Speeding up writing output

[AminTorabi-NOAA]

This PR, improve writing output. Before we used df.apply() in the aggregation process, which can be slow for large datasets because it iterates over the rows in a less optimized way. To improve performance, we restructured the code by:

Grouping Without .apply(): Instead of using apply(), which is slower, we leveraged vectorized operations like groupby() combined with direct masking and conditional aggregation. This allows for more efficient handling of grouped data.

Additions

Removals

Changes

Testing

Screenshots

Notes

Todos

Checklist

• PR has an informative and human-readable title
• Changes are limited to a single goal (no scope creep)
• Code can be automatically merged (no conflicts)
• Code follows project standards (link if applicable)
• Passes all existing automated tests
• Any change in functionality is tested
• New functions are documented (with a description, list of inputs, and expected output)
• Placeholder code is flagged / future todos are captured in comments
• Visually tested in supported browsers and devices (see checklist below 👇)
• Project documentation has been updated (including the "Unreleased" section of the CHANGELOG)
• Reviewers requested with the Reviewers tool ➡️

Testing checklist

Target Environment support

• Windows
• Linux
• Browser

Accessibility

• Keyboard friendly
• Screen reader friendly

Other

• Is useable without CSS
• Is useable without JS
• Flexible from small to large screens
• No linting errors or warnings
• JavaScript tests are passing

[kumdonoaa]

Great speed gain. With Lower Colorado domain, output writing took 18 sec vs 2 sec with 24hrs simulation period. With CONUS domain & 24 hrs simulation, the current code took this:

This PR took this: