orcaflex-visualization-orcawave-mesh-export-via-diffraction

Sub-skill of orcaflex-visualization: OrcaWave Mesh Export via Diffraction (+4).

5 stars

Best use case

orcaflex-visualization-orcawave-mesh-export-via-diffraction is best used when you need a repeatable AI agent workflow instead of a one-off prompt.

Sub-skill of orcaflex-visualization: OrcaWave Mesh Export via Diffraction (+4).

Teams using orcaflex-visualization-orcawave-mesh-export-via-diffraction should expect a more consistent output, faster repeated execution, less prompt rewriting.

When to use this skill

  • You want a reusable workflow that can be run more than once with consistent structure.

When not to use this skill

  • You only need a quick one-off answer and do not need a reusable workflow.
  • You cannot install or maintain the underlying files, dependencies, or repository context.

Installation

Claude Code / Cursor / Codex

$curl -o ~/.claude/skills/orcawave-mesh-export-via-diffraction/SKILL.md --create-dirs "https://raw.githubusercontent.com/vamseeachanta/workspace-hub/main/.agents/skills/_archive/engineering/marine-offshore/orcaflex-visualization/orcawave-mesh-export-via-diffraction/SKILL.md"

Manual Installation

  1. Download SKILL.md from GitHub
  2. Place it in .claude/skills/orcawave-mesh-export-via-diffraction/SKILL.md inside your project
  3. Restart your AI agent — it will auto-discover the skill

How orcaflex-visualization-orcawave-mesh-export-via-diffraction Compares

Feature / Agentorcaflex-visualization-orcawave-mesh-export-via-diffractionStandard Approach
Platform SupportNot specifiedLimited / Varies
Context Awareness High Baseline
Installation ComplexityUnknownN/A

Frequently Asked Questions

What does this skill do?

Sub-skill of orcaflex-visualization: OrcaWave Mesh Export via Diffraction (+4).

Where can I find the source code?

You can find the source code on GitHub using the link provided at the top of the page.

SKILL.md Source

# OrcaWave Mesh Export via Diffraction (+4)

## OrcaWave Mesh Export via Diffraction


`OrcFxAPI.Diffraction` exposes mesh export methods that output GDF geometry files.
These can be parsed and rendered programmatically (Plotly 3D mesh → Figure 30 style).

```python
import OrcFxAPI as ofx

d = ofx.Diffraction()
d.LoadData("model.owd")           # or d.LoadResults("model.owr")

# Body mesh (GDF format) — corresponds to red mesh in Figure 30

*See sub-skills for full details.*

## Programmatic Mesh Rendering (Plotly — Figure 30 style)


Since `SaveModelView` is not available, render mesh from GDF/panelGeometry data:

```python
import numpy as np
import plotly.graph_objects as go

def gdf_to_mesh3d(gdf_path, color="red"):
    """Parse WAMIT GDF file and return Plotly Mesh3d trace."""
    panels = []
    with open(gdf_path) as f:

*See sub-skills for full details.*

## OrcaFlex: Canonical SaveModelView Pattern


```python
import OrcFxAPI as ofx

def save_model_view(model: ofx.Model, filepath: str, **view_params):
    """Save OrcaFlex model screenshot. NOT available for OrcaWave."""
    vp = model.defaultViewParameters
    for k, v in view_params.items():
        if hasattr(vp, k):
            setattr(vp, k, v)
    model.SaveModelView(str(filepath), vp)

*See sub-skills for full details.*

## Existing Codebase Implementations


| Script / Module | Coverage | Notes |
|----------------|----------|-------|
| `scripts/capture_riser_views.py` | Minimal `SaveModelView` | OrcaFlex `.dat`; verified |
| `src/.../orcaflex/pipeline_schematic.py` | Multi-view YAML config | `OrcaFlexViewCapture` class |
| `src/.../orcaflex/opp_visualization.py` | Batch parallel (30 workers) | `save_all_views()` |
| `scripts/benchmark/qtf_postprocessing.py` | `panelGeometry` 3D scatter | OrcaWave body panels, used in benchmark report |
| `scripts/build_sme_report.py` | `img_to_base64()` | HTML embedding of BMP/PNG/JPG |

For HTML embedding use `img_to_base64()` from `scripts/build_sme_report.py` —
converts BMP/PNG/JPG to `data:image/...;base64` URI for inline `<img>` tags.

## panelGeometry Structured Array Schema


`d.panelGeometry` returns a numpy structured array (one record per panel):

```
dtype fields:
  objectId          int32       body object index
  objectName        str (U31)   body name e.g. 'test101 bottom mounted cylinder'
  vertices          float64 (4,3) four quad panel corners [x, y, z] metres
  centroid          float64 (3)  panel centroid [x, y, z]
  normal            float64 (3)  outward unit normal vector
  area              float64      panel area (m²)

*See sub-skills for full details.*

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