Getting Started#

Prerequisites#

  1. Familiarity with xarray, since this package is an extension of it

2. xCDAT is distributed through conda, which is available through Anaconda and Miniconda. The instruction to install conda from Miniconda is provided as follows:

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 yes in 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 conda to your path.

Note: After installation completes you may need to type bash to restart your shell (if you use bash). Alternatively, you can log out and log back in.

Installation#

  1. Create a conda environment from scratch with xcdat (conda create)

    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 xcdat conda environment with xesmf (a recommended dependency), run:

    >>> conda create -n <ENV_NAME> -c conda-forge xcdat xesmf
>>> conda activate <ENV_NAME>

    Note that xesmf is an optional dependency, which is required for using xesmf based horizontal regridding APIs in xcdat. xesmf is not currently supported on osx-arm64 or windows because esmpy is not yet available on these platforms. Windows users can try WSL2 as a workaround.

  1. Install xcdat in an existing conda environment (conda install)

    You can also install xcdat in 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, xesmf is an optional dependency.

  2. [Optional] Some packages that are commonly used with xcdat can 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 ipykernel.

    • matplotlib: a library for creating visualizations in Python.

    • cartopy: an add-on package for matplotlib and specialized for geospatial data processing.