Prepare Documentation with Presentation-Ready Jupyter Notebooks

[DaniJonesOcean]

Task Description:

Create and finalize one or more Jupyter notebooks that document the key aspects of the GreatLakes-TempSensors project. These notebooks should be polished and presentation-ready, including code, explanatory text, and visualizations (e.g., graphs, charts, and images). Once completed, upload these notebooks to the shared GitHub repository for the project.

Checklist:

• Notebook Content:

  • [ ] Introduction:
  • Provide an overview of the project objectives and research questions.
  • [ ] Methods:
  • Include code snippets and explanations for the key methodologies used (e.g., convolutional Gaussian neural processes).
  • Describe the dataset and preprocessing steps.
  • [ ] Results:
  • Present the findings from the sensor placement experiments.
  • Include visualizations such as charts, graphs, and maps to effectively communicate the data.
  • [ ] Discussion:
  • Provide interpretations of the results and highlight any limitations or challenges faced.
  • [ ] Future Work:
  • Suggest potential expansions and future research opportunities.

• Notebook Quality:

  • [ ] Ensure all code cells are well-commented and easy to follow.
  • [ ] Include markdown cells to provide context and explanations for each section.
  • [ ] Use high-quality plots and visual elements to enhance clarity.
  • [ ] Verify that the notebooks run without errors from start to finish.

• Uploading to GitHub:

  • [ ] Save the final versions of the Jupyter notebooks.
  • [ ] Ensure that paths to all necessary data files or dependencies are mentioned or included (e.g. masks, input file locations on U-M HPC).
  • [ ] Upload the notebooks to the shared GitHub repository under an appropriate directory (e.g. notebooks/reports).
  • [ ] Add a README.md file in the directory to very briefly explain the contents of each notebook.

Additional Notes:

Collaborate with mentors to review the notebooks for accuracy and completeness. Ensure that the notebooks are well-organized and that they effectively communicate the project's findings and methods.

[DaniJonesOcean]

Plan is to prepare a small set of Jupyter notebooks by the end of August

[DaniJonesOcean]

Output is available on turbo volume at this location:

/nfs/turbo/seas-dannes/TempSensors-GreatLakes-Aug2024-Fellowship

which includes output, masks, and some *.py files with scripts.