RNetCDF (version 2.4-2)

var.put.nc: Write Data to a NetCDF Variable

Description

Write the contents of a NetCDF variable.

Usage

var.put.nc(ncfile, variable, data, start=NA, count=NA, na.mode=4, pack=FALSE,
  cache_bytes=NA, cache_slots=NA, cache_preemption=NA)

Arguments

ncfile

Object of class "NetCDF" which points to the NetCDF dataset (as returned from open.nc).

variable

ID or name of the NetCDF variable.

data

An R vector or array of data to be written to the NetCDF variable. Values are taken from data in the order of R vector elements, so that leftmost indices vary fastest over an array.

start

A vector of indices specifying the element where writing starts along each dimension of variable. Indices are numbered from 1 onwards, and the order of dimensions is shown by print.nc (array elements are stored sequentially with leftmost indices varying fastest). By default (start=NA), all dimensions of variable are written from the first element onwards. Otherwise, start must be a vector whose length is not less than the number of dimensions in variable (excess elements are ignored). Any NA values in vector start are set to 1.

count

A vector of integers specifying the number of values to write along each dimension of variable. The order of dimensions is the same as for start. By default (count=NA), count is set to dim(data) for an array or length(data) for a vector. Otherwise, count must be a vector whose length is not less than the number of dimensions in variable (excess elements are ignored). Any NA value in vector count indicates that the corresponding dimension should be written from the start index to the end of the dimension. Note that an unlimited dimension initially has zero length, and the dimension is extended by setting the corresponding element of count greater than the current length.

na.mode

Set the mode for handling missing values (NA) in numeric variables: 0=accept _FillValue, then missing_value attribute; 1=accept only _FillValue attribute; 2=accept only missing_value attribute; 3=no missing value conversion; 4=valid range from valid_min and valid_max or valid_range, fill value from _FillValue, with defaults for each type except NC_BYTE and NC_UBYTE (see https://www.unidata.ucar.edu/software/netcdf/docs/attribute_conventions.html).

pack

Variables are packed if pack=TRUE and the attributes add_offset and scale_factor are defined. Default is FALSE.

cache_bytes

Size of chunk cache in bytes. Value of NA (default) implies no change.

cache_slots

Number of slots in chunk cache. Value of NA (default) implies no change.

cache_preemption

Value between 0 and 1 (inclusive) that biases the cache scheme towards eviction of chunks that have been fully read. Value of NA (default) implies no change.

Details

This function writes values to a NetCDF variable. Data values in R are automatically converted to the correct type of NetCDF variable.

Text represented by R type character can be written to NetCDF types NC_CHAR and NC_STRING, and R type raw can be written to NetCDF type NC_CHAR. When writing to NC_CHAR variables, character variables have an implied dimension corresponding to the string length. This implied dimension must be defined explicitly as the fastest-varying dimension of the NC_CHAR variable, and it must be included as the first element of arguments start and count taken by this function.

Due to the lack of native support for 64-bit integers in R, NetCDF types NC_INT64 and NC_UINT64 require special attention. This function accepts the usual R integer (signed 32-bit) and numeric (double precision) types, but to represent integers larger than about 53-bits without truncation, \codeinteger64 vectors are also supported.

NetCDF numeric variables cannot portably represent NA values from R. NetCDF does allow attributes to be defined for variables, and several conventions exist for attributes that define missing values and valid ranges. The convention in use can be specified by argument na.mode. Values of NA in argument data are converted to a missing or fill value before writing to the NetCDF variable. Unusual cases can be handled directly in user code by setting na.mode=3.

Variables of user-defined types are supported, subject to conditions on the corresponding data structures in R. "compound" arrays must be stored in R as lists, with items named for the compound fields; items of base NetCDF data types are stored as R arrays, with leading dimensions from the field dimensions (if any) and trailing dimensions from the NetCDF variable. "enum" arrays are stored in R as factor arrays. "opaque" arrays are stored in R as raw (byte) arrays, with a leading dimension for bytes of the opaque type and trailing dimensions from the NetCDF variable. "vlen" arrays are stored in R as a list with dimensions of the NetCDF variable; items in the list may have different lengths; base NetCDF data types are stored as R vectors.

To reduce the storage space required by a NetCDF file, numeric variables can be "packed" into types of lower precision. The packing operation involves subtraction of attribute add_offset before division by attribute scale_factor. This packing operation is performed automatically for variables defined with the two attributes add_offset and scale_factor if argument pack is set to TRUE. If pack is FALSE, data values are assumed to be packed correctly and are written to the variable without alteration.

Data in a NetCDF variable is represented as a multi-dimensional array. The number and length of dimensions is determined when the variable is created. The start and count arguments of this routine indicate where the writing starts and the number of values to write along each dimension.

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).

References

https://www.unidata.ucar.edu/software/netcdf/

Examples

# NOT RUN {
##  Create a new NetCDF dataset and define two dimensions
file1 <- tempfile("var.put_", fileext=".nc")
nc <- create.nc(file1)

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 _FillValue attribute for temperature
att.put.nc(nc, "temperature", "_FillValue", "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")

dim(mytemperature) <- c(5,2)

##  Put subsets of the data:
var.put.nc(nc, "time", mytime, start=2, count=1)
var.put.nc(nc, "temperature", mytemperature[3:4,2], start=c(3,2), count=c(2,1))
var.put.nc(nc, "name", myname[3:4], start=c(NA,3), count=c(NA,2))
sync.nc(nc)

##  Put all of the data:
var.put.nc(nc, "time", mytime)
var.put.nc(nc, "temperature", mytemperature)
var.put.nc(nc, "name", myname)

close.nc(nc)
unlink(file1)
# }