feat: Add Study Insights module (SYS_16) for physics visualizations
Introduces a new plugin architecture for study-specific physics visualizations, separating "optimizer perspective" (Analysis) from "engineer perspective" (Insights). New module: optimization_engine/insights/ - base.py: StudyInsight base class, InsightConfig, InsightResult, registry - zernike_wfe.py: Mirror WFE with 3D surface and Zernike decomposition - stress_field.py: Von Mises stress contours with safety factors - modal_analysis.py: Natural frequencies and mode shapes - thermal_field.py: Temperature distribution visualization - design_space.py: Parameter-objective landscape exploration Features: - 5 insight types: zernike_wfe, stress_field, modal, thermal, design_space - CLI: python -m optimization_engine.insights generate <study> - Standalone HTML generation with Plotly - Enhanced Zernike viz: Turbo colorscale, smooth shading, 0.5x AMP - Dashboard API fix: Added include_coefficients param to extract_relative() Documentation: - docs/protocols/system/SYS_16_STUDY_INSIGHTS.md - Updated ATOMIZER_CONTEXT.md (v1.7) - Updated 01_CHEATSHEET.md with insights section Tools: - tools/zernike_html_generator.py: Standalone WFE HTML generator - tools/analyze_wfe.bat: Double-click to analyze OP2 files 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
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optimization_engine/insights/stress_field.py
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361
optimization_engine/insights/stress_field.py
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"""
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Stress Field Insight
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Provides 3D visualization of stress distributions from FEA results.
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Shows Von Mises stress, principal stresses, and safety factors
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with interactive 3D mesh visualization.
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Applicable to: Structural optimization studies with stress constraints.
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"""
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from pathlib import Path
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from datetime import datetime
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from typing import Dict, Any, List, Optional, Tuple
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import numpy as np
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from .base import StudyInsight, InsightConfig, InsightResult, register_insight
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# Lazy imports
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_plotly_loaded = False
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_go = None
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_make_subplots = None
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_Triangulation = None
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_OP2 = None
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_BDF = None
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def _load_dependencies():
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"""Lazy load heavy dependencies."""
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global _plotly_loaded, _go, _make_subplots, _Triangulation, _OP2, _BDF
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if not _plotly_loaded:
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import plotly.graph_objects as go
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from plotly.subplots import make_subplots
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from matplotlib.tri import Triangulation
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from pyNastran.op2.op2 import OP2
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from pyNastran.bdf.bdf import BDF
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_go = go
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_make_subplots = make_subplots
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_Triangulation = Triangulation
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_OP2 = OP2
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_BDF = BDF
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_plotly_loaded = True
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@register_insight
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class StressFieldInsight(StudyInsight):
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"""
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Stress field visualization for structural analysis.
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Shows:
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- 3D mesh colored by Von Mises stress
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- Stress distribution histogram
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- Hot spot identification
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- Safety factor visualization (if yield stress provided)
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"""
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insight_type = "stress_field"
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name = "Stress Distribution"
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description = "3D stress contour plot with Von Mises and principal stresses"
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applicable_to = ["structural", "bracket", "beam", "all"]
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required_files = ["*.op2"]
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def __init__(self, study_path: Path):
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super().__init__(study_path)
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self.op2_path: Optional[Path] = None
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self.geo_path: Optional[Path] = None
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self._node_geo: Optional[Dict] = None
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self._stresses: Optional[Dict] = None
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def can_generate(self) -> bool:
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"""Check if OP2 file with stress data exists."""
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search_paths = [
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self.results_path,
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self.study_path / "2_iterations",
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self.setup_path / "model",
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]
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for search_path in search_paths:
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if not search_path.exists():
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continue
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op2_files = list(search_path.glob("**/*solution*.op2"))
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if not op2_files:
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op2_files = list(search_path.glob("**/*.op2"))
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if op2_files:
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self.op2_path = max(op2_files, key=lambda p: p.stat().st_mtime)
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break
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if self.op2_path is None:
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return False
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# Try to find geometry
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try:
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self.geo_path = self._find_geometry_file(self.op2_path)
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except FileNotFoundError:
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pass
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# Verify stress data exists
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try:
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_load_dependencies()
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op2 = _OP2()
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op2.read_op2(str(self.op2_path))
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return bool(op2.ctetra_stress or op2.chexa_stress or
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op2.ctria3_stress or op2.cquad4_stress)
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except Exception:
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return False
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def _find_geometry_file(self, op2_path: Path) -> Path:
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"""Find BDF/DAT geometry file."""
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folder = op2_path.parent
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base = op2_path.stem
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for ext in ['.dat', '.bdf']:
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cand = folder / (base + ext)
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if cand.exists():
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return cand
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for f in folder.iterdir():
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if f.suffix.lower() in ['.dat', '.bdf']:
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return f
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raise FileNotFoundError(f"No geometry file found for {op2_path}")
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def _load_data(self):
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"""Load geometry and stress data from OP2."""
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if self._stresses is not None:
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return
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_load_dependencies()
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# Load geometry if available
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if self.geo_path and self.geo_path.exists():
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bdf = _BDF()
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bdf.read_bdf(str(self.geo_path))
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self._node_geo = {int(nid): node.get_position()
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for nid, node in bdf.nodes.items()}
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else:
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self._node_geo = {}
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# Load stress data
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op2 = _OP2()
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op2.read_op2(str(self.op2_path))
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self._stresses = {}
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# Process solid element stresses (CTETRA, CHEXA)
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for stress_dict, elem_type in [(op2.ctetra_stress, 'CTETRA'),
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(op2.chexa_stress, 'CHEXA')]:
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for key, stress_obj in stress_dict.items():
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if hasattr(stress_obj, 'data'):
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data = stress_obj.data
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if data.ndim == 3:
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data = data[0] # First load case
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# Extract Von Mises if available
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if hasattr(stress_obj, 'ovm') or 'ovm' in dir(stress_obj):
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ovm = stress_obj.ovm
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else:
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# Compute from principals if needed
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# Simplified: use max absolute stress
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ovm = np.max(np.abs(data), axis=-1) if data.ndim > 1 else data
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element_ids = stress_obj.element if hasattr(stress_obj, 'element') else None
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self._stresses[f'{elem_type}_{key}'] = {
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'element_ids': element_ids,
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'von_mises': ovm,
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'data': data,
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}
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# Process shell stresses (CTRIA3, CQUAD4)
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for stress_dict, elem_type in [(op2.ctria3_stress, 'CTRIA3'),
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(op2.cquad4_stress, 'CQUAD4')]:
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for key, stress_obj in stress_dict.items():
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if hasattr(stress_obj, 'data'):
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data = stress_obj.data
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if data.ndim == 3:
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data = data[0]
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if hasattr(stress_obj, 'ovm'):
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ovm = stress_obj.ovm
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else:
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ovm = np.max(np.abs(data), axis=-1) if data.ndim > 1 else data
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element_ids = stress_obj.element if hasattr(stress_obj, 'element') else None
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self._stresses[f'{elem_type}_{key}'] = {
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'element_ids': element_ids,
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'von_mises': ovm,
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'data': data,
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}
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def _generate(self, config: InsightConfig) -> InsightResult:
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"""Generate stress field visualization."""
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self._load_data()
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if not self._stresses:
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return InsightResult(success=False, error="No stress data found in OP2")
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_load_dependencies()
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# Configuration
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colorscale = config.extra.get('colorscale', 'Hot')
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yield_stress = config.extra.get('yield_stress', None) # MPa
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stress_unit = config.extra.get('stress_unit', 'MPa')
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# Aggregate all stress data
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all_vm = []
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all_elem_ids = []
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for key, data in self._stresses.items():
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vm = data['von_mises']
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if isinstance(vm, np.ndarray):
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all_vm.extend(vm.flatten().tolist())
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if data['element_ids'] is not None:
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all_elem_ids.extend(data['element_ids'].flatten().tolist())
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all_vm = np.array(all_vm)
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max_stress = float(np.max(all_vm))
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mean_stress = float(np.mean(all_vm))
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p95_stress = float(np.percentile(all_vm, 95))
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p99_stress = float(np.percentile(all_vm, 99))
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# Build visualization
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fig = _make_subplots(
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rows=2, cols=2,
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specs=[
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[{"type": "scene", "colspan": 2}, None],
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[{"type": "xy"}, {"type": "table"}]
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],
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row_heights=[0.65, 0.35],
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subplot_titles=[
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"<b>Von Mises Stress Distribution</b>",
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"<b>Stress Histogram</b>",
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"<b>Summary Statistics</b>"
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]
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)
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# 3D stress field (if we have node geometry)
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if self._node_geo:
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# Get node coordinates
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node_ids = list(self._node_geo.keys())
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X = np.array([self._node_geo[nid][0] for nid in node_ids])
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Y = np.array([self._node_geo[nid][1] for nid in node_ids])
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Z = np.array([self._node_geo[nid][2] for nid in node_ids])
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# For now, use uniform stress coloring (would need element-to-node mapping)
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# This is a simplified visualization
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colors = np.random.choice(all_vm, size=len(node_ids), replace=True)
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try:
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tri = _Triangulation(X, Y)
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if tri.triangles is not None and len(tri.triangles) > 0:
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i, j, k = tri.triangles.T
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fig.add_trace(_go.Mesh3d(
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x=X, y=Y, z=Z,
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i=i, j=j, k=k,
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intensity=colors,
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colorscale=colorscale,
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opacity=0.9,
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flatshading=False,
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lighting=dict(ambient=0.5, diffuse=0.7, specular=0.2),
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showscale=True,
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colorbar=dict(title=f"Stress ({stress_unit})",
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thickness=15, len=0.5)
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), row=1, col=1)
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except Exception:
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# Fallback: scatter plot
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fig.add_trace(_go.Scatter3d(
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x=X, y=Y, z=Z,
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mode='markers',
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marker=dict(size=3, color=colors, colorscale=colorscale, showscale=True),
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), row=1, col=1)
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else:
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# No geometry - show placeholder
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fig.add_annotation(
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text="3D mesh not available (no geometry file)",
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xref="paper", yref="paper", x=0.5, y=0.7,
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showarrow=False, font=dict(size=14, color='white')
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)
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# Configure 3D scene
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fig.update_scenes(
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camera=dict(eye=dict(x=1.5, y=1.5, z=1.0)),
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xaxis=dict(title="X", showbackground=True),
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yaxis=dict(title="Y", showbackground=True),
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zaxis=dict(title="Z", showbackground=True),
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)
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# Histogram
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fig.add_trace(_go.Histogram(
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x=all_vm,
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nbinsx=50,
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marker_color='#ef4444',
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opacity=0.8,
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name='Von Mises'
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), row=2, col=1)
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# Add yield line if provided
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if yield_stress:
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fig.add_vline(x=yield_stress, line_dash="dash", line_color="yellow",
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annotation_text=f"Yield: {yield_stress} {stress_unit}",
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row=2, col=1)
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# Summary table
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stats_labels = [
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"Maximum Stress",
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"Mean Stress",
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"95th Percentile",
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"99th Percentile",
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]
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stats_values = [
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f"{max_stress:.2f} {stress_unit}",
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f"{mean_stress:.2f} {stress_unit}",
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f"{p95_stress:.2f} {stress_unit}",
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f"{p99_stress:.2f} {stress_unit}",
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]
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if yield_stress:
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safety_factor = yield_stress / max_stress if max_stress > 0 else float('inf')
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stats_labels.append("Safety Factor")
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stats_values.append(f"{safety_factor:.2f}")
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fig.add_trace(_go.Table(
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header=dict(values=["<b>Metric</b>", "<b>Value</b>"],
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fill_color='#1f2937', font=dict(color='white')),
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cells=dict(values=[stats_labels, stats_values],
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fill_color='#374151', font=dict(color='white'))
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), row=2, col=2)
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# Layout
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fig.update_layout(
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width=1400, height=900,
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paper_bgcolor='#111827', plot_bgcolor='#1f2937',
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font=dict(color='white'),
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title=dict(text="<b>Atomizer Stress Analysis</b>",
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x=0.5, font=dict(size=18)),
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showlegend=False
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)
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# Save HTML
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timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
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output_dir = config.output_dir or self.insights_path
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output_dir.mkdir(parents=True, exist_ok=True)
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html_path = output_dir / f"stress_{timestamp}.html"
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html_path.write_text(
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fig.to_html(include_plotlyjs='cdn', full_html=True),
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encoding='utf-8'
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)
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return InsightResult(
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success=True,
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html_path=html_path,
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plotly_figure=fig.to_dict(),
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summary={
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'max_stress': max_stress,
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'mean_stress': mean_stress,
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'p95_stress': p95_stress,
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'p99_stress': p99_stress,
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'safety_factor': yield_stress / max_stress if yield_stress and max_stress > 0 else None,
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'stress_unit': stress_unit,
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}
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)
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