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Variables

Variables (like e.g. CFVariable) are the quantities contained within a NetCDF dataset. See the Datasets page on how to obtain them from a dataset.

Different type of arrays are involved when working with NCDatasets. For instance assume that test.nc is a file with a Float32 variable called variable.

using NCDatasets
ds = NCDataset("test.nc")
ncvar_cf = ds["variable"]

The variable ncvar_cf has the type CFVariable. No data is actually loaded from disk, but you can query its size, number of dimensions, number elements, etc., using the functions size, ndims, length as if ncvar_cf was an ordinary Julia array.

To load the variable ncvar_cf in memory you can convert it into an array with:

data = Array(ncvar_cf)
# or
data = ncvar_cf |> Array
# or if ndims(ncvar_cf) == 2
data = ncvar_cf[:,:]

Since NCDatasets 0.13, the syntax ncvar_cf[:] flattens the array, and is not equivalent with the above (unless ncvar_cf is a vector).

You can only load sub-parts of it in memory via indexing each dimension:

ncvar_cf[1:5, 10:20]

A scalar variable can be loaded using [], for example:

using NCDatasets
NCDataset("test_scalar.nc","c") do ds
    # the list of dimension names is simple `()` as a scalar does not have dimensions
    defVar(ds,"scalar",42,())
end

ds = NCDataset("test_scalar.nc")
value = ds["scalar"][] # 42

To load all a variable in a NetCDF file ignoring attributes like scale_factor, add_offset, _FillValue and time units one can use the property var or the function variable for example:

using NCDatasets
using Dates
data = [DateTime(2000,1,1), DateTime(2000,1,2)]
NCDataset("test_file.nc","c") do ds
    defVar(ds,"time",data,("time",), attrib = Dict(
               "units" => "days since 2000-01-01"))
end;

ds = NCDataset("test_file.nc")
ncvar = ds["time"].var
# or
ncvar = variable(ds,"time")
data = ncvar[:] # here [0., 1.]

The variable ncvar can be indexed in the same way as ncvar_cf explained above.

!!! note NCDatasets.Variable and NCDatasets.CFVariable implement the interface of AbstractArray. It is thus possible to call any function that accepts an AbstractArray. But functions like mean, sum (and many more) would load every element individually which is very inefficient for large fields read from disk. You should instead convert such a variable to a standard Julia Array and then do computations with it. See also the [performance tips](@ref performance_tips) for more information.

The following functions are convenient for working with variables:

Base.size(v::NCDatasets.CFVariable)
dimnames
dimsize
name
renameVar
NCDataset(var::NCDatasets.CFVariable)
nomissing
fillvalue

loadragged
NCDatasets.load!

Creating a variable

defVar

Storage parameter of a variable

chunking
deflate
checksum

Coordinate variables and cell boundaries

coord
bounds