PACEToolkit - hackweek 2024

We are a team representing diverse end users of PACE data. During PACE Hackweek 2024, we collaborating on tools and tutorials that would help our end users access and use PACE data.

See the Tutorials link in sidebar of our Jupyter Book or the notenooks folder for our tutorials developed during the hackweek. See the scripts folder for functions we created.

Collaborators

Name	Affiliation	Tutorial	Links
Eli Holmes	NOAA Fisheries, Office of Science and Technology	Simple matchup on tracks	website
Prem Maheshwarkar	Universite Paris Est Creteil Val de Marne	Multi-source aerosol data visualization
Thiago Nobrega	University of Sao Paulo	Re-gridding PACE data
Bingqing Liu	University of Louisiana Lafayette	CyanoHABs	HyperCoast website
Jiaxu Zhang	University of Washington (CICOES)/NOAA PMEL	Chl-a products of multiple sources
Rui Jin	University of Washington (CICOES)	Simple PACE data manipulation	website
Han Huynh	University of Colorado at Boulder (CIRES)/NOAA CSL	Multi-source aerosol data visualization

Additional resources or background reading

• Our JupyterBook
• HyperCoast
• EDM Workshop -- similar tutorials
• CoastWatch -- lots of Python tutorials on some typical science tasks with remote-sensing data

Running notebook on CryoCloud

Note, all the earthaccess code will work fine on your laptop if you already have Python installed. We can edit environment.yml as we add needed modules. To clone this into the JupyterHub. Open a terminal (big button on the Launcher).

cd ~
git clone https://github.com/PACEHackWeek/proj_2024_PACEToolkit

To be able to push to the repo, run this from the terminal

gh-scoped-creds

Files and folders

• contributors/
  Each team member can create their own folder under contributors, within which they can work on their own scripts, notebooks, and other files. Having a dedicated folder for each person helps to prevent conflicts when merging with the main branch. This is a good place for team members to start off exploring data and methods for the project.
• notebooks/
  Notebooks that are considered delivered results for the project should go in here.
• scripts/
  Code that is shared by the team should go in here (e.g. functions or subroutines). These will be files other than Jupyter Notebooks such as Python scripts (.py).
• .gitignore
  This file sets the files that will be globally ignored by git for the project. (e.g. you may want git to ignore temporary files or large data files, read more about ignoring files here)
• environment.yml
conda environment description needed to run this project.
• README.md
  Description of the project (see suggested headings below)
• model-card.md
  Description (following a metadata standard) of any machine learning models used in the project

To add notebooks

1. Make sure your notebook has a markdown cell with a level 1 header at the top. For example

# My title

2. Do not include any html, like <div> or <h1> in your notebook. It will break the book build.
3. Add your notebook to the notebooks folder
4. Add your notebook to the _toc.yml file
5. Push to the repo and the book will automatically rebuild.
6. Watch the Actions tab on the repo to see when the rebuild is finished.

To build book

Do pip install ghp-import if needed. Then build book and push to GitHub. Set Pages to use gh-pages branch.

jupyter-book build . --keep-going
ghp-import -n -p -f _build/html