Tutorial: Using xarray to work with multidimensional geospatial datasets

- Thursday, November 7, 2024, 1:15 PM - 2:45 PM ET
  - Lead: Erika Peirce
  - Prerequisites:
    - Have a SCINet account and be able to login
    - Familiarity with Python and Jupyter notebooks.
    - Familiarity with handling data using pandas.

In this tutorial you will learn the basic concepts of the Python library xarray while exploring its capabilities when working with multidimensional datasets with dimensions of time, latitude, and longitude. We will also touch on the capabilities of parallel processing using another Python library called dask.

Additional details and instructions for the tutorial will be added closer to the event. A recording of the tutorial will be added after the workshop concludes.

Tutorial setup instructions

Steps to prepare for the tutorial sessions:

Login to Ceres Open OnDemand at https://ceres-ood.scinet.usda.gov/. For more information on login procedures for web-based SCINet access, see the SCINet access user guide.
Open a command-line session by clicking on “Clusters” -> “Ceres Shell Access” on the top menu. This will open a new tab with a command-line session on Ceres’ login node.
Create and/or update your workshop working directory and copy the tutorial materials into it by running the following commands. Note: you do not have to edit the commands with your username as it will be determined by the $USER variable. These commands will work for everyone, including if you attending previous workshop sessions or not.
```
 mkdir -p /90daydata/shared/$USER/
 cd /90daydata/shared/$USER/
 cp -r /project/geospatialworkshop/2024/tutorial4 .
 source /project/geospatialworkshop/2024/grwg_2024_env/bin/activate
 ipython kernel install --name "grwg_2024_env" --user
```
Launch a JupyterLab Server session. Under the Interactive Apps menu, select Jupyter. Specify the following input values on the page:
- Account: geospatialworkshop
- Queue: short———Max Time: 2-00:00:00
- QOS: 400thread
- Number of hours: 2
- Number of cores: 8
- Memory required: 16GB
- Optional Slurm Arguments: --reservation=workshop
- Jupyter Notebook vs Lab: Lab
- Working Directory: /90daydata/shared/${USER}
Click Launch. The screen will update to the Interactive Sessions page. When your Jupyter session is ready, the top card will update from Queued to Running and a Connect to Jupyter button will appear. Click Connect to Jupyter.
For the notebook used in the tutorial, select the grwg_2024_env kernel.