Read data from a file (or source) using the NetCDF library directly.

read_ncdf(
  .x,
  ...,
  var = NULL,
  ncsub = NULL,
  curvilinear = character(0),
  eps = sqrt(.Machine$double.eps),
  ignore_bounds = FALSE,
  make_time = TRUE,
  make_units = TRUE,
  proxy = NULL,
  downsample = 0
)

Arguments

.x

NetCDF file or source as a character vector or an nc_proxy object.

...

ignored

var

variable name or names (they must be on matching grids)

ncsub

matrix of start, count columns (see Details)

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.

eps

numeric; dimension value increases are considered identical when they differ less than eps

ignore_bounds

logical; should bounds values for dimensions, if present, be ignored?

make_time

if TRUE (the default), an atttempt is made to provide a date-time class from the "time" variable

make_units

if TRUE (the default), an attempt is made to set the units property of each variable

proxy

logical; if TRUE, an object of class stars_proxy is read which contains array metadata only; if FALSE the full array data is read in memory. If not set, defaults to TRUE when the number of cells to be read is larger than options(stars.n_proxy), or to 1e8 if that option was not set.

downsample

integer; number of cells to omit between samples along each dimension. e.g. c(1,1,2) would return every other cell in x and y and every third cell in the third dimension (z or t). If 0, no downsampling is applied. Note that this transformation is applied AFTER NetCDF data are read using st_downsample. As such, if proxy=TRUE, this option is ignored.

Details

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 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 var is not set the first set of variables on a shared grid is used.

start and count columns of ncsub must correspond to the variable dimemsion (nrows) and be valid index using var.get.nc convention (start is 1-based). If the count value is NA then all steps are included. Axis order must match that of the variable/s being read.

Examples

f <- system.file("nc/reduced.nc", package = "stars")
if (require(ncmeta, quietly = TRUE)) {
 read_ncdf(f)
 read_ncdf(f, var = c("anom"))
 read_ncdf(f, ncsub = cbind(start = c(1, 1, 1, 1), count = c(10, 12, 1, 1)))
}
#> no 'var' specified, using sst, anom, err, ice
#> other available variables:
#>  lon, lat, zlev, time
#> 0-360 longitude crossing the international dateline encountered.
#> Longitude coordinates will be0-360 in output.
#> Will return stars object with 16200 cells.
#> No projection information found in nc file. 
#>  Coordinate variable units found to be degrees, 
#>  assuming WGS84 Lat/Lon.
#> 0-360 longitude crossing the international dateline encountered.
#> Longitude coordinates will be0-360 in output.
#> Will return stars object with 16200 cells.
#> No projection information found in nc file. 
#>  Coordinate variable units found to be degrees, 
#>  assuming WGS84 Lat/Lon.
#> no 'var' specified, using sst, anom, err, ice
#> other available variables:
#>  lon, lat, zlev, time
#> Will return stars object with 120 cells.
#> No projection information found in nc file. 
#>  Coordinate variable units found to be degrees, 
#>  assuming WGS84 Lat/Lon.
#> stars object with 4 dimensions and 4 attributes
#> attribute(s):
#>                Min. 1st Qu. Median        Mean 3rd Qu. Max. NA's
#> sst [°C]      -1.39 -0.7200 -0.515 -0.53399999  -0.275 0.03   90
#> anom [°C]     -1.07 -0.3625  0.195  0.05866667   0.555 0.92   90
#> err [°C]       0.30  0.3000  0.300  0.30299999   0.300 0.32   90
#> ice [percent]  0.01  0.1100  0.170  0.20937500   0.255 0.52  104
#> dimension(s):
#>      from to offset delta  refsys point         values x/y
#> lon     1 10     -1     2  WGS 84    NA           NULL [x]
#> lat     1 12    -90     2  WGS 84    NA           NULL [y]
#> zlev    1  1     NA    NA      NA    NA              0    
#> time    1  1     NA    NA POSIXct    NA 1981-12-31 UTC    

if (require(ncmeta, quietly = TRUE)) {
 #' precipitation data in a curvilinear NetCDF
 prec_file = system.file("nc/test_stageiv_xyt.nc", package = "stars")
 prec = read_ncdf(prec_file, curvilinear = c("lon", "lat"), ignore_bounds = TRUE)
}
#> no 'var' specified, using Total_precipitation_surface_1_Hour_Accumulation
#> other available variables:
#>  lat, lon, time
#> Will return stars object with 236118 cells.
#> No projection information found in nc file. 
#>  Coordinate variable units found to be degrees, 
#>  assuming WGS84 Lat/Lon.

##plot(prec) ## gives error about unique breaks
## remove NAs, zeros, and give a large number
## of breaks (used for validating in detail)
qu_0_omit = function(x, ..., n = 22) {
  x = units::drop_units(na.omit(x))
  c(0, quantile(x[x > 0], seq(0, 1, length.out = n)))
}
if (require(dplyr, quietly = TRUE)) {
  prec_slice = slice(prec, index = 17, along = "time")
  plot(prec_slice, border = NA, breaks = qu_0_omit(prec_slice[[1]]), reset = FALSE)
  nc = sf::read_sf(system.file("gpkg/nc.gpkg", package = "sf"), "nc.gpkg")
  plot(st_geometry(nc), add = TRUE, reset = FALSE, col = NA)
}