When we look into the list of variables: 1-dim prec_sfc and 2-dim T_p, we realize that they have different dimensions(!). In this example, they are measurement data taken at 147.4E 2.1S, Manus, Papua New Guinea by ARM. Global attributes (not printed above) tells us how the data is collected and pre-processed. You can see dimensions, variables, and other metadata which are quite self-explanatory. Go to the directory of your dataset and try $ ncdump -h twparmbeatmC1.c1.Īs we do not need to see the values of every data entry at the moment, -h ensures only header (metadata) is shown. The former gives text representation of your netCDF dataset (basically metadata and the data itself), while the latter is a very powerful graphical interface for instant data visualization. Assume you have installed netCDF4-python and the only two commands you need are ncdump and ncview. It is always a good idea to ‘preview’ and ‘get to know’ your data, its metadata and data structures. Things should check out if you install xarray through conda. To check the version of packages, use pip freeze or conda list. You can also check if Python3 is installed by $ python3 -version Python 3.4.9 If you want to visualize your dataset, you will probably need these:įor absolute beginners, you can check your default version of Python by $ python -version Python 2.7.5
netCDF4-python for basic netCDF operation such as reading/writing.bottleneck for speeding up NaN-skipping.I made a list of dependencies that you need to check: That is why the most convenient way to get everything installed is to use the following command: $ conda install xarray dask netCDF4 bottleneckĮxperienced Python programmers are recommended check the relevant documentation for more details. As you might know, package dependency is a pain in Python.
Long story short, it builds upon numpy (and dask) libraries and leverages the power of pandas, but you probably don’t need to know about it. We will use xarray library in Python for data processing.
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