Atomizer/tools/generate_optical_report.py.backup

#!/usr/bin/env python3
"""
Atomizer Optical Performance Report Generator
===============================================

Generates a comprehensive, CDR-ready HTML report for the optical
performance of an M1 mirror design from FEA results (OP2 file).

The report combines:
1. Executive Summary with pass/fail vs design targets
2. Per-Angle Wavefront Error Analysis (3D surface plots)
3. Zernike Trajectory Analysis (mode-specific metrics across elevation)
4. Sensitivity Matrix (axial vs lateral load response)
5. Manufacturing Analysis (90° correction metrics)
6. Full Zernike coefficient tables

Usage:
    conda activate atomizer
    python generate_optical_report.py "path/to/solution.op2"

    # With annular aperture
    python generate_optical_report.py "path/to/solution.op2" --inner-radius 135.75

    # Custom targets
    python generate_optical_report.py "path/to/solution.op2" --target-40 4.0 --target-60 10.0 --target-mfg 20.0

    # Include design parameters from study database
    python generate_optical_report.py "path/to/solution.op2" --study-db "path/to/study.db" --trial 725

Output:
    Creates a single comprehensive HTML file:
    {basename}_OPTICAL_REPORT_{timestamp}.html

Author: Atomizer / Atomaste
Created: 2026-01-29
"""

import sys
import os
import argparse
import json
from pathlib import Path
from math import factorial
from datetime import datetime
import numpy as np
from numpy.linalg import LinAlgError

# Add Atomizer root to path
ATOMIZER_ROOT = Path(__file__).parent.parent
if str(ATOMIZER_ROOT) not in sys.path:
    sys.path.insert(0, str(ATOMIZER_ROOT))

try:
    import plotly.graph_objects as go
    from plotly.subplots import make_subplots
    from matplotlib.tri import Triangulation
    from pyNastran.op2.op2 import OP2
    from pyNastran.bdf.bdf import BDF
except ImportError as e:
    print(f"ERROR: Missing dependency: {e}")
    print("Run: conda activate atomizer")
    sys.exit(1)

# Import Atomizer extractors
from optimization_engine.extractors.extract_zernike import (
    compute_zernike_coefficients,
    compute_rms_metrics,
    compute_aberration_magnitudes,
    compute_rms_with_custom_filter,
    zernike_noll,
    zernike_label,
    zernike_name,
    noll_indices,
    read_node_geometry,
    find_geometry_file,
    extract_displacements_by_subcase,
    UNIT_TO_NM,
    DEFAULT_N_MODES,
    DEFAULT_FILTER_ORDERS,
)

from optimization_engine.extractors.extract_zernike_trajectory import (
    ZernikeTrajectoryExtractor,
    MODE_GROUPS,
    MODE_NAMES,
    compute_trajectory_params,
)


# ============================================================================
# Configuration
# ============================================================================

N_MODES = 50
FILTER_LOW_ORDERS = 4
PLOT_DOWNSAMPLE = 12000
COLORSCALE = 'Turbo'

# Default design targets (nm)
DEFAULT_TARGETS = {
    'wfe_40_20': 4.0,
    'wfe_60_20': 10.0,
    'mfg_90': 20.0,
}

# Default annular aperture for M1 (271.5mm central hole diameter)
DEFAULT_INNER_RADIUS = 135.75  # mm

DISP_UNIT = 'mm'
NM_SCALE = UNIT_TO_NM[DISP_UNIT]

# Subcase mapping: subcase_id -> angle
SUBCASE_ANGLE_MAP = {
    '1': 90, '2': 20, '3': 40, '4': 60,
    '90': 90, '20': 20, '40': 40, '60': 60,
}

# ── Professional light-theme Plotly layout defaults ──
_PLOTLY_LIGHT_LAYOUT = dict(
    paper_bgcolor='#ffffff',
    plot_bgcolor='#f8fafc',
    font=dict(family="Inter, system-ui, sans-serif", color='#1e293b', size=12),
)

# Professional blue palette for charts
_BLUE_PALETTE = ['#2563eb', '#3b82f6', '#60a5fa', '#93c5fd', '#1d4ed8', '#1e40af']
_MODE_COLORS = ['#2563eb', '#dc2626', '#16a34a', '#9333ea', '#ea580c', '#0891b2', '#4f46e5']
_MODE_DASHES = ['solid', 'dash', 'dot', 'dashdot', 'longdash', 'longdashdot', 'solid']


# ============================================================================
# Data Extraction Helpers
# ============================================================================

def load_study_params(db_path: str, trial_id: int = None) -> dict:
    """Load design parameters from study database."""
    import sqlite3
    conn = sqlite3.connect(db_path)
    c = conn.cursor()

    if trial_id is None:
        # Find best trial by weighted sum
        c.execute('''
            SELECT t.trial_id, tua.key, tua.value_json
            FROM trials t
            JOIN trial_user_attributes tua ON t.trial_id = tua.trial_id
            WHERE t.state = 'COMPLETE' AND tua.key = 'weighted_sum'
            ORDER BY CAST(tua.value_json AS REAL) ASC
            LIMIT 1
        ''')
        row = c.fetchone()
        if row:
            trial_id = row[0]
        else:
            conn.close()
            return {}

    # Get all attributes for the trial
    c.execute('''
        SELECT key, value_json
        FROM trial_user_attributes
        WHERE trial_id = ?
    ''', (trial_id,))
    attrs = {row[0]: json.loads(row[1]) for row in c.fetchall()}

    # Get parameters
    c.execute('''
        SELECT tp.key, tp.value_json
        FROM trial_params tp
        WHERE tp.trial_id = ?
    ''', (trial_id,))
    params = {row[0]: json.loads(row[1]) for row in c.fetchall()}

    conn.close()

    return {
        'trial_id': trial_id,
        'attributes': attrs,
        'parameters': params,
    }


def build_wfe_arrays(node_ids, disp, node_geo):
    """Build X, Y, WFE arrays from displacement data."""
    X, Y, WFE = [], [], []
    for nid, vec in zip(node_ids, disp):
        geo = node_geo.get(int(nid))
        if geo is None:
            continue
        X.append(geo[0])
        Y.append(geo[1])
        WFE.append(vec[2] * 2.0 * NM_SCALE)
    return np.array(X), np.array(Y), np.array(WFE)


def compute_relative_wfe(X1, Y1, WFE1, nids1, X2, Y2, WFE2, nids2):
    """Compute WFE1 - WFE2 for common nodes."""
    ref_map = {int(n): w for n, w in zip(nids2, WFE2)}
    Xr, Yr, Wr = [], [], []
    for nid, x, y, w in zip(nids1, X1, Y1, WFE1):
        nid = int(nid)
        if nid in ref_map:
            Xr.append(x)
            Yr.append(y)
            Wr.append(w - ref_map[nid])
    return np.array(Xr), np.array(Yr), np.array(Wr)


def zernike_fit(X, Y, W, n_modes=N_MODES, inner_radius=None):
    """Compute Zernike fit with optional annular masking."""
    Xc = X - np.mean(X)
    Yc = Y - np.mean(Y)
    R_outer = float(np.max(np.hypot(Xc, Yc)))
    r = np.hypot(Xc, Yc) / R_outer
    th = np.arctan2(Yc, Xc)

    # Annular mask
    if inner_radius is not None:
        r_inner_norm = inner_radius / R_outer
        mask = (r >= r_inner_norm) & (r <= 1.0) & ~np.isnan(W)
    else:
        mask = (r <= 1.0) & ~np.isnan(W)

    idx = np.nonzero(mask)[0]
    m = int(n_modes)
    G = np.zeros((m, m), dtype=np.float64)
    h = np.zeros((m,), dtype=np.float64)
    v = W.astype(np.float64)

    for start in range(0, len(idx), 100000):
        sl = idx[start:start+100000]
        Zb = np.column_stack([zernike_noll(j, r[sl].astype(np.float32), th[sl].astype(np.float32)).astype(np.float32)
                              for j in range(1, m+1)])
        G += (Zb.T @ Zb).astype(np.float64)
        h += (Zb.T @ v[sl]).astype(np.float64)

    try:
        coeffs = np.linalg.solve(G, h)
    except LinAlgError:
        coeffs = np.linalg.lstsq(G, h, rcond=None)[0]

    # Compute residuals
    Z_all = np.column_stack([zernike_noll(j, r.astype(np.float32), th.astype(np.float32))
                              for j in range(1, m+1)])
    W_low4 = Z_all[:, :FILTER_LOW_ORDERS].dot(coeffs[:FILTER_LOW_ORDERS])
    W_low3 = Z_all[:, :3].dot(coeffs[:3])
    W_res_j4 = W - W_low4  # J1-J4 removed
    W_res_j3 = W - W_low3  # J1-J3 removed

    global_rms = float(np.sqrt(np.mean(W[mask]**2)))
    filtered_rms = float(np.sqrt(np.mean(W_res_j4[mask]**2)))
    rms_j1to3 = float(np.sqrt(np.mean(W_res_j3[mask]**2)))

    return {
        'coefficients': coeffs,
        'R_outer': R_outer,
        'global_rms': global_rms,
        'filtered_rms': filtered_rms,
        'rms_j1to3': rms_j1to3,
        'W_res_filt': W_res_j4,
        'mask': mask,
        'n_masked': int(np.sum(mask)),
        'n_total': len(W),
    }


def aberration_magnitudes(coeffs):
    """Get individual aberration magnitudes from Zernike coefficients."""
    defocus = float(abs(coeffs[3]))
    astig = float(np.sqrt(coeffs[4]**2 + coeffs[5]**2))
    coma = float(np.sqrt(coeffs[6]**2 + coeffs[7]**2))
    trefoil = float(np.sqrt(coeffs[8]**2 + coeffs[9]**2))
    spherical = float(abs(coeffs[10])) if len(coeffs) > 10 else 0.0
    return {
        'defocus': defocus, 'astigmatism': astig, 'coma': coma,
        'trefoil': trefoil, 'spherical': spherical,
    }


def compute_spatial_freq_metrics(coefficients):
    """Compute spatial frequency band metrics from Zernike coefficients.

    Decomposes the Zernike spectrum into Low Spatial Frequency (form/figure)
    and Mid Spatial Frequency (ripple) bands based on radial order.

    Args:
        coefficients: array of Zernike coefficients (50 modes, Noll convention)

    Returns:
        dict with band RMS values and per-radial-order breakdown
    """
    n_modes = min(len(coefficients), 50)
    coeffs = np.array(coefficients[:n_modes])

    # Build radial order mapping using noll_indices
    order_map = {}  # n -> list of coeff values
    for j in range(1, n_modes + 1):
        n, m = noll_indices(j)
        if n not in order_map:
            order_map[n] = []
        order_map[n].append(coeffs[j - 1])

    # Band definitions (by Noll index, 1-based)
    # LSF: J4-J15 (n=2..4) — Low Spatial Frequency (form/figure)
    # MSF: J16-J50 (n=5..9) — Mid Spatial Frequency (ripple)
    lsf_coeffs = coeffs[3:15]              # indices 3..14 → J4..J15
    msf_coeffs = coeffs[15:n_modes]        # indices 15..49 → J16..J50
    total_coeffs = coeffs[3:n_modes]       # J4..J50 (excluding piston/tilt)

    lsf_rms = float(np.sqrt(np.sum(lsf_coeffs**2)))
    msf_rms = float(np.sqrt(np.sum(msf_coeffs**2)))
    total_filtered_rms = float(np.sqrt(np.sum(total_coeffs**2)))
    lsf_msf_ratio = lsf_rms / msf_rms if msf_rms > 0 else float('inf')

    # Per-radial-order RMS
    per_order = {}
    for n in sorted(order_map.keys()):
        order_coeffs = np.array(order_map[n])
        per_order[n] = float(np.sqrt(np.sum(order_coeffs**2)))

    return {
        'lsf_rms': lsf_rms,
        'msf_rms': msf_rms,
        'total_filtered_rms': total_filtered_rms,
        'lsf_msf_ratio': lsf_msf_ratio,
        'per_order': per_order,
    }


def _spatial_freq_html(metrics):
    """Generate HTML card for spatial frequency band metrics."""
    m = metrics

    # Per-order breakdown for n=2..9
    order_items = ""
    for n in range(2, 10):
        val = m['per_order'].get(n, 0.0)
        order_items += (
            f'<div class="sf-order-item">'
            f'<span class="sf-order-n">n={n}:</span>'
            f'<span class="sf-order-val">{val:.2f}</span>'
            f'</div>\n'
        )

    ratio_str = (
        f"{m['lsf_msf_ratio']:.1f}\u00d7"
        if m['lsf_msf_ratio'] < 1000
        else "\u221e"
    )

    return f"""
    <div class="sf-breakdown">
        <h4>Spatial Frequency Breakdown</h4>
        <div class="sf-band-grid">
            <div class="sf-band-card sf-lsf">
                <div class="sf-band-label">LSF (Form)</div>
                <div class="sf-band-range">J4\u2013J15 &nbsp; n\u22644</div>
                <div class="sf-band-value">{m['lsf_rms']:.2f} <span class="sf-unit">nm</span></div>
            </div>
            <div class="sf-band-card sf-msf">
                <div class="sf-band-label">MSF (Ripple)</div>
                <div class="sf-band-range">J16\u2013J50 &nbsp; n\u22655</div>
                <div class="sf-band-value">{m['msf_rms']:.2f} <span class="sf-unit">nm</span></div>
            </div>
            <div class="sf-band-card sf-ratio">
                <div class="sf-band-label">LSF / MSF</div>
                <div class="sf-band-range">Ratio</div>
                <div class="sf-band-value">{ratio_str}</div>
            </div>
        </div>
        <div class="sf-order-grid">
            <div class="sf-order-title">Per-Order RMS (nm)</div>
            <div class="sf-order-items">{order_items}</div>
        </div>
    </div>
    """


# ============================================================================
# HTML Report Generation
# ============================================================================

def _metric_color(value, target):
    """Return a CSS color class based on value vs target."""
    if value <= target:
        return 'color: #16a34a; font-weight: 700;'  # green
    ratio = value / target
    if ratio < 1.5:
        return 'color: #d97706; font-weight: 700;'  # amber
    return 'color: #dc2626; font-weight: 700;'  # red


def make_surface_plot(X, Y, W_res, mask, inner_radius=None, title="", amp=0.5, downsample=PLOT_DOWNSAMPLE):
    """Create a 3D surface plot of residual WFE."""
    Xm, Ym, Wm = X[mask], Y[mask], W_res[mask]

    n = len(Xm)
    if n > downsample:
        rng = np.random.default_rng(42)
        sel = rng.choice(n, size=downsample, replace=False)
        Xp, Yp, Wp = Xm[sel], Ym[sel], Wm[sel]
    else:
        Xp, Yp, Wp = Xm, Ym, Wm

    res_amp = amp * Wp
    max_amp = float(np.max(np.abs(res_amp))) if res_amp.size else 1.0

    traces = []
    try:
        tri = Triangulation(Xp, Yp)
        if tri.triangles is not None and len(tri.triangles) > 0:
            # Filter triangles spanning central hole
            if inner_radius is not None:
                cx, cy = np.mean(X), np.mean(Y)
                valid = []
                for t in tri.triangles:
                    vx = Xp[t] - cx
                    vy = Yp[t] - cy
                    vr = np.hypot(vx, vy)
                    if np.any(vr < inner_radius * 0.9):
                        continue
                    p0, p1, p2 = Xp[t] + 1j*Yp[t]
                    if max(abs(p1-p0), abs(p2-p1), abs(p0-p2)) > 2*inner_radius:
                        continue
                    valid.append(t)
                if valid:
                    tri_arr = np.array(valid)
                else:
                    tri_arr = tri.triangles
            else:
                tri_arr = tri.triangles

            i, j, k = tri_arr.T
            traces.append(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="nm", side="right", font=dict(color='#1e293b')), thickness=12, len=0.5, tickformat=".1f",
                              tickfont=dict(color='#1e293b')),
                hovertemplate="X: %{x:.1f}<br>Y: %{y:.1f}<br>Residual: %{z:.2f} nm<extra></extra>"
            ))

            # Inner hole circle
            if inner_radius:
                theta_c = np.linspace(0, 2*np.pi, 80)
                traces.append(go.Scatter3d(
                    x=cx + inner_radius*np.cos(theta_c),
                    y=cy + inner_radius*np.sin(theta_c),
                    z=np.zeros(80),
                    mode='lines', line=dict(color='#64748b', width=2),
                    name='Central Hole', showlegend=False, hoverinfo='name'
                ))
    except Exception:
        traces.append(go.Scatter3d(
            x=Xp, y=Yp, z=res_amp,
            mode='markers', marker=dict(size=2, color=res_amp, colorscale=COLORSCALE, showscale=True),
            showlegend=False
        ))

    fig = go.Figure(data=traces)
    fig.update_layout(
        scene=dict(
            camera=dict(eye=dict(x=1.2, y=1.2, z=0.8), up=dict(x=0, y=0, z=1)),
            xaxis=dict(title=dict(text="X (mm)", font=dict(color='#1e293b')), showgrid=True, gridcolor='#e2e8f0',
                       showbackground=True, backgroundcolor='#f1f5f9',
                       tickfont=dict(color='#475569')),
            yaxis=dict(title=dict(text="Y (mm)", font=dict(color='#1e293b')), showgrid=True, gridcolor='#e2e8f0',
                       showbackground=True, backgroundcolor='#f1f5f9',
                       tickfont=dict(color='#475569')),
            zaxis=dict(title=dict(text="Residual (nm)", font=dict(color='#1e293b')), range=[-max_amp*3.0, max_amp*3.0],
                       showgrid=True, gridcolor='#e2e8f0',
                       showbackground=True, backgroundcolor='#eff6ff',
                       tickfont=dict(color='#475569')),
            aspectmode='manual',
            aspectratio=dict(x=1, y=1, z=0.4),
        ),
        margin=dict(t=30, b=10, l=10, r=10),
        **_PLOTLY_LIGHT_LAYOUT,
        height=650,
        width=1200,
    )
    return fig.to_html(include_plotlyjs=False, full_html=False, div_id=f"surface_{title.replace(' ','_')}")


def make_bar_chart(coeffs, title="Zernike Coefficients", max_modes=50):
    """Create horizontal bar chart of Zernike coefficient magnitudes."""
    n = min(len(coeffs), max_modes)
    labels = [str(zernike_label(j))[:40] for j in range(1, n+1)]
    vals = np.abs(coeffs[:n])
    text_vals = [f"{v:.3f}" for v in vals]

    fig = go.Figure(go.Bar(
        x=vals, y=labels, orientation='h',
        marker_color=_BLUE_PALETTE[0],
        text=text_vals,
        textposition='outside',
        textfont=dict(size=9, color='#475569'),
        hovertemplate="%{y}<br>|c| = %{x:.3f} nm<extra></extra>",
    ))
    fig.update_layout(
        height=max(400, n*22),
        margin=dict(t=30, b=10, l=220, r=60),
        **_PLOTLY_LIGHT_LAYOUT,
        xaxis=dict(title=dict(text="|Coefficient| (nm)", font=dict(color='#1e293b')),
                   gridcolor='#e2e8f0', zeroline=True,
                   zerolinecolor='#cbd5e1',
                   tickfont=dict(color='#475569')),
        yaxis=dict(autorange='reversed', tickfont=dict(size=10, color='#1e293b')),
    )
    return fig.to_html(include_plotlyjs=False, full_html=False, div_id=f"bar_{title.replace(' ','_')}")


def make_trajectory_plot(angles, coefficients_relative, mode_groups, sensitivity, title=""):
    """Create trajectory visualization: Zernike modes vs elevation angle."""
    fig = go.Figure()

    color_idx = 0

    for group_name, noll_indices in mode_groups.items():
        indices = [n - 5 for n in noll_indices if 5 <= n < 5 + coefficients_relative.shape[1]]
        if not indices:
            continue

        color = _MODE_COLORS[color_idx % len(_MODE_COLORS)]
        dash = _MODE_DASHES[color_idx % len(_MODE_DASHES)]

        # RSS of modes in this group at each angle
        rss = np.sqrt(np.sum(coefficients_relative[:, indices]**2, axis=1))
        display_name = MODE_NAMES.get(group_name, group_name)

        # Group RSS trace (thick)
        fig.add_trace(go.Scatter(
            x=angles, y=rss,
            mode='lines+markers',
            name=f"{display_name} (RSS)",
            line=dict(color=color, width=3, dash=dash),
            marker=dict(size=10, symbol='circle'),
            hovertemplate=f"{display_name} RSS<br>%{{x:.1f}}°: %{{y:.3f}} nm<extra></extra>",
            legendgroup=group_name,
        ))

        # Individual mode traces (thin, same color, lighter)
        for idx_i, noll_idx in enumerate(noll_indices):
            col_idx = noll_idx - 5
            if col_idx < 0 or col_idx >= coefficients_relative.shape[1]:
                continue
            mode_vals = np.abs(coefficients_relative[:, col_idx])
            mode_label = f"J{noll_idx}"
            fig.add_trace(go.Scatter(
                x=angles, y=mode_vals,
                mode='lines+markers',
                name=f"  {mode_label}",
                line=dict(color=color, width=1, dash='dot'),
                marker=dict(size=5, symbol='diamond'),
                opacity=0.5,
                hovertemplate=f"{mode_label}<br>%{{x:.1f}}°: %{{y:.3f}} nm<extra></extra>",
                legendgroup=group_name,
                showlegend=True,
            ))

        color_idx += 1

    # Check if log scale would help (values span >2 orders of magnitude)
    all_y = []
    for trace in fig.data:
        all_y.extend([v for v in trace.y if v > 0])
    use_log = False
    if all_y:
        y_min, y_max = min(all_y), max(all_y)
        if y_max > 0 and y_min > 0 and (y_max / y_min) > 100:
            use_log = True

    yaxis_cfg = dict(
        title=dict(text="RMS (nm)", font=dict(color='#1e293b')),
        gridcolor='#e2e8f0',
        zeroline=True, zerolinecolor='#cbd5e1',
        tickfont=dict(color='#475569'),
    )
    if use_log:
        yaxis_cfg['type'] = 'log'
        yaxis_cfg['title'] = dict(text="RMS (nm) — log scale", font=dict(color='#1e293b'))

    fig.update_layout(
        height=450,
        margin=dict(t=30, b=50, l=70, r=20),
        **_PLOTLY_LIGHT_LAYOUT,
        xaxis=dict(title=dict(text="Elevation Angle (°)", font=dict(color='#1e293b')),
                   gridcolor='#e2e8f0',
                   tickvals=list(angles), dtick=10,
                   tickfont=dict(color='#475569')),
        yaxis=yaxis_cfg,
        legend=dict(x=0.01, y=0.99, bgcolor='rgba(255,255,255,0.9)',
                    bordercolor='#e2e8f0', borderwidth=1,
                    font=dict(size=10, color='#1e293b')),
    )
    return fig.to_html(include_plotlyjs=False, full_html=False, div_id="trajectory_plot")


def make_sensitivity_bar(sensitivity_dict):
    """Create stacked bar chart of axial vs lateral sensitivity per mode."""
    modes = list(sensitivity_dict.keys())
    axial = [sensitivity_dict[m]['axial'] for m in modes]
    lateral = [sensitivity_dict[m]['lateral'] for m in modes]
    labels = [str(MODE_NAMES.get(m, m)) for m in modes]

    fig = go.Figure()
    fig.add_trace(go.Bar(
        y=labels, x=axial, orientation='h',
        name='Axial (sin \u03b8)', marker_color='#2563eb',
        text=[f"{v:.3f}" for v in axial], textposition='outside',
        textfont=dict(size=9, color='#475569'),
        hovertemplate="%{y}<br>Axial: %{x:.3f} nm/unit<extra></extra>"
    ))
    fig.add_trace(go.Bar(
        y=labels, x=lateral, orientation='h',
        name='Lateral (cos \u03b8)', marker_color='#7c3aed',
        text=[f"{v:.3f}" for v in lateral], textposition='outside',
        textfont=dict(size=9, color='#475569'),
        hovertemplate="%{y}<br>Lateral: %{x:.3f} nm/unit<extra></extra>"
    ))
    fig.update_layout(
        barmode='group',
        height=max(300, len(modes)*45),
        margin=dict(t=30, b=40, l=180, r=60),
        **_PLOTLY_LIGHT_LAYOUT,
        xaxis=dict(title=dict(text="Sensitivity (nm per load fraction)", font=dict(color='#1e293b')),
                   gridcolor='#e2e8f0',
                   zeroline=True, zerolinecolor='#cbd5e1',
                   tickfont=dict(color='#475569')),
        yaxis=dict(autorange='reversed', tickfont=dict(size=11, color='#1e293b')),
        legend=dict(x=0.55, y=0.99, bgcolor='rgba(255,255,255,0.9)',
                    bordercolor='#e2e8f0', borderwidth=1,
                    font=dict(color='#1e293b')),
    )
    return fig.to_html(include_plotlyjs=False, full_html=False, div_id="sensitivity_bar")


def make_per_angle_rms_plot(angle_rms_data, ref_angle=20):
    """Create bar chart of per-angle RMS relative to reference."""
    angles = sorted(angle_rms_data.keys())
    rms_vals = [angle_rms_data[a] for a in angles]
    labels = [f"{a}\u00b0 vs {ref_angle}\u00b0" for a in angles]

    fig = go.Figure(go.Bar(
        x=labels, y=rms_vals,
        marker_color=_BLUE_PALETTE[0],
        text=[f"{v:.2f} nm" for v in rms_vals],
        textposition='outside',
        textfont=dict(size=11, color='#1e293b'),
        hovertemplate="%{x}: %{y:.2f} nm<extra></extra>"
    ))
    fig.update_layout(
        height=350,
        margin=dict(t=30, b=40, l=60, r=20),
        **_PLOTLY_LIGHT_LAYOUT,
        yaxis=dict(title=dict(text="Filtered RMS WFE (nm)", font=dict(color='#1e293b')),
                   gridcolor='#e2e8f0',
                   zeroline=True, zerolinecolor='#cbd5e1',
                   tickfont=dict(color='#475569')),
        xaxis=dict(tickfont=dict(color='#1e293b', size=12)),
    )
    return fig.to_html(include_plotlyjs=False, full_html=False, div_id="per_angle_rms")


# ============================================================================
# Main Report Builder
# ============================================================================

def generate_report(
    op2_path: Path,
    inner_radius: float = None,
    targets: dict = None,
    study_db: str = None,
    trial_id: int = None,
    title: str = "M1 Mirror Optical Performance Report",
    study_name: str = None,
) -> Path:
    """Generate comprehensive optical performance HTML report."""

    targets = targets or DEFAULT_TARGETS
    timestamp = datetime.now().strftime("%Y-%m-%d %H:%M")
    ts_file = datetime.now().strftime("%Y%m%d_%H%M%S")

    print("=" * 70)
    print("  ATOMIZER OPTICAL PERFORMANCE REPORT GENERATOR")
    print("=" * 70)
    print(f"\nOP2 File: {op2_path.name}")
    print(f"Inner Radius: {inner_radius} mm" if inner_radius else "Aperture: Full disk")

    # ------------------------------------------------------------------
    # 1. Load geometry & displacement data
    # ------------------------------------------------------------------
    print("\n[1/5] Loading data...")
    geo_path = find_geometry_file(op2_path)
    node_geo = read_node_geometry(geo_path)
    print(f"  Geometry: {geo_path.name} ({len(node_geo)} nodes)")

    op2 = OP2()
    op2.read_op2(str(op2_path))
    displacements = extract_displacements_by_subcase(op2_path)
    print(f"  Subcases: {list(displacements.keys())}")

    # Map subcases to angles
    subcase_map = {}
    for angle in ['90', '20', '40', '60']:
        if angle in displacements:
            subcase_map[angle] = angle
    if len(subcase_map) < 4:
        if all(str(i) in displacements for i in range(1, 5)):
            subcase_map = {'90': '1', '20': '2', '40': '3', '60': '4'}
    print(f"  Subcase map: {subcase_map}")

    # Also detect intermediate angles (30, 50) if present
    extra_angles = []
    for a in ['30', '50']:
        if a in displacements:
            extra_angles.append(a)
    if extra_angles:
        print(f"  Extra angles detected: {extra_angles}")

    # ------------------------------------------------------------------
    # 2. Per-angle Zernike analysis
    # ------------------------------------------------------------------
    print("\n[2/5] Per-angle Zernike analysis...")

    ref_label = subcase_map['20']
    ref_data = displacements[ref_label]
    X_ref, Y_ref, WFE_ref = build_wfe_arrays(ref_data['node_ids'], ref_data['disp'], node_geo)

    # Analysis results storage
    angle_results = {}  # angle -> {rms_data, X, Y, WFE, ...}

    for angle_name, label in subcase_map.items():
        data = displacements[label]
        X, Y, WFE = build_wfe_arrays(data['node_ids'], data['disp'], node_geo)

        # Absolute fit
        rms_abs = zernike_fit(X, Y, WFE, inner_radius=inner_radius)

        # Relative fit (vs 20 deg reference)
        if angle_name != '20':
            Xr, Yr, Wr = compute_relative_wfe(
                X, Y, WFE, data['node_ids'],
                X_ref, Y_ref, WFE_ref, ref_data['node_ids']
            )
            rms_rel = zernike_fit(Xr, Yr, Wr, inner_radius=inner_radius)
        else:
            Xr, Yr, Wr = X, Y, np.zeros_like(WFE)
            rms_rel = {'filtered_rms': 0.0, 'rms_j1to3': 0.0, 'coefficients': np.zeros(N_MODES)}

        angle_results[int(angle_name)] = {
            'X': X, 'Y': Y, 'WFE': WFE,
            'X_rel': Xr, 'Y_rel': Yr, 'WFE_rel': Wr,
            'rms_abs': rms_abs,
            'rms_rel': rms_rel,
            'aberrations_abs': aberration_magnitudes(rms_abs['coefficients']),
            'aberrations_rel': aberration_magnitudes(rms_rel['coefficients']) if angle_name != '20' else None,
        }
        print(f"  {angle_name}° - Abs Filt: {rms_abs['filtered_rms']:.2f} nm, "
              f"Rel Filt: {rms_rel['filtered_rms']:.2f} nm")

    # Extra angles (30, 50)
    for ea in extra_angles:
        data = displacements[ea]
        X, Y, WFE = build_wfe_arrays(data['node_ids'], data['disp'], node_geo)
        Xr, Yr, Wr = compute_relative_wfe(
            X, Y, WFE, data['node_ids'],
            X_ref, Y_ref, WFE_ref, ref_data['node_ids']
        )
        rms_abs = zernike_fit(X, Y, WFE, inner_radius=inner_radius)
        rms_rel = zernike_fit(Xr, Yr, Wr, inner_radius=inner_radius)
        angle_results[int(ea)] = {
            'X': X, 'Y': Y, 'WFE': WFE,
            'X_rel': Xr, 'Y_rel': Yr, 'WFE_rel': Wr,
            'rms_abs': rms_abs,
            'rms_rel': rms_rel,
            'aberrations_abs': aberration_magnitudes(rms_abs['coefficients']),
            'aberrations_rel': aberration_magnitudes(rms_rel['coefficients']),
        }
        print(f"  {ea}° - Abs Filt: {rms_abs['filtered_rms']:.2f} nm, "
              f"Rel Filt: {rms_rel['filtered_rms']:.2f} nm")

    # ------------------------------------------------------------------
    # 3. Trajectory analysis
    # ------------------------------------------------------------------
    print("\n[3/5] Trajectory analysis...")
    traj_result = None
    try:
        traj_extractor = ZernikeTrajectoryExtractor(
            op2_file=op2_path,
            bdf_file=geo_path,
            reference_angle=20.0,
            unit=DISP_UNIT,
            n_modes=N_MODES,
            inner_radius=inner_radius,
        )
        traj_result = traj_extractor.extract_trajectory(exclude_angles=[90.0])
        print(f"  R² fit: {traj_result['linear_fit_r2']:.4f}")
        print(f"  Dominant mode: {traj_result['dominant_mode']}")
        print(f"  Total filtered RMS: {traj_result['total_filtered_rms_nm']:.2f} nm")
    except Exception as e:
        print(f"  [WARN] Trajectory analysis failed: {e}")

    # ------------------------------------------------------------------
    # 4. Manufacturing analysis
    # ------------------------------------------------------------------
    print("\n[4/5] Manufacturing analysis...")
    r90 = angle_results[90]
    mfg_abs_aberr = r90['aberrations_abs']
    mfg_correction = aberration_magnitudes(angle_results[90]['rms_rel']['coefficients'])
    mfg_rms_j1to3 = r90['rms_rel']['rms_j1to3']
    print(f"  MFG 90 (J1-J3 filtered): {mfg_rms_j1to3:.2f} nm")
    print(f"  Correction - Astigmatism: {mfg_correction['astigmatism']:.2f} nm, "
          f"Coma: {mfg_correction['coma']:.2f} nm")

    # ------------------------------------------------------------------
    # 5. Load study params (optional)
    # ------------------------------------------------------------------
    study_params = None
    if study_db:
        print("\n[5/5] Loading study parameters...")
        try:
            study_params = load_study_params(study_db, trial_id)
            print(f"  Trial #{study_params.get('trial_id', '?')}")
        except Exception as e:
            print(f"  [WARN] Could not load study params: {e}")
    else:
        print("\n[5/5] No study database provided (skipping design parameters)")

    # ------------------------------------------------------------------
    # Key metrics for executive summary
    # ------------------------------------------------------------------
    wfe_40_20 = angle_results[40]['rms_rel']['filtered_rms']
    wfe_60_20 = angle_results[60]['rms_rel']['filtered_rms']
    mfg_90 = mfg_rms_j1to3

    # Weighted sum
    ws = 6*wfe_40_20 + 5*wfe_60_20 + 3*mfg_90

    # ------------------------------------------------------------------
    # Generate HTML
    # ------------------------------------------------------------------
    print("\nGenerating HTML report...")

    # Surface plots
    surf_40 = make_surface_plot(
        angle_results[40]['X_rel'], angle_results[40]['Y_rel'],
        angle_results[40]['rms_rel']['W_res_filt'], angle_results[40]['rms_rel']['mask'],
        inner_radius=inner_radius, title="40 vs 20"
    )
    surf_60 = make_surface_plot(
        angle_results[60]['X_rel'], angle_results[60]['Y_rel'],
        angle_results[60]['rms_rel']['W_res_filt'], angle_results[60]['rms_rel']['mask'],
        inner_radius=inner_radius, title="60 vs 20"
    )
    surf_90 = make_surface_plot(
        angle_results[90]['X'], angle_results[90]['Y'],
        angle_results[90]['rms_abs']['W_res_filt'], angle_results[90]['rms_abs']['mask'],
        inner_radius=inner_radius, title="90 abs"
    )

    # Bar charts
    bar_40 = make_bar_chart(angle_results[40]['rms_rel']['coefficients'], title="40v20 coeffs")
    bar_60 = make_bar_chart(angle_results[60]['rms_rel']['coefficients'], title="60v20 coeffs")
    bar_90 = make_bar_chart(angle_results[90]['rms_abs']['coefficients'], title="90abs coeffs")

    # Spatial frequency band metrics
    sfm_40 = _spatial_freq_html(compute_spatial_freq_metrics(angle_results[40]['rms_rel']['coefficients']))
    sfm_60 = _spatial_freq_html(compute_spatial_freq_metrics(angle_results[60]['rms_rel']['coefficients']))
    sfm_90 = _spatial_freq_html(compute_spatial_freq_metrics(angle_results[90]['rms_abs']['coefficients']))

    # Per-angle RMS plot
    angle_rms_data = {}
    for ang in sorted(angle_results.keys()):
        if ang != 20:
            angle_rms_data[ang] = angle_results[ang]['rms_rel']['filtered_rms']
    per_angle_plot = make_per_angle_rms_plot(angle_rms_data)

    # Trajectory & sensitivity plots
    traj_plot_html = ""
    sens_plot_html = ""
    if traj_result:
        coeffs_rel = np.array(traj_result['coefficients_relative'])
        traj_plot_html = make_trajectory_plot(
            traj_result['angles_deg'], coeffs_rel, MODE_GROUPS,
            traj_result['sensitivity_matrix']
        )
        sens_plot_html = make_sensitivity_bar(traj_result['sensitivity_matrix'])

    # Design parameters table
    params_html = ""
    if study_params and study_params.get('parameters'):
        params = study_params['parameters']
        rows = ""
        for k, v in sorted(params.items()):
            unit = "\u00b0" if "angle" in k else "mm"
            rows += f"<tr><td>{k}</td><td>{v:.4f} {unit}</td></tr>\n"
        params_html = f"""
        <div class="section">
            <h2>7. Design Parameters (Trial #{study_params.get('trial_id', '?')})</h2>
            <table class="data-table"><thead><tr><th>Parameter</th><th>Value</th></tr></thead>
            <tbody>{rows}</tbody></table>
        </div>
        """

    # Per-angle detail table
    angle_detail_rows = ""
    for ang in sorted(angle_results.keys()):
        r = angle_results[ang]
        rel_filt = r['rms_rel']['filtered_rms']
        abs_filt = r['rms_abs']['filtered_rms']
        abs_glob = r['rms_abs']['global_rms']
        ab = r['aberrations_abs']
        angle_detail_rows += f"""<tr>
            <td><b>{ang}\u00b0</b></td>
            <td>{abs_glob:.2f}</td><td>{abs_filt:.2f}</td>
            <td>{rel_filt:.2f}</td>
            <td>{ab['astigmatism']:.2f}</td><td>{ab['coma']:.2f}</td>
            <td>{ab['trefoil']:.2f}</td><td>{ab['spherical']:.2f}</td>
        </tr>"""

    # Trajectory metrics table
    traj_metrics_html = ""
    if traj_result:
        traj_metrics_html = f"""
        <div class="metrics-grid">
            <div class="metric-card">
                <div class="metric-label">Coma RMS</div>
                <div class="metric-value">{traj_result['coma_rms_nm']:.2f} nm</div>
            </div>
            <div class="metric-card">
                <div class="metric-label">Astigmatism RMS</div>
                <div class="metric-value">{traj_result['astigmatism_rms_nm']:.2f} nm</div>
            </div>
            <div class="metric-card">
                <div class="metric-label">Trefoil RMS</div>
                <div class="metric-value">{traj_result['trefoil_rms_nm']:.2f} nm</div>
            </div>
            <div class="metric-card">
                <div class="metric-label">Spherical RMS</div>
                <div class="metric-value">{traj_result['spherical_rms_nm']:.2f} nm</div>
            </div>
            <div class="metric-card">
                <div class="metric-label">Total Filtered RMS</div>
                <div class="metric-value">{traj_result['total_filtered_rms_nm']:.2f} nm</div>
            </div>
            <div class="metric-card">
                <div class="metric-label">Linear Fit R\u00b2</div>
                <div class="metric-value">{traj_result['linear_fit_r2']:.4f}</div>
            </div>
        </div>
        <p class="note">Dominant aberration mode: <b>{MODE_NAMES.get(traj_result['dominant_mode'], traj_result['dominant_mode'])}</b></p>
        <p class="note">Mode ranking: {' \u2192 '.join(traj_result['mode_ranking'][:5])}</p>
        """

    # Manufacturing details
    mfg_html = f"""
    <table class="data-table">
        <thead><tr><th>Metric</th><th>Absolute 90\u00b0</th><th>Correction (90\u00b0\u221220\u00b0)</th></tr></thead>
        <tbody>
            <tr><td>Defocus (J4)</td><td>{mfg_abs_aberr['defocus']:.2f} nm</td><td>{mfg_correction['defocus']:.2f} nm</td></tr>
            <tr><td>Astigmatism (J5+J6)</td><td>{mfg_abs_aberr['astigmatism']:.2f} nm</td><td>{mfg_correction['astigmatism']:.2f} nm</td></tr>
            <tr><td>Coma (J7+J8)</td><td>{mfg_abs_aberr['coma']:.2f} nm</td><td>{mfg_correction['coma']:.2f} nm</td></tr>
            <tr><td>Trefoil (J9+J10)</td><td>{mfg_abs_aberr['trefoil']:.2f} nm</td><td>{mfg_correction['trefoil']:.2f} nm</td></tr>
            <tr><td>Spherical (J11)</td><td>{mfg_abs_aberr['spherical']:.2f} nm</td><td>{mfg_correction['spherical']:.2f} nm</td></tr>
            <tr class="highlight"><td><b>J1\u2212J3 Filtered RMS</b></td><td>{r90['rms_abs']['rms_j1to3']:.2f} nm</td><td><b>{mfg_rms_j1to3:.2f} nm</b></td></tr>
        </tbody>
    </table>
    """

    # Executive summary metric styling
    style_40 = _metric_color(wfe_40_20, targets['wfe_40_20'])
    style_60 = _metric_color(wfe_60_20, targets['wfe_60_20'])
    style_mfg = _metric_color(mfg_90, targets['mfg_90'])

    # Section numbering: adjust if trajectory present
    sec_surface = 3
    sec_traj = 4
    sec_mfg = 5 if traj_result else 4
    sec_params = sec_mfg + 1
    sec_zernike = sec_params + 1 if (study_params and study_params.get('parameters')) else sec_mfg + 1
    sec_method = sec_zernike + 1

    # Assemble full HTML
    html = f"""<!DOCTYPE html>
<html lang="en">
<head>
<meta charset="UTF-8">
<meta name="viewport" content="width=device-width, initial-scale=1.0">
<title>{title}</title>
<script src="https://cdn.plot.ly/plotly-3.3.1.min.js"></script>
<style>
    :root {{
        --bg-primary: #ffffff;
        --bg-secondary: #f8fafc;
        --bg-card: #ffffff;
        --text-primary: #1e293b;
        --text-secondary: #64748b;
        --accent: #2563eb;
        --accent-light: #dbeafe;
        --success: #16a34a;
        --warning: #d97706;
        --danger: #dc2626;
        --border: #e2e8f0;
        --border-strong: #cbd5e1;
    }}
    * {{ margin: 0; padding: 0; box-sizing: border-box; }}
    body {{
        font-family: 'Inter', -apple-system, BlinkMacSystemFont, 'Segoe UI', system-ui, sans-serif;
        background: var(--bg-secondary);
        color: var(--text-primary);
        line-height: 1.6;
    }}
    .container {{ max-width: 1400px; margin: 0 auto; padding: 2rem; }}

    /* Header */
    .header {{
        background: var(--bg-card);
        border: 1px solid var(--border);
        border-radius: 8px;
        padding: 2rem 3rem;
        margin-bottom: 2rem;
        display: flex;
        justify-content: space-between;
        align-items: center;
    }}
    .header h1 {{ font-size: 1.6rem; font-weight: 700; color: var(--text-primary); }}
    .header .subtitle {{ color: var(--text-secondary); font-size: 0.9rem; margin-top: 0.3rem; }}
    .header .branding {{
        text-align: right;
        font-size: 0.85rem;
        color: var(--text-secondary);
    }}
    .header .branding .by-line {{ color: #94a3b8; font-size: 0.8rem; margin-top: 0.2rem; }}
    .header .branding .tagline {{ color: var(--text-secondary); font-size: 0.8rem; margin-top: 0.15rem; }}

    /* Sections */
    .section {{
        background: var(--bg-card);
        border: 1px solid var(--border);
        border-radius: 8px;
        padding: 1.5rem 2rem;
        margin-bottom: 1.5rem;
    }}
    .section h2 {{
        font-size: 1.2rem;
        font-weight: 600;
        color: var(--text-primary);
        margin-bottom: 1rem;
        padding-bottom: 0.5rem;
        border-bottom: 1px solid var(--border);
    }}

    /* Executive Summary */
    .exec-grid {{
        display: grid;
        grid-template-columns: repeat(auto-fit, minmax(280px, 1fr));
        gap: 1rem;
        margin: 1rem 0;
    }}
    .exec-card {{
        background: var(--bg-secondary);
        border: 1px solid var(--border);
        border-radius: 6px;
        padding: 1.2rem;
    }}
    .exec-card .label {{ font-size: 0.85rem; color: var(--text-secondary); margin-bottom: 0.3rem; }}
    .exec-card .value {{ font-size: 1.8rem; font-weight: 700; }}
    .exec-card .unit {{ font-size: 0.9rem; font-weight: 400; color: var(--text-secondary); }}
    .exec-footnote {{
        margin-top: 1rem;
        padding: 0.75rem 1rem;
        background: var(--bg-secondary);
        border-left: 3px solid var(--accent);
        border-radius: 0 4px 4px 0;
        font-size: 0.82rem;
        color: var(--text-secondary);
    }}

    /* Tables */
    .data-table {{
        width: 100%;
        border-collapse: collapse;
        margin: 0.5rem 0;
    }}
    .data-table th, .data-table td {{
        padding: 0.6rem 1rem;
        text-align: left;
        border-bottom: 1px solid var(--border);
    }}
    .data-table th {{
        background: var(--bg-secondary);
        font-weight: 600;
        font-size: 0.82rem;
        text-transform: uppercase;
        letter-spacing: 0.05em;
        color: var(--text-secondary);
    }}
    .data-table tr:hover {{ background: #f1f5f9; }}
    .data-table tr.highlight td {{
        background: var(--accent-light);
        font-weight: 600;
    }}

    /* Metrics Grid */
    .metrics-grid {{
        display: grid;
        grid-template-columns: repeat(auto-fit, minmax(170px, 1fr));
        gap: 0.8rem;
        margin: 1rem 0;
    }}
    .metric-card {{
        background: var(--bg-secondary);
        border: 1px solid var(--border);
        border-radius: 6px;
        padding: 1rem;
        text-align: center;
    }}
    .metric-label {{ font-size: 0.8rem; color: var(--text-secondary); margin-bottom: 0.3rem; }}
    .metric-value {{ font-size: 1.2rem; font-weight: 700; color: var(--accent); }}

    /* Plots */
    .plot-grid {{
        display: grid;
        grid-template-columns: repeat(auto-fit, minmax(1100px, 1fr));
        gap: 1rem;
    }}
    .plot-container {{
        background: var(--bg-card);
        border: 1px solid var(--border);
        border-radius: 6px;
        padding: 1rem;
        min-height: 400px;
    }}
    .plot-container h3 {{
        font-size: 0.95rem;
        font-weight: 600;
        margin-bottom: 0.5rem;
        color: var(--text-secondary);
    }}

    /* Spatial Frequency Breakdown */
    .sf-breakdown {{
        margin: 1rem 0 1.5rem 0;
        padding: 1.2rem;
        background: var(--bg-secondary);
        border: 1px solid var(--border);
        border-radius: 8px;
    }}
    .sf-breakdown h4 {{
        font-size: 0.9rem;
        font-weight: 600;
        color: var(--text-secondary);
        text-transform: uppercase;
        letter-spacing: 0.05em;
        margin-bottom: 0.8rem;
    }}
    .sf-band-grid {{
        display: grid;
        grid-template-columns: 1fr 1fr auto;
        gap: 0.8rem;
        margin-bottom: 1rem;
    }}
    .sf-band-card {{
        background: var(--bg-card);
        border: 1px solid var(--border);
        border-radius: 6px;
        padding: 0.8rem 1rem;
        text-align: center;
    }}
    .sf-band-label {{
        font-size: 0.82rem;
        font-weight: 600;
        color: var(--text-primary);
    }}
    .sf-band-range {{
        font-size: 0.75rem;
        color: var(--text-secondary);
        margin: 0.15rem 0 0.4rem 0;
    }}
    .sf-band-value {{
        font-size: 1.3rem;
        font-weight: 700;
        color: var(--accent);
    }}
    .sf-unit {{
        font-size: 0.8rem;
        font-weight: 400;
        color: var(--text-secondary);
    }}
    .sf-lsf {{ border-top: 3px solid #2563eb; }}
    .sf-msf {{ border-top: 3px solid #7c3aed; }}
    .sf-ratio {{ border-top: 3px solid #16a34a; min-width: 100px; }}
    .sf-order-grid {{
        background: var(--bg-card);
        border: 1px solid var(--border);
        border-radius: 6px;
        padding: 0.8rem 1rem;
    }}
    .sf-order-title {{
        font-size: 0.8rem;
        font-weight: 600;
        color: var(--text-secondary);
        margin-bottom: 0.5rem;
    }}
    .sf-order-items {{
        display: flex;
        flex-wrap: wrap;
        gap: 0.3rem 1.2rem;
    }}
    .sf-order-item {{
        display: flex;
        gap: 0.3rem;
        font-size: 0.85rem;
    }}
    .sf-order-n {{
        color: var(--text-secondary);
        font-weight: 500;
    }}
    .sf-order-val {{
        color: var(--text-primary);
        font-weight: 600;
    }}

    /* Collapsible */
    details {{ margin: 0.5rem 0; }}
    summary {{
        cursor: pointer;
        font-weight: 600;
        padding: 0.6rem 0.8rem;
        background: var(--bg-secondary);
        border-radius: 6px;
        border: 1px solid var(--border);
        color: var(--text-primary);
        font-size: 0.95rem;
    }}
    summary:hover {{ background: var(--accent-light); }}
    details > div {{ padding: 1rem; }}

    .note {{ color: var(--text-secondary); font-size: 0.88rem; margin: 0.5rem 0; }}

    .two-col {{ display: grid; grid-template-columns: 1fr 1fr; gap: 1.5rem; }}
    @media (max-width: 900px) {{ .two-col {{ grid-template-columns: 1fr; }} }}

    /* Print styles */
    @media print {{
        body {{ background: white; color: black; font-size: 10pt; }}
        .container {{ max-width: 100%; padding: 0.5cm; }}
        .header {{ border: 1px solid #999; }}
        .section {{ border: 1px solid #ccc; page-break-inside: avoid; margin-bottom: 0.5cm; }}
        .plot-container {{ page-break-inside: avoid; }}
        details {{ display: block; }}
        details > summary {{ display: none; }}
        details > div {{ padding: 0; }}
        .exec-grid {{ grid-template-columns: repeat(2, 1fr); }}
        .no-print {{ display: none; }}
    }}

    /* Footer */
    .footer {{
        text-align: center;
        padding: 2rem;
        color: var(--text-secondary);
        font-size: 0.8rem;
        border-top: 1px solid var(--border);
        margin-top: 1rem;
    }}
</style>
</head>
<body>
<div class="container">

<!-- Header -->
<div class="header">
    <div>
        <h1>{title}</h1>
        <div class="subtitle">Generated {timestamp} &nbsp;|&nbsp; OP2: {op2_path.name}</div>
        {'<div class="subtitle">Study: ' + study_name + '</div>' if study_name else ''}
    </div>
    <div class="branding">
        <svg width="120" height="32" viewBox="0 0 120 32" xmlns="http://www.w3.org/2000/svg">
            <text x="0" y="24" font-family="Inter, system-ui, sans-serif" font-size="22" font-weight="700" fill="#2563eb">ATOMIZER</text>
        </svg>
        <div class="by-line">by Atomaste</div>
        <div class="tagline">FEA Optimization Platform</div>
    </div>
</div>

<!-- 1. Executive Summary -->
<div class="section">
    <h2>1. Executive Summary</h2>
    <div class="exec-grid">
        <div class="exec-card">
            <div class="label">WFE 40\u00b0 vs 20\u00b0 (Tracking)</div>
            <div class="value" style="{style_40}">{wfe_40_20:.2f} <span class="unit">nm</span></div>
        </div>
        <div class="exec-card">
            <div class="label">WFE 60\u00b0 vs 20\u00b0 (Tracking)</div>
            <div class="value" style="{style_60}">{wfe_60_20:.2f} <span class="unit">nm</span></div>
        </div>
        <div class="exec-card">
            <div class="label">MFG 90\u00b0 (J1\u2212J3 Filtered)</div>
            <div class="value" style="{style_mfg}">{mfg_90:.2f} <span class="unit">nm</span></div>
        </div>
        <div class="exec-card">
            <div class="label">Weighted Sum (6\u00b7W40 + 5\u00b7W60 + 3\u00b7MFG)</div>
            <div class="value" style="color: var(--accent); font-weight: 700;">{ws:.1f}</div>
        </div>
    </div>
    <div class="exec-footnote">
        Design targets &mdash;
        WFE 40\u00b0\u221220\u00b0 \u2264 {targets['wfe_40_20']:.1f} nm &nbsp;|&nbsp;
        WFE 60\u00b0\u221220\u00b0 \u2264 {targets['wfe_60_20']:.1f} nm &nbsp;|&nbsp;
        MFG 90\u00b0 \u2264 {targets['mfg_90']:.1f} nm &nbsp;|&nbsp;
        Weighted sum: lower is better.
        {'<br>Annular aperture: inner radius = ' + f'{inner_radius:.1f} mm (\u00f8{2*inner_radius:.1f} mm central hole)' if inner_radius else ''}
    </div>
</div>

<!-- 2. Per-Angle Summary -->
<div class="section">
    <h2>2. Per-Angle RMS Summary</h2>
    {per_angle_plot}
    <table class="data-table" style="margin-top:1rem">
        <thead>
            <tr>
                <th>Angle</th><th>Abs Global RMS</th><th>Abs Filtered RMS</th>
                <th>Rel Filtered RMS</th>
                <th>Astigmatism</th><th>Coma</th><th>Trefoil</th><th>Spherical</th>
            </tr>
        </thead>
        <tbody>{angle_detail_rows}</tbody>
    </table>
    <p class="note">All values in nm. Filtered = J1\u2212J4 removed. Relative = vs 20\u00b0 reference. Aberrations are absolute.</p>
</div>

<!-- 3. Surface Plots -->
<div class="section">
    <h2>{sec_surface}. Wavefront Error Surface Maps</h2>
    <p class="note">3D residual surfaces after removing piston, tip, tilt, and defocus (J1\u2212J4). Interactive \u2014 drag to rotate.</p>
    <div class="plot-grid">
        <div class="plot-container">
            <h3>40\u00b0 vs 20\u00b0 (Relative)</h3>
            {surf_40}
        </div>
        <div class="plot-container">
            <h3>60\u00b0 vs 20\u00b0 (Relative)</h3>
            {surf_60}
        </div>
    </div>
    <div class="plot-container" style="margin-top:1rem">
        <h3>90\u00b0 Manufacturing (Absolute)</h3>
        {surf_90}
    </div>
</div>

<!-- Trajectory Analysis -->
{'<div class="section"><h2>' + str(sec_traj) + '. Zernike Trajectory Analysis</h2>' +
 '<p class="note">Mode-specific integrated RMS across the operating elevation range. ' +
 'The linear model c<sub>j</sub>(\u03b8) = a<sub>j</sub>\u00b7\u0394sin\u03b8 + b<sub>j</sub>\u00b7\u0394cos\u03b8 decomposes gravity into axial and lateral components.</p>' +
 traj_metrics_html +
 '<div class="two-col" style="margin-top:1rem">' +
 '<div class="plot-container"><h3>Mode RMS vs Elevation Angle</h3>' + traj_plot_html + '</div>' +
 '<div class="plot-container"><h3>Axial vs Lateral Sensitivity</h3>' + sens_plot_html + '</div>' +
 '</div></div>' if traj_result else ''}

<!-- Manufacturing Analysis -->
<div class="section">
    <h2>{sec_mfg}. Manufacturing Analysis (90\u00b0 Orientation)</h2>
    <p class="note">
        The mirror is manufactured (polished) at 90\u00b0 orientation. The "Correction" column shows the
        aberrations that must be polished out to achieve the 20\u00b0 operational figure.
    </p>
    {mfg_html}
</div>

<!-- Design Parameters -->
{params_html}

<!-- Zernike Coefficient Details -->
<div class="section">
    <h2>{sec_zernike}. Zernike Coefficient Details</h2>
    <details>
        <summary>40\u00b0 vs 20\u00b0 \u2014 Relative Coefficients</summary>
        <div>{sfm_40}{bar_40}</div>
    </details>
    <details>
        <summary>60\u00b0 vs 20\u00b0 \u2014 Relative Coefficients</summary>
        <div>{sfm_60}{bar_60}</div>
    </details>
    <details>
        <summary>90\u00b0 \u2014 Absolute Coefficients</summary>
        <div>{sfm_90}{bar_90}</div>
    </details>
</div>

<!-- Methodology -->
<div class="section">
    <h2>{sec_method}. Methodology</h2>
    <table class="data-table">
        <tbody>
            <tr><td><b>Zernike Modes</b></td><td>{N_MODES} (Noll convention)</td></tr>
            <tr><td><b>Filtered Modes</b></td><td>J1\u2212J4 (Piston, Tip, Tilt, Defocus)</td></tr>
            <tr><td><b>WFE Calculation</b></td><td>WFE = 2 \u00d7 Surface Error (reflective)</td></tr>
            <tr><td><b>Displacement Unit</b></td><td>{DISP_UNIT} \u2192 nm ({NM_SCALE:.0e}\u00d7)</td></tr>
            <tr><td><b>Aperture</b></td><td>{'Annular (inner R = ' + f'{inner_radius:.1f} mm)' if inner_radius else 'Full disk'}</td></tr>
            <tr><td><b>Reference Angle</b></td><td>20\u00b0 (polishing/measurement orientation)</td></tr>
            <tr><td><b>MFG Objective</b></td><td>90\u00b0\u221220\u00b0 relative, J1\u2212J3 filtered (optician workload)</td></tr>
            <tr><td><b>Weighted Sum</b></td><td>6\u00d7WFE(40\u221220) + 5\u00d7WFE(60\u221220) + 3\u00d7MFG(90)</td></tr>
            {'<tr><td><b>Trajectory R\u00b2</b></td><td>' + f'{traj_result["linear_fit_r2"]:.6f}' + '</td></tr>' if traj_result else ''}
        </tbody>
    </table>
</div>

<!-- Footer -->
<div class="footer">
    Generated by <b>Atomizer</b> Optical Report Generator &nbsp;|&nbsp; {timestamp}<br>
    \u00a9 Atomaste &nbsp;|&nbsp; atomaste.ca
</div>

</div>
</body>
</html>"""

    # Write output
    output_path = op2_path.parent / f"{op2_path.stem}_OPTICAL_REPORT_{ts_file}.html"
    output_path.write_text(html, encoding='utf-8')

    print(f"\n{'=' * 70}")
    print(f"REPORT GENERATED: {output_path.name}")
    print(f"{'=' * 70}")
    print(f"\nLocation: {output_path}")
    print(f"Size: {output_path.stat().st_size / 1024:.0f} KB")

    return output_path


# ============================================================================
# CLI
# ============================================================================

def main():
    parser = argparse.ArgumentParser(
        description='Atomizer Optical Performance Report Generator',
        epilog='Generates a comprehensive CDR-ready HTML report from FEA results.'
    )
    parser.add_argument('op2_file', nargs='?', help='Path to OP2 results file')
    parser.add_argument('--inner-radius', '-r', type=float, default=None,
                       help=f'Inner radius of central hole in mm (default: {DEFAULT_INNER_RADIUS}mm for M1)')
    parser.add_argument('--inner-diameter', '-d', type=float, default=None,
                       help='Inner diameter of central hole in mm')
    parser.add_argument('--no-annular', action='store_true',
                       help='Disable annular aperture (treat as full disk)')
    parser.add_argument('--target-40', type=float, default=DEFAULT_TARGETS['wfe_40_20'],
                       help=f'WFE 40-20 target in nm (default: {DEFAULT_TARGETS["wfe_40_20"]})')
    parser.add_argument('--target-60', type=float, default=DEFAULT_TARGETS['wfe_60_20'],
                       help=f'WFE 60-20 target in nm (default: {DEFAULT_TARGETS["wfe_60_20"]})')
    parser.add_argument('--target-mfg', type=float, default=DEFAULT_TARGETS['mfg_90'],
                       help=f'MFG 90 target in nm (default: {DEFAULT_TARGETS["mfg_90"]})')
    parser.add_argument('--study-db', type=str, default=None,
                       help='Path to study.db for design parameters')
    parser.add_argument('--trial', type=int, default=None,
                       help='Trial ID (default: best trial)')
    parser.add_argument('--title', type=str, default="M1 Mirror Optical Performance Report",
                       help='Report title')
    parser.add_argument('--study-name', type=str, default=None,
                       help='Study name for report header')

    args = parser.parse_args()

    # Find OP2 file
    if args.op2_file:
        op2_path = Path(args.op2_file)
        if not op2_path.exists():
            print(f"ERROR: File not found: {op2_path}")
            sys.exit(1)
    else:
        # Search current directory
        cwd = Path.cwd()
        candidates = list(cwd.glob("*solution*.op2")) + list(cwd.glob("*.op2"))
        if not candidates:
            print("ERROR: No OP2 file found. Specify path as argument.")
            sys.exit(1)
        op2_path = max(candidates, key=lambda p: p.stat().st_mtime)
        print(f"Found: {op2_path}")

    # Handle inner radius
    inner_radius = DEFAULT_INNER_RADIUS  # Default to M1 annular
    if args.no_annular:
        inner_radius = None
    elif args.inner_diameter is not None:
        inner_radius = args.inner_diameter / 2.0
    elif args.inner_radius is not None:
        inner_radius = args.inner_radius

    targets = {
        'wfe_40_20': args.target_40,
        'wfe_60_20': args.target_60,
        'mfg_90': args.target_mfg,
    }

    try:
        generate_report(
            op2_path=op2_path,
            inner_radius=inner_radius,
            targets=targets,
            study_db=args.study_db,
            trial_id=args.trial,
            title=args.title,
            study_name=args.study_name,
        )
    except Exception as e:
        print(f"\nERROR: {e}")
        import traceback
        traceback.print_exc()
        sys.exit(1)


if __name__ == '__main__':
    main()