NCDatasets

NCDatasets allows one to read and create netCDF files. NetCDF data set and attribute list behave like Julia dictionaries and variables like Julia arrays. This package implements the CommonDataModel.jl interface, which mean that the datasets can be accessed in the same way as GRIB files (GRIBDatasets.jl) and Zarr files (ZarrDatasets.jl).

The module NCDatasets provides support for the following netCDF CF conventions:

• _FillValue will be returned as missing (more information)
• scale_factor and add_offset are applied if present
• time variables (recognized by the units attribute) are returned as DateTime objects.
• support of the CF calendars (standard, gregorian, proleptic gregorian, julian, all leap, no leap, 360 day) using CFTime
• the raw data can also be accessed (without the transformations above).
• contiguous ragged array representation

Other features include:

• Support for NetCDF 4 compression and variable-length arrays (i.e. arrays of vectors where each vector can have potentailly a different length)
• The module also includes an utility function ncgen which generates the Julia code that would produce a netCDF file with the same metadata as a template netCDF file.

Installation

Inside the Julia shell, you can download and install the package by issuing:

using Pkg
Pkg.add("NCDatasets")

Manual

This Manual is a quick introduction in using NCDatasets.jl. For more details you can read the stable or latest documentation.

• Create a netCDF file
• Explore the content of a netCDF file
• Load a netCDF file
• Edit an existing netCDF file

Create a netCDF file

The following gives an example of how to create a netCDF file by defining dimensions, variables and attributes.

using NCDatasets
using DataStructures: OrderedDict
# This creates a new NetCDF file called file.nc.
# The mode "c" stands for creating a new file (clobber)
ds = NCDataset("file.nc","c")

# Define the dimension "lon" and "lat" with the size 100 and 110 resp.
defDim(ds,"lon",100)
defDim(ds,"lat",110)

# Define a global attribute
ds.attrib["title"] = "this is a test file"

# Define the variables temperature with the attribute units
v = defVar(ds,"temperature",Float32,("lon","lat"), attrib = OrderedDict(
    "units" => "degree Celsius",
    "scale_factor" => 10,
))

# add additional attributes
v.attrib["comments"] = "this is a string attribute with Unicode Ω ∈ ∑ ∫ f(x) dx"

# Generate some example data
data = [Float32(i+j) for i = 1:100, j = 1:110];

# write a single column
v[:,1] = data[:,1];

# write a the complete data set
v[:,:] = data;

close(ds)

It is also possible to create the dimensions, the define the variable and set its value with a single call to defVar:

using NCDatasets
ds = NCDataset("/tmp/test2.nc","c")
data = [Float32(i+j) for i = 1:100, j = 1:110]
v = defVar(ds,"temperature",data,("lon","lat"))
close(ds)

Explore the content of a netCDF file

Before reading the data from a netCDF file, it is often useful to explore the list of variables and attributes defined in it.

For interactive use, the following commands (without ending semicolon) display the content of the file similarly to ncdump -h file.nc:

using NCDatasets
ds = NCDataset("file.nc")

This creates the central structure of NCDatasets.jl, NCDataset, which represents the contents of the netCDF file (without immediatelly loading everything in memory). NCDataset is an alias for Dataset.

The following displays the information just for the variable varname:

ds["varname"]

while to get the global attributes you can do:

ds.attrib

NCDataset("file.nc") produces a listing like:

Dataset: file.nc
Group: /

Dimensions
   lon = 100
   lat = 110

Variables
  temperature   (100 × 110)
    Datatype:    Float32 (Float32)
    Dimensions:  lon × lat
    Attributes:
     units                = degree Celsius
     scale_factor         = 10
     comments             = this is a string attribute with Unicode Ω ∈ ∑ ∫ f(x) dx

Global attributes
  title                = this is a test file

Load a netCDF file

Loading a variable with known structure can be achieved by accessing the variables and attributes directly by their name.

# The mode "r" stands for read-only. The mode "r" is the default mode and the parameter can be omitted.
ds = NCDataset("file.nc","r")
v = ds["temperature"]

# load a subset
subdata = v[10:30,30:5:end]

# load all data
data = v[:,:]

# load all data ignoring attributes like scale_factor, add_offset, _FillValue and time units
data2 = v.var[:,:];


# load an attribute
unit = v.attrib["units"]
close(ds)

In the example above, the subset can also be loaded with:

subdata = NCDataset("file.nc")["temperature"][10:30,30:5:end]

This might be useful in an interactive session. However, the file test.nc is not directly closed (closing the file will be triggered by Julia's garbage collector), which can be a problem if you open many files. On Linux the number of opened files is often limited to 1024 (soft limit). If you write to a file, you should also always close the file to make sure that the data is properly written to the disk.

An alternative way to ensure the file has been closed is to use a do block: the file will be closed automatically when leaving the block.

data = NCDataset(filename,"r") do ds
    ds["temperature"][:,:]
end # ds is closed

Edit an existing netCDF file

When you need to modify variables or attributes in a netCDF file, you have to open it with the "a" option. Here, for example, we add a global attribute creator to the file created in the previous step.

ds = NCDataset("file.nc","a")
ds.attrib["creator"] = "your name"
close(ds);

Benchmark

The benchmark loads a variable of the size 1000x500x100 in slices of 1000x500 (applying the scaling of the CF conventions) and computes the maximum of each slice and the average of each maximum over all slices. This operation is repeated 100 times. The code is available at https://github.com/Alexander-Barth/NCDatasets.jl/tree/master/test/perf .

Module	median	minimum	mean	std. dev.
R-ncdf4	0.407	0.384	0.407	0.010
python-netCDF4	0.475	0.463	0.476	0.010
julia-NCDatasets	0.265	0.249	0.267	0.011

All runtimes are in seconds. We use Julia 1.10.0 (with NCDatasets 0.14.0), R 4.1.2 (with ncdf4 1.22) and Python 3.10.12 (with netCDF4 1.6.5) on a i5-1135G7 CPU and NVMe SSD (WDC WDS100T2B0C).

Filing an issue

When you file an issue, please include sufficient information that would allow somebody else to reproduce the issue, in particular:

1. Provide the code that generates the issue.
2. If necessary to run your code, provide the used netCDF file(s).
3. Make your code and netCDF file(s) as simple as possible (while still showing the error and being runnable). A big thank you for the 5-star-premium-gold users who do not forget this point! 👍🏅🏆
4. The full error message that you are seeing (in particular file names and line numbers of the stack-trace).
5. Which version of Julia and NCDatasets are you using? Please include the output of:

versioninfo()
using Pkg
Pkg.installed()["NCDatasets"]

6. Does NCDatasets pass its test suite? Please include the output of:

using Pkg
Pkg.test("NCDatasets")

Alternative

The package NetCDF.jl from Fabian Gans and contributors is an alternative to this package which supports a more Matlab/Octave-like interface for reading and writing NetCDF files.

Credits

netcdf_c.jl and the error handling code of the NetCDF C API are from NetCDF.jl by Fabian Gans (Max-Planck-Institut für Biogeochemie, Jena, Germany) released under the MIT license.