Cloud Workspace Setup

If you plan to use this repository with the Openscapes 2i2c JupyterHub Cloud Workspace there are no additional setup requirements for the Python environment. All packages needed are included unless specified within a notebook, in which case a cell will be dedicated to installing the necessary Python libraries using the appropriate package manager.

After completing the prerequisites you will have access to the Openscapes 2i2c JupyterHub cloud workspace. Click here to start JupyterLab. Use your email and the provided password to sign in. This password will be provided in the workshop. If you’re interested in using the 2i2c cloud workspace outside of the workshop, please contact us.

After signing in you will be prompted for some server options:

Be sure to select the radio button for Python and a size of 28 GB RAM and up to 4 CPUs.

Cloning the nasa-neon-hsi-workshop Repository

Once the Python environment is spun up, you can clone the workshop repository.

To clone the repository, navigate to the directory where you want to store the repository on Openscapes, or on your local machine, then type the following from the terminal or command prompt:

git clone https://github.com/NEONScience/neon-nasa-hsi-workshop.git

To locate and start running the NEON notebooks, change directories (cd) to neon-nasa-hsi-workshop/neon

cd neon-nasa-hsi-workshop/neon

If you plan to edit or contribute to this NEON NASA Airborne Hyperspectral Workshop Repository repository, or the NEON-Data-Skills repository, we recommend following a fork and pull workflow: first fork the repository, then clone your fork to your local machine, make changes, push changes to your fork, then make a pull request back to the main repository.

Troubleshooting

We recommend Shutting down all kernels after running each notebook. This will clear the memory used by the previous notebook, and is necessary to run some of the more memory intensive notebooks.

No single notebook exceeds roughly the limit using the provided data, but if you choose to use your own data in the notebook, or have 2 notebooks open and do not shut down the kernel, you may get an out of memory error.

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