Reading a NetCDF file with Python, Matlab and R

Visualisation tools

There are several turn-key desktop tools designed to data and image visualization of a NetCDF file like:

•  Panoply designed essentially for NetCDF files that use the Climate Forecast conventions metadata
• HDFview : able to open all NetCDF (and HDF) files

Language

In the following examples, the NetCDF file downloadable here is used.

PYTHON

1. Using the netCDF4 library:

# First, you need to install the netCDF4 library if you haven’t already
# pip install netCDF4

import netCDF4 as nc

# Specify the file path
file_path = 'sresa1b_ncar_ccsm3-example.nc'

# Open the NetCDF file
ds = nc.Dataset(file_path)

# Print the file details
print(ds)

# Access a specific variable
tas = ds.variables['tas'][:]

# Check the dimensions and attributes of the variable
print(ds.variables['tas'].dimensions)
print(ds.variables['tas'].shape)
print(ds.variables['tas'].units)

# Close the dataset after using it
ds.close()

2. Using the xarray library:

# Install xarray and netCDF4 together if you haven’t done so
# pip install xarray netCDF4

import xarray as xr

# Open the NetCDF file using xarray
ds = xr.open_dataset('sresa1b_ncar_ccsm3-example.nc')

# Print the dataset details
print(ds)

# Access a specific variable
tas = ds['tas']

# Print the values of the lat variable
print(tas.values)

# You can also access dimensions and coordinates
print(ds.dims)
print(ds.coords)

# Close the dataset (optional, handled automatically with xarray)
ds.close()

MATLAB

file_name = 'sresa1b_ncar_ccsm3-example.nc';
var_name = 'tas';
group_name = '/grid2/';
tmp = var_name;  % or tmp = group_name
 
% Display global information about a netcdf file
ncdisp(file_name);
% About a variable or a group
ncdisp(file_name, tmp);
% In the specified format 'full' or 'min'
ncdisp(file_name, tmp, 'min')
   

% Get information about a netcdf file
info = ncinfo(file_name)
% About a variable
var_info = ncinfo(file_name, var_name)
% About a group
gr_info = ncinfo(file_name, group_name)
 
 
% Read data from a vraiable of a netcdf file
data = ncread(file_name, var_name);
% Or
% data = ncread(file_name, var_name, start, count, stride) 
 
 
% Read attribute
attr =  ncreadatt(file_name, '/', 'creation_date')
var_attr = ncreadatt(file_name, var_name, 'add_offset')
gr_attr = ncreadatt(file_name, group_name, 'description')

R

#Intall the NetCDF libraries: sudo apt-get install libnetcdf-dev 
#Install the ncdf package in R: install.packages("ncdf")
 
library("ncdf")
 
filename = "sresa1b_ncar_ccsm3-example.nc"
var_name = "tas"
attr_name = "units"
 
# Open a netcdf file
fid <- open.ncdf(filename, write=FALSE)
 
# Get a netcdf file info
print(fid)
 
# Get the variable identifier
var_id <- varid.inq.ncdf(fid, var_name)
print(var_id)
 
# Get the name of a variable id 
my_var_name <- varname.inq.ncdf(fid, var_id)
print(my_var_name)
 
# Get a file attribute
attr <- att.get.ncdf(fid, 0,"creation_date")
print(attr)

# Get a variable attribute
attr_value <- att.get.ncdf(fid, var_name, attr_name)
print(attr_value)
 
# Get the size of a variable
size <- varsize.ncdf(fid, var_id)
print (size)
 
# Get dimensions of a variable
ndims <- varndims.ncdf(fid, var_id)
print (ndims)
 
# Read data from a netcdf file
data <- get.var.ncdf(fid, var_name)
print(data)
 
# Get the variable objet
var_object <- var.inq.ncdf(fid,var_id )
print(var_object)
 
# Get the name from the variable object
name <- var_object$name
print (name)
 
# Get variable object units
n_attr <- var_object[["units"]]
print(n_attr)
 
# Close a netcdf file
close.ncdf(fid)