], n[i] + 1) args[[i+1]] = sq # $values: if (!is.null(dims[[i]]$values)) dims[[i]]$values = dims[[i]]$values[sq] } } x = eval(rlang::expr(x[!!!args])) if (regular) { d_new = st_dimensions(x) for (i in seq_along(d)) { dims[[i]]$delta = dims[[i]]$delta * (n[i] + 1) dims[[i]]$from = d_new[[i]]$from dims[[i]]$to = d_new[[i]]$to } x = structure(x, dimensions = dims) } } x } #' @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/factors.R0000644000175000017500000000333114144300304013516 0ustar nileshnilesh#' 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/tidyverse.R0000644000175000017500000002750114144300304014100 0ustar nileshnilesh# convert arrays to data.frame, in long form to_df = function(x) { as.data.frame(lapply(x, function(y) structure(y, dim = NULL)), stringsAsFactors = FALSE) } 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) #' library(dplyr) #' 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, ...) { collect(.data, match.call(), "slice", ".data", env = parent.frame()) } #' @name st_coordinates #' @param .x object to be converted to a tibble as_tibble.stars = function(.x, ..., add_max = FALSE, center = NA) { if (!requireNamespace("dplyr", quietly = TRUE)) stop("package dplyr required, please install it first") # nocov dplyr::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}. #' @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. #' @export #' @examples #' system.file("tif/L7_ETMs.tif", package = "stars") %>% read_stars() -> x #' library(ggplot2) #' 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)) geom_stars = function(mapping = NULL, data = NULL, ..., downsample = 0, sf = FALSE) { if (!requireNamespace("ggplot2", quietly = TRUE)) stop("package ggplot2 required, please install it first") # nocov if (is.null(data)) stop("argument data should not set to 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) if (is_curvilinear(data) || sf) data = st_xy2sfc(data, as_points = FALSE) 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)) { if (is.null(mapping)) mapping = ggplot2::aes(x = !!rlang::sym(xy[1]), y = !!rlang::sym(xy[2]), fill = !!rlang::sym(names(data)[1])) ggplot2::geom_raster(mapping = mapping, data = dplyr::as_tibble(data), ...) } else { # rectilinear: use geom_rect, passing on cell boundaries xy_max = paste0(xy, "_max") if (is.null(mapping)) 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])) ggplot2::geom_rect(mapping = mapping, data = dplyr::as_tibble(data, add_max = TRUE), ...) } } else if (has_sfc(d)) { if (is.null(mapping)) mapping = ggplot2::aes(fill = !!rlang::sym(names(data)[1])) 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", "as_tibble", "stars") 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("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/mosaic.R0000644000175000017500000000505713777653456013375 0ustar nileshnilesh#' 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 #' @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 imput images can be multi-band, but not higher-dimensional data cubes or stars objects with multiple attributes #' #' 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 } objs = if (missing(.x)) list(...) else append(list(.x), list(...)) src = sapply(objs, lst_write) on.exit(unlink(src)) if (missing(dst)) on.exit(unlink(dst)) st_mosaic(src, dst = dst, file_ext = file_ext, options = options) setNames(read_stars(dst), names(objs[[1]])[1]) } #' @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) 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/spatstat.R0000644000175000017500000000307314144045432013733 0ustar nileshnileshcheck_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, ...) { d = dim(.x) nd = create_dimensions( list(x = create_dimension(1, d[1], .x$xrange[1], diff(.x$xrange)/d[1]), y = create_dimension(1, d[2], .x$yrange[1], diff(.x$yrange)/d[2])), get_raster(affine = c(0.0, 0.0), dimensions = c("x", "y"), curvilinear = FALSE)) st_stars(list(v = t(.x$v)), nd) } as.im.stars = function(X, ...) { check_spatstat("spatstat.geom", X) if (!has_raster(X)) stop("as.im.stars only works for stars objects with 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/ops.R0000644000175000017500000002250414145541577012705 0ustar nileshnilesh#' 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) { ret = if (missing(e2)) lapply(e1, .Generic) else if (!inherits(e2, "stars")) lapply(e1, .Generic, e2 = e2) else { # https://github.com/r-spatial/stars/issues/187#issuecomment-834020710 : # if (!all(dim(e1) == dim(e2))) { if (!is.null(dim(e1)) && !isTRUE(all.equal(dim(e1), dim(e2), check.attributes = FALSE))) { stopifnot(length(e2) == 1) lapply(e1, .Generic, e2 = as.vector(e2[[1]])) } 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")) setNames(st_as_stars(ret, dimensions = st_dimensions(e2)), names(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()) } 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/inst/0000755000175000017500000000000014157340043012516 5ustar nileshnileshstars/inst/tif/0000755000175000017500000000000014144045432013300 5ustar nileshnileshstars/inst/tif/olinda_dem_utm25s.tif.aux.xml0000644000175000017500000000523713777664215020741 0ustar nileshnilesh -1.0445 88.0445 1000 0 0 1|0|0|0|0|0|0|0|0|0|0|2054|0|0|0|0|0|0|0|0|0|0|16|0|0|0|0|0|0|0|0|0|0|0|35|0|0|0|0|0|0|0|0|0|0|98|0|0|0|0|0|0|0|0|0|0|204|0|0|0|0|0|0|0|0|0|0|315|0|0|0|0|0|0|0|0|0|0|0|429|0|0|0|0|0|0|0|0|0|0|506|0|0|0|0|0|0|0|0|0|0|670|0|0|0|0|0|0|0|0|0|0|634|0|0|0|0|0|0|0|0|0|0|552|0|0|0|0|0|0|0|0|0|0|0|407|0|0|0|0|0|0|0|0|0|0|282|0|0|0|0|0|0|0|0|0|0|219|0|0|0|0|0|0|0|0|0|0|193|0|0|0|0|0|0|0|0|0|0|0|188|0|0|0|0|0|0|0|0|0|0|151|0|0|0|0|0|0|0|0|0|0|161|0|0|0|0|0|0|0|0|0|0|168|0|0|0|0|0|0|0|0|0|0|137|0|0|0|0|0|0|0|0|0|0|0|135|0|0|0|0|0|0|0|0|0|0|118|0|0|0|0|0|0|0|0|0|0|128|0|0|0|0|0|0|0|0|0|0|122|0|0|0|0|0|0|0|0|0|0|0|132|0|0|0|0|0|0|0|0|0|0|109|0|0|0|0|0|0|0|0|0|0|118|0|0|0|0|0|0|0|0|0|0|122|0|0|0|0|0|0|0|0|0|0|0|128|0|0|0|0|0|0|0|0|0|0|118|0|0|0|0|0|0|0|0|0|0|118|0|0|0|0|0|0|0|0|0|0|110|0|0|0|0|0|0|0|0|0|0|86|0|0|0|0|0|0|0|0|0|0|0|91|0|0|0|0|0|0|0|0|0|0|107|0|0|0|0|0|0|0|0|0|0|89|0|0|0|0|0|0|0|0|0|0|83|0|0|0|0|0|0|0|0|0|0|0|88|0|0|0|0|0|0|0|0|0|0|68|0|0|0|0|0|0|0|0|0|0|90|0|0|0|0|0|0|0|0|0|0|82|0|0|0|0|0|0|0|0|0|0|95|0|0|0|0|0|0|0|0|0|0|0|84|0|0|0|0|0|0|0|0|0|0|65|0|0|0|0|0|0|0|0|0|0|74|0|0|0|0|0|0|0|0|0|0|87|0|0|0|0|0|0|0|0|0|0|0|102|0|0|0|0|0|0|0|0|0|0|76|0|0|0|0|0|0|0|0|0|0|97|0|0|0|0|0|0|0|0|0|0|98|0|0|0|0|0|0|0|0|0|0|91|0|0|0|0|0|0|0|0|0|0|0|106|0|0|0|0|0|0|0|0|0|0|85|0|0|0|0|0|0|0|0|0|0|104|0|0|0|0|0|0|0|0|0|0|87|0|0|0|0|0|0|0|0|0|0|0|92|0|0|0|0|0|0|0|0|0|0|86|0|0|0|0|0|0|0|0|0|0|106|0|0|0|0|0|0|0|0|0|0|94|0|0|0|0|0|0|0|0|0|0|72|0|0|0|0|0|0|0|0|0|0|0|99|0|0|0|0|0|0|0|0|0|0|92|0|0|0|0|0|0|0|0|0|0|87|0|0|0|0|0|0|0|0|0|0|77|0|0|0|0|0|0|0|0|0|0|0|60|0|0|0|0|0|0|0|0|0|0|58|0|0|0|0|0|0|0|0|0|0|62|0|0|0|0|0|0|0|0|0|0|53|0|0|0|0|0|0|0|0|0|0|0|40|0|0|0|0|0|0|0|0|0|0|36|0|0|0|0|0|0|0|0|0|0|43|0|0|0|0|0|0|0|0|0|0|48|0|0|0|0|0|0|0|0|0|0|35|0|0|0|0|0|0|0|0|0|0|0|33|0|0|0|0|0|0|0|0|0|0|27|0|0|0|0|0|0|0|0|0|0|25|0|0|0|0|0|0|0|0|0|0|22|0|0|0|0|0|0|0|0|0|0|0|16|0|0|0|0|0|0|0|0|0|0|9|0|0|0|0|0|0|0|0|0|0|8|0|0|0|0|0|0|0|0|0|0|10|0|0|0|0|0|0|0|0|0|0|8|0|0|0|0|0|0|0|0|0|0|0|2|0|0|0|0|0|0|0|0|0|0|1|0|0|0|0|0|0|0|0|0|0|2|0|0|0|0|0|0|0|0|0|0|4|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|1 88 21.665205746287 -1 20.974640760798 stars/inst/tif/na.tif0000644000175000017500000000137613777653456014437 0ustar nileshnileshII*   n S    Vf??fV@#  mtr@@TXAWGS 84|}?>k7?b?&>G?ї=+&>:e?l>i>P>v=m?D?p?|>?/<6>sF=AU?>c@=ҹ>N(?;c?>5V>hp?W>A>E=&>İI=m>Bg,?Q?B3?Ca?\:?"$? >c ?< l?T>7?>=F/?7{?2??W>JSD?(w?q?j>~(?-7?>2?ۦ3?Yk?5o>?m*?_? 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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 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gridcomment291-hourly model 2D output starting at validtime plus 1 hrcomment38Generated 2x per day following the 0,12 Z Forecast runs validtime22-AUG-2019 12:00 GMT validtime_DOY234, 2019 12:00 GMTauthorgregory.lang@noaa.gov references%http://www.glerl.noaa.gov/res/glcfs/ creation_dateThu Aug 22 13:12:09 2019 GMT disclaimer+http://www.glerl.noaa.gov/home/notice.htmlhistoryMon Sep 16 14:56:50 2019: ncks -v wvh c201923412.out1_3.nc c201923412.out1_4.nc Mon Sep 16 14:54:47 2019: ncks -d nsigma,1,2 c201923412.out1_2.nc c201923412.out1_3.nc Mon Sep 16 14:52:48 2019: ncks -d time,1 c201923412.out1.nc c201923412.out1_2.ncNCO`netCDF Operators version 4.8.1 (Homepage = http://nco.sf.net, Code = http://github.com/nco/nco) lat  long_name Latitudeunitsdegrees_north standard_name latitudezXlon  long_name Longitudeunits degrees_east standard_name longitudezXtime  long_nameUTCaxisTunits)seconds since 1970-01-01 00:00:00 +00:00 base_date 1970,1,1,0 standard_nametimehwvh  long_nameSignificant Wave 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>SS_>U>K[}>+<>$OO>0>#=OOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOO====n=\=->I>w>$">,>3L>;6>Dh>N>W>[z>YL>gq>[I5>O>@1>4ɻ>0̑>1K>&\8>'>!>>OOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOO=G=OOO==>>!>>!}>*>>6H>@>Ht>LuE>HU>E>HP >:>A[>=>7>2>0 >27> >V=OOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOO=>T$>H>dy>w/>'>1>8:><˲>=*E>9>8+>/y1>2Q>/[>(L>&t>!d>x><=Ie=w-OOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOO=X=ፏ=/ => =>f>!>&>'>'n>(bC>(> >&?d>>>> 1(>~==ă'=eOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOO==˜B=ֹm=->t>$>V>D>D> >Q > >B:>1>r=e==F=-Q=~2=WOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOO={3=k==̿=&=}==_>{>>z=Y==ԓ=श=`-=7U=~a= .=hOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOO=6====П,===L==ݓ=== UOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOO=~==@= ==o~== =p=h=8OOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOO=^|=g= ={z=OOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOO==YOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOstars/inst/nc/reduced.R0000644000175000017500000000036713777653456014710 0ustar nileshnileshlibrary(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/sub.nc0000644000175000017500000002017013777653456014257 0ustar nileshnileshCDF latitude level longitude time  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 BPBOBNBMBLBKBJBIBH9R@@@@@@@@@zzwsq"p-qrtxfwtolllmnutpl&i8gfgeel-jQgd}c a_z^_]@[Z[d\]ZXY9RQtRpVL[^\VSSNPSTiZ~aNdaNXKQW[`dg#gbZS:ejlljgkd<`}\'lljohhDhj.l}o*lk@haffgirkdmn.liGffKfDeefRjigfff=cb a``6`cefb_]$VV[W\bbae2aZiSQR7TZ 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ڏ o?2G𞷆5|ƭn yk[1pLc5Xo |k8[9pNs5\o |k8[0&5ѭan cvk[0=hȁ?p-kr4\ hfGõ;p-kz4\ۣ hGõ?09q4̍a sh3G909u4̭a shsG;09y4ͣa shG=09}4a shG?Ѱ5q4a khX3G9Ѱ5u4a khXsG;Ѱ5y4ͣa khXG=Ѱ5}4a khXG?b p4Ć!F hGC8bKr4Ė! h9GC9bt4Ħ!F hYGC:bˎv4Ķ! hyGC;b x4ƣ!F hGC<bKz4֣! hC=b|bˏ~ýx<돇{p?x` 68x`v8`8#8#PK6U O 3$EURO-CORDEX_81_DOMAIN000_mask_int.ncUTQ]ux  PKjjstars/inst/nc/timeseries.nc0000644000175000017500000000411413777653456015637 0ustar nileshnileshCDF station time  featureType timeSeries ConventionsCF-1.7 num  long_nameStation numbercf_role timeseries_id(<time units"days since 1970-01-01 00:00:00 UTC long_nametimecalendar gregorianPdpr units kg m-2 s-1 _FillValue long_nameTotal precipitation flux coordinateslat lon alt num standard_nameprecipitation_flux lat units degrees_north long_nameStation latitude standard_namelatitude(lon units degrees_east long_nameStation longitude standard_name longitude(alt unitsm long_name#Vertical distance above the surface standard_nameheight($ *,;-/013]46779:;=Z>@4ACD|EBBXBB BABBBBBdAB$B(BBBBPBA@ABBBBBBBBABB BBlB@BB\BB0A@B`BBBBBPBBB0AB,BAApAB0B@AABh@A@@BBABPBBBBB4AABB(BBBA?ABBHBB`BDBhAABBBABBBBp@BA@BABHBAB<BBdBABBBB@B8BBBB0BB A BB|A BBpABB`BB\BB AA`A B$AABBB8BBB ABBABx?BhBBBB\A BLA0BAABTBBAA@BABBABLB`BABBBBB,BAB ABBBA@BBPBBBA`A@@šAB8C|°B%B|(A CAB GT1B|B(Bstars/inst/nc/ncdf_tidync.R0000644000175000017500000002061213777653456015554 0ustar nileshnilesh### 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/3B42_Daily.19991231.7.test.nc0000644000175000017500000000326413777653456017273 0ustar nileshnileshCDF 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/tos_O1_2001-2002.nc0000644000175000017500001320004013777653456015655 0ustar nileshnileshCDF lonlattimebnds titleIIPSL model output prepared for IPCC Fourth Assessment SRES A2 experiment institution3IPSL (Institut Pierre Simon Laplace, Paris, France)sourceIPSL-CM4_v1 (2003) : atmosphere : LMDZ (IPSL-CM4_IPCC, 96x71x19) ; ocean ORCA2 (ipsl_cm4_v1_8, 2x2L31); sea ice LIM (ipsl_cm4_vcontact2Sebastien Denvil, sebastien.denvil@ipsl.jussieu.fr project_idIPCC Fourth Assessmenttable_idTable O1 (13 November 2004) experiment_idSRES A2 experiment realization cmor_version?u ConventionsCF-1.0historyYYYY/MM/JJ: data generated; YYYY/MM/JJ+1 data transformed At 16:37:23 on 01/11/2005, CMOR rewrote data to comply with CF standards and IPCC Fourth Assessment requirements references7Dufresne et al, Journal of Climate, 2015, vol XX, p 136comment Test drive lon  standard_name longitude long_name longitudeunits degrees_eastaxisXboundslon_bndsoriginal_units@degrees_east lon_bnds @lat  standard_namelatitude long_namelatitudeunits degrees_northaxisYboundslat_bndsoriginal_units@degrees_northPlat_bnds @time  standard_nametime long_nametimeunits=days since 2001-1-1axisTcalendar360_daybounds time_bndsoriginal_unitsseconds since 2001-1-1* time_bnds*tos standard_namesea_surface_temperature long_nameSea Surface TemperatureunitsK cell_methods!time: mean (interval: 30 minutes) _FillValue`x missing_value`x original_namesosstsstoriginal_unitsdegChistory At 16:37:23 on 01/11/2005: CMOR altered the data in the following ways: added 2.73150E+02 to yield output units; Cyclical dimension was output starting at a different lon; *?@@@@"@&@*@.@1@3@5@7@9@;@=@?@@@A@B@C@D@E@F@G@H@I@J@K@L@M@N@O@P@@P@Q@@Q@R@@R@S@@S@T@@T@U@@U@V@@V@W@@W@X@@X@Y@@Y@Z@@Z@[@@[@\@@\@]@@]@^@@^@_@@_@` @``@`@`@a @a`@a@a@b @b`@b@b@c @c`@c@c@d @d`@d@d@e @e`@e@e@f @f`@f@f@g @g`@g@g@h @h`@h@h@i @i`@i@i@j @j`@j@j@k @k`@k@k@l @l`@l@l@m @m`@m@m@n @n`@n@n@o @o`@o@o@p@p0@pP@pp@p@p@p@p@q@q0@qP@qp@q@q@q@q@r@r0@rP@rp@r@r@r@r@s@s0@sP@sp@s@s@s@s@t@t0@tP@tp@t@t@t@t@u@u0@uP@up@u@u@u@u@v@v0@vP@vp@@@@@@@ @ 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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 4.7.6 (Homepage = http://nco.sf.net, Code = http://github.com/nco/nco) /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 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BBB^B(BBBgBBoB^tBMB;B*RBBvBBBB~BBbBB{2BiBWBFAB4B"B"BcBBBB2BYB{BBpB^BLB:B(BBBBBΟBBdB?BBsBaBOzB=BBBUB̨BB=BBBsBb)BPWB>B,BBBBB BBB BBxBfBTBBB0BXB *BBBՄBCBBBaBz BgBUSBBB0BB B/BB1BjBӢBBB(BJBfBn|B]BLB;B*BBB|BfBJB)BBBBnBn2B\BKB:\B) BBUBBBBB3BB4BzBiBWBEB4XB"BBbBBBB,BBOBBB~B.BBB!BuBcUBPB>uB+BBB|B5BiBBBBBB)B5BnBBaBBnBBeBBGBxBgBUsBCB2!B oBBBBsBBBBB0BFB}XBkdBYjBGlB5iB#`BSB@B(B BBBBfB0BmB[BIpB7&B$BB*BBiBBB$BB2BlBZ-BGB5BIB8B(B>BXBlB{BӅBˆBBBqB~_BmFB\)BKB9B(B{BABBBuB%BBwBB{BjHBXBGdB5B$jBBZBB5B̛BBVBBBtGBbBPB? B-?BpB BBBBB)B5B=Bz?BhBBBۇBBJB B%B9BGBOB|RBkOBZGBI9B8&B' BBBBpBB֙BBBB BB BB{BjBYBHB7B&fB7BBBBGBBBYBByBh:BVBE_B3B"nBBhBBLBʶBBzBB(BrwB`BOB=EB+BBB B2BRBlBBBBxBfBTBBB0BqB WB9BBBÿBBTBBzBhBVABCB1B>B ByBB՟B+BB4BBy)BfBT BAsB.B7B BBBBBBBBBBB{zBjXBY0BHB6B%B[BBBB-BBtBBBx7BfBUHBCB2DB B*BBBZBȵB B[BBBp+B^fBLB:B(BBBBB}BBjBWBz*Bi.BX,BG$B6B%BBBBBPBBBB[ByBgBViBEB3B"FBBhBBtBBkBBLBBsBatBOB>B,lBBB4BlBӟBBBB4BzLBh_BVmBDuB2xB vBoBbBQB:BBBBB}zBkDBY BFB4B":BBB=BB{BBB2B|Bj>BWBE6B2B B BBKBզBBOBBBx(AAAAAAAAA A(A0A8A@AHAPAXA`AhApAxA߀A߈Aߐstars/inst/nc/test_adaptor.cams_regional_fc.nc0000644000175000017500000000416013777653456021432 0ustar nileshnileshCDF latitude level longitude time title/PM10/PM25 Air Pollutant ANALYSIS at the Surface institutionData produced by Meteo FrancesourceData from ENSEMBLE modelhistoryTue Apr 28 08:16:38 2020: ncks -v pm10_conc -d longitude,245,255 -d latitude,210,220 -d time,1,1 adaptor.cams_regional_fc.retrieve-1587939061.8606012-22402-6-64f0e215-609d-4ca6-b65c-a9ef7dc53370.nc test.nc Model ENSEMBLE ANALYSISANALYSIS#Europe, 20200101-20200102+[12H_12H]summarysENSEMBLE model hourly ANALYSIS of PM10/PM25 concentration at the Surface from 20200101-20200102+[12H_12H] on Europeproject-MACC-RAQ (http://macc-raq.gmes-atmosphere.eu)NCO`netCDF Operators version 4.9.2 (Homepage = http://nco.sf.net, Code = http://github.com/nco/nco) latitude  long_namelatitudeunits degrees_north,,level  long_namelevelunitsmX longitude  long_name longitudeunits degrees_east,\ pm10_conc  _FillValueyspecies PM10 Aerosolunitsµg/m3value hourly values standard_name1mass_concentration_of_pm10_ambient_aerosol_in_airtime  long_nameANALYSIS time from 20200101unitshourslBKBKffBKBJBJ33BIBIffBIBHBH33BGCfC3CCC=L>>>33>ff? APAPAJA>A4A/ܷA-iA5ؠA?4 A9}A7AKFAAA2A+[A.$A2A*A+A1SA:!ADAF]A1_A-gA&>A*gA0_A' A&A%onA'5A@#A\CARBAW"uA,u;A',A(A*A+pA+pA*AN $A{3A|Ao[A\A^߀A]pAUuAOAJXAJ0ACtAA_A Az9yA]AX`ANVADeABHA?A5 0], seq(0, 1, length.out = n))) } plot(stars::slice.stars(x, index = 17, along = "time"), border = NA, breaks = qu_0_omit(x[[1]][,,17]), reset = FALSE, col = viridis::viridis(22)) plot(sf::read_sf(system.file("gpkg/nc.gpkg", package = "sf"), "nc.gpkg"), add = TRUE, reset = FALSE, col = NA) stars/inst/plumber/0000755000175000017500000000000013777653456014212 5ustar nileshnileshstars/inst/plumber/client_blog.R0000644000175000017500000000317513777653456016624 0ustar nileshnileshlibrary(httr) # GET, POST, PUT library(jsonlite) # base64_dec, base64_enc, toJSON, fromJSON library(tibble) # print get_data = function(url, expr = NULL) { if (is.null(expr)) fromJSON( content(GET(url), "text", encoding = "UTF-8")) else unserialize(base64_dec(fromJSON( content(POST(url, body = list(expr = expr), encode = "json"), "text", encoding = "UTF-8") ))) } put_data = function(url, name, value) { value = toJSON(base64_enc(serialize(value, NULL))) PUT(url, body = list(name = name, value = value), encode = "json") } # from where? url = "http://localhost:8000/data" get_data(url) # list library(stars) md = get_data(url, "md") # select a country: nl = st_as_sf(raster::getData("GADM", country = "NLD", level = 0)) %>% st_transform(st_crs(md)) get_data(url) # any data there? md = get_data(url, "md") proxy = md[nl,]$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/plumber/api.R0000644000175000017500000000025713777653456015112 0ustar nileshnileshlibrary(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/server.R0000644000175000017500000000100013777653456015632 0ustar nileshnilesh# 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/client.R0000644000175000017500000000211213777653456015607 0ustar nileshnileshlibrary(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.R0000644000175000017500000000253313777653456016651 0ustar nileshnilesh# 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/docker/0000755000175000017500000000000013777653456014013 5ustar nileshnileshstars/inst/docker/rdevel/0000755000175000017500000000000013777653456015274 5ustar nileshnileshstars/inst/docker/rdevel/Dockerfile0000644000175000017500000000601413777653456017267 0ustar nileshnileshFROM 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 http://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 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/fedora/0000755000175000017500000000000013777653456015253 5ustar nileshnileshstars/inst/docker/fedora/Dockerfile0000644000175000017500000000464413777653456017255 0ustar nileshnileshFROM 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/0000755000175000017500000000000013777653456015365 5ustar nileshnileshstars/inst/docker/trusty/Dockerfile0000644000175000017500000000313713777653456017363 0ustar nileshnileshFROM 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 http://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/docker/ubuntu/0000755000175000017500000000000013777653456015335 5ustar nileshnileshstars/inst/docker/ubuntu/Dockerfile0000644000175000017500000000231213777653456017325 0ustar nileshnileshFROM 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 http://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/gpkg/0000755000175000017500000000000013777653456013474 5ustar nileshnileshstars/inst/gpkg/s2.gpkg0000644000175000017500000030000013777653456014663 0ustar nileshnileshSQLite 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/inst/doc/0000755000175000017500000000000014157340043013263 5ustar nileshnileshstars/inst/doc/stars1.html0000644000175000017500000146230514157340010015373 0ustar nileshnilesh 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

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

(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:

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:

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 offset 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 generalisation of the tbl_cube found in cubelyr; we can convert to that by

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

Switching attributes to dimensions and back

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:

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:

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,

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:

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

Reading a raster time series: NetCDF

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

We see that

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

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:

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

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

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:

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:

We can now crop the grid to those cells falling in

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/4.0/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 10.0143      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 3...,...,MULTIPOLYGON (((-78.65572 3...
## destination MULTIPOLYGON (((-81.47276 3...,...,MULTIPOLYGON (((-78.65572 3...
## 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:

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

stars/inst/doc/stars5.Rmd0000644000175000017500000001205214157337763015167 0ustar nileshnilesh--- title: "5. vector-raster conversions, reprojection, warping" author: "Edzer Pebesma" output: rmarkdown::html_vignette vignette: > %\VignetteIndexEntry{5. vector-raster and raster-vector conversion} %\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$set(echo = TRUE) knitr::opts_chunk$set(collapse = TRUE) knitr::opts_chunk$set(fig.height = 4.5) knitr::opts_chunk$set(fig.width = 6) suppressPackageStartupMessages(library(sf)) ev = sf_extSoftVersion()["GDAL"] >= "2.4.0" ``` 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/stars6.Rmd0000644000175000017500000002007514157337763015174 0ustar nileshnilesh--- title: "6. How `raster` functions map to `stars` functions" author: "Sébastien Rochette, Adriano Fantini, Edzer Pebesma" output: rmarkdown::html_vignette vignette: > %\VignetteIndexEntry{6-raster-stars-mapping} %\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$set(echo = FALSE, collapse = TRUE, comment = "#>") knitr::opts_chunk$set(fig.height = 4.5) knitr::opts_chunk$set(fig.width = 6) suppressPackageStartupMessages(library(dplyr)) ``` 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", "[ ] <-", "", "cut", "cut", "", "subs", "", "", "reclassify", "mutate with case_when", "or forcats::fct_recode ?", "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_dimensions, look for delta", "may not be a constant in case of rectilinear or curvilinear grids", "yres", "st_dimensions, look for delta", "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", "", "", "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$to)", "raster columns are not always named 'y'", "setValues", "[ ] <-", "", "writeRaster", "write_stars", "currently uses GDAL, somewhat limited", "KML", "", "" ) %>% knitr::kable() ``` The format of this table follows the `raster-package` entry in the raster manual, found at `https://cran.r-project.org/web/packages/raster/raster.pdf`. stars/inst/doc/stars1.Rmd0000644000175000017500000003017514157337763015171 0ustar nileshnilesh--- title: "1. introduction" author: "Edzer Pebesma" output: rmarkdown::html_vignette vignette: > %\VignetteIndexEntry{1. introduction} %\VignetteEncoding{UTF-8} %\VignetteEngine{knitr::rmarkdown} --- **For a better version of the stars vignettes see** https://r-spatial.github.io/stars/articles/ ```{r setup, include=FALSE} knitr::opts_chunk$set(echo = TRUE) knitr::opts_chunk$set(collapse = TRUE) knitr::opts_chunk$set(fig.height = 4.5) knitr::opts_chunk$set(fig.width = 6) ev = suppressWarnings(require(starsdata, quietly = TRUE)) ``` 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 offset 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 generalisation 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 eval=FALSE} 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=FALSE} st %>% as.tbl_cube() ``` The following demonstrates how `dplyr` can filter bike travel, and compute mean bike traffic by hour of day: ```{r eval=FALSE} 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)) ``` stars/inst/doc/stars2.Rmd0000644000175000017500000003206714157337763015174 0ustar nileshnilesh--- 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) knitr::opts_chunk$set(collapse = TRUE) knitr::opts_chunk$set(fig.height = 4.5) knitr::opts_chunk$set(fig.width = 6) set.seed(13579) EVAL = x = suppressWarnings(require(starsdata, quietly = TRUE)) && sf::sf_extSoftVersion()["GDAL"] >= "2.2.0" ``` 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", 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]) 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$file.sep, "img6.tif") write_stars(s4, fn4) write_stars(s5, fn5) write_stars(s6, fn6) (r2 = read_stars(c(fn4, fn5, fn6), proxy = TRUE)) st_as_stars(r2) %>% merge() %>% plot(breaks = "equal", text_values = TRUE, text_color = 'orange', axes = TRUE) st_as_stars(r2[,2:4,2:4]) %>% merge() %>% plot(breaks = "equal", text_values = TRUE, text_color = 'orange', axes = TRUE) ``` Finally, an example where the first raster has the higher resolution: ```{r eval=TRUE} (r3 = read_stars(c(fn6, fn5, fn4), proxy = TRUE)) st_as_stars(r3) %>% merge() %>% plot(breaks = "equal", text_values = TRUE, text_color = 'orange', axes = TRUE) st_as_stars(r3[,2:6,3:6]) %>% merge() %>% plot(breaks = "equal", text_values = TRUE, text_color = 'orange', axes = TRUE) ``` stars/inst/doc/stars3.Rmd0000644000175000017500000000534614157337763015175 0ustar nileshnilesh--- title: "3. stars tidyverse methods" author: "Edzer Pebesma" output: rmarkdown::html_vignette vignette: > %\VignetteIndexEntry{3. stars tidyverse methods} %\VignetteEngine{knitr::rmarkdown} %\VignetteEncoding{UTF-8} --- **For a better version of the stars vignettes see** https://r-spatial.github.io/stars/articles/ ```{r setup, include=FALSE} knitr::opts_chunk$set(echo = TRUE) knitr::opts_chunk$set(collapse = TRUE) knitr::opts_chunk$set(fig.height = 4.5) knitr::opts_chunk$set(fig.width = 6) #ev = FALSE ev = TRUE ``` 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/stars1.R0000644000175000017500000001372214157340007014630 0ustar nileshnilesh## ----setup, include=FALSE----------------------------------------------------- knitr::opts_chunk$set(echo = TRUE) knitr::opts_chunk$set(collapse = TRUE) knitr::opts_chunk$set(fig.height = 4.5) knitr::opts_chunk$set(fig.width = 6) ev = suppressWarnings(require(starsdata, quietly = TRUE)) ## ----------------------------------------------------------------------------- library(stars) ## ----------------------------------------------------------------------------- methods(class = "stars") ## ----------------------------------------------------------------------------- tif = system.file("tif/L7_ETMs.tif", package = "stars") x = read_stars(tif) plot(x, axes = TRUE) ## ----------------------------------------------------------------------------- x ## ----eval=ev------------------------------------------------------------------ library(cubelyr) as.tbl_cube(x) ## ----------------------------------------------------------------------------- (x.spl = split(x, "band")) merge(x.spl) ## ----------------------------------------------------------------------------- merge(x.spl) %>% setNames(names(x)) %>% st_set_dimensions(3, values = paste0("band", 1:6)) %>% st_set_dimensions(names = c("x", "y", "band")) ## ----------------------------------------------------------------------------- class(x[[1]]) dim(x[[1]]) x$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) ## ----eval=FALSE--------------------------------------------------------------- # 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=FALSE--------------------------------------------------------------- # st %>% as.tbl_cube() ## ----eval=FALSE--------------------------------------------------------------- # 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)) stars/inst/doc/stars4.R0000644000175000017500000001131214157340041014622 0ustar nileshnilesh## ----setup, include=FALSE----------------------------------------------------- knitr::opts_chunk$set(echo = TRUE) knitr::opts_chunk$set(collapse = TRUE) knitr::opts_chunk$set(fig.height = 4.5) knitr::opts_chunk$set(fig.width = 6) set.seed(13579) ## ----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---------------------------------------------------------------- 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")) { threshold = units::set_units(9e+36, mol/m^2) } else { threshold = 9e+36 } nit.c[[1]][nit.c[[1]] > threshold] = NA st_crs(nit.c) = 4326 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 = stars:::st_downsample(nit.c, 8)) 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') stars/inst/doc/stars2.R0000644000175000017500000001677714157340015014645 0ustar nileshnilesh## ----setup, include=FALSE----------------------------------------------------- knitr::opts_chunk$set(echo = TRUE) knitr::opts_chunk$set(collapse = TRUE) knitr::opts_chunk$set(fig.height = 4.5) knitr::opts_chunk$set(fig.width = 6) set.seed(13579) EVAL = x = suppressWarnings(require(starsdata, quietly = TRUE)) && sf::sf_extSoftVersion()["GDAL"] >= "2.2.0" ## ----eval=FALSE--------------------------------------------------------------- # install.packages("starsdata", repos = "http://gis-bigdata.uni-muenster.de", 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]) 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/stars5.html0000644000175000017500000065552514157340043015414 0ustar nileshnilesh 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.

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:

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):

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:

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

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

or merge polygons that have identical pixel values;

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

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.

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

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:

and then warping the old raster to the new

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

stars/inst/doc/stars4.html0000644000175000017500000234671514157340041015411 0ustar nileshnilesh 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
  • an attribute called dimensions with a dimensions object carrying 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,
    • 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)

Clearly, offset and delta only apply to regularly discretized dimensions, and are NA if this is not the case. from and to will usually be 1 and the dimension size, but from may be larger than 1 in case a regular sub-grid got cut out or was cropped. Rectilinear and curvilinear grids need grid values in values; this can be irregularly spaced coordinate values, or coordinate intervals of irregular width, or spatial geometries encoded in an sfc vector (“list-column”), or a matrix with grid cell centre values (longitude or latitude) for curvilinear grids.

Grid type

Regular grids

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

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:
  • 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,

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 origing \((0,0)\) did not change:

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:

Raster attributes, rotated and sheared grids

Dimension tables of stars objects carry a raster attribute:

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:

The rotation angle, in degrees, is

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

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

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:

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:

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:

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

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, one with the latitudes and one with the longitudes for every grid cell. Using the GDAL interface, we need to compose a stars object from the array and a long and lat matrix. The matrices with longitude and latitude values are stored in the values field of their respective dimensions.

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

The dataset is found here:

We can construct the curvilinear stars raster by calling st_as_stars on three arrays, with data, longitude and latitudes:

nit.c = read_stars(s5p, sub = "//PRODUCT/SUPPORT_DATA/DETAILED_RESULTS/nitrogendioxide_summed_total_column",
    curvilinear = c("//PRODUCT/longitude", "//PRODUCT/latitude"), driver = NULL)
## 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.
## /PRODUCT/longitude,
## 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.
## /PRODUCT/latitude,
## 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.
## /PRODUCT/SUPPORT_DATA/DETAILED_RESULTS/nitrogendioxide_summed_total_column,
## 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: dimension #2 (ground_pixel) is not a
## Longitude/X dimension.
## Warning in CPL_read_gdal(as.character(x), as.character(options),
## as.character(driver), : GDAL Message 1: dimension #1 (scanline) is not a
## Latitude/Y dimension.
## Warning in CPL_read_gdal(as.character(x), as.character(options),
## as.character(driver), : GDAL Message 1: No 1D variable is indexed by dimension
## time
if (inherits(nit.c[[1]], "units")) {
    threshold = units::set_units(9e+36, mol/m^2)
} else {
    threshold = 9e+36
}
nit.c[[1]][nit.c[[1]] > threshold] = NA
st_crs(nit.c) = 4326
nit.c
## stars object with 3 dimensions and 1 attribute
## attribute(s):
##                                                     Min.      1st Qu.
## nitrogendioxide_summed_total_c... [mol/m^2] 3.650813e-05 7.451281e-05
##                                                   Median         Mean
## nitrogendioxide_summed_total_c... [mol/m^2] 8.296968e-05 8.581399e-05
##                                                  3rd Qu.         Max. NA's
## nitrogendioxide_summed_total_c... [mol/m^2] 9.397442e-05 0.0004535302  330
## dimension(s):
##      from  to offset delta refsys point                                 values
## x       1 450     NA    NA WGS 84    NA [450x278] -5.81066 [°],...,30.9468 [°]
## y       1 278     NA    NA WGS 84    NA  [450x278] 28.3605 [°],...,51.4686 [°]
## time    1   1     NA    NA     NA    NA                                   NULL
##      x/y
## x    [x]
## y    [y]
## time    
## curvilinear grid

We can downsample the data by

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

stars/inst/doc/stars3.R0000644000175000017500000000302014157340017014621 0ustar nileshnilesh## ----setup, include=FALSE----------------------------------------------------- knitr::opts_chunk$set(echo = TRUE) knitr::opts_chunk$set(collapse = TRUE) knitr::opts_chunk$set(fig.height = 4.5) knitr::opts_chunk$set(fig.width = 6) #ev = FALSE ev = TRUE ## ----------------------------------------------------------------------------- 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/stars3.html0000644000175000017500000140200214157340020015362 0ustar nileshnilesh 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

Methods now available for class stars are

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

filter

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

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:

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

stars/inst/doc/stars6.html0000644000175000017500000005756314157340043015413 0ustar nileshnilesh 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 [ ] <-
cut cut
subs
reclassify mutate with case_when or forcats::fct_recode ?
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_dimensions, look for delta may not be a constant in case of rectilinear or curvilinear grids
yres st_dimensions, look for delta 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
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/stars5.R0000644000175000017500000000441214157340043014630 0ustar nileshnilesh## ----setup, include=FALSE----------------------------------------------------- knitr::opts_chunk$set(echo = TRUE) knitr::opts_chunk$set(collapse = TRUE) knitr::opts_chunk$set(fig.height = 4.5) knitr::opts_chunk$set(fig.width = 6) suppressPackageStartupMessages(library(sf)) ev = sf_extSoftVersion()["GDAL"] >= "2.4.0" ## ----------------------------------------------------------------------------- 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/stars7.html0000644000175000017500000001063214157340043015376 0ustar nileshnilesh 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.Rmd0000644000175000017500000000055114157337763015172 0ustar nileshnilesh--- 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.R0000644000175000017500000001655314157340043014642 0ustar nileshnilesh## ---- include = FALSE--------------------------------------------------------- knitr::opts_chunk$set(echo = FALSE, collapse = TRUE, comment = "#>") knitr::opts_chunk$set(fig.height = 4.5) knitr::opts_chunk$set(fig.width = 6) suppressPackageStartupMessages(library(dplyr)) ## ----------------------------------------------------------------------------- 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", "[ ] <-", "", "cut", "cut", "", "subs", "", "", "reclassify", "mutate with case_when", "or forcats::fct_recode ?", "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_dimensions, look for delta", "may not be a constant in case of rectilinear or curvilinear grids", "yres", "st_dimensions, look for delta", "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", "", "", "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$to)", "raster columns are not always named 'y'", "setValues", "[ ] <-", "", "writeRaster", "write_stars", "currently uses GDAL, somewhat limited", "KML", "", "" ) %>% knitr::kable() stars/inst/doc/stars4.Rmd0000644000175000017500000002556414157337763015202 0ustar nileshnilesh--- title: '4. stars data model' author: "Edzer Pebesma" output: rmarkdown::html_vignette vignette: > %\VignetteIndexEntry{4. stars data model} %\VignetteEngine{knitr::rmarkdown} %\VignetteEncoding{UTF-8} --- **For a better version of the stars vignettes see** https://r-spatial.github.io/stars/articles/ ```{r setup, include=FALSE} knitr::opts_chunk$set(echo = TRUE) knitr::opts_chunk$set(collapse = TRUE) knitr::opts_chunk$set(fig.height = 4.5) knitr::opts_chunk$set(fig.width = 6) set.seed(13579) ``` 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 * an attribute called `dimensions` with a `dimensions` object carrying 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`, * 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) Clearly, `offset` and `delta` only apply to _regularly_ discretized dimensions, and are `NA` if this is not the case. `from` and `to` will usually be 1 and the dimension size, but `from` may be larger than 1 in case a regular sub-grid got cut out or was cropped. Rectilinear and curvilinear grids need grid values in `values`; this can be irregularly _spaced_ coordinate values, or coordinate _intervals_ of irregular width, or spatial geometries encoded in an `sfc` vector ("list-column"), or a matrix with grid cell centre values (longitude or latitude) for curvilinear grids. ## 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 origing $(0,0)$ 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, one with the latitudes and one with the longitudes for every grid cell. Using the GDAL interface, we need to compose a `stars` object from the array and a long and lat matrix. The matrices with longitude and latitude values are stored in the `values` field of their respective dimensions. 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 `st_as_stars` on three arrays, with data, longitude and latitudes: ```{r eval=EVAL} 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")) { threshold = units::set_units(9e+36, mol/m^2) } else { threshold = 9e+36 } nit.c[[1]][nit.c[[1]] > threshold] = NA st_crs(nit.c) = 4326 nit.c ``` ```{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 = stars:::st_downsample(nit.c, 8)) 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') ``` which doesn't look nice, but plotting the cells as polygons looks better: ```{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') ``` stars/inst/doc/stars2.html0000644000175000017500000211226414157340015015376 0ustar nileshnilesh 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

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 at a different resolution

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

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:

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

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

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

##    user  system elapsed 
##   0.966   0.252   0.297

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

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

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:

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

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.

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:

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

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:

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

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):

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

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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/NAMESPACE0000644000175000017500000001233014157337762012775 0ustar nileshnilesh# Generated by roxygen2: do not edit by hand S3method("$<-",stars) S3method("[",dimension) S3method("[",dimensions) S3method("[",intervals_list) 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(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(aperm,stars) S3method(aperm,stars_proxy) S3method(as.data.frame,dimensions) S3method(as.data.frame,stars) S3method(as.data.frame,stars_proxy) S3method(as.list,intervals) 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,stars) S3method(droplevels,stars) S3method(droplevels,stars_proxy) S3method(format,intervals) S3method(format,intervals_list) S3method(hist,stars) S3method(hist,stars_proxy) S3method(image,stars) S3method(is.na,stars) S3method(is.na,stars_proxy) S3method(merge,stars) S3method(merge,stars_proxy) S3method(plot,stars) S3method(plot,stars_proxy) S3method(predict,stars) S3method(predict,stars_proxy) S3method(print,dimensions) S3method(print,stars) S3method(print,stars_proxy) S3method(print,stars_raster) 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,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,STFDF) S3method(st_as_stars,STSDF) S3method(st_as_stars,SpatRaster) S3method(st_as_stars,Spatial) S3method(st_as_stars,bbox) 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,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,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_interpolate_aw,stars) S3method(st_intersects,stars) S3method(st_join,stars) S3method(st_mosaic,character) S3method(st_mosaic,stars) S3method(st_mosaic,stars_proxy) S3method(st_normalize,stars) S3method(st_redimension,stars) S3method(st_redimension,stars_proxy) 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_transform_proj,stars) S3method(write_stars,stars) S3method(write_stars,stars_proxy) export("st_dimensions<-") export(as.tbl_cube.stars) export(detect.driver) export(geom_stars) export(is_big) export(make_intervals) export(read_ncdf) export(read_stars) export(st_apply) export(st_as_stars) export(st_contour) export(st_dim_to_attr) export(st_dimensions) export(st_downsample) export(st_extract) export(st_flip) export(st_get_dimension_values) export(st_mosaic) export(st_raster_type) export(st_rasterize) export(st_redimension) export(st_rgb) export(st_set_bbox) export(st_set_dimensions) export(st_sfc2xy) export(st_transform_proj) export(st_warp) export(st_xy2sfc) export(write_stars) exportMethods("%in%") import(sf) import(units) importFrom(abind,abind) importFrom(abind,adrop) importFrom(abind,asub) importFrom(classInt,classIntervals) 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,text) importFrom(graphics,title) importFrom(lwgeom,st_transform_proj) importFrom(methods,as) importFrom(methods,new) importFrom(methods,slot) importFrom(methods,slotNames) importFrom(parallel,parApply) importFrom(rlang,"%||%") importFrom(stats,aggregate) importFrom(stats,na.omit) importFrom(stats,predict) importFrom(stats,quantile) importFrom(stats,runif) importFrom(stats,setNames) importFrom(stats,var) importFrom(tools,file_ext) importFrom(utils,head) importFrom(utils,packageVersion) importFrom(utils,setTxtProgressBar) importFrom(utils,tail) importFrom(utils,txtProgressBar)