Attributes

The NetCDF dataset (as returned by NCDataset or NetCDF groups) and the NetCDF variables (as returned by getindex, variable or defVar) have the field attrib which has the type NCDatasets.Attributes and behaves like a julia dictionary.

Base.getindex — Method

getindex(a::Attributes,name::SymbolOrString)

Return the value of the attribute called name from the attribute list a. Generally the attributes are loaded by indexing, for example:

using NCDatasets
ds = NCDataset("file.nc")
title = ds.attrib["title"]

source

Base.setindex! — Method

Base.setindex!(a::Attributes,data,name::SymbolOrString)

Set the attribute called name to the value data in the attribute list a. data can be a vector or a scalar. A scalar is handeld as a vector with one element in the NetCDF data model.

Generally the attributes are defined by indexing, for example:

ds = NCDataset("file.nc","c")
ds.attrib["title"] = "my title"
close(ds)

source

Base.keys — Method

Base.keys(a::Attributes)

Return a list of the names of all attributes.

source

Base.delete! — Method

Base.delete!(a::Attributes, name)

Delete the attribute name from the attribute list a.

source

Loading all attributes as a Dict can be achieved by passing ds.attrib (where ds is the NCDataset) as argument to Dict.

using NCDatasets
ncfile = download("https://www.unidata.ucar.edu/software/netcdf/examples/sresa1b_ncar_ccsm3-example.nc");
ds = NCDataset(ncfile);
attributes_as_dictionary = Dict(ds.attrib)
typeof(attributes_as_dictionary)
# returns Dict{String,Any}

Possible type promotion in Julia

There is a subtle problem with the following code:

ncv1 = defVar(ds,"v1", UInt8, ("longitude", "latitude", "time"), attrib = [
    "add_offset"                => -1.0,
    "scale_factor"              => 5.0,
    "_FillValue"                => UInt8(255),
])

Julia effectively promotes the _FillValue to Float64 which leads to a "NetCDF: Not a valid data type or _FillValue type mismatch" as the fillvalue has to have exactly the same type as the NetCDF data type. Other parameters could be equally promoted.

[
           "add_offset"                => -1.0,
           "scale_factor"              => 5.0,
           "_FillValue"                => UInt8(255),
]
# returns
# 3-element Array{Pair{String,Float64},1}:
#   "add_offset" => -1.0
# "scale_factor" => 5.0
#   "_FillValue" => 255.0

Note the type of the second element of the Pair.

Using a Julia Dict does not show this behaviour:

ncv1 = defVar(ds,"v1", UInt8, ("longitude", "latitude", "time"), attrib = Dict(
    "add_offset"                => -1.0,
    "scale_factor"              => 5.0,
    "_FillValue"                => UInt8(255),
))

Note that Dict does not perserve the order of the attributes. Therefore an OrderedDict from the package DataStructures is preferable.

Or one could use simply the fillvalue parameter of defVar.

ncv1 = defVar(ds,"v1", UInt8, ("longitude", "latitude", "time"), fillvalue = UInt8(255), attrib = [
    "add_offset"                => -1.0,
    "scale_factor"              => 5.0,
])