xarray, since this package is an extension of it
We highly recommend visiting the xarray tutorial and xarray documentation pages if you aren’t familiar with
xCDAT is distributed through conda, which is available through Anaconda and Miniconda.
We recommend following these steps to install Miniconda:
wget https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh bash Miniconda3-latest-Linux-x86_64.sh
Then follow the instructions for installation. To have conda added to your path you will need to type
yesin response to
"Do you wish the installer to initialize Miniconda3 by running conda init?"(we recommend that you do this). Note that this will modify your shell profile (e.g.,
~/.bashrc) to add
condato your path.
Note: After installation completes you may need to type
bashto restart your shell (if you use bash). Alternatively, you can log out and log back in.
Create a conda environment from scratch with
We recommend using the Conda environment creation procedure to install
xcdat. The advantage with following this approach is that Conda will attempt to resolve dependencies (e.g.
python >= 3.8) for compatibility.
To create an
xcdatconda environment with
xesmf(a recommended dependency), run:
>>> conda create -n <ENV_NAME> -c conda-forge xcdat xesmf >>> conda activate <ENV_NAME>
xesmfis an optional dependency, which is required for using
xesmfbased horizontal regridding APIs in
xesmfis not currently supported on osx-arm64 or windows because
esmpyis not yet available on these platforms. Windows users can try WSL2 as a workaround.
xcdatin an existing conda environment (conda install)
You can also install
xcdatin an existing Conda environment, granted that Conda is able to resolve the compatible dependencies.
>>> conda activate <ENV_NAME> >>> conda install -c conda-forge xcdat xesmf
Note: As above,
xesmfis an optional dependency.
[Optional] Some packages that are commonly used with
xcdatcan be installed either in step 1 or step 2 above:
jupyterlab: a web-based interactive development environment for notebooks, code, and data. This package also includes
matplotlib: a library for creating visualizations in Python.
cartopy: an add-on package for
matplotliband specialized for geospatial data processing.