Junior Colleague (@jcoll) Request

[ChristinaB]

@jcoll wrote:

Hi 👋

Thank you so much for helping me get the driver data prepped before I plug it into the model. As you recommended earlier, I separated my download and processing code into two different scripts. I can't wait to share this with the project team - they have been looking for a way to use code to prep their files in a more reliable way! Here I am following your advice of submitting a pull request with my initial effort so you can take a look at it before I move on to the modeling steps.

I haven't downloaded data from this site before, so I may have missed something obvious for how to download the precipitation files. I ended up doing it manually for all 42 years of data and then wrote my code to look for those files locally. I made a comment indicating this manual step in the download script. Finally, I added a plotting step to visually verify that my annual summaries are working properly.

I am using some common libraries in python so hopefully you can get those installed and run this yourself. Thanks, again! I know you are super busy and I really appreciate you taking the time to help me out.

😊

[ChristinaB]

@jcoll Great work! I was able to get my local computer updated and the libraries installed. I am using Github Issues to break down my feedback on different parts of your coding. For your final and next versions of the .py files, before further distribution, I suggest removing all the comments from me (and you) that aren't necessary for our next colleagues. They will just want to run the code and review the outputs.

My Issues for you to Review

Download Data Driver Subset Netcdf Data Combine Data & Plot Reproducible Versioning Methods

Let's keep track of questions and comments using these Github issues. Comment away!

Thanks for your time, attention and great energy on this project!

[ChristinaB]

Suggested additions to think about next - we can discuss the pros/cons in our next meeting:

• Add a plotting step to the tutorial version in the Jupyter Notebook to investigate netcdf file using xarray plotting functions.
• You may find that using an xarray data array instead of pandas data dictionaries and data frames is more efficient.
• Storing the basin intermediate files as netcdf allows the workflow to maintain the metadata from the original netcdf file.
• The final data product that gets generated for publication will need to have all the metadata attached.

Draft checklist of additional information we need to include future users:

• [ ] Size of download
• [ ] Time to download
• [ ] Commenting updated for future users
• [ ] Instructions on where to find tutorial and local versions
• [ ] Instructions for public on how you want to manage feedback, comments, issues and pull requests