---

name: gempy description: "Use GemPy 3.x for CPU-first 3-D implicit geological modeling, structural data preparation, grid evaluation, visualization, persistence, and optional integrations." disable-model-invocation: true metadata: disco-role: operating license: EUPL 1.2

GemPy operating skill

Use this skill when a Researcher needs to build, compute, inspect, serialize, or troubleshoot a GemPy 3.x geological model. It is distilled from a versioned public package surface and is self-contained: workflows use caller-owned arrays, tables, and paths rather than checkout-relative examples or downloaded data.

Route the request

Choose the narrowest route before writing code:

• Create, map, validate, compute, faults, or unconformities: modeling
• Surface points, orientations, CSV input, IDs, elements, groups, or table mutation: data-and-structure
• Dense/custom/section/centered/topography grids, coordinate queries, or plots: grids-and-visualization
• .gempy/JSON persistence, mesh extraction, gravity, topology, properties, Subsurface, or legacy adapters: serialization-and-advanced
• Installation, imports, package drift, backend/device, optional packages, or headless rendering: environment-and-troubleshooting

For a multi-stage request, follow the usual order: environment → data/structure → modeling → grid/evaluation → persistence or visualization. Return to the owning route when a failure crosses a boundary; do not mask a data error by changing the backend or installing unrelated optional packages.

Install and verify the public package

Use a fresh Python 3.10+ environment and keep pip attached to that interpreter:

python -m pip install gempy
python -m pip check
python -c "import gempy, gempy_engine; print(gempy.__version__)"

Add the base extra only for pandas/table readers or GemPy Viewer workflows:

python -m pip install "gempy[base]"

Install optional scientific, plugin, Subsurface, PyVista, or PyTorch packages only for the corresponding route. The environment route's checker is read-only: python sub-skills/environment-and-troubleshooting/scripts/check_environment.py --json.

Core operating rules

1. Prefer import gempy as gp and public gp.data/gp.* APIs. Verify the installed GemPy, gempy_engine, and (if used) gempy_viewer release line before relying on a changed signature.
2. Start with a small dense grid and NumPy: GemPyEngineConfig(backend=gp.data.AvailableBackends.numpy, use_gpu=False). PyTorch/CUDA, PyKeOps, viewer/PyVista, SciPy/scikit-image, GSTools, gempy_plugins, Subsurface, and gempy_legacy are optional boundaries, not proof obligations for the core route.
3. Build or load a StructuralFrame, add caller-owned input data, map elements to final structural groups, then call model.validate() before gp.compute_model(). Preserve ModelValidationError.reason, .field, and .context when repair is needed.
4. Grid setters activate flags without necessarily clearing earlier components; inspect model.grid.active_grids, use reset=True intentionally, and recompute after grid changes. compute_model_at is stateful and leaves a custom grid active.
5. Keep .gempy archives and JSON files in caller-owned temporary or output paths. Load and compare structure/input counts before trusting a round trip; recompute restored models when solutions are needed.
6. Do not fetch network examples, open interactive viewers in headless jobs, or claim an optional backend works from a CPU import. See references/troubleshooting.md for the cross-cutting decision sequence.

Bundled diagnostics

The sub-skills contain deterministic helpers that do not install packages, fetch data, or read the source checkout:

• sub-skills/environment-and-troubleshooting/scripts/check_environment.py checks Python, core imports, optional modules, and selected backends.
• sub-skills/modeling/scripts/tiny_model_smoke.py builds and computes a tiny in-memory CPU model.
• sub-skills/grids-and-visualization/scripts/grid_smoke.py checks dense, custom, active-grid, and section semantics without a viewer.
• sub-skills/data-and-structure/scripts/inspect_tables.py reports caller-owned CSV table shape, names, IDs, and finite-value counts without modifying files.
• sub-skills/serialization-and-advanced/scripts/json_roundtrip_smoke.py checks tiny .gempy and JSON round trips in temporary output.

Run these from the generated skill directory or by passing their absolute path; read the owning route first so a smoke result is not mistaken for geological correctness.

Freshness and limits

The package facts are tied to the commit and evidence listed in references/repo-provenance.md. Optional plugins, GPU/autodiff, external data readers, and interactive 3-D rendering are explicitly bounded; if a request depends on one, verify that dependency and runtime separately. The review artifacts under skills/tests/gempy/ are construction and verification records, not runtime dependencies.