Add GeometryPredictor example notebook and fix NaN handling#183
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- Add geometry_predictor.ipynb example notebook demonstrating
GeometryPredictor with trained KAN on CONUS MERIT reaches
- Add v0.5.2 trained weights (n, q_spatial, p_spatial learned)
and geometry_config.yaml for the notebook
- Fix GeometryPredictor.from_checkpoint: resolve checkpoint path
before config validation, fix mode enum ("route" -> "routing")
- Fill NaN attributes with training mean in _prepare_attributes,
matching the geodataset loader behavior
- Update plot_parameter_map.ipynb to save plots to plots/ directory
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Summary
geometry_predictor.ipynbexample notebook demonstrating standalone channel geometry prediction from a trained KAN — loads checkpoint, predicts on all 346K CONUS MERIT reaches, visualizes learned parameters (n, p, q), geometry scaling with discharge, and Leopold & Maddock power lawp_spatialand ageometry_config.yamlso the notebook is self-containedGeometryPredictor.from_checkpoint: mode enum typo ("route"→"routing"), checkpoint path not resolved before Pydantic validation, and NaN attributes propagating through the KAN instead of being filled with training meanplot_parameter_map.ipynbto write plots to aplots/subdirectoryTest plan
geometry_predictor.ipynbexecutes end-to-end vianbconvertwith zero NaN outputs across all 346K reachesplot_parameter_map.ipynbsaves toplots/when run interactively🤖 Generated with Claude Code