Using JupyterLab for flood map development: Approaches for improving productivity and reproducibility
JupyterCon, August 24, 2018
New York City, NY
- (Proprietary) Tool diversity
- Cost
- Learning curve
- Silos
- Conceptualizing Workflows- technical & non-technical
- Reproducibility & Traceability
- Copy/Paste
- Standard Models
- Interactivity
- Quality Control
- Batch processing
Notebooks created by STARR II (Strategic Alliance for Risk Reduction) in support of FEMA.
- Monte Carlo Approaches (html) Conceptualizing Workflows
- USGS API Copy/Paste
- Model Development (html) Standard Models, Reproducibility & Traceability
- Data Visualization (html) Interactivity
Notebooks developed by Tyler Miesse for production runs of the coupled hydrodynamic and wave models ADCIRC + SWAN.
- Model Development Silos, Learning curve
- Model Validation Copy/Paste, Interactivity
- Production QAQC Quality Control
- Troubleshooting QAQC Quality Control, Visualization*
Notebooks for working with vector and raster datasets common in flood risk analysis.
- Vector Datasets Learning Curve, Cost, Reproducibility & Traceability
- Raster Datasets Learning Curve, Cost, Reproducibility & Traceability
- Batch Processing with CL Tools Batch Processing