Refactor non-dashboard modules
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22 changed files with 122 additions and 186 deletions
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@ -22,7 +22,10 @@ This will set up the complete environment including RAPIDS, PyTorch, and all geo
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### Code Organization
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- **`src/entropice/`**: Core modules (grids, data sources, training, inference)
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- **`src/entropice/ingest/`**: Data ingestion modules (darts, era5, arcticdem, alphaearth)
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- **`src/entropice/spatial/`**: Spatial operations (grids, aggregators, watermask, xvec)
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- **`src/entropice/ml/`**: Machine learning components (dataset, training, inference)
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- **`src/entropice/utils/`**: Utilities (paths, codecs)
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- **`src/entropice/dashboard/`**: Streamlit visualization dashboard
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- **`scripts/`**: Data processing pipeline scripts (numbered 00-05)
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- **`notebooks/`**: Exploratory analysis and validation notebooks
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@ -30,11 +33,12 @@ This will set up the complete environment including RAPIDS, PyTorch, and all geo
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### Key Modules
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- `grids.py`: H3/HEALPix spatial grid systems
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- `darts.py`, `era5.py`, `arcticdem.py`, `alphaearth.py`: Data source processors
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- `dataset.py`: Dataset assembly and feature engineering
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- `training.py`: Model training with eSPA, XGBoost, Random Forest, KNN
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- `inference.py`: Prediction generation
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- `spatial/grids.py`: H3/HEALPix spatial grid systems
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- `ingest/darts.py`, `ingest/era5.py`, `ingest/arcticdem.py`, `ingest/alphaearth.py`: Data source processors
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- `ml/dataset.py`: Dataset assembly and feature engineering
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- `ml/training.py`: Model training with eSPA, XGBoost, Random Forest, KNN
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- `ml/inference.py`: Prediction generation
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- `utils/paths.py`: Centralized path management
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## Coding Standards
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@ -58,7 +62,7 @@ This will set up the complete environment including RAPIDS, PyTorch, and all geo
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- Follow the numbered script sequence: `00grids.sh` → `01darts.sh` → ... → `05train.sh`
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- Each stage should produce reproducible intermediate outputs
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- Document data dependencies in module docstrings
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- Use `paths.py` for consistent path management
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- Use `utils/paths.py` for consistent path management
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## Testing
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