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>
This commit is contained in:
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optimization_engine/extractors/extract_zernike_opd.py
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optimization_engine/extractors/extract_zernike_opd.py
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"""
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Analytic (Parabola-Based) Zernike Extractor for Telescope Mirror Optimization
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==============================================================================
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This extractor computes OPD using an ANALYTICAL parabola formula, accounting for
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lateral (X, Y) displacements in addition to axial (Z) displacements.
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NOTE: For most robust OPD analysis, prefer ZernikeOPDExtractor from extract_zernike_figure.py
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which uses actual mesh geometry as reference (no shape assumptions).
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This analytic extractor is useful when:
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- You know the optical prescription (focal length)
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- The surface is a parabola (or close to it)
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- You want to compare against theoretical parabola
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WHY LATERAL CORRECTION MATTERS:
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-------------------------------
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The standard Zernike approach uses original (x, y) coordinates with only Z-displacement.
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This is INCORRECT when lateral displacements are significant because:
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1. A node at (x₀, y₀) moves to (x₀+Δx, y₀+Δy, z₀+Δz)
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2. The parabola surface at the NEW position has a different expected Z
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3. The true surface error is: (z₀+Δz) - z_parabola(x₀+Δx, y₀+Δy)
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PARABOLA EQUATION:
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------------------
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For a paraboloid with vertex at origin and optical axis along Z:
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z = (x² + y²) / (4 * f)
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where f = focal length = R / 2 (R = radius of curvature at vertex)
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For a concave mirror (like telescope primary):
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z = -r² / (4 * f) (negative because surface curves away from +Z)
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We support both conventions via the `concave` parameter.
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Author: Atomizer Framework
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Date: 2024
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"""
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from pathlib import Path
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from typing import Dict, Any, Optional, List, Tuple, Union
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import numpy as np
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from numpy.linalg import LinAlgError
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# Import base Zernike functionality from existing module
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from .extract_zernike import (
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compute_zernike_coefficients,
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compute_aberration_magnitudes,
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zernike_noll,
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zernike_label,
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read_node_geometry,
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find_geometry_file,
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extract_displacements_by_subcase,
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UNIT_TO_NM,
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DEFAULT_N_MODES,
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DEFAULT_FILTER_ORDERS,
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)
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try:
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from pyNastran.op2.op2 import OP2
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from pyNastran.bdf.bdf import BDF
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except ImportError:
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raise ImportError("pyNastran is required. Install with: pip install pyNastran")
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# ============================================================================
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# OPD Calculation Functions
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# ============================================================================
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def compute_parabola_z(
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x: np.ndarray,
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y: np.ndarray,
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focal_length: float,
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concave: bool = True
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) -> np.ndarray:
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"""
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Compute the Z coordinate on a paraboloid surface.
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For a paraboloid: z = ±(x² + y²) / (4 * f)
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Args:
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x, y: Coordinates on the surface
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focal_length: Focal length of the parabola (f = R_vertex / 2)
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concave: If True, mirror curves toward -Z (typical telescope primary)
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Returns:
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Z coordinates on the parabola surface
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"""
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r_squared = x**2 + y**2
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z = r_squared / (4.0 * focal_length)
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if concave:
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z = -z # Concave mirror curves toward -Z
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return z
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def compute_true_opd(
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x0: np.ndarray,
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y0: np.ndarray,
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z0: np.ndarray,
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dx: np.ndarray,
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dy: np.ndarray,
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dz: np.ndarray,
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focal_length: float,
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concave: bool = True
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) -> Tuple[np.ndarray, np.ndarray, np.ndarray, np.ndarray]:
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"""
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Compute true Optical Path Difference accounting for lateral displacement.
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The key insight: when a node moves laterally, we must compare its new Z
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position against what the parabola Z SHOULD BE at that new (x, y) location.
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The CORRECT formulation:
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- Original surface: z0 = parabola(x0, y0) + manufacturing_errors
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- Deformed surface: z0 + dz = parabola(x0 + dx, y0 + dy) + total_errors
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The surface error we want is:
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error = (z0 + dz) - parabola(x0 + dx, y0 + dy)
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But since z0 ≈ parabola(x0, y0), we can compute the DIFFERENTIAL:
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error ≈ dz - [parabola(x0 + dx, y0 + dy) - parabola(x0, y0)]
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error ≈ dz - Δz_parabola
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where Δz_parabola is the change in ideal parabola Z due to lateral movement.
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Args:
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x0, y0, z0: Original node coordinates (undeformed)
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dx, dy, dz: Displacement components from FEA
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focal_length: Parabola focal length
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concave: Whether mirror is concave (typical)
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Returns:
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Tuple of:
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- x_def: Deformed X coordinates (for Zernike fitting)
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- y_def: Deformed Y coordinates (for Zernike fitting)
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- surface_error: True surface error at deformed positions
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- lateral_magnitude: Magnitude of lateral displacement (diagnostic)
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"""
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# Deformed coordinates
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x_def = x0 + dx
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y_def = y0 + dy
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# Compute the CHANGE in parabola Z due to lateral displacement
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# For parabola: z = ±r²/(4f), so:
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# Δz_parabola = z(x_def, y_def) - z(x0, y0)
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# = [(x0+dx)² + (y0+dy)² - x0² - y0²] / (4f)
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# = [2*x0*dx + dx² + 2*y0*dy + dy²] / (4f)
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# Original r² and deformed r²
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r0_sq = x0**2 + y0**2
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r_def_sq = x_def**2 + y_def**2
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# Change in r² due to lateral displacement
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delta_r_sq = r_def_sq - r0_sq
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# Change in parabola Z (the Z shift that would keep the node on the ideal parabola)
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delta_z_parabola = delta_r_sq / (4.0 * focal_length)
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if concave:
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delta_z_parabola = -delta_z_parabola
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# TRUE surface error = actual dz - expected dz (to stay on parabola)
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# If node moves laterally outward (larger r), parabola Z changes
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# The error is the difference between actual Z movement and expected Z movement
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surface_error = dz - delta_z_parabola
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# Diagnostic: lateral displacement magnitude
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lateral_magnitude = np.sqrt(dx**2 + dy**2)
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return x_def, y_def, surface_error, lateral_magnitude
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def estimate_focal_length_from_geometry(
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x: np.ndarray,
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y: np.ndarray,
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z: np.ndarray,
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concave: bool = True
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) -> float:
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"""
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Estimate focal length from node geometry by fitting a paraboloid.
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Uses least-squares fit: z = a * (x² + y²) + b
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where focal_length = 1 / (4 * |a|)
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Args:
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x, y, z: Node coordinates
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concave: Whether mirror is concave
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Returns:
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Estimated focal length
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"""
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r_squared = x**2 + y**2
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# Fit z = a * r² + b
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A = np.column_stack([r_squared, np.ones_like(r_squared)])
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try:
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coeffs, _, _, _ = np.linalg.lstsq(A, z, rcond=None)
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a = coeffs[0]
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except LinAlgError:
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# Fallback: use simple ratio
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mask = r_squared > 0
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a = np.median(z[mask] / r_squared[mask])
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# For concave mirror, a should be negative
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# focal_length = 1 / (4 * |a|)
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if abs(a) < 1e-12:
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raise ValueError("Cannot estimate focal length - surface appears flat")
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focal_length = 1.0 / (4.0 * abs(a))
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return focal_length
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# ============================================================================
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# Main Analytic (Parabola-Based) Extractor Class
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# ============================================================================
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class ZernikeAnalyticExtractor:
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"""
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Analytic (parabola-based) Zernike extractor for telescope mirror optimization.
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This extractor accounts for lateral (X, Y) displacements when computing
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wavefront error, using an analytical parabola formula as reference.
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NOTE: For most robust analysis, prefer ZernikeOPDExtractor from
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extract_zernike_figure.py which uses actual mesh geometry (no shape assumptions).
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Use this extractor when:
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- You know the focal length / optical prescription
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- The surface is parabolic (or near-parabolic)
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- You want to compare against theoretical parabola shape
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Example:
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extractor = ZernikeAnalyticExtractor(
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op2_file, bdf_file,
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focal_length=5000.0 # mm
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)
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result = extractor.extract_subcase('20')
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# Metrics available:
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print(f"Max lateral displacement: {result['max_lateral_disp_um']:.2f} µm")
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print(f"RMS lateral displacement: {result['rms_lateral_disp_um']:.2f} µm")
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"""
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def __init__(
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self,
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op2_path: Union[str, Path],
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bdf_path: Optional[Union[str, Path]] = None,
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focal_length: Optional[float] = None,
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concave: bool = True,
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displacement_unit: str = 'mm',
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n_modes: int = DEFAULT_N_MODES,
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filter_orders: int = DEFAULT_FILTER_ORDERS,
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auto_estimate_focal: bool = True
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):
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"""
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Initialize OPD-based Zernike extractor.
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Args:
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op2_path: Path to OP2 results file
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bdf_path: Path to BDF/DAT geometry file (auto-detected if None)
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focal_length: Parabola focal length in same units as geometry
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If None and auto_estimate_focal=True, will estimate from mesh
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concave: Whether mirror is concave (curves toward -Z)
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displacement_unit: Unit of displacement in OP2 ('mm', 'm', 'um', 'nm')
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n_modes: Number of Zernike modes to fit
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filter_orders: Number of low-order modes to filter
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auto_estimate_focal: If True, estimate focal length from geometry
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"""
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self.op2_path = Path(op2_path)
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self.bdf_path = Path(bdf_path) if bdf_path else find_geometry_file(self.op2_path)
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self.focal_length = focal_length
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self.concave = concave
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self.displacement_unit = displacement_unit
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self.n_modes = n_modes
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self.filter_orders = filter_orders
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self.auto_estimate_focal = auto_estimate_focal
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# Unit conversion factor
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self.nm_scale = UNIT_TO_NM.get(displacement_unit.lower(), 1e6)
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self.um_scale = self.nm_scale / 1000.0 # For lateral displacement reporting
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# WFE = 2 * surface error (optical convention for reflection)
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self.wfe_factor = 2.0 * self.nm_scale
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# Lazy-loaded data
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self._node_geo = None
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self._displacements = None
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self._estimated_focal = None
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@property
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def node_geometry(self) -> Dict[int, np.ndarray]:
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"""Lazy-load node geometry from BDF."""
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if self._node_geo is None:
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self._node_geo = read_node_geometry(self.bdf_path)
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return self._node_geo
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@property
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def displacements(self) -> Dict[str, Dict[str, np.ndarray]]:
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"""Lazy-load displacements from OP2."""
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if self._displacements is None:
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self._displacements = extract_displacements_by_subcase(self.op2_path)
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return self._displacements
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def get_focal_length(self, x: np.ndarray, y: np.ndarray, z: np.ndarray) -> float:
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"""
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Get focal length, estimating from geometry if not provided.
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"""
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if self.focal_length is not None:
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return self.focal_length
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if self._estimated_focal is not None:
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return self._estimated_focal
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if self.auto_estimate_focal:
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self._estimated_focal = estimate_focal_length_from_geometry(
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x, y, z, self.concave
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)
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return self._estimated_focal
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raise ValueError(
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"focal_length must be provided or auto_estimate_focal must be True"
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)
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def _build_opd_data(
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self,
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subcase_label: str
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) -> Dict[str, np.ndarray]:
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"""
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Build OPD data arrays for a subcase using rigorous method.
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Returns:
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Dict with:
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- x_original, y_original, z_original: Original coordinates
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- dx, dy, dz: Displacement components
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- x_deformed, y_deformed: Deformed coordinates (for Zernike fitting)
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- surface_error: True surface error (mm or model units)
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- wfe_nm: Wavefront error in nanometers
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- lateral_disp: Lateral displacement magnitude
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"""
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if subcase_label not in self.displacements:
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available = list(self.displacements.keys())
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raise ValueError(f"Subcase '{subcase_label}' not found. Available: {available}")
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data = self.displacements[subcase_label]
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node_ids = data['node_ids']
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disp = data['disp']
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# Build arrays
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x0, y0, z0 = [], [], []
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dx_arr, dy_arr, dz_arr = [], [], []
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for nid, vec in zip(node_ids, disp):
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geo = self.node_geometry.get(int(nid))
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if geo is None:
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continue
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x0.append(geo[0])
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y0.append(geo[1])
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z0.append(geo[2])
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dx_arr.append(vec[0])
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dy_arr.append(vec[1])
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dz_arr.append(vec[2])
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x0 = np.array(x0)
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y0 = np.array(y0)
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z0 = np.array(z0)
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dx_arr = np.array(dx_arr)
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dy_arr = np.array(dy_arr)
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dz_arr = np.array(dz_arr)
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# Get focal length
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focal = self.get_focal_length(x0, y0, z0)
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# Compute true OPD
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x_def, y_def, surface_error, lateral_disp = compute_true_opd(
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x0, y0, z0, dx_arr, dy_arr, dz_arr, focal, self.concave
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)
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# Convert to WFE (nm)
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wfe_nm = surface_error * self.wfe_factor
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return {
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'x_original': x0,
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'y_original': y0,
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'z_original': z0,
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'dx': dx_arr,
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'dy': dy_arr,
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'dz': dz_arr,
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'x_deformed': x_def,
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'y_deformed': y_def,
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'surface_error': surface_error,
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'wfe_nm': wfe_nm,
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'lateral_disp': lateral_disp,
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'focal_length': focal
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}
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def extract_subcase(
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self,
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subcase_label: str,
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include_coefficients: bool = False,
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include_diagnostics: bool = True
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) -> Dict[str, Any]:
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"""
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Extract Zernike metrics for a single subcase using rigorous OPD method.
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Args:
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subcase_label: Subcase identifier (e.g., '20', '90')
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include_coefficients: Whether to include all Zernike coefficients
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include_diagnostics: Whether to include lateral displacement diagnostics
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Returns:
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Dict with RMS metrics, aberrations, and lateral displacement info
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"""
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opd_data = self._build_opd_data(subcase_label)
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# Use DEFORMED coordinates for Zernike fitting
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X = opd_data['x_deformed']
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Y = opd_data['y_deformed']
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WFE = opd_data['wfe_nm']
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# Fit Zernike coefficients
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coeffs, R_max = compute_zernike_coefficients(X, Y, WFE, self.n_modes)
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# Compute RMS metrics
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# Center on deformed coordinates
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x_c = X - np.mean(X)
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y_c = Y - np.mean(Y)
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r = np.hypot(x_c / R_max, y_c / R_max)
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theta = np.arctan2(y_c, x_c)
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# Build low-order Zernike basis
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Z_low = np.column_stack([
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zernike_noll(j, r, theta) for j in range(1, self.filter_orders + 1)
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])
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# Filtered WFE
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wfe_filtered = WFE - Z_low @ coeffs[:self.filter_orders]
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global_rms = float(np.sqrt(np.mean(WFE**2)))
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filtered_rms = float(np.sqrt(np.mean(wfe_filtered**2)))
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# J1-J3 filtered (optician workload)
|
||||
Z_j1to3 = np.column_stack([
|
||||
zernike_noll(j, r, theta) for j in range(1, 4)
|
||||
])
|
||||
wfe_j1to3 = WFE - Z_j1to3 @ coeffs[:3]
|
||||
rms_j1to3 = float(np.sqrt(np.mean(wfe_j1to3**2)))
|
||||
|
||||
# Aberration magnitudes
|
||||
aberrations = compute_aberration_magnitudes(coeffs)
|
||||
|
||||
result = {
|
||||
'subcase': subcase_label,
|
||||
'method': 'opd_rigorous',
|
||||
'global_rms_nm': global_rms,
|
||||
'filtered_rms_nm': filtered_rms,
|
||||
'rms_filter_j1to3_nm': rms_j1to3,
|
||||
'n_nodes': len(X),
|
||||
'focal_length_used': opd_data['focal_length'],
|
||||
**aberrations
|
||||
}
|
||||
|
||||
if include_diagnostics:
|
||||
lateral = opd_data['lateral_disp']
|
||||
result.update({
|
||||
'max_lateral_disp_um': float(np.max(lateral) * self.um_scale),
|
||||
'rms_lateral_disp_um': float(np.sqrt(np.mean(lateral**2)) * self.um_scale),
|
||||
'mean_lateral_disp_um': float(np.mean(lateral) * self.um_scale),
|
||||
'p99_lateral_disp_um': float(np.percentile(lateral, 99) * self.um_scale),
|
||||
})
|
||||
|
||||
if include_coefficients:
|
||||
result['coefficients'] = coeffs.tolist()
|
||||
result['coefficient_labels'] = [
|
||||
zernike_label(j) for j in range(1, self.n_modes + 1)
|
||||
]
|
||||
|
||||
return result
|
||||
|
||||
def extract_comparison(
|
||||
self,
|
||||
subcase_label: str
|
||||
) -> Dict[str, Any]:
|
||||
"""
|
||||
Extract metrics using BOTH methods for comparison.
|
||||
|
||||
Useful for understanding the impact of the rigorous method
|
||||
vs the standard Z-only approach.
|
||||
|
||||
Returns:
|
||||
Dict with metrics from both methods and the delta
|
||||
"""
|
||||
from .extract_zernike import ZernikeExtractor
|
||||
|
||||
# Rigorous OPD method
|
||||
opd_result = self.extract_subcase(subcase_label, include_diagnostics=True)
|
||||
|
||||
# Standard Z-only method
|
||||
standard_extractor = ZernikeExtractor(
|
||||
self.op2_path, self.bdf_path,
|
||||
self.displacement_unit, self.n_modes, self.filter_orders
|
||||
)
|
||||
standard_result = standard_extractor.extract_subcase(subcase_label)
|
||||
|
||||
# Compute deltas
|
||||
delta_global = opd_result['global_rms_nm'] - standard_result['global_rms_nm']
|
||||
delta_filtered = opd_result['filtered_rms_nm'] - standard_result['filtered_rms_nm']
|
||||
|
||||
return {
|
||||
'subcase': subcase_label,
|
||||
'opd_method': {
|
||||
'global_rms_nm': opd_result['global_rms_nm'],
|
||||
'filtered_rms_nm': opd_result['filtered_rms_nm'],
|
||||
},
|
||||
'standard_method': {
|
||||
'global_rms_nm': standard_result['global_rms_nm'],
|
||||
'filtered_rms_nm': standard_result['filtered_rms_nm'],
|
||||
},
|
||||
'delta': {
|
||||
'global_rms_nm': delta_global,
|
||||
'filtered_rms_nm': delta_filtered,
|
||||
'percent_difference_filtered': 100.0 * delta_filtered / standard_result['filtered_rms_nm'] if standard_result['filtered_rms_nm'] > 0 else 0.0,
|
||||
},
|
||||
'lateral_displacement': {
|
||||
'max_um': opd_result['max_lateral_disp_um'],
|
||||
'rms_um': opd_result['rms_lateral_disp_um'],
|
||||
'p99_um': opd_result['p99_lateral_disp_um'],
|
||||
},
|
||||
'recommendation': _get_method_recommendation(opd_result)
|
||||
}
|
||||
|
||||
def extract_all_subcases(
|
||||
self,
|
||||
reference_subcase: Optional[str] = None
|
||||
) -> Dict[str, Dict[str, Any]]:
|
||||
"""
|
||||
Extract metrics for all available subcases.
|
||||
|
||||
Args:
|
||||
reference_subcase: Reference for relative calculations (None to skip)
|
||||
|
||||
Returns:
|
||||
Dict mapping subcase label to metrics dict
|
||||
"""
|
||||
results = {}
|
||||
|
||||
for label in self.displacements.keys():
|
||||
results[label] = self.extract_subcase(label)
|
||||
|
||||
return results
|
||||
|
||||
def get_diagnostic_data(
|
||||
self,
|
||||
subcase_label: str
|
||||
) -> Dict[str, np.ndarray]:
|
||||
"""
|
||||
Get raw data for visualization/debugging.
|
||||
|
||||
Returns arrays suitable for plotting lateral displacement maps,
|
||||
comparing methods, etc.
|
||||
"""
|
||||
return self._build_opd_data(subcase_label)
|
||||
|
||||
|
||||
def _get_method_recommendation(opd_result: Dict[str, Any]) -> str:
|
||||
"""
|
||||
Generate recommendation based on lateral displacement magnitude.
|
||||
"""
|
||||
max_lateral = opd_result.get('max_lateral_disp_um', 0)
|
||||
rms_lateral = opd_result.get('rms_lateral_disp_um', 0)
|
||||
|
||||
if max_lateral > 10.0: # > 10 µm max lateral
|
||||
return "CRITICAL: Large lateral displacements detected. OPD method strongly recommended."
|
||||
elif max_lateral > 1.0: # > 1 µm
|
||||
return "RECOMMENDED: Significant lateral displacements. OPD method provides more accurate results."
|
||||
elif max_lateral > 0.1: # > 0.1 µm
|
||||
return "OPTIONAL: Small lateral displacements. OPD method provides minor improvement."
|
||||
else:
|
||||
return "EQUIVALENT: Negligible lateral displacements. Both methods give similar results."
|
||||
|
||||
|
||||
# ============================================================================
|
||||
# Convenience Functions
|
||||
# ============================================================================
|
||||
|
||||
def extract_zernike_analytic(
|
||||
op2_file: Union[str, Path],
|
||||
bdf_file: Optional[Union[str, Path]] = None,
|
||||
subcase: Union[int, str] = 1,
|
||||
focal_length: Optional[float] = None,
|
||||
displacement_unit: str = 'mm',
|
||||
**kwargs
|
||||
) -> Dict[str, Any]:
|
||||
"""
|
||||
Convenience function to extract Zernike metrics using analytic parabola method.
|
||||
|
||||
NOTE: For most robust analysis, prefer extract_zernike_opd() from
|
||||
extract_zernike_figure.py which uses actual mesh geometry.
|
||||
|
||||
Args:
|
||||
op2_file: Path to OP2 results file
|
||||
bdf_file: Path to BDF geometry (auto-detected if None)
|
||||
subcase: Subcase identifier
|
||||
focal_length: Parabola focal length (auto-estimated if None)
|
||||
displacement_unit: Unit of displacement in OP2
|
||||
**kwargs: Additional arguments for ZernikeAnalyticExtractor
|
||||
|
||||
Returns:
|
||||
Dict with RMS metrics, aberrations, and lateral displacement info
|
||||
"""
|
||||
extractor = ZernikeAnalyticExtractor(
|
||||
op2_file, bdf_file,
|
||||
focal_length=focal_length,
|
||||
displacement_unit=displacement_unit,
|
||||
**kwargs
|
||||
)
|
||||
return extractor.extract_subcase(str(subcase))
|
||||
|
||||
|
||||
def extract_zernike_analytic_filtered_rms(
|
||||
op2_file: Union[str, Path],
|
||||
subcase: Union[int, str] = 1,
|
||||
**kwargs
|
||||
) -> float:
|
||||
"""
|
||||
Extract filtered RMS WFE using analytic parabola method.
|
||||
|
||||
NOTE: For most robust analysis, prefer extract_zernike_opd_filtered_rms()
|
||||
from extract_zernike_figure.py.
|
||||
"""
|
||||
result = extract_zernike_analytic(op2_file, subcase=subcase, **kwargs)
|
||||
return result['filtered_rms_nm']
|
||||
|
||||
|
||||
def compare_zernike_methods(
|
||||
op2_file: Union[str, Path],
|
||||
bdf_file: Optional[Union[str, Path]] = None,
|
||||
subcase: Union[int, str] = 1,
|
||||
focal_length: Optional[float] = None,
|
||||
**kwargs
|
||||
) -> Dict[str, Any]:
|
||||
"""
|
||||
Compare standard vs analytic (parabola) Zernike methods.
|
||||
|
||||
Use this to understand how much lateral displacement affects your results.
|
||||
"""
|
||||
extractor = ZernikeAnalyticExtractor(
|
||||
op2_file, bdf_file,
|
||||
focal_length=focal_length,
|
||||
**kwargs
|
||||
)
|
||||
return extractor.extract_comparison(str(subcase))
|
||||
|
||||
|
||||
# Backwards compatibility aliases
|
||||
ZernikeOPDExtractor = ZernikeAnalyticExtractor # Deprecated: use ZernikeAnalyticExtractor
|
||||
extract_zernike_opd = extract_zernike_analytic # Deprecated: use extract_zernike_analytic
|
||||
extract_zernike_opd_filtered_rms = extract_zernike_analytic_filtered_rms # Deprecated
|
||||
|
||||
|
||||
# ============================================================================
|
||||
# Module Exports
|
||||
# ============================================================================
|
||||
|
||||
__all__ = [
|
||||
# Main extractor class (new name)
|
||||
'ZernikeAnalyticExtractor',
|
||||
|
||||
# Convenience functions (new names)
|
||||
'extract_zernike_analytic',
|
||||
'extract_zernike_analytic_filtered_rms',
|
||||
'compare_zernike_methods',
|
||||
|
||||
# Utility functions
|
||||
'compute_true_opd',
|
||||
'compute_parabola_z',
|
||||
'estimate_focal_length_from_geometry',
|
||||
|
||||
# Backwards compatibility (deprecated)
|
||||
'ZernikeOPDExtractor',
|
||||
'extract_zernike_opd',
|
||||
'extract_zernike_opd_filtered_rms',
|
||||
]
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
import sys
|
||||
|
||||
if len(sys.argv) > 1:
|
||||
op2_file = Path(sys.argv[1])
|
||||
focal = float(sys.argv[2]) if len(sys.argv) > 2 else None
|
||||
|
||||
print(f"Analyzing: {op2_file}")
|
||||
print(f"Focal length: {focal if focal else 'auto-estimate'}")
|
||||
print("=" * 60)
|
||||
|
||||
try:
|
||||
extractor = ZernikeOPDExtractor(op2_file, focal_length=focal)
|
||||
|
||||
print(f"\nAvailable subcases: {list(extractor.displacements.keys())}")
|
||||
|
||||
for label in extractor.displacements.keys():
|
||||
print(f"\n{'=' * 60}")
|
||||
print(f"Subcase {label}")
|
||||
print('=' * 60)
|
||||
|
||||
# Compare methods
|
||||
comparison = extractor.extract_comparison(label)
|
||||
|
||||
print(f"\nStandard (Z-only) method:")
|
||||
print(f" Global RMS: {comparison['standard_method']['global_rms_nm']:.2f} nm")
|
||||
print(f" Filtered RMS: {comparison['standard_method']['filtered_rms_nm']:.2f} nm")
|
||||
|
||||
print(f"\nRigorous OPD method:")
|
||||
print(f" Global RMS: {comparison['opd_method']['global_rms_nm']:.2f} nm")
|
||||
print(f" Filtered RMS: {comparison['opd_method']['filtered_rms_nm']:.2f} nm")
|
||||
|
||||
print(f"\nDifference (OPD - Standard):")
|
||||
print(f" Filtered RMS: {comparison['delta']['filtered_rms_nm']:+.2f} nm ({comparison['delta']['percent_difference_filtered']:+.1f}%)")
|
||||
|
||||
print(f"\nLateral Displacement:")
|
||||
print(f" Max: {comparison['lateral_displacement']['max_um']:.3f} µm")
|
||||
print(f" RMS: {comparison['lateral_displacement']['rms_um']:.3f} µm")
|
||||
print(f" P99: {comparison['lateral_displacement']['p99_um']:.3f} µm")
|
||||
|
||||
print(f"\n→ {comparison['recommendation']}")
|
||||
|
||||
except Exception as e:
|
||||
print(f"Error: {e}")
|
||||
import traceback
|
||||
traceback.print_exc()
|
||||
sys.exit(1)
|
||||
else:
|
||||
print("OPD-Based Zernike Extractor")
|
||||
print("=" * 40)
|
||||
print("\nUsage: python extract_zernike_opd.py <op2_file> [focal_length]")
|
||||
print("\nThis module provides rigorous OPD-based Zernike extraction")
|
||||
print("that accounts for lateral (X, Y) displacements.")
|
||||
print("\nKey features:")
|
||||
print(" - True optical path difference calculation")
|
||||
print(" - Accounts for node lateral shift due to pinching/clamping")
|
||||
print(" - Lateral displacement diagnostics")
|
||||
print(" - Method comparison (standard vs OPD)")
|
||||
Reference in New Issue
Block a user