Regional Lab Dry Plan on Headwater 100 km2#

Note

This page and its static assets are auto-generated by python -m tools.doc_gallery. The Sphinx build only reads committed PNG and JSON artifacts.

This case documents the orchestration layer rather than one child run. It uses the first committed regional_lab example in dry-plan mode to show how a small site catalog is filtered, clustered, expanded into recipes, and reported as runnable cases or explicit coverage gaps.

See also

Read the Simulation walkthrough if you want the parameter mapping, a recommended reading order, and the first modifications to try.

Regional lab dry-plan synthesis for the headwater 100 km2 example
Dry-plan synthesis for the committed regional-lab example: site/recipe matrix, recipe coverage, and planning summary.

Case Setup#

  • Launcher family: regional_lab, sitting above child simulation and comparison launchers.

  • Example scope: one small Brittany site catalog with one fully runnable headwater site and several inventory-only or screening sites.

  • The committed example starts with execute = false, so the page documents planning, selection, and reporting rather than child-run results.

What It Shows#

  • How regional_lab separates site inventory, recipe definitions, and execution/reporting layers.

  • How one selected site population expands into planned child runs plus explicit coverage gaps when required configs are missing.

  • How a dry-run can be used as a planning and coverage-audit tool before any child simulation is actually launched.

Key Parameters#

  • [regional_lab.catalog] defines how site metadata and path-like config references are loaded from the catalog.

  • [regional_lab.selection] tags = [“mesh_ready”] filters the population before any recipe expansion happens.

  • [[regional_lab.cluster_rule]] enriches catalog rows into reusable clusters/families/scales instead of relying only on static columns.

  • [[regional_lab.recipe]] turns one selected site population into concrete child launcher plans, with required_fields making coverage gaps explicit.

  • execute = false keeps the example in dry-plan mode, which is exactly what this page documents.

How To Read It#

  • Start with the site-by-recipe matrix to see which sites are runnable and which ones remain coverage gaps.

  • Use the recipe bars next to understand how much of the selected population each recipe actually covers.

  • Read the text summary last: it explains why the example is valuable even with zero executed child runs.

Next Steps#

  • Switch execute = true in the example config when the dry plan looks correct and you want to launch the child workflows.

  • Use this page as the orchestration complement to the individual simulation and comparison cases already exposed elsewhere in the gallery.

Reproduce#

Run the underlying example or validation case with:

python -m tools.doc_gallery

Refresh the committed gallery artifacts with:

python -m tools.doc_gallery

Source Pointers#

  • docs/source/_static/capability_gallery/simulation/regional_lab_headwater_100km2_dry_plan_plan.json

  • docs/source/_static/capability_gallery/simulation/regional_lab_headwater_100km2_dry_plan_report.json

  • docs/source/_static/capability_gallery/simulation/regional_lab_headwater_100km2_dry_plan_summary.md

  • docs/source/_static/capability_gallery/simulation/regional_lab_headwater_100km2_dry_plan_site_inventory.csv

  • docs/source/_static/capability_gallery/simulation/regional_lab_headwater_100km2_dry_plan_recipe_summary.csv

  • docs/source/_static/capability_gallery/simulation/regional_lab_headwater_100km2_dry_plan_cluster_summary.csv

  • docs/source/_static/capability_gallery/simulation/regional_lab_headwater_100km2_dry_plan_case_matrix.csv

  • hydromodpy/analysis/testbed/__init__.py

  • hydromodpy/analysis/testbed/regional_lab_config.py

  • hydromodpy/analysis/testbed/regional_lab.py

Artifacts#

  • docs/source/_static/capability_gallery/simulation/regional_lab_headwater_100km2_dry_plan.png

  • docs/source/_static/capability_gallery/simulation/regional_lab_headwater_100km2_dry_plan_summary.json stores the displayed metrics plus source hashes used by python -m tools.doc_gallery --check.