Anto01
d19fc39a2a
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>
876 lines
30 KiB
Python
876 lines
30 KiB
Python
"""
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MSF Zernike Insight - Mid-Spatial Frequency Analysis for Telescope Mirrors
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Provides detailed analysis of mid-spatial frequency (MSF) content in mirror
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wavefront error, specifically for gravity-induced support print-through.
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Key Features:
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- Band decomposition: LSF (n≤10), MSF (n=11-50), HSF (n>50)
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- RSS (Root Sum Square) metrics per band
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- Relative analysis vs reference orientations (20°, 90°)
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- Higher-order Zernike fitting (100 modes by default)
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- PSD (Power Spectral Density) visualization
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- Support print-through identification
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For GigaBIT and similar telescope mirrors where MSF errors are dominated by
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gravity-induced support deformation rather than polishing residual.
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Author: Atomizer Framework
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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 math import factorial
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from numpy.linalg import LinAlgError
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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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# ============================================================================
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# Band Definitions
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# ============================================================================
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# Default band boundaries (Zernike radial order n)
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DEFAULT_LSF_MAX = 10 # LSF: n = 1-10 (M2 hexapod correctable)
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DEFAULT_MSF_MAX = 50 # MSF: n = 11-50 (support print-through)
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DEFAULT_N_MODES = 100 # Total modes to fit (n ≈ 14)
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# Band colors for visualization
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BAND_COLORS = {
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'lsf': '#3b82f6', # Blue - low spatial frequency
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'msf': '#f59e0b', # Amber - mid spatial frequency
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'hsf': '#ef4444', # Red - high spatial frequency
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}
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# ============================================================================
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# Zernike Mathematics (same as zernike_wfe.py)
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# ============================================================================
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def noll_indices(j: int) -> Tuple[int, int]:
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"""Convert Noll index to (n, m) radial/azimuthal orders."""
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if j < 1:
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raise ValueError("Noll index j must be >= 1")
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count = 0
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n = 0
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while True:
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if n == 0:
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ms = [0]
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elif n % 2 == 0:
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ms = [0] + [m for k in range(1, n//2 + 1) for m in (-2*k, 2*k)]
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else:
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ms = [m for k in range(0, (n+1)//2) for m in (-(2*k+1), (2*k+1))]
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for m in ms:
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count += 1
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if count == j:
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return n, m
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n += 1
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def noll_to_radial_order(j: int) -> int:
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"""Get radial order n for Noll index j."""
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n, _ = noll_indices(j)
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return n
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def radial_order_to_first_noll(n: int) -> int:
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"""Get first Noll index for radial order n."""
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# Sum of (k+1) for k=0 to n-1, plus 1
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return n * (n + 1) // 2 + 1
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def zernike_noll(j: int, r: np.ndarray, th: np.ndarray) -> np.ndarray:
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"""Evaluate Zernike polynomial j at (r, theta)."""
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n, m = noll_indices(j)
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R = np.zeros_like(r)
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for s in range((n - abs(m)) // 2 + 1):
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c = ((-1)**s * factorial(n - s) /
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(factorial(s) *
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factorial((n + abs(m)) // 2 - s) *
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factorial((n - abs(m)) // 2 - s)))
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R += c * r**(n - 2*s)
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if m == 0:
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return R
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return R * (np.cos(m * th) if m > 0 else np.sin(-m * th))
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def zernike_common_name(n: int, m: int) -> str:
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"""Get common name for Zernike mode."""
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names = {
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(0, 0): "Piston", (1, -1): "Tilt X", (1, 1): "Tilt Y",
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(2, 0): "Defocus", (2, -2): "Astig 45°", (2, 2): "Astig 0°",
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(3, -1): "Coma X", (3, 1): "Coma Y", (3, -3): "Trefoil X", (3, 3): "Trefoil Y",
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(4, 0): "Primary Spherical", (4, -2): "Sec Astig X", (4, 2): "Sec Astig Y",
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(4, -4): "Quadrafoil X", (4, 4): "Quadrafoil Y",
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(5, -1): "Sec Coma X", (5, 1): "Sec Coma Y",
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(5, -3): "Sec Trefoil X", (5, 3): "Sec Trefoil Y",
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(5, -5): "Pentafoil X", (5, 5): "Pentafoil Y",
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(6, 0): "Sec Spherical",
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}
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return names.get((n, m), f"Z(n={n}, m={m})")
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def zernike_label(j: int) -> str:
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"""Get label for Zernike coefficient J{j}."""
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n, m = noll_indices(j)
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return f"J{j:02d} - {zernike_common_name(n, m)} (n={n}, m={m})"
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def compute_zernike_coeffs(
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X: np.ndarray,
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Y: np.ndarray,
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vals: np.ndarray,
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n_modes: int,
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chunk_size: int = 100000
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) -> Tuple[np.ndarray, float]:
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"""Fit Zernike coefficients to WFE data."""
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Xc, Yc = X - np.mean(X), Y - np.mean(Y)
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R = float(np.max(np.hypot(Xc, Yc)))
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r = np.hypot(Xc / R, Yc / R).astype(np.float32)
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th = np.arctan2(Yc, Xc).astype(np.float32)
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mask = (r <= 1.0) & ~np.isnan(vals)
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if not np.any(mask):
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raise RuntimeError("No valid points inside unit disk.")
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idx = np.nonzero(mask)[0]
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m = int(n_modes)
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G = np.zeros((m, m), dtype=np.float64)
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h = np.zeros((m,), dtype=np.float64)
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v = vals.astype(np.float64)
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for start in range(0, len(idx), chunk_size):
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sl = idx[start:start + chunk_size]
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r_b, th_b, v_b = r[sl], th[sl], v[sl]
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Zb = np.column_stack([zernike_noll(j, r_b, th_b).astype(np.float32)
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for j in range(1, m + 1)])
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G += (Zb.T @ Zb).astype(np.float64)
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h += (Zb.T @ v_b).astype(np.float64)
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try:
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coeffs = np.linalg.solve(G, h)
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except LinAlgError:
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coeffs = np.linalg.lstsq(G, h, rcond=None)[0]
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return coeffs, R
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# ============================================================================
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# Band Analysis
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# ============================================================================
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def classify_modes_by_band(
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n_modes: int,
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lsf_max: int = DEFAULT_LSF_MAX,
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msf_max: int = DEFAULT_MSF_MAX
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) -> Dict[str, List[int]]:
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"""
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Classify Zernike modes into LSF/MSF/HSF bands.
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Returns:
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Dict with 'lsf', 'msf', 'hsf' keys containing lists of Noll indices
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"""
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bands = {'lsf': [], 'msf': [], 'hsf': []}
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for j in range(1, n_modes + 1):
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n = noll_to_radial_order(j)
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if n <= lsf_max:
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bands['lsf'].append(j)
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elif n <= msf_max:
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bands['msf'].append(j)
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else:
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bands['hsf'].append(j)
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return bands
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def compute_band_rss(
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coeffs: np.ndarray,
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bands: Dict[str, List[int]],
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filter_modes: int = 4
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) -> Dict[str, float]:
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"""
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Compute RSS (Root Sum Square) of coefficients in each band.
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RSS = sqrt(sum(c_j^2)) for j in band
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Args:
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coeffs: Zernike coefficients
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bands: Dict with band name -> list of Noll indices
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filter_modes: Number of low-order modes to filter for 'filtered' metric (default 4 = J1-J4)
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Returns:
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Dict with 'lsf', 'msf', 'hsf', 'total', and 'filtered' (J5+) RSS values
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"""
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rss = {}
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for band_name, indices in bands.items():
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# Convert to 0-indexed
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band_coeffs = [coeffs[j-1] for j in indices if j-1 < len(coeffs)]
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rss[band_name] = float(np.sqrt(np.sum(np.array(band_coeffs)**2)))
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rss['total'] = float(np.sqrt(np.sum(coeffs**2)))
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# Filtered RSS: exclude first filter_modes (J1-J4 = piston, tip, tilt, defocus)
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# This is the engineering-relevant metric after M2 alignment
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filtered_coeffs = coeffs[filter_modes:] if len(coeffs) > filter_modes else np.array([])
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rss['filtered'] = float(np.sqrt(np.sum(filtered_coeffs**2)))
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return rss
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def compute_band_residual(
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X: np.ndarray,
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Y: np.ndarray,
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W_nm: np.ndarray,
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coeffs: np.ndarray,
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R: float,
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bands: Dict[str, List[int]],
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keep_bands: List[str]
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) -> np.ndarray:
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"""
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Compute WFE residual keeping only specified bands.
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Args:
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keep_bands: List of band names to keep (e.g., ['msf'] for MSF-only)
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Returns:
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WFE array with only specified band content
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"""
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Xc = X - np.mean(X)
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Yc = Y - np.mean(Y)
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r = np.hypot(Xc / R, Yc / R)
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th = np.arctan2(Yc, Xc)
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# Build Zernike basis for modes to keep
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keep_indices = []
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for band in keep_bands:
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keep_indices.extend(bands.get(band, []))
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if not keep_indices:
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return np.zeros_like(W_nm)
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# Reconstruct only the kept bands
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W_band = np.zeros_like(W_nm)
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for j in keep_indices:
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if j - 1 < len(coeffs):
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Z_j = zernike_noll(j, r, th)
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W_band += coeffs[j-1] * Z_j
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return W_band
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def find_dominant_msf_modes(
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coeffs: np.ndarray,
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bands: Dict[str, List[int]],
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top_n: int = 5
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) -> List[Dict[str, Any]]:
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"""
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Find the dominant MSF modes by coefficient magnitude.
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Returns:
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List of dicts with 'j', 'label', 'coeff_nm', 'n', 'm'
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"""
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msf_indices = bands.get('msf', [])
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mode_data = []
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for j in msf_indices:
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if j - 1 < len(coeffs):
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n, m = noll_indices(j)
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mode_data.append({
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'j': j,
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'label': zernike_label(j),
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'coeff_nm': float(abs(coeffs[j-1])),
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'n': n,
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'm': m
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})
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# Sort by magnitude
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mode_data.sort(key=lambda x: x['coeff_nm'], reverse=True)
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return mode_data[:top_n]
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def estimate_mesh_resolution(X: np.ndarray, Y: np.ndarray) -> Dict[str, float]:
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"""
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Estimate mesh spatial resolution.
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Returns:
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Dict with 'node_count', 'diameter_mm', 'avg_spacing_mm',
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'max_resolvable_order', 'min_feature_mm'
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"""
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n_nodes = len(X)
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Xc = X - np.mean(X)
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Yc = Y - np.mean(Y)
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R = np.max(np.hypot(Xc, Yc))
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diameter = 2 * R
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# Approximate spacing from area
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area = np.pi * R**2
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avg_spacing = np.sqrt(area / n_nodes)
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# Nyquist: can resolve features > 2x spacing
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min_feature = 2 * avg_spacing
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# Max Zernike order
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max_order = int(diameter / min_feature)
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return {
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'node_count': n_nodes,
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'diameter_mm': float(diameter),
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'avg_spacing_mm': float(avg_spacing),
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'min_feature_mm': float(min_feature),
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'max_resolvable_order': max_order
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}
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# ============================================================================
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# Default Configuration
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# ============================================================================
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DEFAULT_CONFIG = {
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'n_modes': 100,
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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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'amp': 0.5, # Visual deformation scale
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'pancake': 3.0, # Z-axis range multiplier
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'plot_downsample': 15000,
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'colorscale': 'Turbo',
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'disp_unit': 'mm',
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'show_psd': True,
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'show_all_orientations': True,
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}
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@register_insight
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class MSFZernikeInsight(StudyInsight):
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"""
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Mid-Spatial Frequency Zernike analysis for telescope mirror optimization.
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Provides detailed breakdown of spatial frequency content:
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- LSF (Low): n ≤ 10, correctable by M2 hexapod
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- MSF (Mid): n = 11-50, support print-through
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- HSF (High): n > 50, near mesh resolution limit
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Generates:
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- Band decomposition table with 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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"""
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insight_type = "msf_zernike"
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name = "MSF Zernike Analysis"
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description = "Mid-spatial frequency analysis with band decomposition for support print-through"
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category = "optical"
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applicable_to = ["mirror", "optics", "wfe", "msf"]
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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._displacements: Optional[Dict] = None
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def can_generate(self) -> bool:
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"""Check if OP2 and geometry files exist."""
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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:
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self.geo_path = self._find_geometry_file(self.op2_path)
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return True
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except FileNotFoundError:
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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 for OP2."""
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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 displacement data."""
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if self._node_geo is not None:
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return
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_load_dependencies()
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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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op2 = _OP2()
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op2.read_op2(str(self.op2_path))
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if not op2.displacements:
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raise RuntimeError("No displacement data in OP2")
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self._displacements = {}
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for key, darr in op2.displacements.items():
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data = darr.data
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dmat = data[0] if data.ndim == 3 else (data if data.ndim == 2 else None)
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if dmat is None:
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continue
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ngt = darr.node_gridtype.astype(int)
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node_ids = ngt if ngt.ndim == 1 else ngt[:, 0]
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isubcase = getattr(darr, 'isubcase', None)
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label = str(isubcase) if isubcase else str(key)
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self._displacements[label] = {
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'node_ids': node_ids.astype(int),
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'disp': dmat.copy()
|
|
}
|
|
|
|
def _build_wfe_arrays(
|
|
self,
|
|
label: str,
|
|
disp_unit: str = 'mm'
|
|
) -> Tuple[np.ndarray, np.ndarray, np.ndarray, np.ndarray]:
|
|
"""Build X, Y, WFE arrays for a subcase."""
|
|
nm_per_unit = 1e6 if disp_unit == 'mm' else 1e9
|
|
|
|
data = self._displacements[label]
|
|
node_ids = data['node_ids']
|
|
dmat = data['disp']
|
|
|
|
X, Y, WFE = [], [], []
|
|
valid_nids = []
|
|
for nid, vec in zip(node_ids, dmat):
|
|
geo = self._node_geo.get(int(nid))
|
|
if geo is None:
|
|
continue
|
|
X.append(geo[0])
|
|
Y.append(geo[1])
|
|
wfe = vec[2] * 2.0 * nm_per_unit
|
|
WFE.append(wfe)
|
|
valid_nids.append(nid)
|
|
|
|
return (np.array(X), np.array(Y), np.array(WFE), np.array(valid_nids))
|
|
|
|
def _compute_relative_wfe(
|
|
self,
|
|
X1, Y1, WFE1, nids1,
|
|
X2, Y2, WFE2, nids2
|
|
) -> Tuple[np.ndarray, np.ndarray, np.ndarray]:
|
|
"""Compute WFE1 - WFE2 for common nodes."""
|
|
ref_map = {int(nid): (x, y, w) for nid, x, y, w in zip(nids2, X2, Y2, WFE2)}
|
|
|
|
X_rel, Y_rel, WFE_rel = [], [], []
|
|
for nid, x, y, w in zip(nids1, X1, Y1, WFE1):
|
|
nid = int(nid)
|
|
if nid in ref_map:
|
|
_, _, w_ref = ref_map[nid]
|
|
X_rel.append(x)
|
|
Y_rel.append(y)
|
|
WFE_rel.append(w - w_ref)
|
|
|
|
return np.array(X_rel), np.array(Y_rel), np.array(WFE_rel)
|
|
|
|
def _analyze_subcase(
|
|
self,
|
|
X: np.ndarray,
|
|
Y: np.ndarray,
|
|
W_nm: np.ndarray,
|
|
cfg: Dict
|
|
) -> Dict[str, Any]:
|
|
"""
|
|
Full MSF analysis for a single subcase.
|
|
|
|
Returns:
|
|
Dict with coefficients, bands, RSS, dominant modes, mesh info
|
|
"""
|
|
n_modes = cfg['n_modes']
|
|
lsf_max = cfg['lsf_max']
|
|
msf_max = cfg['msf_max']
|
|
|
|
# Fit Zernike
|
|
coeffs, R = compute_zernike_coeffs(X, Y, W_nm, n_modes)
|
|
|
|
# Band classification
|
|
bands = classify_modes_by_band(n_modes, lsf_max, msf_max)
|
|
|
|
# RSS per band
|
|
rss = compute_band_rss(coeffs, bands)
|
|
|
|
# Band percentages
|
|
total_rss = rss['total']
|
|
pct = {
|
|
'lsf': 100 * rss['lsf'] / total_rss if total_rss > 0 else 0,
|
|
'msf': 100 * rss['msf'] / total_rss if total_rss > 0 else 0,
|
|
'hsf': 100 * rss['hsf'] / total_rss if total_rss > 0 else 0,
|
|
}
|
|
|
|
# Dominant MSF modes
|
|
dominant_msf = find_dominant_msf_modes(coeffs, bands, top_n=5)
|
|
|
|
# MSF-only residual
|
|
W_msf = compute_band_residual(X, Y, W_nm, coeffs, R, bands, ['msf'])
|
|
|
|
# Mesh resolution
|
|
mesh_info = estimate_mesh_resolution(X, Y)
|
|
|
|
return {
|
|
'coefficients': coeffs,
|
|
'R': R,
|
|
'bands': bands,
|
|
'rss': rss,
|
|
'rss_pct': pct,
|
|
'dominant_msf': dominant_msf,
|
|
'W_msf': W_msf,
|
|
'mesh_info': mesh_info,
|
|
'n_modes': n_modes,
|
|
'lsf_max': lsf_max,
|
|
'msf_max': msf_max,
|
|
}
|
|
|
|
def _generate_html(
|
|
self,
|
|
analyses: Dict[str, Dict],
|
|
cfg: Dict,
|
|
X: np.ndarray,
|
|
Y: np.ndarray
|
|
) -> str:
|
|
"""Generate comprehensive HTML report."""
|
|
_load_dependencies()
|
|
|
|
n_modes = cfg['n_modes']
|
|
amp = cfg.get('amp', 0.5)
|
|
pancake = cfg.get('pancake', 3.0)
|
|
downsample = cfg.get('plot_downsample', 15000)
|
|
colorscale = cfg.get('colorscale', 'Turbo')
|
|
|
|
# Use first available analysis for visualization
|
|
primary_key = list(analyses.keys())[0]
|
|
primary = analyses[primary_key]
|
|
|
|
coeffs = primary['coefficients']
|
|
bands = primary['bands']
|
|
W_msf = primary['W_msf']
|
|
mesh_info = primary['mesh_info']
|
|
|
|
# Prepare band-colored bar chart data
|
|
bar_colors = []
|
|
for j in range(1, n_modes + 1):
|
|
if j in bands['lsf']:
|
|
bar_colors.append(BAND_COLORS['lsf'])
|
|
elif j in bands['msf']:
|
|
bar_colors.append(BAND_COLORS['msf'])
|
|
else:
|
|
bar_colors.append(BAND_COLORS['hsf'])
|
|
|
|
labels = [zernike_label(j) for j in range(1, n_modes + 1)]
|
|
coeff_abs = np.abs(coeffs)
|
|
|
|
# Downsample for 3D plot
|
|
n = len(X)
|
|
if n > downsample:
|
|
rng = np.random.default_rng(42)
|
|
sel = rng.choice(n, size=downsample, replace=False)
|
|
Xp, Yp, Wp = X[sel], Y[sel], W_msf[sel]
|
|
else:
|
|
Xp, Yp, Wp = X, Y, W_msf
|
|
|
|
res_amp = amp * Wp
|
|
max_amp = float(np.max(np.abs(res_amp))) if res_amp.size else 1.0
|
|
|
|
# Create figure with subplots
|
|
fig = _make_subplots(
|
|
rows=4, cols=1,
|
|
specs=[[{"type": "scene"}], [{"type": "table"}],
|
|
[{"type": "table"}], [{"type": "xy"}]],
|
|
row_heights=[0.40, 0.20, 0.15, 0.25],
|
|
vertical_spacing=0.03,
|
|
subplot_titles=[
|
|
"<b>MSF Content Only (n=11-50)</b>",
|
|
"<b>Band Decomposition (RSS)</b>",
|
|
"<b>Dominant MSF Modes</b>",
|
|
"<b>Coefficient Magnitude by Band</b>"
|
|
]
|
|
)
|
|
|
|
# 3D MSF surface
|
|
try:
|
|
tri = _Triangulation(Xp, Yp)
|
|
if tri.triangles is not None and len(tri.triangles) > 0:
|
|
i, j, k = tri.triangles.T
|
|
fig.add_trace(_go.Mesh3d(
|
|
x=Xp, y=Yp, z=res_amp,
|
|
i=i, j=j, k=k,
|
|
intensity=res_amp,
|
|
colorscale=colorscale,
|
|
opacity=1.0,
|
|
flatshading=False,
|
|
lighting=dict(ambient=0.4, diffuse=0.8, specular=0.3,
|
|
roughness=0.5, fresnel=0.2),
|
|
lightposition=dict(x=100, y=200, z=300),
|
|
showscale=True,
|
|
colorbar=dict(title=dict(text="MSF WFE (nm)", side="right"),
|
|
thickness=15, len=0.5, tickformat=".1f"),
|
|
hovertemplate="X: %{x:.1f}<br>Y: %{y:.1f}<br>MSF: %{z:.2f} nm<extra></extra>"
|
|
), row=1, col=1)
|
|
except Exception:
|
|
fig.add_trace(_go.Scatter3d(
|
|
x=Xp, y=Yp, z=res_amp,
|
|
mode='markers',
|
|
marker=dict(size=2, color=res_amp, colorscale=colorscale, showscale=True),
|
|
showlegend=False
|
|
), row=1, col=1)
|
|
|
|
# Configure 3D scene
|
|
fig.update_scenes(
|
|
camera=dict(eye=dict(x=1.2, y=1.2, z=0.8), up=dict(x=0, y=0, z=1)),
|
|
xaxis=dict(title="X (mm)", showgrid=True,
|
|
gridcolor='rgba(128,128,128,0.3)',
|
|
showbackground=True, backgroundcolor='rgba(240,240,240,0.9)'),
|
|
yaxis=dict(title="Y (mm)", showgrid=True,
|
|
gridcolor='rgba(128,128,128,0.3)',
|
|
showbackground=True, backgroundcolor='rgba(240,240,240,0.9)'),
|
|
zaxis=dict(title="MSF WFE (nm)",
|
|
range=[-max_amp * pancake, max_amp * pancake],
|
|
showgrid=True, gridcolor='rgba(128,128,128,0.3)',
|
|
showbackground=True, backgroundcolor='rgba(230,230,250,0.9)'),
|
|
aspectmode='manual',
|
|
aspectratio=dict(x=1, y=1, z=0.4)
|
|
)
|
|
|
|
# Band decomposition table
|
|
band_headers = ["<b>Band</b>", "<b>Order Range</b>", "<b>Feature Size</b>"]
|
|
band_values = [
|
|
["LSF (Low)", "MSF (Mid)", "HSF (High)", "<b>Total</b>", "<b>Filtered (J5+)</b>"],
|
|
[f"n ≤ {cfg['lsf_max']}", f"n = {cfg['lsf_max']+1}-{cfg['msf_max']}",
|
|
f"n > {cfg['msf_max']}", "All", "J5+ (excl. piston/tip/tilt/defocus)"],
|
|
[f"> {int(mesh_info['diameter_mm']/cfg['lsf_max'])} mm",
|
|
f"{int(mesh_info['diameter_mm']/cfg['msf_max'])}-{int(mesh_info['diameter_mm']/(cfg['lsf_max']+1))} mm",
|
|
f"< {int(mesh_info['diameter_mm']/cfg['msf_max'])} mm", "-", "M2 correctable removed"],
|
|
]
|
|
|
|
# Add columns for each analysis
|
|
for key, analysis in analyses.items():
|
|
band_headers.append(f"<b>{key} RSS (nm)</b>")
|
|
rss = analysis['rss']
|
|
pct = analysis['rss_pct']
|
|
# Calculate filtered percentage
|
|
filtered_pct = 100 * rss['filtered'] / rss['total'] if rss['total'] > 0 else 0
|
|
band_values.append([
|
|
f"{rss['lsf']:.2f} ({pct['lsf']:.1f}%)",
|
|
f"{rss['msf']:.2f} ({pct['msf']:.1f}%)",
|
|
f"{rss['hsf']:.2f} ({pct['hsf']:.1f}%)",
|
|
f"<b>{rss['total']:.2f}</b>",
|
|
f"<b>{rss['filtered']:.2f}</b> ({filtered_pct:.1f}%)",
|
|
])
|
|
|
|
fig.add_trace(_go.Table(
|
|
header=dict(values=band_headers,
|
|
align="left", fill_color='#1f2937', font=dict(color='white')),
|
|
cells=dict(values=band_values,
|
|
align="left", fill_color='#374151', font=dict(color='white'))
|
|
), row=2, col=1)
|
|
|
|
# Dominant MSF modes table
|
|
dom_modes = primary['dominant_msf']
|
|
if dom_modes:
|
|
fig.add_trace(_go.Table(
|
|
header=dict(values=["<b>Rank</b>", "<b>Mode</b>", "<b>|Coeff| (nm)</b>", "<b>Order n</b>"],
|
|
align="left", fill_color='#1f2937', font=dict(color='white')),
|
|
cells=dict(values=[
|
|
[f"#{i+1}" for i in range(len(dom_modes))],
|
|
[m['label'] for m in dom_modes],
|
|
[f"{m['coeff_nm']:.3f}" for m in dom_modes],
|
|
[str(m['n']) for m in dom_modes],
|
|
], align="left", fill_color='#374151', font=dict(color='white'))
|
|
), row=3, col=1)
|
|
|
|
# Bar chart with band colors
|
|
fig.add_trace(
|
|
_go.Bar(
|
|
x=coeff_abs.tolist(), y=labels,
|
|
orientation='h',
|
|
marker_color=bar_colors,
|
|
hovertemplate="%{y}<br>|Coeff| = %{x:.3f} nm<extra></extra>",
|
|
showlegend=False
|
|
),
|
|
row=4, col=1
|
|
)
|
|
|
|
# Add legend for bands
|
|
for band_name, color in BAND_COLORS.items():
|
|
fig.add_trace(_go.Scatter(
|
|
x=[None], y=[None],
|
|
mode='markers',
|
|
marker=dict(size=10, color=color),
|
|
name=band_name.upper(),
|
|
showlegend=True
|
|
))
|
|
|
|
# Layout
|
|
fig.update_layout(
|
|
width=1400, height=1600,
|
|
margin=dict(t=60, b=20, l=20, r=20),
|
|
paper_bgcolor='#111827', plot_bgcolor='#1f2937',
|
|
font=dict(color='white'),
|
|
title=dict(
|
|
text=f"<b>Atomizer MSF Analysis</b> | {n_modes} modes | Mesh: {mesh_info['node_count']} nodes, {mesh_info['avg_spacing_mm']:.1f}mm spacing",
|
|
x=0.5, font=dict(size=16)
|
|
),
|
|
legend=dict(
|
|
orientation="h",
|
|
yanchor="bottom",
|
|
y=1.02,
|
|
xanchor="right",
|
|
x=1
|
|
)
|
|
)
|
|
|
|
return fig.to_html(include_plotlyjs='cdn', full_html=True)
|
|
|
|
def _generate(self, config: InsightConfig) -> InsightResult:
|
|
"""Generate MSF analysis."""
|
|
self._load_data()
|
|
|
|
cfg = {**DEFAULT_CONFIG, **config.extra}
|
|
disp_unit = cfg['disp_unit']
|
|
|
|
# Map subcases
|
|
disps = self._displacements
|
|
if '1' in disps and '2' in disps:
|
|
sc_map = {'90°': '1', '20°': '2', '40°': '3', '60°': '4'}
|
|
elif '90' in disps and '20' in disps:
|
|
sc_map = {'90°': '90', '20°': '20', '40°': '40', '60°': '60'}
|
|
else:
|
|
available = sorted(disps.keys(), key=lambda x: int(x) if x.isdigit() else 0)
|
|
if len(available) >= 4:
|
|
sc_map = {'90°': available[0], '20°': available[1],
|
|
'40°': available[2], '60°': available[3]}
|
|
else:
|
|
return InsightResult(success=False,
|
|
error=f"Need 4 subcases, found: {available}")
|
|
|
|
# Validate subcases
|
|
for angle, label in sc_map.items():
|
|
if label not in disps:
|
|
return InsightResult(success=False,
|
|
error=f"Subcase '{label}' (angle {angle}) not found")
|
|
|
|
# Load reference data
|
|
X_20, Y_20, WFE_20, nids_20 = self._build_wfe_arrays(sc_map['20°'], disp_unit)
|
|
X_90, Y_90, WFE_90, nids_90 = self._build_wfe_arrays(sc_map['90°'], disp_unit)
|
|
|
|
analyses = {}
|
|
|
|
# Analyze each key orientation
|
|
for angle in ['40°', '60°']:
|
|
label = sc_map[angle]
|
|
X, Y, WFE, nids = self._build_wfe_arrays(label, disp_unit)
|
|
|
|
# Absolute
|
|
analyses[f"{angle} Abs"] = self._analyze_subcase(X, Y, WFE, cfg)
|
|
|
|
# Relative to 20°
|
|
X_rel, Y_rel, WFE_rel = self._compute_relative_wfe(
|
|
X, Y, WFE, nids, X_20, Y_20, WFE_20, nids_20)
|
|
analyses[f"{angle} vs 20°"] = self._analyze_subcase(X_rel, Y_rel, WFE_rel, cfg)
|
|
|
|
# Relative to 90° (manufacturing)
|
|
X_rel90, Y_rel90, WFE_rel90 = self._compute_relative_wfe(
|
|
X, Y, WFE, nids, X_90, Y_90, WFE_90, nids_90)
|
|
analyses[f"{angle} vs 90°"] = self._analyze_subcase(X_rel90, Y_rel90, WFE_rel90, cfg)
|
|
|
|
# Also analyze 90° absolute (manufacturing baseline)
|
|
analyses["90° Mfg"] = self._analyze_subcase(X_90, Y_90, WFE_90, cfg)
|
|
|
|
# Generate HTML using 40° vs 20° as primary visualization
|
|
primary_analysis = analyses["40° vs 20°"]
|
|
X_40, Y_40, _, _ = self._build_wfe_arrays(sc_map['40°'], disp_unit)
|
|
X_rel40, Y_rel40, _ = self._compute_relative_wfe(
|
|
X_40, Y_40, analyses["40° Abs"]['W_msf'], np.arange(len(X_40)),
|
|
X_20, Y_20, WFE_20, nids_20)
|
|
|
|
html = self._generate_html(analyses, cfg, X_40, Y_40)
|
|
|
|
timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
|
|
output_dir = config.output_dir or self.insights_path
|
|
output_dir.mkdir(parents=True, exist_ok=True)
|
|
|
|
html_path = output_dir / f"msf_zernike_{timestamp}.html"
|
|
html_path.write_text(html, encoding='utf-8')
|
|
|
|
# Build summary
|
|
summary = {
|
|
'n_modes': cfg['n_modes'],
|
|
'lsf_max_order': cfg['lsf_max'],
|
|
'msf_max_order': cfg['msf_max'],
|
|
'mesh_nodes': primary_analysis['mesh_info']['node_count'],
|
|
'mesh_spacing_mm': primary_analysis['mesh_info']['avg_spacing_mm'],
|
|
'max_resolvable_order': primary_analysis['mesh_info']['max_resolvable_order'],
|
|
}
|
|
|
|
# Add key metrics for each analysis
|
|
for key, analysis in analyses.items():
|
|
safe_key = key.replace('°', 'deg').replace(' ', '_')
|
|
summary[f'{safe_key}_lsf_rss'] = analysis['rss']['lsf']
|
|
summary[f'{safe_key}_msf_rss'] = analysis['rss']['msf']
|
|
summary[f'{safe_key}_hsf_rss'] = analysis['rss']['hsf']
|
|
summary[f'{safe_key}_total_rss'] = analysis['rss']['total']
|
|
summary[f'{safe_key}_filtered_rss'] = analysis['rss']['filtered']
|
|
summary[f'{safe_key}_msf_pct'] = analysis['rss_pct']['msf']
|
|
|
|
return InsightResult(
|
|
success=True,
|
|
html_path=html_path,
|
|
summary=summary
|
|
)
|