var.get.nc {RNetCDF} | R Documentation |
Get data from a NetCDF variable.
var.get.nc(ncfile, variable, start=NA, count=NA, na.mode=0, collapse=TRUE)
ncfile |
Object of class "NetCDF " which points to the NetCDF dataset (as returned from open.nc ). |
variable |
ID or name of the variable. |
start |
A vector of indices indicating where to start reading the values (beginning at 1). The length of this vector must equal the number of dimensions the variable has. Order is leftmost varying fastest (as got from print.nc ; opposite to the CDL conventions). If not specified (start=NA ), reading starts at index 1. |
count |
A vector of integers indicating the count of values to read along each dimension. Order is leftmost varying fastest (as got from print.nc ; opposite to the CDL conventions). The length of this vector must equal the number of dimensions the variable has. If not specified (count=NA ), the entire variable or all values along the corresponding dimension(s) are read. |
na.mode |
Set the mode how missing values (NA ) are handled: 0=accept _FillValue or missing_value attribute, 1=accept only _FillValue attribute, 2=accept only missing_value attribute, 3=no missing value conversion. |
collapse |
TRUE if degenerated dimensions (length=1) should be omitted. |
This function returns the value of a variable. Returned values are always in ordinary R double precision (apart from character variables), no matter what precision they are in the on-disk dataset.
Values of NA
are supported; values in the data file that match the variable's missing value attribute (as defined in na.mode
) are automatically converted to NA
before being returned to the user. If na.mode=0
and both attributes are defined, the value of _FillValue
is used.
Data in a NetCDF file is conceived as being a multi-dimensional array. The number and length of dimensions is determined when the variable is created. The start
and count
indices that this routine takes indicate where the reading starts along each dimension, and the count of values along each dimension to read.
The argument collapse
allows to keep degenerated dimensions (if set to FALSE
). As default, array dimensions with length=1 are omitted (e.g., an array with dimensions [2,1,3,4] in the NetCDF dataset is returned as [2,3,4]).
Awkwardness arises mainly from one thing: NetCDF data are written with the last dimension varying fastest, whereas R works opposite. Thus, the order of the dimensions according to the CDL conventions (e.g., time, latitude, longitude) is reversed in the R array (e.g., longitude, latitude, time).
A multidimensional array of type numeric
or character
if the data type is NC_CHAR
. No distinction is made between the different storage types of numeric objects. The dimension order according to the CDL conventions is swapped in the R array, because NetCDF data are written with the last dimension varying fastest, whereas R works opposite. Arrays of type character
loose their first dimension, because strings can be indexed with one dimension in R and the first dimension (usually max_string_length
) is therefore needless.
NC_BYTE
is always interpreted as signed.
Pavel Michna
http://www.unidata.ucar.edu/packages/netcdf/
## Create a new NetCDF dataset and define two dimensions nc <- create.nc("foo.nc") dim.def.nc(nc, "station", 5) dim.def.nc(nc, "time", unlim=TRUE) dim.def.nc(nc, "max_string_length", 32) ## Create three variables, one as coordinate variable var.def.nc(nc, "time", "NC_INT", "time") var.def.nc(nc, "temperature", "NC_DOUBLE", c(0,1)) var.def.nc(nc, "name", "NC_CHAR", c("max_string_length", "station")) ## Put some missing_value attribute for temperature att.put.nc(nc, "temperature", "missing_value", "NC_DOUBLE", -99999.9) ## Define variable values mytime <- c(1:2) mytemperature <- c(1.1, 2.2, 3.3, 4.4, 5.5, 6.6, 7.7, NA, NA, 9.9) myname <- c("alfa", "bravo", "charlie", "delta", "echo") ## Put the data var.put.nc(nc, "time", mytime, 1, length(mytime)) var.put.nc(nc, "temperature", mytemperature, c(1,1), c(5,2)) var.put.nc(nc, "name", myname, c(1,1), c(32,5)) sync.nc(nc) ## Get the data (or a subset) var.get.nc(nc, 0) var.get.nc(nc, "temperature") var.get.nc(nc, "temperature", c(NA,2), c(NA,1)) var.get.nc(nc, "name") var.get.nc(nc, "name", c(1,2), c(4,2)) var.get.nc(nc, "name", c(1,2), c(NA,2)) close.nc(nc)