GEOS694 Final Project Review_Saurabh

[ss2098]

Instructions

• Remember this is just a class assignment, and your classmate's work. Please be respectful when providing feedback or comments.
• Open an issue in your classmate's GitHub repository and copy-paste this rubric.
• Go through the checklist, reflection questions and task below while consulting the repository.
• If any checklist items require more explanation or links to code blocks, include them.

Checklist

• Adheres to PEP-8 formatting (or consistent and explained when deviating)
  Based on the visible commit history and file organization, there is clear evidence that PEP-8 compliance was considered during development. Several notebook files in the DAS workflow are associated with commit messages explicitly mentioning PEP-8 compliance, which suggests that formatting consistency was an intentional part of the project.

• [~] Commit history that reflects small and frequent commits since project assignment
  The repository does show active development, and the commit history suggests ongoing work. From the repository view alone, I cannot fully judge whether all commits were small and frequent, so I would mark this as partially satisfied.

• [~] Clear installation instructions, installation tested if available
  I did not see clear installation or setup instructions for someone new to the project. Since this is a notebook-based workflow with supporting files, it would help to include a short section on dependencies, required packages, and the recommended order for running notebooks.

• Current version number (note the current version number)
  I did not see a version number clearly listed in the repository.

• Example problem with required data either provided or with a data fetching script
  This seems to be present. The repository includes notebooks along with project files such as TERRA_phase_amplitudes.csv and event_metadata_cache.json, which suggests that at least some of the required data or cached metadata are included.

• Appropriate use of functions, tasks are broken into logical chunks
  At the repository level, the project does seem to be broken into logical parts. The folder and notebook names suggest a separation between bathymetry work, frequency plots, the main DAS notebook, and supporting scripts.

• Readability, organization, accessibility: Code is well-commented where necessary, files are organized in a logical manner, naming is intuitive or explanatory
  The file and folder names are descriptive and make it fairly easy to understand the broad purpose of each part of the project. The repository organization makes sense for an active research workflow, even if the author notes that it is still somewhat messy while in use.

• [~] Documentation is explanative of: what, why, how
  The README gives a good start, especially in explaining what the repository is and what the das folder contains. The What: section is helpful, but the How: and Why: sections look unfinished right now, so I would mark this as partially complete.

• Task 1 Selected (dataclass implementation)

• Task 2 Selected (parameter input system and tests)

Reflection Questions

1. What does this code do?

This repository appears to organize and analyze DAS (Distributed Acoustic Sensing) research data. Based on the README and notebook names, the project seems focused on seismology using fiber-optic cables, with work related to bathymetry, frequency filtering, event arrival windows, and plotting of DAS observations.

2. How does this code do it?

The workflow seems to be built mainly through Jupyter notebooks, with MS-DAS.ipynb acting as the main active research environment and other notebooks or subfolders handling more specialized or older analyses. From the README, it looks like the author develops actively inside the main notebook and then gradually breaks out inactive or mostly finalized scripts into separate notebooks.

3. What data does this code work with?

The project works with DAS-related research data, including bathymetry files for cable coordinates, filtered signal outputs, and metadata-type files such as TERRA_phase_amplitudes.csv and event_metadata_cache.json. Based on the README, it also seems to work with observed arrival time data and calculated arrival windows for multiple events.

4. What do you find cool/interesting/unique about this code?

What stood out to me most is that this feels like a real working research repository rather than just a cleaned-up class example. I liked that the README is honest about the repo still being actively used. The combination of bathymetry, frequency analysis, and seismic arrival-time plotting makes the project feel scientifically grounded and interesting.

5. How can this code be improved (e.g., restructuring, optimization, improved examples, clearer explanation? - provide some input into how you could implement that)

I think the biggest improvement would be making the workflow easier for a new user to follow. A few things that would help are:

• finishing the How: and Why: sections in the README
• adding a short setup section with required packages or an environment file
• describing the recommended order for running notebooks
• separating reusable logic into helper Python files where possible
• clarifying which files are active notebooks, which are archived/supporting notebooks, and which are output products

Task: Pull Request

1. Find an error/typo/bug/inconsistency in the code, it can be really minor, this is just to practice making a pull request.
   I identified a small documentation/repository-cleanup issue that would work well for a practice pull request.

2. Fork your classmate's repository and clone the repo to your own computer.
   Completed.

3. Fix the error that you found on your local version and push the change to your forked repository.
   Completed. I made a small README cleanup change.

4. Open a pull request in your classmate's repository requesting the change you made be incorporated into their repository.
   Completed.

5. In the pull request, write a small message describing the change, and any additional information you'd like to include.
   Completed.

6. Link to your pull request in this issue, and link to this issue in your pull request.
   Pull request link: Clean up README formatting and fix path naming #2