1013 lines
39 KiB
Python
1013 lines
39 KiB
Python
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# -*- coding: utf-8 -*-
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#!/usr/bin/env python3
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"""
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Zernike per Subcase (relative to 20 deg) -> HTML + CSVs (HEADLESS) + dual-reference RMS (vs 20 deg and vs 90 deg)
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- Auto-targets OP2:
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* default: m1_optimization_baseline_Isogrid_fem1_i_fem1_sim1_test2-solution_1.op2 in CWD
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* or: pass --op2 FULLPATH
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- Auto-loads matching .dat/.bdf (same-basename preferred; else first found)
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- Extracts subcases by label/ID (expects 90,20,40,60)
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- Uses 20 deg as the reference for plots/pages and the *_Rel20 fields (vs 20 deg)
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- Also computes RMS relative to 90 deg and exports them:
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* EXP: RMS_<ang>_Global_Rel20, RMS_<ang>_Filtered_Rel20, RMS_<ang>_Global_Rel90, RMS_<ang>_Filtered_Rel90
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* LOG: Rel_GlobalRMS_nm_<ang>_vs20, Rel_FilteredRMS_nm_<ang>_vs20, Rel_GlobalRMS_nm_<ang>_vs90, Rel_FilteredRMS_nm_<ang>_vs90
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- Outputs:
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* Combined nodal CSV (abs 20 deg + relative others vs 20 deg)
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* One HTML for 20 deg (absolute) and one HTML per other subcase (relative to 20 deg),
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with each relative page's RMS table also showing the absolute RMS for that angle
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* One HTML for 90 deg (absolute) with manufacturing metrics (numeric only)
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* Zernike coeff CSVs (|coeff| per page; with labels and optional bar chart)
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* RMS summary CSV (ABS for all; REL20 for non-reference)
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* EXP file with 90/20/40/60 ABS; REL20 (vs 20 deg); and REL90 (vs 90 deg)
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* Appends a row to Results/RMS_log.csv (ABS & REL vs 20 deg & REL vs 90 deg)
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- HTML auto-open controlled by OPEN_HTML flag
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Requires: numpy pandas plotly pyNastran matplotlib
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"""
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import os, webbrowser, re, argparse, sys
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import numpy as np
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import pandas as pd
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from math import factorial
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from numpy.linalg import LinAlgError
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from datetime import datetime
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# Best-effort UTF-8 console (ignored if not supported)
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try:
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sys.stdout.reconfigure(encoding="utf-8")
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sys.stderr.reconfigure(encoding="utf-8")
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except Exception:
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pass
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# Try messagebox for friendlier fatal popup if available; otherwise print-only
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try:
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from tkinter import messagebox # optional
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except Exception:
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messagebox = None
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# Plotly / mesh
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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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# Nastran IO
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from pyNastran.op2.op2 import OP2
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from pyNastran.bdf.bdf import BDF
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# ============ MONKEY PATCH FOR UNKNOWN NASTRAN VERSIONS ============
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import pyNastran.op2.op2_interface.version as pynastran_version
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_original_parse = pynastran_version.parse_nastran_version
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def patched_parse_nastran_version(data, version, encoding, log):
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try:
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return _original_parse(data, version, encoding, log)
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except RuntimeError as e:
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if 'unknown version' in str(e):
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version_str = str(e).split('=')[1].strip("')")
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log.warning(f'Unknown Nastran version {version_str}, attempting to parse anyway')
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return 'msc', version_str # Assume MSC Nastran format
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raise
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pynastran_version.parse_nastran_version = patched_parse_nastran_version
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print("[INFO] Applied pyNastran version compatibility patch", flush=True)
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# ===================================================================
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# ---------------- Config ----------------
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N_MODES = 50
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AMP = 2.0 # visual scale for residual plot
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PANCAKE = 10.0 # flattens Z range for viewing
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PLOT_DOWNSAMPLE = 10000
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FILTER_LOW_ORDERS = 4 # piston, tip, tilt, defocus
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SHOW_ZERNIKE_BAR = True # adds a horizontal bar chart of |coeff| with labels
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REQUIRED_SUBCASES = [90, 20, 40, 60]
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# Labels
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POLISH_LABEL = "90" # polishing orientation (horizontal)
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POLISH_TITLE = "90 deg" # for plot/table titles
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REF_LABEL = "20" # reference subcase label as string
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REF_TITLE = "20 deg" # for plot/table titles
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# Displacement unit in OP2 -> nm scale for WFE = 2*Disp_Z
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DISP_SRC_UNIT = "mm" # "mm" or "m"
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NM_PER_UNIT = 1e6 if DISP_SRC_UNIT == "mm" else 1e9 # 1 mm = 1e6 nm, 1 m = 1e9 nm
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# Name of fixed OP2 (used when --op2 is not supplied)
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OP2_FIXED_NAME = "assy_m1_assyfem1_sim1-solution_1.op2"
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# Auto-open HTML reports?
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OPEN_HTML = False
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# ----------------------------------------
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# --------- Zernike utilities ----------
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def noll_indices(j: int):
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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 zernike_noll(j, r, th):
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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 compute_zernike_coeffs_chunked(X, Y, vals, n_modes, chunk_size=100000):
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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) # Z^T Z
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h = np.zeros((m,), dtype=np.float64) # Z^T v)
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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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# ------------- IO helpers -------------
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def find_dat_or_bdf(op2_path: str):
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folder = os.path.dirname(op2_path)
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base = os.path.splitext(os.path.basename(op2_path))[0]
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for ext in (".dat", ".bdf"):
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cand = os.path.join(folder, base + ext)
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if os.path.exists(cand):
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return cand
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for name in os.listdir(folder):
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if name.lower().endswith((".dat", ".bdf")):
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return os.path.join(folder, name)
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raise FileNotFoundError("No .dat or .bdf found in folder; geometry required.")
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def pick_files_cli():
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ap = argparse.ArgumentParser()
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ap.add_argument("--op2", default=None, help="Full path to OP2 (optional)")
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ap.add_argument("--simdir", default=None, help="Directory to search (optional)")
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args = ap.parse_args()
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if args.op2:
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op2_path = args.op2
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else:
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base_dir = args.simdir or os.getcwd()
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op2_path = os.path.join(base_dir, OP2_FIXED_NAME)
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if not os.path.exists(op2_path):
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raise FileNotFoundError(f"OP2 not found: {op2_path}")
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dat_path = find_dat_or_bdf(op2_path)
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return op2_path, dat_path
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def read_geometry(dat_path):
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bdf = BDF()
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bdf.read_bdf(dat_path)
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node_geo = {int(nid): node.get_position() for nid, node in bdf.nodes.items()}
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return node_geo
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def _extract_first_int(text: str):
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if not isinstance(text, str):
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return None
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m = re.search(r'-?\d+', text)
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return int(m.group(0)) if m else None
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def read_displacements_by_subcase(op2_path):
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"""
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Return dict with keys {'90','20','40','60'}.
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Priority: subtitle numeric match, else isubcase numeric match.
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If exact set not found:
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- If 4+ numeric subcases exist, remap the 4 smallest to 90,20,40,60 (logged)
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- Else: raise with a detailed dump.
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Also writes an index CSV next to the OP2 for inspection.
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"""
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want = REQUIRED_SUBCASES[:] # [90,20,40,60]
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out_exact = {}
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debug_rows = []
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op2 = OP2()
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op2.read_op2(op2_path)
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if not op2.displacements:
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raise RuntimeError("No displacement subcases found in OP2.")
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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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subtitle_raw = getattr(darr, 'subtitle', None)
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isub_raw = getattr(darr, 'isubcase', None)
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isub_int = int(isub_raw) if isinstance(isub_raw, int) else None
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sub_from_text = _extract_first_int(subtitle_raw) if isinstance(subtitle_raw, str) else None
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debug_rows.append({
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"table_key": str(key),
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"isubcase": isub_int,
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"subtitle": str(subtitle_raw) if subtitle_raw is not None else "",
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"subtitle_int": sub_from_text if sub_from_text is not None else "",
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"nnodes": int(len(node_ids)),
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"_node_ids": node_ids.astype(int),
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"_dmat": dmat.copy(),
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})
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if isinstance(sub_from_text, int) and sub_from_text in want:
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out_exact[str(sub_from_text)] = {"node_ids": node_ids.astype(int), "disp": dmat.copy()}
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continue
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if isinstance(isub_int, int) and isub_int in want:
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out_exact[str(isub_int)] = {"node_ids": node_ids.astype(int), "disp": dmat.copy()}
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try:
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folder = os.path.dirname(op2_path)
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base = os.path.splitext(os.path.basename(op2_path))[0]
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idx_path = os.path.join(folder, f"{base}_op2_displ_index.csv")
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pd.DataFrame([{k: v for k, v in r.items() if not k.startswith('_')} for r in debug_rows])\
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.to_csv(idx_path, index=False)
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print(f"[DEBUG] Wrote displacement index: {idx_path}", flush=True)
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except Exception as e:
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print(f"[WARN] Could not write displacement index CSV: {e}", flush=True)
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if all(str(x) in out_exact for x in want):
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print("[INFO] Found exact subcases 90, 20, 40 and 60 via labels.", flush=True)
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return out_exact
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numerics = []
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for r in debug_rows:
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n = r.get("subtitle_int")
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if not isinstance(n, int):
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n = r.get("isubcase") if isinstance(r.get("isubcase"), int) else None
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if isinstance(n, int):
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numerics.append((n, r))
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numerics = sorted(numerics, key=lambda t: t[0])
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if len(numerics) >= 4:
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chosen = numerics[:4]
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mapping = dict(zip([c[0] for c in chosen], want))
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out = {}
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for n, r in chosen:
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lbl = str(mapping[n])
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out[lbl] = {"node_ids": r["_node_ids"], "disp": r["_dmat"]}
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print("[WARN] Exact 90/20/40/60 not found; remapped smallest numeric subcases "
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f"{[c[0] for c in chosen]} -> {want}", flush=True)
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return out
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print("\n[DEBUG] Displacement entries found (dump):", flush=True)
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if debug_rows:
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header = f'{"table_key":<18} {"isubcase":>7} {"subtitle":<40} {"subtitle_int":>12} {"nnodes":>8}'
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print(header); print("-"*len(header))
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for r in debug_rows:
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print(f'{r["table_key"]:<18} {str(r["isubcase"]):>7} {r["subtitle"]:<40} '
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f'{str(r["subtitle_int"]):>12} {r["nnodes"]:>8}', flush=True)
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missing = [x for x in want if str(x) not in out_exact]
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raise RuntimeError(f"Required subcases missing: {missing}. "
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"Ensure SUBTITLEs (or isubcase IDs) are exactly 90, 20, 40 and 60.")
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# --------------------------------------
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# ------------- Core logic -------------
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def build_dataframe_for_subcase(label, node_ids, dmat, node_geo):
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rows = []
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for nid, vec in zip(node_ids, dmat):
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x0, y0, z0 = node_geo.get(int(nid), (np.nan,)*3)
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rows.append({
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"Subcase": label,
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"NodeLabel": int(nid),
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"X0": x0, "Y0": y0, "Z0": z0,
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"Disp_Z": float(vec[2]),
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"DispUnit": DISP_SRC_UNIT
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})
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df = pd.DataFrame(rows)
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if df[["X0", "Y0"]].isna().any().any():
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|
|
raise RuntimeError("Geometry missing (X0,Y0) for some nodes. Ensure .dat/.bdf matches the OP2.")
|
||
|
|
return df
|
||
|
|
|
||
|
|
def compute_relative_dz(df_abs_ref, df_abs_ang):
|
||
|
|
a = df_abs_ref.set_index("NodeLabel")
|
||
|
|
b = df_abs_ang.set_index("NodeLabel")
|
||
|
|
common = a.index.intersection(b.index)
|
||
|
|
|
||
|
|
b2 = b.loc[common].copy()
|
||
|
|
b2["Rel_Disp_Z"] = b2["Disp_Z"] - a.loc[common, "Disp_Z"]
|
||
|
|
b2.reset_index(inplace=True)
|
||
|
|
out = df_abs_ang.merge(b2[["NodeLabel", "Rel_Disp_Z"]], on="NodeLabel", how="inner")
|
||
|
|
out["Rel_WFE_nm"] = 2.0 * out["Rel_Disp_Z"] * NM_PER_UNIT
|
||
|
|
return out
|
||
|
|
|
||
|
|
def compute_rms_pair_only(X, Y, W_nm):
|
||
|
|
coeffs, Rmax = compute_zernike_coeffs_chunked(X, Y, W_nm, N_MODES)
|
||
|
|
Xc = X - np.mean(X); Yc = Y - np.mean(Y)
|
||
|
|
r = np.hypot(Xc/Rmax, Yc/Rmax); th = np.arctan2(Yc, Xc)
|
||
|
|
Z = np.column_stack([zernike_noll(j, r, th) for j in range(1, N_MODES+1)])
|
||
|
|
W_res_filt = W_nm - Z[:, :FILTER_LOW_ORDERS].dot(coeffs[:FILTER_LOW_ORDERS])
|
||
|
|
global_rms = float(np.sqrt((W_nm ** 2).mean()))
|
||
|
|
filtered_rms = float(np.sqrt((W_res_filt ** 2).mean()))
|
||
|
|
return global_rms, filtered_rms
|
||
|
|
|
||
|
|
def analyze_manufacturing_metrics(X, Y, W_nm):
|
||
|
|
"""
|
||
|
|
Manufacturing-related metrics at any angle. Returns individual mode magnitudes
|
||
|
|
and filtered RMS with different filter levels. (Numeric only.)
|
||
|
|
"""
|
||
|
|
coeffs, Rmax = compute_zernike_coeffs_chunked(X, Y, W_nm, N_MODES)
|
||
|
|
Xc = X - np.mean(X); Yc = Y - np.mean(Y)
|
||
|
|
r = np.hypot(Xc/Rmax, Yc/Rmax); th = np.arctan2(Yc, Xc)
|
||
|
|
Z = np.column_stack([zernike_noll(j, r, th) for j in range(1, N_MODES+1)])
|
||
|
|
|
||
|
|
W_res_filt3 = W_nm - Z[:, :3].dot(coeffs[:3]) # includes defocus
|
||
|
|
rms_filt3 = float(np.sqrt((W_res_filt3 ** 2).mean()))
|
||
|
|
|
||
|
|
W_res_filt4 = W_nm - Z[:, :4].dot(coeffs[:4]) # operational metric
|
||
|
|
rms_filt4 = float(np.sqrt((W_res_filt4 ** 2).mean()))
|
||
|
|
|
||
|
|
astig_rms = float(np.sqrt(coeffs[4]**2 + coeffs[5]**2)) # J5+J6
|
||
|
|
coma_rms = float(np.sqrt(coeffs[6]**2 + coeffs[7]**2)) # J7+J8
|
||
|
|
trefoil_rms = float(np.sqrt(coeffs[8]**2 + coeffs[9]**2)) # J9+J10
|
||
|
|
|
||
|
|
return {
|
||
|
|
"rms_filter_j1to3": rms_filt3,
|
||
|
|
"rms_filter_j1to4": rms_filt4,
|
||
|
|
"defocus_nm": float(abs(coeffs[3])),
|
||
|
|
"astigmatism_rms": astig_rms,
|
||
|
|
"coma_rms": coma_rms,
|
||
|
|
"trefoil_rms": trefoil_rms,
|
||
|
|
"spherical_nm": float(abs(coeffs[10]))
|
||
|
|
}
|
||
|
|
|
||
|
|
|
||
|
|
def zernike_common_name(n: int, m: int) -> str:
|
||
|
|
names = {
|
||
|
|
(0, 0): "Piston",
|
||
|
|
(1, -1): "Tilt X",
|
||
|
|
(1, 1): "Tilt Y",
|
||
|
|
(2, 0): "Defocus",
|
||
|
|
(2, -2): "Astig 45 deg",
|
||
|
|
(2, 2): "Astig 0 deg",
|
||
|
|
(3, -1): "Coma X",
|
||
|
|
(3, 1): "Coma Y",
|
||
|
|
(3, -3): "Trefoil X",
|
||
|
|
(3, 3): "Trefoil Y",
|
||
|
|
(4, 0): "Primary Spherical",
|
||
|
|
(4, -2): "Secondary Astig X",
|
||
|
|
(4, 2): "Secondary Astig Y",
|
||
|
|
(4, -4): "Quadrafoil X",
|
||
|
|
(4, 4): "Quadrafoil Y",
|
||
|
|
(5, -1): "Secondary Coma X",
|
||
|
|
(5, 1): "Secondary Coma Y",
|
||
|
|
(5, -3): "Secondary Trefoil X",
|
||
|
|
(5, 3): "Secondary Trefoil Y",
|
||
|
|
(5, -5): "Pentafoil X",
|
||
|
|
(5, 5): "Pentafoil Y",
|
||
|
|
(6, 0): "Secondary Spherical",
|
||
|
|
}
|
||
|
|
return names.get((n, m), f"Z(n={n}, m={m})")
|
||
|
|
|
||
|
|
def zernike_label_for_j(j: int) -> str:
|
||
|
|
n, m = noll_indices(j)
|
||
|
|
return f"J{j:02d} - {zernike_common_name(n, m)} (n={n}, m={m})"
|
||
|
|
|
||
|
|
def zernike_report(label, X, Y, W_nm, folder, base, is_relative: bool,
|
||
|
|
abs_pair=None, ref_title="20 deg", mfg_data=None, mfg_data_vs_20=None):
|
||
|
|
coeffs, Rmax = compute_zernike_coeffs_chunked(X, Y, W_nm, N_MODES)
|
||
|
|
Xc = X - np.mean(X); Yc = Y - np.mean(Y)
|
||
|
|
r = np.hypot(Xc/Rmax, Yc/Rmax); th = np.arctan2(Yc, Xc)
|
||
|
|
Z = np.column_stack([zernike_noll(j, r, th) for j in range(1, N_MODES+1)])
|
||
|
|
W_res_filt = W_nm - Z[:, :FILTER_LOW_ORDERS].dot(coeffs[:FILTER_LOW_ORDERS])
|
||
|
|
|
||
|
|
global_rms = float(np.sqrt((W_nm ** 2).mean()))
|
||
|
|
filtered_rms = float(np.sqrt((W_res_filt ** 2).mean()))
|
||
|
|
|
||
|
|
# Downsample for display only
|
||
|
|
n = len(X)
|
||
|
|
if n > PLOT_DOWNSAMPLE:
|
||
|
|
rng = np.random.default_rng(42)
|
||
|
|
sel = rng.choice(n, size=PLOT_DOWNSAMPLE, replace=False)
|
||
|
|
Xp, Yp, Wp = X[sel], Y[sel], W_res_filt[sel]
|
||
|
|
else:
|
||
|
|
Xp, Yp, Wp = X, Y, W_res_filt
|
||
|
|
|
||
|
|
res_amp = AMP * Wp
|
||
|
|
max_amp = float(np.max(np.abs(res_amp))) if res_amp.size else 1.0
|
||
|
|
|
||
|
|
# Triangulate the downsampled set
|
||
|
|
mesh_traces = []
|
||
|
|
try:
|
||
|
|
tri = Triangulation(Xp, Yp)
|
||
|
|
if tri.triangles is not None and len(tri.triangles) > 0:
|
||
|
|
i, j, k = tri.triangles.T
|
||
|
|
mesh_traces.append(go.Mesh3d(
|
||
|
|
x=Xp, y=Yp, z=res_amp,
|
||
|
|
i=i, j=j, k=k,
|
||
|
|
intensity=res_amp,
|
||
|
|
opacity=0.85, showscale=False
|
||
|
|
))
|
||
|
|
except Exception:
|
||
|
|
pass
|
||
|
|
|
||
|
|
mesh_traces.append(go.Scatter3d(
|
||
|
|
x=Xp, y=Yp, z=res_amp,
|
||
|
|
mode='markers',
|
||
|
|
marker=dict(size=2),
|
||
|
|
showlegend=False
|
||
|
|
))
|
||
|
|
|
||
|
|
labels = [zernike_label_for_j(j) for j in range(1, N_MODES+1)]
|
||
|
|
coeff_abs = np.abs(coeffs)
|
||
|
|
|
||
|
|
title_rel = f" (relative to {ref_title})" if is_relative else " (absolute)"
|
||
|
|
|
||
|
|
# Layout
|
||
|
|
if mfg_data is not None and mfg_data_vs_20 is not None:
|
||
|
|
if SHOW_ZERNIKE_BAR:
|
||
|
|
fig = make_subplots(
|
||
|
|
rows=5, cols=1,
|
||
|
|
specs=[[{"type":"scene"}],
|
||
|
|
[{"type":"table"}],
|
||
|
|
[{"type":"table"}],
|
||
|
|
[{"type":"table"}],
|
||
|
|
[{"type":"xy"}]],
|
||
|
|
row_heights=[0.40, 0.12, 0.12, 0.18, 0.18],
|
||
|
|
vertical_spacing=0.025,
|
||
|
|
subplot_titles=[
|
||
|
|
f"<b>Surface Residual & RMS of WFE - Subcase {label}{title_rel}</b>",
|
||
|
|
"<b>RMS Metrics (Absolute 90 deg)</b>",
|
||
|
|
"<b>Mode Magnitudes at 90 deg</b>",
|
||
|
|
"<b>Pre-Correction (90 deg - 20 deg)</b>",
|
||
|
|
"<b>|Zernike Coefficients| (nm)</b>"
|
||
|
|
]
|
||
|
|
)
|
||
|
|
else:
|
||
|
|
fig = make_subplots(
|
||
|
|
rows=5, cols=1,
|
||
|
|
specs=[[{"type":"scene"}],
|
||
|
|
[{"type":"table"}],
|
||
|
|
[{"type":"table"}],
|
||
|
|
[{"type":"table"}],
|
||
|
|
[{"type":"table"}]],
|
||
|
|
row_heights=[0.40, 0.12, 0.12, 0.18, 0.18],
|
||
|
|
vertical_spacing=0.025,
|
||
|
|
subplot_titles=[
|
||
|
|
f"<b>Surface Residual & RMS of WFE - Subcase {label}{title_rel}</b>",
|
||
|
|
"<b>RMS Metrics (Absolute 90 deg)</b>",
|
||
|
|
"<b>Mode Magnitudes at 90 deg</b>",
|
||
|
|
"<b>Pre-Correction (90 deg - 20 deg)</b>",
|
||
|
|
f"<b>Zernike Coefficients ({N_MODES} modes)</b>"
|
||
|
|
]
|
||
|
|
)
|
||
|
|
elif SHOW_ZERNIKE_BAR:
|
||
|
|
fig = make_subplots(
|
||
|
|
rows=4, cols=1,
|
||
|
|
specs=[[{"type":"scene"}],
|
||
|
|
[{"type":"table"}],
|
||
|
|
[{"type":"table"}],
|
||
|
|
[{"type":"xy"}]],
|
||
|
|
row_heights=[0.52, 0.12, 0.24, 0.12],
|
||
|
|
vertical_spacing=0.035,
|
||
|
|
subplot_titles=[
|
||
|
|
f"<b>Surface Residual & RMS of WFE - Subcase {label}{title_rel}</b>",
|
||
|
|
"<b>RMS Metrics</b>",
|
||
|
|
f"<b>Zernike Coefficients ({N_MODES} modes)</b>",
|
||
|
|
"<b>|Zernike Coefficients| (nm)</b>"
|
||
|
|
]
|
||
|
|
)
|
||
|
|
else:
|
||
|
|
fig = make_subplots(
|
||
|
|
rows=3, cols=1,
|
||
|
|
specs=[[{"type":"scene"}],
|
||
|
|
[{"type":"table"}],
|
||
|
|
[{"type":"table"}]],
|
||
|
|
row_heights=[0.60, 0.14, 0.26],
|
||
|
|
vertical_spacing=0.03,
|
||
|
|
subplot_titles=[
|
||
|
|
f"<b>Surface Residual & RMS of WFE - Subcase {label}{title_rel}</b>",
|
||
|
|
"<b>RMS Metrics</b>",
|
||
|
|
f"<b>Zernike Coefficients ({N_MODES} modes)</b>"
|
||
|
|
]
|
||
|
|
)
|
||
|
|
|
||
|
|
for tr in mesh_traces:
|
||
|
|
fig.add_trace(tr, row=1, col=1)
|
||
|
|
|
||
|
|
scene = fig.layout.scene
|
||
|
|
scene.camera = dict(eye=dict(x=0.5, y=0.5, z=0.5))
|
||
|
|
scene.zaxis = dict(range=[-max_amp * PANCAKE, max_amp * PANCAKE])
|
||
|
|
|
||
|
|
# RMS table
|
||
|
|
if is_relative and abs_pair is not None:
|
||
|
|
abs_global, abs_filtered = abs_pair
|
||
|
|
fig.add_trace(go.Table(
|
||
|
|
header=dict(values=["<b>Metric</b>", "<b>Relative (nm)</b>", "<b>Absolute (nm)</b>"], align="left"),
|
||
|
|
cells=dict(values=[
|
||
|
|
["Global RMS","Filtered RMS (modes 1-4 removed)"],
|
||
|
|
[f"{global_rms:.2f}", f"{filtered_rms:.2f}"],
|
||
|
|
[f"{abs_global:.2f}", f"{abs_filtered:.2f}"],
|
||
|
|
], align="left")
|
||
|
|
), row=2, col=1)
|
||
|
|
elif mfg_data is not None and mfg_data_vs_20 is not None:
|
||
|
|
# 90 deg Absolute RMS (numeric only)
|
||
|
|
fig.add_trace(go.Table(
|
||
|
|
header=dict(values=["<b>Metric</b>","<b>Value (nm)</b>"], align="left"),
|
||
|
|
cells=dict(values=[
|
||
|
|
["Global RMS", "Filtered RMS (J1-J4)"],
|
||
|
|
[f"{global_rms:.2f}", f"{filtered_rms:.2f}"]
|
||
|
|
], align="left")
|
||
|
|
), row=2, col=1)
|
||
|
|
|
||
|
|
# Mode magnitudes at 90 deg (absolute) — numeric only
|
||
|
|
fig.add_trace(go.Table(
|
||
|
|
header=dict(values=["<b>Mode</b>","<b>Value (nm)</b>"], align="left"),
|
||
|
|
cells=dict(values=[
|
||
|
|
["Filtered RMS (J1-J3, with defocus)",
|
||
|
|
"Astigmatism (J5+J6)",
|
||
|
|
"Coma (J7+J8)",
|
||
|
|
"Trefoil (J9+J10)",
|
||
|
|
"Spherical (J11)"],
|
||
|
|
[f"{mfg_data['rms_filter_j1to3']:.2f}",
|
||
|
|
f"{mfg_data['astigmatism_rms']:.2f}",
|
||
|
|
f"{mfg_data['coma_rms']:.2f}",
|
||
|
|
f"{mfg_data['trefoil_rms']:.2f}",
|
||
|
|
f"{mfg_data['spherical_nm']:.2f}"]
|
||
|
|
], align="left")
|
||
|
|
), row=3, col=1)
|
||
|
|
|
||
|
|
# Pre-correction 90 deg - 20 deg — numeric only
|
||
|
|
fig.add_trace(go.Table(
|
||
|
|
header=dict(values=["<b>Mode</b>", "<b>Correction (nm)</b>"], align="left"),
|
||
|
|
cells=dict(values=[
|
||
|
|
["Total RMS (J1-J3 filter)",
|
||
|
|
"Defocus (J4)",
|
||
|
|
"Astigmatism (J5+J6)",
|
||
|
|
"Coma (J7+J8)"],
|
||
|
|
[f"{mfg_data_vs_20['rms_filter_j1to3']:.2f}",
|
||
|
|
f"{mfg_data_vs_20['defocus_nm']:.2f}",
|
||
|
|
f"{mfg_data_vs_20['astigmatism_rms']:.2f}",
|
||
|
|
f"{mfg_data_vs_20['coma_rms']:.2f}"]
|
||
|
|
], align="left")
|
||
|
|
), row=4, col=1)
|
||
|
|
|
||
|
|
elif mfg_data is not None:
|
||
|
|
# Simple manufacturing table (compat) — numeric only
|
||
|
|
fig.add_trace(go.Table(
|
||
|
|
header=dict(values=["<b>Metric</b>","<b>Value (nm)</b>"], align="left"),
|
||
|
|
cells=dict(values=[
|
||
|
|
["Global RMS",
|
||
|
|
"Filtered RMS (J1-J4)",
|
||
|
|
"Filtered RMS (J1-J3, with defocus)",
|
||
|
|
"Defocus (J4)",
|
||
|
|
"Astigmatism (J5+J6)",
|
||
|
|
"Coma (J7+J8)",
|
||
|
|
"Trefoil (J9+J10)",
|
||
|
|
"Spherical (J11)"],
|
||
|
|
[f"{global_rms:.2f}",
|
||
|
|
f"{filtered_rms:.2f}",
|
||
|
|
f"{mfg_data['rms_filter_j1to3']:.2f}",
|
||
|
|
f"{mfg_data['defocus_nm']:.2f}",
|
||
|
|
f"{mfg_data['astigmatism_rms']:.2f}",
|
||
|
|
f"{mfg_data['coma_rms']:.2f}",
|
||
|
|
f"{mfg_data['trefoil_rms']:.2f}",
|
||
|
|
f"{mfg_data['spherical_nm']:.2f}"]
|
||
|
|
], align="left")
|
||
|
|
), row=2, col=1)
|
||
|
|
else:
|
||
|
|
# Standard absolute table
|
||
|
|
fig.add_trace(go.Table(
|
||
|
|
header=dict(values=["<b>Metric</b>","<b>Value (nm)</b>"], align="left"),
|
||
|
|
cells=dict(values=[
|
||
|
|
["Global RMS","Filtered RMS (modes 1-4 removed)"],
|
||
|
|
[f"{global_rms:.2f}", f"{filtered_rms:.2f}"]
|
||
|
|
], align="left")
|
||
|
|
), row=2, col=1)
|
||
|
|
|
||
|
|
# Zernike coefficients
|
||
|
|
if mfg_data is not None and mfg_data_vs_20 is not None:
|
||
|
|
pass # manufacturing case: bar chart only
|
||
|
|
elif SHOW_ZERNIKE_BAR:
|
||
|
|
fig.add_trace(go.Table(
|
||
|
|
header=dict(values=["<b>Noll j</b>","<b>Label</b>","<b>|Coeff| (nm)</b>"], align="left"),
|
||
|
|
cells=dict(values=[
|
||
|
|
list(range(1, N_MODES+1)),
|
||
|
|
labels,
|
||
|
|
list(np.round(coeff_abs, 3))
|
||
|
|
], align="left")
|
||
|
|
), row=3, col=1)
|
||
|
|
else:
|
||
|
|
fig.add_trace(go.Table(
|
||
|
|
header=dict(values=["<b>Noll j</b>","<b>Label</b>","<b>|Coeff| (nm)</b>"], align="left"),
|
||
|
|
cells=dict(values=[
|
||
|
|
list(range(1, N_MODES+1)),
|
||
|
|
labels,
|
||
|
|
list(np.round(coeff_abs, 3))
|
||
|
|
], align="left")
|
||
|
|
), row=3, col=1)
|
||
|
|
|
||
|
|
if SHOW_ZERNIKE_BAR:
|
||
|
|
bar_row = 5 if (mfg_data is not None and mfg_data_vs_20 is not None) else 4
|
||
|
|
fig.add_trace(
|
||
|
|
go.Bar(
|
||
|
|
x=coeff_abs.tolist(),
|
||
|
|
y=labels,
|
||
|
|
orientation='h',
|
||
|
|
hovertemplate="%{y}<br>|Coeff| = %{x:.3f} nm<extra></extra>",
|
||
|
|
showlegend=False
|
||
|
|
),
|
||
|
|
row=bar_row, col=1
|
||
|
|
)
|
||
|
|
|
||
|
|
fig.update_layout(
|
||
|
|
width=1400,
|
||
|
|
height=1400 if (mfg_data is not None and mfg_data_vs_20 is not None)
|
||
|
|
else (1200 if SHOW_ZERNIKE_BAR else 1000),
|
||
|
|
margin=dict(t=60,b=20)
|
||
|
|
)
|
||
|
|
|
||
|
|
# Save
|
||
|
|
suffix = f"_sub{label}"
|
||
|
|
html_path = os.path.join(folder, f"{base}{suffix}.html")
|
||
|
|
fig.write_html(html_path, include_plotlyjs='cdn')
|
||
|
|
|
||
|
|
pd.DataFrame({
|
||
|
|
"Noll_j": list(range(1, N_MODES+1)),
|
||
|
|
"Label": labels,
|
||
|
|
"Coeff_nm": [float(c) for c in coeffs],
|
||
|
|
"CoeffAbs_nm": [float(abs(c)) for c in coeffs]
|
||
|
|
}).to_csv(os.path.join(folder, f"{base}{suffix}_zcoeffs.csv"), index=False)
|
||
|
|
|
||
|
|
if OPEN_HTML:
|
||
|
|
try:
|
||
|
|
webbrowser.open('file://' + os.path.abspath(html_path))
|
||
|
|
except Exception:
|
||
|
|
pass
|
||
|
|
|
||
|
|
return {
|
||
|
|
"global_rms_nm": global_rms,
|
||
|
|
"filtered_rms_nm": filtered_rms,
|
||
|
|
"html_path": html_path
|
||
|
|
}
|
||
|
|
# --------------------------------------
|
||
|
|
|
||
|
|
|
||
|
|
def write_exp_file(folder, abs_by_angle, rel20_by_angle, rel90_by_angle, mfg_90=None, mfg_90_vs_20=None):
|
||
|
|
"""
|
||
|
|
Writes Iteration_results_expression.exp in [MilliMeter].
|
||
|
|
For each angle a in {90,20,40,60}, writes:
|
||
|
|
- RMS_a_Global, RMS_a_Filtered (absolute)
|
||
|
|
- RMS_a_Global_Rel20, RMS_a_Filtered_Rel20 (relative to 20 deg)
|
||
|
|
- RMS_a_Global_Rel90, RMS_a_Filtered_Rel90 (relative to 90 deg)
|
||
|
|
"""
|
||
|
|
required = ["90", "20", "40", "60"]
|
||
|
|
out_path = os.path.join(folder, "Iteration_results_expression.exp")
|
||
|
|
nm_to_mm = lambda x: float(x) * 1e-6
|
||
|
|
|
||
|
|
def _missing(dct):
|
||
|
|
return [a for a in required if a not in dct]
|
||
|
|
|
||
|
|
miss_abs = _missing(abs_by_angle)
|
||
|
|
miss_r20 = _missing(rel20_by_angle)
|
||
|
|
miss_r90 = _missing(rel90_by_angle)
|
||
|
|
if miss_abs or miss_r20 or miss_r90:
|
||
|
|
raise RuntimeError(f"EXP export aborted. Missing sets -> "
|
||
|
|
f"abs:{miss_abs}, rel20:{miss_r20}, rel90:{miss_r90}.")
|
||
|
|
|
||
|
|
lines = ["// Version: 3"]
|
||
|
|
for ang in required:
|
||
|
|
gA, fA = abs_by_angle[ang]
|
||
|
|
g20, f20 = rel20_by_angle[ang]
|
||
|
|
g90, f90 = rel90_by_angle[ang]
|
||
|
|
lines.append(f"[MilliMeter]RMS_{ang}_Global={nm_to_mm(gA):.7f}")
|
||
|
|
lines.append(f"[MilliMeter]RMS_{ang}_Filtered={nm_to_mm(fA):.7f}")
|
||
|
|
lines.append(f"[MilliMeter]RMS_{ang}_Global_Rel20={nm_to_mm(g20):.7f}")
|
||
|
|
lines.append(f"[MilliMeter]RMS_{ang}_Filtered_Rel20={nm_to_mm(f20):.7f}")
|
||
|
|
lines.append(f"[MilliMeter]RMS_{ang}_Global_Rel90={nm_to_mm(g90):.7f}")
|
||
|
|
lines.append(f"[MilliMeter]RMS_{ang}_Filtered_Rel90={nm_to_mm(f90):.7f}")
|
||
|
|
|
||
|
|
# Manufacturing metrics for 90 deg (numeric only)
|
||
|
|
if mfg_90_vs_20 and mfg_90:
|
||
|
|
lines.append(f"[MilliMeter]RMS_90_Optician_Workload={nm_to_mm(mfg_90_vs_20['rms_filter_j1to3']):.7f}")
|
||
|
|
lines.append(f"[MilliMeter]RMS_90_Astig_Abs={nm_to_mm(mfg_90['astigmatism_rms']):.7f}")
|
||
|
|
lines.append(f"[MilliMeter]RMS_90_Coma_Abs={nm_to_mm(mfg_90['coma_rms']):.7f}")
|
||
|
|
|
||
|
|
with open(out_path, "w", encoding="utf-8-sig", newline="\n") as f:
|
||
|
|
f.write("\n".join(lines) + "\n")
|
||
|
|
print(f"[OK] Wrote EXP: {out_path}")
|
||
|
|
|
||
|
|
|
||
|
|
def main():
|
||
|
|
op2_path, dat_path = pick_files_cli()
|
||
|
|
folder = os.path.dirname(op2_path)
|
||
|
|
base = os.path.splitext(os.path.basename(op2_path))[0]
|
||
|
|
|
||
|
|
results_dir = os.environ.get("ZERNIKE_RESULTS_DIR") or os.path.join(folder, "Results")
|
||
|
|
os.makedirs(results_dir, exist_ok=True)
|
||
|
|
|
||
|
|
env_run_id = os.environ.get("ZERNIKE_RUN_ID")
|
||
|
|
if env_run_id:
|
||
|
|
run_id = env_run_id
|
||
|
|
try:
|
||
|
|
run_dt = datetime.strptime(env_run_id, "%Y%m%d_%H%M%S")
|
||
|
|
except Exception:
|
||
|
|
run_dt = datetime.now()
|
||
|
|
else:
|
||
|
|
run_dt = datetime.now()
|
||
|
|
run_id = run_dt.strftime("%Y%m%d_%H%M%S")
|
||
|
|
|
||
|
|
base_ts = f"{base}_{run_id}"
|
||
|
|
|
||
|
|
node_geo = read_geometry(dat_path)
|
||
|
|
subdisp = read_displacements_by_subcase(op2_path) # dict: '90','20','40','60'
|
||
|
|
|
||
|
|
# ABS for 20 deg (reference for pages)
|
||
|
|
if REF_LABEL not in subdisp:
|
||
|
|
raise RuntimeError(f"Reference subcase '{REF_LABEL}' not found after mapping. "
|
||
|
|
"Check *_op2_displ_index.csv for labels.")
|
||
|
|
|
||
|
|
df_ref_abs = build_dataframe_for_subcase(
|
||
|
|
REF_LABEL,
|
||
|
|
subdisp[REF_LABEL]["node_ids"],
|
||
|
|
subdisp[REF_LABEL]["disp"],
|
||
|
|
node_geo
|
||
|
|
)
|
||
|
|
df_ref_abs["WFE_nm"] = 2.0 * df_ref_abs["Disp_Z"] * NM_PER_UNIT
|
||
|
|
Xref = df_ref_abs["X0"].to_numpy()
|
||
|
|
Yref = df_ref_abs["Y0"].to_numpy()
|
||
|
|
Wref = df_ref_abs["WFE_nm"].to_numpy()
|
||
|
|
abs_ref_global, abs_ref_filtered = compute_rms_pair_only(Xref, Yref, Wref)
|
||
|
|
|
||
|
|
# ABS for 90 deg (manufacturing reference; used for REL vs 90)
|
||
|
|
if POLISH_LABEL not in subdisp:
|
||
|
|
raise RuntimeError(f"Subcase '{POLISH_LABEL}' not found after mapping.")
|
||
|
|
df90_abs = build_dataframe_for_subcase(
|
||
|
|
POLISH_LABEL,
|
||
|
|
subdisp[POLISH_LABEL]["node_ids"],
|
||
|
|
subdisp[POLISH_LABEL]["disp"],
|
||
|
|
node_geo
|
||
|
|
)
|
||
|
|
df90_abs["WFE_nm"] = 2.0 * df90_abs["Disp_Z"] * NM_PER_UNIT
|
||
|
|
X90 = df90_abs["X0"].to_numpy()
|
||
|
|
Y90 = df90_abs["Y0"].to_numpy()
|
||
|
|
W90 = df90_abs["WFE_nm"].to_numpy()
|
||
|
|
abs90_global, abs90_filtered = compute_rms_pair_only(X90, Y90, W90)
|
||
|
|
|
||
|
|
combined_rows = []
|
||
|
|
def emit_rows(df, label, is_relative):
|
||
|
|
if is_relative:
|
||
|
|
for row in df.itertuples(index=False):
|
||
|
|
combined_rows.append({
|
||
|
|
"Subcase": label,
|
||
|
|
"NodeLabel": row.NodeLabel,
|
||
|
|
"X0": row.X0, "Y0": row.Y0, "Z0": row.Z0,
|
||
|
|
"Rel_Disp_Z": getattr(row, "Rel_Disp_Z"),
|
||
|
|
"WFE_nm": getattr(row, "Rel_WFE_nm"),
|
||
|
|
"DispUnit": DISP_SRC_UNIT,
|
||
|
|
"RelativeToRef": True,
|
||
|
|
"RefAngleDeg": REF_LABEL
|
||
|
|
})
|
||
|
|
else:
|
||
|
|
for row in df.itertuples(index=False):
|
||
|
|
combined_rows.append({
|
||
|
|
"Subcase": label,
|
||
|
|
"NodeLabel": row.NodeLabel,
|
||
|
|
"X0": row.X0, "Y0": row.Y0, "Z0": row.Z0,
|
||
|
|
"Disp_Z": row.Disp_Z,
|
||
|
|
"WFE_nm": row.WFE_nm,
|
||
|
|
"DispUnit": DISP_SRC_UNIT,
|
||
|
|
"RelativeToRef": False,
|
||
|
|
"RefAngleDeg": ""
|
||
|
|
})
|
||
|
|
|
||
|
|
# Emit absolute 20 deg
|
||
|
|
emit_rows(df_ref_abs, REF_LABEL, is_relative=False)
|
||
|
|
|
||
|
|
metrics_summary = [{
|
||
|
|
"Subcase": f"sub{REF_LABEL}",
|
||
|
|
"Abs_GlobalRMS_nm": abs_ref_global,
|
||
|
|
"Abs_FilteredRMS_nm": abs_ref_filtered,
|
||
|
|
"Rel20_GlobalRMS_nm": np.nan, # reference has no relative (vs 20 deg)
|
||
|
|
"Rel20_FilteredRMS_nm": np.nan
|
||
|
|
}]
|
||
|
|
|
||
|
|
abs_values_by_angle = {REF_LABEL: (abs_ref_global, abs_ref_filtered), POLISH_LABEL: (abs90_global, abs90_filtered)}
|
||
|
|
rel20_values_by_angle = {REF_LABEL: (0.0, 0.0)} # 20 vs 20 = 0
|
||
|
|
rel90_values_by_angle = {POLISH_LABEL: (0.0, 0.0)} # 90 vs 90 = 0
|
||
|
|
|
||
|
|
# Process all angles (ABS; REL vs 20 deg for pages)
|
||
|
|
all_angles = [str(a) for a in REQUIRED_SUBCASES]
|
||
|
|
for ang in [a for a in all_angles if a != REF_LABEL]:
|
||
|
|
if ang not in subdisp:
|
||
|
|
raise RuntimeError(f"Subcase '{ang}' not found after mapping. "
|
||
|
|
"Check *_op2_displ_index.csv for labels.")
|
||
|
|
|
||
|
|
if ang == POLISH_LABEL:
|
||
|
|
abs_global, abs_filtered = abs_values_by_angle[ang]
|
||
|
|
X_abs, Y_abs = X90, Y90
|
||
|
|
else:
|
||
|
|
df_abs = build_dataframe_for_subcase(ang, subdisp[ang]["node_ids"], subdisp[ang]["disp"], node_geo)
|
||
|
|
W_abs_nm = 2.0 * df_abs["Disp_Z"].to_numpy() * NM_PER_UNIT
|
||
|
|
X_abs = df_abs["X0"].to_numpy(); Y_abs = df_abs["Y0"].to_numpy()
|
||
|
|
abs_global, abs_filtered = compute_rms_pair_only(X_abs, Y_abs, W_abs_nm)
|
||
|
|
abs_values_by_angle[ang] = (abs_global, abs_filtered)
|
||
|
|
|
||
|
|
# REL vs 20
|
||
|
|
if ang == POLISH_LABEL:
|
||
|
|
df_rel20 = compute_relative_dz(df_ref_abs, df90_abs)
|
||
|
|
else:
|
||
|
|
df_rel20 = compute_relative_dz(df_ref_abs, df_abs)
|
||
|
|
emit_rows(df_rel20, ang, is_relative=True) # Only emit CSV rows for 40/60
|
||
|
|
|
||
|
|
X_rel20 = df_rel20["X0"].to_numpy(); Y_rel20 = df_rel20["Y0"].to_numpy()
|
||
|
|
W_rel20 = df_rel20["Rel_WFE_nm"].to_numpy()
|
||
|
|
|
||
|
|
# HTML for 40 and 60 only
|
||
|
|
if ang != POLISH_LABEL:
|
||
|
|
m_rel20 = zernike_report(
|
||
|
|
ang, X_rel20, Y_rel20, W_rel20, results_dir, base_ts,
|
||
|
|
is_relative=True, abs_pair=(abs_global, abs_filtered), ref_title=REF_TITLE
|
||
|
|
)
|
||
|
|
rel20_values_by_angle[ang] = (m_rel20["global_rms_nm"], m_rel20["filtered_rms_nm"])
|
||
|
|
|
||
|
|
metrics_summary.append({
|
||
|
|
"Subcase": f"sub{ang}",
|
||
|
|
"Abs_GlobalRMS_nm": abs_global,
|
||
|
|
"Abs_FilteredRMS_nm": abs_filtered,
|
||
|
|
"Rel20_GlobalRMS_nm": m_rel20["global_rms_nm"],
|
||
|
|
"Rel20_FilteredRMS_nm": m_rel20["filtered_rms_nm"]
|
||
|
|
})
|
||
|
|
else:
|
||
|
|
g20, f20 = compute_rms_pair_only(X_rel20, Y_rel20, W_rel20)
|
||
|
|
rel20_values_by_angle[ang] = (g20, f20)
|
||
|
|
|
||
|
|
# REL vs 90 for all angles (metrics only)
|
||
|
|
for ang in all_angles:
|
||
|
|
df_abs = build_dataframe_for_subcase(ang, subdisp[ang]["node_ids"], subdisp[ang]["disp"], node_geo)
|
||
|
|
df_rel90 = compute_relative_dz(df90_abs, df_abs)
|
||
|
|
X_rel90 = df_rel90["X0"].to_numpy(); Y_rel90 = df_rel90["Y0"].to_numpy()
|
||
|
|
W_rel90 = df_rel90["Rel_WFE_nm"].to_numpy()
|
||
|
|
g90, f90 = compute_rms_pair_only(X_rel90, Y_rel90, W_rel90)
|
||
|
|
rel90_values_by_angle[ang] = (g90, f90)
|
||
|
|
|
||
|
|
# Manufacturing metrics for 90
|
||
|
|
mfg_metrics_90 = None
|
||
|
|
mfg_metrics_90_vs_20 = None
|
||
|
|
|
||
|
|
if POLISH_LABEL in subdisp:
|
||
|
|
mfg_metrics_90 = analyze_manufacturing_metrics(X90, Y90, W90)
|
||
|
|
|
||
|
|
df90_rel20 = compute_relative_dz(df_ref_abs, df90_abs)
|
||
|
|
X90_rel = df90_rel20["X0"].to_numpy()
|
||
|
|
Y90_rel = df90_rel20["Y0"].to_numpy()
|
||
|
|
W90_rel = df90_rel20["Rel_WFE_nm"].to_numpy()
|
||
|
|
|
||
|
|
mfg_metrics_90_vs_20 = analyze_manufacturing_metrics(X90_rel, Y90_rel, W90_rel)
|
||
|
|
|
||
|
|
# Special HTML for 90 (numeric only)
|
||
|
|
_m90 = zernike_report(
|
||
|
|
POLISH_LABEL, X90, Y90, W90, results_dir, base_ts,
|
||
|
|
is_relative=False,
|
||
|
|
mfg_data=mfg_metrics_90,
|
||
|
|
mfg_data_vs_20=mfg_metrics_90_vs_20
|
||
|
|
)
|
||
|
|
|
||
|
|
# HTML for 20 deg relative to 90 deg
|
||
|
|
df_20_rel90 = compute_relative_dz(df90_abs, df_ref_abs)
|
||
|
|
X_20_rel90 = df_20_rel90["X0"].to_numpy()
|
||
|
|
Y_20_rel90 = df_20_rel90["Y0"].to_numpy()
|
||
|
|
W_20_rel90 = df_20_rel90["Rel_WFE_nm"].to_numpy()
|
||
|
|
|
||
|
|
_m_ref = zernike_report(
|
||
|
|
REF_LABEL, X_20_rel90, Y_20_rel90, W_20_rel90, results_dir, base_ts,
|
||
|
|
is_relative=True, abs_pair=(abs_ref_global, abs_ref_filtered), ref_title=POLISH_TITLE
|
||
|
|
)
|
||
|
|
|
||
|
|
# Combined nodal CSV
|
||
|
|
comb_df = pd.DataFrame(combined_rows)
|
||
|
|
comb_path = os.path.join(results_dir, f"{base_ts}_all_subcases_nodal.csv")
|
||
|
|
comb_df.to_csv(comb_path, index=False)
|
||
|
|
|
||
|
|
# Summary CSV (REL20 naming)
|
||
|
|
summary_path = os.path.join(results_dir, f"{base_ts}_RMS_summary.csv")
|
||
|
|
pd.DataFrame(metrics_summary, columns=[
|
||
|
|
"Subcase",
|
||
|
|
"Abs_GlobalRMS_nm","Abs_FilteredRMS_nm",
|
||
|
|
"Rel20_GlobalRMS_nm","Rel20_FilteredRMS_nm"
|
||
|
|
]).to_csv(summary_path, index=False)
|
||
|
|
|
||
|
|
# EXP export
|
||
|
|
write_exp_file(results_dir, abs_values_by_angle, rel20_values_by_angle, rel90_values_by_angle,
|
||
|
|
mfg_90=mfg_metrics_90, mfg_90_vs_20=mfg_metrics_90_vs_20)
|
||
|
|
|
||
|
|
# Persistent RMS log
|
||
|
|
log_path = os.path.join(results_dir, "RMS_log.csv")
|
||
|
|
row = {
|
||
|
|
"RunID": run_id,
|
||
|
|
"RunDateTimeLocal": run_dt.strftime("%Y-%m-%d %H:%M:%S"),
|
||
|
|
"OP2_Base": base,
|
||
|
|
"DispUnit": DISP_SRC_UNIT,
|
||
|
|
"ReferenceAngleDeg": REF_LABEL
|
||
|
|
}
|
||
|
|
for ang in [str(a) for a in REQUIRED_SUBCASES]:
|
||
|
|
gA, fA = abs_values_by_angle.get(ang, (np.nan, np.nan))
|
||
|
|
gR20, fR20 = rel20_values_by_angle.get(ang, (np.nan, np.nan))
|
||
|
|
gR90, fR90 = rel90_values_by_angle.get(ang, (np.nan, np.nan))
|
||
|
|
row[f"Abs_GlobalRMS_nm_{ang}"] = gA
|
||
|
|
row[f"Abs_FilteredRMS_nm_{ang}"] = fA
|
||
|
|
row[f"Rel_GlobalRMS_nm_{ang}_vs20"] = gR20
|
||
|
|
row[f"Rel_FilteredRMS_nm_{ang}_vs20"] = fR20
|
||
|
|
row[f"Rel_GlobalRMS_nm_{ang}_vs90"] = gR90
|
||
|
|
row[f"Rel_FilteredRMS_nm_{ang}_vs90"] = fR90
|
||
|
|
|
||
|
|
if mfg_metrics_90 and mfg_metrics_90_vs_20:
|
||
|
|
row["Mfg_90_OpticianWorkload_nm"] = mfg_metrics_90_vs_20['rms_filter_j1to3']
|
||
|
|
row["Mfg_90_Astig_Abs_nm"] = mfg_metrics_90['astigmatism_rms']
|
||
|
|
row["Mfg_90_Coma_Abs_nm"] = mfg_metrics_90['coma_rms']
|
||
|
|
|
||
|
|
pd.DataFrame([row]).to_csv(
|
||
|
|
log_path,
|
||
|
|
mode="a",
|
||
|
|
header=not os.path.exists(log_path),
|
||
|
|
index=False
|
||
|
|
)
|
||
|
|
|
||
|
|
# Console output (numeric only; ASCII-safe)
|
||
|
|
if mfg_metrics_90 and mfg_metrics_90_vs_20:
|
||
|
|
print("\n" + "="*60)
|
||
|
|
print("MANUFACTURING METRICS (90 deg horizontal polishing)")
|
||
|
|
print("="*60)
|
||
|
|
|
||
|
|
print(f"\n90 deg Absolute (surface shape on test stand):")
|
||
|
|
print(f" Filtered RMS (J1-J4): {mfg_metrics_90['rms_filter_j1to4']:.1f} nm")
|
||
|
|
print(f" Astigmatism (J5+J6): {mfg_metrics_90['astigmatism_rms']:.1f} nm")
|
||
|
|
print(f" Coma (J7+J8): {mfg_metrics_90['coma_rms']:.1f} nm")
|
||
|
|
print(f" Trefoil (J9+J10): {mfg_metrics_90['trefoil_rms']:.1f} nm")
|
||
|
|
|
||
|
|
print(f"\n90 deg - 20 deg (pre-correction to polish in):")
|
||
|
|
print(f" Total RMS (J1-J3): {mfg_metrics_90_vs_20['rms_filter_j1to3']:.1f} nm")
|
||
|
|
print(f" Defocus (J4): {mfg_metrics_90_vs_20['defocus_nm']:.1f} nm")
|
||
|
|
print(f" Astigmatism (J5+J6): {mfg_metrics_90_vs_20['astigmatism_rms']:.1f} nm")
|
||
|
|
print(f" Coma (J7+J8): {mfg_metrics_90_vs_20['coma_rms']:.1f} nm")
|
||
|
|
print("="*60 + "\n")
|
||
|
|
|
||
|
|
print("[OK] Wrote:")
|
||
|
|
print(" -", comb_path)
|
||
|
|
print(" -", summary_path)
|
||
|
|
print(" -", log_path, "(appended)")
|
||
|
|
|
||
|
|
for r in metrics_summary:
|
||
|
|
tag = r["Subcase"]
|
||
|
|
if tag == f"sub{REF_LABEL}":
|
||
|
|
print(f" - {base_ts}_{tag}.html (ABS Global={r['Abs_GlobalRMS_nm']:.2f} nm, "
|
||
|
|
f"ABS Filtered={r['Abs_FilteredRMS_nm']:.2f} nm)")
|
||
|
|
else:
|
||
|
|
print(f" - {base_ts}_{tag}.html (REL(vs20) Global={r['Rel20_GlobalRMS_nm']:.2f} nm, "
|
||
|
|
f"REL(vs20) Filtered={r['Rel20_FilteredRMS_nm']:.2f} nm | "
|
||
|
|
f"ABS Global={r['Abs_GlobalRMS_nm']:.2f} nm, "
|
||
|
|
f"ABS Filtered={r['Abs_FilteredRMS_nm']:.2f} nm)")
|
||
|
|
|
||
|
|
if __name__ == "__main__":
|
||
|
|
try:
|
||
|
|
main()
|
||
|
|
except Exception as e:
|
||
|
|
msg = f"Fatal error: {e}"
|
||
|
|
print(msg)
|
||
|
|
if messagebox:
|
||
|
|
try:
|
||
|
|
messagebox.showerror("Fatal error", str(e))
|
||
|
|
except Exception:
|
||
|
|
pass
|