feat: Add OPD method support to Zernike visualization with Standard/OPD toggle
Major improvements to Zernike WFE visualization: - Add ZernikeDashboardInsight: Unified dashboard with all orientations (40°, 60°, 90°) on one page with light theme and executive summary - Add OPD method toggle: Switch between Standard (Z-only) and OPD (X,Y,Z) methods in ZernikeWFEInsight with interactive buttons - Add lateral displacement maps: Visualize X,Y displacement for each orientation - Add displacement component views: Toggle between WFE, ΔX, ΔY, ΔZ in relative views - Add metrics comparison table showing both methods side-by-side New extractors: - extract_zernike_figure.py: ZernikeOPDExtractor using BDF geometry interpolation - extract_zernike_opd.py: Parabola-based OPD with focal length Key finding: OPD method gives 8-11% higher WFE values than Standard method (more conservative/accurate for surfaces with lateral displacement under gravity) Documentation updates: - SYS_12: Added E22 ZernikeOPD as recommended method - SYS_16: Added ZernikeDashboard, updated ZernikeWFE with OPD features - Cheatsheet: Added Zernike method comparison table 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
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@@ -26,12 +26,32 @@ Study Insights provide **physics understanding** of optimization results through
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| Type ID | Name | Applicable To | Data Required |
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|---------|------|---------------|---------------|
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| `zernike_dashboard` | **Zernike Dashboard (RECOMMENDED)** | Mirror, optics | OP2 with displacement subcases |
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| `zernike_wfe` | Zernike WFE Analysis | Mirror, optics | OP2 with displacement subcases |
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| `zernike_opd_comparison` | Zernike OPD Method Comparison | Mirror, optics, lateral | OP2 with displacement subcases |
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| `msf_zernike` | MSF Zernike Analysis | Mirror, optics | OP2 with displacement subcases |
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| `stress_field` | Stress Distribution | Structural, bracket, beam | OP2 with stress results |
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| `modal` | Modal Analysis | Vibration, dynamic | OP2 with eigenvalue/eigenvector |
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| `thermal` | Thermal Analysis | Thermo-structural | OP2 with temperature results |
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| `design_space` | Design Space Explorer | All optimization studies | study.db with 5+ trials |
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### Zernike Method Comparison: Standard vs OPD
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The Zernike insights now support **two WFE computation methods**:
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| Method | Description | When to Use |
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|--------|-------------|-------------|
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| **Standard (Z-only)** | Uses only Z-displacement at original (x,y) coordinates | Quick analysis, negligible lateral displacement |
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| **OPD (X,Y,Z)** ← RECOMMENDED | Accounts for lateral (X,Y) displacement via interpolation | Any surface with gravity loads, most rigorous |
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**How OPD method works**:
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1. Builds interpolator from undeformed BDF mesh geometry
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2. For each deformed node at `(x+dx, y+dy, z+dz)`, interpolates `Z_ideal` at new XY position
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3. Computes `WFE = z_deformed - Z_ideal(x_def, y_def)`
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4. Fits Zernike polynomials to the surface error map
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**Typical difference**: OPD method gives **8-11% higher** WFE values than Standard (more conservative/accurate).
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---
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## Architecture
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@@ -40,11 +60,13 @@ Study Insights provide **physics understanding** of optimization results through
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```
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optimization_engine/insights/
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├── __init__.py # Registry and public API
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├── base.py # StudyInsight base class, InsightConfig, InsightResult
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├── zernike_wfe.py # Mirror wavefront error visualization
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├── stress_field.py # Stress contour visualization
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├── modal_analysis.py # Mode shape visualization
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├── __init__.py # Registry and public API
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├── base.py # StudyInsight base class, InsightConfig, InsightResult
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├── zernike_wfe.py # Mirror wavefront error visualization (50 modes)
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├── zernike_opd_comparison.py # OPD vs Standard method comparison (lateral disp. analysis)
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├── msf_zernike.py # MSF band decomposition (100 modes, LSF/MSF/HSF)
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├── stress_field.py # Stress contour visualization
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├── modal_analysis.py # Mode shape visualization
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├── thermal_field.py # Temperature distribution
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└── design_space.py # Parameter-objective exploration
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```
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@@ -53,7 +75,10 @@ optimization_engine/insights/
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```python
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StudyInsight (ABC)
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├── ZernikeWFEInsight
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├── ZernikeDashboardInsight # RECOMMENDED: Unified dashboard with all views
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├── ZernikeWFEInsight # Standard 50-mode WFE analysis (with OPD toggle)
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├── ZernikeOPDComparisonInsight # OPD method comparison (lateral displacement)
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├── MSFZernikeInsight # 100-mode MSF band analysis
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├── StressFieldInsight
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├── ModalInsight
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├── ThermalInsight
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@@ -167,14 +192,54 @@ result = insight.generate(config)
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## Insight Type Details
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### 0. Zernike Dashboard (`zernike_dashboard`) - RECOMMENDED
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**Purpose**: Unified dashboard with all orientations (40°, 60°, 90°) and MSF band analysis on one page. Light theme, executive summary, and method comparison.
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**Generates**: 1 comprehensive HTML file with:
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- Executive summary with metric cards (40-20, 60-20, MFG workload)
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- MSF band analysis (LSF/MSF/HSF decomposition)
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- 3D surface plots for each orientation
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- Zernike coefficient bar charts color-coded by band
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**Configuration**:
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```python
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config = InsightConfig(
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extra={
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'n_modes': 50,
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'filter_low_orders': 4,
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'theme': 'light', # Light theme for reports
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}
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)
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```
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**Summary Output**:
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```python
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{
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'40_vs_20_filtered_rms': 6.53, # nm (OPD method)
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'60_vs_20_filtered_rms': 14.21, # nm (OPD method)
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'90_optician_workload': 26.34, # nm (J1-J3 filtered)
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'msf_rss_40': 2.1, # nm (MSF band contribution)
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}
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```
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### 1. Zernike WFE Analysis (`zernike_wfe`)
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**Purpose**: Visualize wavefront error for mirror optimization with Zernike polynomial decomposition.
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**Purpose**: Visualize wavefront error for mirror optimization with Zernike polynomial decomposition. **Now includes Standard/OPD method toggle and lateral displacement maps**.
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**Generates**: 3 HTML files
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- `zernike_*_40_vs_20.html` - 40° vs 20° relative WFE
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- `zernike_*_60_vs_20.html` - 60° vs 20° relative WFE
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- `zernike_*_90_mfg.html` - 90° manufacturing (absolute)
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**Generates**: 6 HTML files
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- `zernike_*_40_vs_20.html` - 40° vs 20° relative WFE (with method toggle)
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- `zernike_*_40_lateral.html` - Lateral displacement map for 40°
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- `zernike_*_60_vs_20.html` - 60° vs 20° relative WFE (with method toggle)
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- `zernike_*_60_lateral.html` - Lateral displacement map for 60°
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- `zernike_*_90_mfg.html` - 90° manufacturing (with method toggle)
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- `zernike_*_90_mfg_lateral.html` - Lateral displacement map for 90°
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**Features**:
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- Toggle buttons to switch between **Standard (Z-only)** and **OPD (X,Y,Z)** methods
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- Toggle between WFE view and **ΔX, ΔY, ΔZ displacement components**
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- Metrics comparison table showing both methods side-by-side
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- Lateral displacement statistics (Max, RMS in µm)
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**Configuration**:
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```python
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@@ -192,14 +257,81 @@ config = InsightConfig(
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**Summary Output**:
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```python
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{
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'40_vs_20_filtered_rms': 45.2, # nm
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'60_vs_20_filtered_rms': 78.3, # nm
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'90_mfg_filtered_rms': 120.5, # nm
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'90_optician_workload': 89.4, # nm (J1-J3 filtered)
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'40_vs_20_filtered_rms_std': 6.01, # nm (Standard method)
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'40_vs_20_filtered_rms_opd': 6.53, # nm (OPD method)
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'60_vs_20_filtered_rms_std': 12.81, # nm
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'60_vs_20_filtered_rms_opd': 14.21, # nm
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'90_mfg_filtered_rms_std': 24.5, # nm
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'90_mfg_filtered_rms_opd': 26.34, # nm
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'90_optician_workload': 26.34, # nm (J1-J3 filtered)
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'lateral_40_max_um': 0.234, # µm max lateral displacement
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'lateral_60_max_um': 0.312, # µm
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'lateral_90_max_um': 0.089, # µm
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}
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```
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### 2. Stress Distribution (`stress_field`)
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### 2. MSF Zernike Analysis (`msf_zernike`)
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**Purpose**: Detailed mid-spatial frequency analysis for telescope mirrors with gravity-induced support print-through.
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**Generates**: 1 comprehensive HTML file with:
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- Band decomposition table (LSF/MSF/HSF RSS metrics)
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- MSF-only 3D surface visualization
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- Coefficient bar chart color-coded by band
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- Dominant MSF mode identification
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- Mesh resolution analysis
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**Band Definitions** (for 1.2m class mirror):
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| Band | Zernike Order | Feature Size | Physical Meaning |
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|------|---------------|--------------|------------------|
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| LSF | n ≤ 10 | > 120 mm | M2 hexapod correctable |
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| MSF | n = 11-50 | 24-109 mm | Support print-through |
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| HSF | n > 50 | < 24 mm | Near mesh resolution limit |
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**Configuration**:
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```python
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config = InsightConfig(
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extra={
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'n_modes': 100, # Higher than zernike_wfe (100 vs 50)
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'lsf_max': 10, # n ≤ 10 is LSF
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'msf_max': 50, # n = 11-50 is MSF
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'disp_unit': 'mm',
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}
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)
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```
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**Analyses Performed**:
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- Absolute WFE at each orientation (40°, 60°, 90°)
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- Relative to 20° (operational reference)
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- Relative to 90° (manufacturing/polishing reference)
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**Summary Output**:
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```python
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{
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'n_modes': 100,
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'lsf_max_order': 10,
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'msf_max_order': 50,
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'mesh_nodes': 78290,
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'mesh_spacing_mm': 4.1,
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'max_resolvable_order': 157,
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'40deg_vs_20deg_lsf_rss': 12.3, # nm
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'40deg_vs_20deg_msf_rss': 8.7, # nm - KEY METRIC
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'40deg_vs_20deg_total_rss': 15.2, # nm
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'40deg_vs_20deg_msf_pct': 33.0, # % of total in MSF band
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# ... similar for 60deg, 90deg
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}
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```
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**When to Use**:
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- Analyzing support structure print-through
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- Quantifying gravity-induced MSF content
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- Comparing MSF at different orientations
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- Validating mesh resolution is adequate for MSF capture
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---
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### 3. Stress Distribution (`stress_field`)
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**Purpose**: Visualize Von Mises stress distribution with hot spot identification.
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@@ -225,7 +357,7 @@ config = InsightConfig(
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}
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```
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### 3. Modal Analysis (`modal`)
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### 4. Modal Analysis (`modal`)
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**Purpose**: Visualize natural frequencies and mode shapes.
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@@ -249,7 +381,7 @@ config = InsightConfig(
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}
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```
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### 4. Thermal Analysis (`thermal`)
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### 5. Thermal Analysis (`thermal`)
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**Purpose**: Visualize temperature distribution and gradients.
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@@ -273,7 +405,7 @@ config = InsightConfig(
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}
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```
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### 5. Design Space Explorer (`design_space`)
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### 6. Design Space Explorer (`design_space`)
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**Purpose**: Visualize parameter-objective relationships from optimization trials.
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@@ -369,31 +501,44 @@ python -m optimization_engine.insights list
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---
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## Dashboard Integration (Future)
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## Dashboard Integration
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The insights module is designed for future dashboard integration:
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The Insights tab in the Atomizer Dashboard provides a 3-step workflow:
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```python
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# Backend API endpoint
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@app.get("/api/study/{study_name}/insights")
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def list_study_insights(study_name: str):
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study_path = Path(f"studies/{study_name}")
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return list_available_insights(study_path)
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### Step 1: Select Iteration
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- Lists all available iterations (iter1, iter2, etc.) and best_design_archive
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- Shows OP2 file name and modification timestamp
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- Auto-selects "Best Design (Recommended)" if available
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@app.post("/api/study/{study_name}/insights/{type}/generate")
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def generate_insight(study_name: str, type: str, config: dict = {}):
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insight = get_insight(type, Path(f"studies/{study_name}"))
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result = insight.generate(InsightConfig(**config))
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return {
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'success': result.success,
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'html_path': str(result.html_path),
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'summary': result.summary
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}
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### Step 2: Choose Insight Type
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- Groups insights by category (Optical, Structural, Thermal, etc.)
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- Shows insight name and description
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- Click to select, then "Generate Insight"
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@app.get("/api/study/{study_name}/insights/{type}/plotly")
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def get_insight_plotly(study_name: str, type: str):
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insight = get_insight(type, Path(f"studies/{study_name}"))
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return insight.get_plotly_data()
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### Step 3: View Result
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- Displays summary metrics (RMS values, etc.)
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- Embedded Plotly visualization (if available)
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- "Open Full View" button for multi-file insights (like Zernike WFE)
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- Fullscreen mode for detailed analysis
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### API Endpoints
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```
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GET /api/insights/studies/{id}/iterations # List available iterations
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GET /api/insights/studies/{id}/available # List available insight types
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GET /api/insights/studies/{id}/generated # List previously generated files
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POST /api/insights/studies/{id}/generate/{type} # Generate insight for iteration
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GET /api/insights/studies/{id}/view/{type} # View generated HTML
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```
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### Generate Request Body
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```json
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{
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"iteration": "best_design_archive", // or "iter5", etc.
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"trial_id": null, // Optional specific trial
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"config": {} // Insight-specific config
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}
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```
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---
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@@ -402,4 +547,7 @@ def get_insight_plotly(study_name: str, type: str):
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| Version | Date | Changes |
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|---------|------|---------|
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| 1.3.0 | 2025-12-22 | Added ZernikeDashboardInsight (unified view), OPD method toggle, lateral displacement maps |
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| 1.2.0 | 2024-12-22 | Dashboard overhaul: 3-step workflow, iteration selection, faster loading |
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| 1.1.0 | 2024-12-21 | Added MSF Zernike Analysis insight (6 insight types) |
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| 1.0.0 | 2024-12-20 | Initial release with 5 insight types |
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