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- Organize outputs into model_validation/ subdirectory - Add diagnostic columns for outlier analysis - Refactor code into focused functions - Generate README documenting validation results - Simplify coefficients table format 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude <noreply@anthropic.com>
- Introduce aoh-prevalence-diagnostician subagent for detailed outlier explanations - Create diagnose-aoh-prevalence command to streamline user interaction - Update model validation functions to include random effects in outputs - Enhance validation pipeline with comprehensive tests for new features 🤖 Generated with [Claude Code](https://claude.com/claude-code)
- Simplify YAML frontmatter to standard format - Add edge case handling for species not found or not flagged - Add example calculation format for clarity - Improve argument mapping between command and subagent - Add usage examples to command documentation 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude <noreply@anthropic.com>
- Add a check for the DATADIR environment variable to ensure data directory is set - Update steps for locating validation outputs to use the DATADIR path - Improve error handling for missing validation files - Renumber steps for clarity and consistency 🤖 Generated with [Claude Code](https://claude.com/claude-code)
- Streamline the diagnostic steps for analyzing outlier species, emphasizing data extraction and calculation processes. - Introduce a structured report format for presenting findings, including model predictions and biological interpretations. - Enhance clarity by renumbering steps and consolidating instructions for user interaction and output generation. 🤖 Generated with [Claude Code](https://claude.com/claude-code)
shaneweisz
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mdales
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Nov 1, 2025
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This looks great, particularly splitting up the logic into more manageable bits where in future we might want to play.
| This directory contains the following scripts: | ||
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| * `collate_data.py` - Then you generate a series of AOH GeoTIFFs, besides each one is a JSON file that contains information required for validation. This script takes a folder containing the AOH output of a run and collates all those JSON files into a single CSV file that can be used for a validation run. | ||
| * `collate_data.py` - When you generate a series of AOH GeoTIFFs, besides each one is a JSON file that contains information required for validation. This script takes a folder containing the AOH output of a run and collates all those JSON files into a single CSV file that can be used for a validation run. |
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This is a right of passage for working on any code base I've touched - I'm sorry :)
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Summary
Adds extra output files that make it easier to do understand and interpret the Dahal et al model validation results.
Changes
Added 4 new output files alongside the outliers CSV:
Added diagnostic columns to the outliers CSV:
For example, the below screenshot suggests that erroneous elevation ranges are the explanation for some of these amphibians being flagged:
Testing
Tested using
aohs.csvproduced in the STAR pipeline.