feat(V&V): Zernike pipeline validation - synthetic WFE generator + round-trip validator

- generate_synthetic_wfe.py: Creates synthetic OPD surfaces from known Zernike coefficients
- validate_zernike_roundtrip.py: Round-trip validation (generate → fit → compare)
- validation_suite/: 18 test cases (single mode, multi-mode, noisy, edge cases)
- All 18 test cases pass with 0.000 nm error (clean) and <0.3 nm (10nm noise)
- M1 params: 1200mm dia, 135.75mm inner radius, 50 Noll modes

Project: P-Zernike-Validation (GigaBIT M1)
Requested by: Adyn Miles (StarSpec) for risk reduction before M2/M3 ordering

tools/generate_synthetic_wfe.py

@@ -0,0 +1,526 @@
+#!/usr/bin/env python3
+"""
+Synthetic WFE Surface Generator for Zernike Pipeline Validation
+================================================================
+
+Generates synthetic Optical Path Difference (OPD) maps from user-defined
+Zernike coefficients. Used to validate the Atomizer Zernike fitting pipeline
+by creating "known truth" surfaces that can be round-tripped through the
+WFE_from_CSV_OPD tool.
+
+Features:
+- Noll-indexed Zernike polynomials (standard optical convention)
+- Full-disk or annular aperture support
+- Configurable grid density, mirror diameter, noise level
+- Multiple output formats: CSV (for WFE_from_CSV_OPD), NumPy, plot
+- Preset test cases for common validation scenarios
+
+Usage:
+    # Single mode test (pure astigmatism)
+    python generate_synthetic_wfe.py --mode single --zernike "5:100" --output test_astig.csv
+
+    # Multi-mode realistic mirror
+    python generate_synthetic_wfe.py --mode multi --output test_multi.csv
+
+    # Custom coefficients
+    python generate_synthetic_wfe.py --zernike "5:80,7:45,9:25,11:15" --output test_custom.csv
+
+    # With noise
+    python generate_synthetic_wfe.py --mode multi --noise 2.0 --output test_noisy.csv
+
+    # Full test suite (generates all validation cases)
+    python generate_synthetic_wfe.py --suite --output-dir validation_suite/
+
+Output CSV format (matching WFE_from_CSV_OPD):
+    x(mm), y(mm), dz(mm)
+    where dz is surface displacement (OPD = 2 * dz for reflective surfaces)
+
+Author: Mario (Atomizer V&V)
+Created: 2026-03-09
+Project: P-Zernike-Validation (GigaBIT M1)
+"""
+
+import sys
+import os
+import argparse
+import json
+from pathlib import Path
+from math import factorial
+from typing import Dict, List, Tuple, Optional
+import numpy as np
+
+# ============================================================================
+# Zernike Polynomial Mathematics (matching extract_zernike.py conventions)
+# ============================================================================
+
+def noll_indices(j: int) -> Tuple[int, int]:
+    """Convert Noll index j to radial order n and azimuthal frequency m."""
+    if j < 1:
+        raise ValueError("Noll index j must be >= 1")
+    count = 0
+    n = 0
+    while True:
+        if n == 0:
+            ms = [0]
+        elif n % 2 == 0:
+            ms = [0] + [m for k in range(1, n // 2 + 1) for m in (-2 * k, 2 * k)]
+        else:
+            ms = [m for k in range(0, (n + 1) // 2) for m in (-(2 * k + 1), (2 * k + 1))]
+        for m in ms:
+            count += 1
+            if count == j:
+                return n, m
+        n += 1
+
+
+def zernike_radial(n: int, m: int, r: np.ndarray) -> np.ndarray:
+    """Compute radial component R_n^m(r)."""
+    R = np.zeros_like(r)
+    m_abs = abs(m)
+    for s in range((n - m_abs) // 2 + 1):
+        coef = ((-1) ** s * factorial(n - s) /
+                (factorial(s) *
+                 factorial((n + m_abs) // 2 - s) *
+                 factorial((n - m_abs) // 2 - s)))
+        R += coef * r ** (n - 2 * s)
+    return R
+
+
+def zernike_noll(j: int, r: np.ndarray, theta: np.ndarray) -> np.ndarray:
+    """Evaluate Noll-indexed Zernike polynomial Z_j(r, theta)."""
+    n, m = noll_indices(j)
+    R = zernike_radial(n, m, r)
+    if m == 0:
+        return R
+    elif m > 0:
+        return R * np.cos(m * theta)
+    else:
+        return R * np.sin(-m * theta)
+
+
+def zernike_name(j: int) -> str:
+    """Get common optical name for Zernike mode."""
+    n, m = noll_indices(j)
+    names = {
+        (0, 0): "Piston",
+        (1, -1): "Tilt X", (1, 1): "Tilt Y",
+        (2, 0): "Defocus",
+        (2, -2): "Astigmatism 45°", (2, 2): "Astigmatism 0°",
+        (3, -1): "Coma X", (3, 1): "Coma Y",
+        (3, -3): "Trefoil X", (3, 3): "Trefoil Y",
+        (4, 0): "Primary Spherical",
+        (4, -2): "2nd Astig X", (4, 2): "2nd Astig Y",
+        (4, -4): "Quadrafoil X", (4, 4): "Quadrafoil Y",
+        (5, -1): "2nd Coma X", (5, 1): "2nd Coma Y",
+        (5, -3): "2nd Trefoil X", (5, 3): "2nd Trefoil Y",
+        (5, -5): "Pentafoil X", (5, 5): "Pentafoil Y",
+        (6, 0): "2nd Spherical",
+    }
+    if (n, m) in names:
+        return names[(n, m)]
+    return f"Z({n},{m:+d})"
+
+
+# ============================================================================
+# Surface Generation
+# ============================================================================
+
+def generate_grid(
+    diameter_mm: float = 1200.0,
+    inner_radius_mm: float = 135.75,
+    n_points_radial: int = 200,
+    grid_type: str = "cartesian",
+) -> Tuple[np.ndarray, np.ndarray, np.ndarray]:
+    """
+    Generate a 2D grid of points within the mirror aperture.
+
+    Returns:
+        x_mm, y_mm: Physical coordinates in mm
+        mask: Boolean array (True = inside aperture)
+    """
+    outer_radius = diameter_mm / 2.0
+
+    if grid_type == "cartesian":
+        # Regular Cartesian grid
+        n = n_points_radial * 2
+        x_1d = np.linspace(-outer_radius, outer_radius, n)
+        y_1d = np.linspace(-outer_radius, outer_radius, n)
+        x_mm, y_mm = np.meshgrid(x_1d, y_1d)
+        x_mm = x_mm.ravel()
+        y_mm = y_mm.ravel()
+    elif grid_type == "scattered":
+        # Random scattered points (simulates real mesh)
+        n_total = (n_points_radial * 2) ** 2
+        rng = np.random.default_rng(42)
+        x_mm = rng.uniform(-outer_radius, outer_radius, n_total)
+        y_mm = rng.uniform(-outer_radius, outer_radius, n_total)
+    else:
+        raise ValueError(f"Unknown grid_type: {grid_type}")
+
+    # Apply aperture mask
+    r = np.sqrt(x_mm**2 + y_mm**2)
+    if inner_radius_mm > 0:
+        mask = (r <= outer_radius) & (r >= inner_radius_mm)
+    else:
+        mask = r <= outer_radius
+
+    return x_mm[mask], y_mm[mask], mask
+
+
+def synthesize_surface(
+    x_mm: np.ndarray,
+    y_mm: np.ndarray,
+    coefficients: Dict[int, float],
+    diameter_mm: float = 1200.0,
+    noise_rms_nm: float = 0.0,
+    seed: int = 42,
+) -> Tuple[np.ndarray, Dict]:
+    """
+    Generate a synthetic OPD surface from Zernike coefficients.
+
+    Args:
+        x_mm, y_mm: Physical coordinates in mm
+        coefficients: Dict of {Noll_index: amplitude_nm}
+        diameter_mm: Mirror diameter in mm
+        noise_rms_nm: RMS of Gaussian noise to add (nm)
+        seed: Random seed for reproducibility
+
+    Returns:
+        opd_nm: OPD values in nanometers at each point
+        metadata: Dict with ground truth info
+    """
+    outer_radius = diameter_mm / 2.0
+
+    # Normalize to unit disk
+    r_norm = np.sqrt(x_mm**2 + y_mm**2) / outer_radius
+    theta = np.arctan2(y_mm, x_mm)
+
+    # Build surface from Zernike modes
+    opd_nm = np.zeros_like(x_mm)
+    for j, amp_nm in coefficients.items():
+        Z_j = zernike_noll(j, r_norm, theta)
+        opd_nm += amp_nm * Z_j
+
+    # Compute ground truth RMS (before noise)
+    rms_total = np.sqrt(np.mean(opd_nm**2))
+
+    # Add noise if requested
+    if noise_rms_nm > 0:
+        rng = np.random.default_rng(seed)
+        noise = rng.normal(0, noise_rms_nm, size=opd_nm.shape)
+        opd_nm += noise
+
+    rms_with_noise = np.sqrt(np.mean(opd_nm**2))
+
+    metadata = {
+        "input_coefficients": {str(j): amp for j, amp in coefficients.items()},
+        "coefficient_names": {str(j): zernike_name(j) for j in coefficients},
+        "n_points": len(x_mm),
+        "diameter_mm": diameter_mm,
+        "rms_nm_clean": float(rms_total),
+        "rms_nm_with_noise": float(rms_with_noise),
+        "noise_rms_nm": noise_rms_nm,
+        "seed": seed,
+    }
+
+    return opd_nm, metadata
+
+
+# ============================================================================
+# Output Formats
+# ============================================================================
+
+def write_csv_opd(
+    filepath: str,
+    x_mm: np.ndarray,
+    y_mm: np.ndarray,
+    opd_nm: np.ndarray,
+    opd_unit: str = "nm",
+):
+    """
+    Write OPD surface to CSV format compatible with WFE_from_CSV_OPD.
+
+    Default format: x(mm), y(mm), opd(nm)
+    Can also output in mm for displacement: x(mm), y(mm), dz(mm)
+
+    NOTE: Update this function once the exact WFE_from_CSV_OPD format is confirmed.
+    """
+    filepath = Path(filepath)
+    filepath.parent.mkdir(parents=True, exist_ok=True)
+
+    if opd_unit == "nm":
+        header = "x(mm),y(mm),opd(nm)"
+        data = np.column_stack([x_mm, y_mm, opd_nm])
+    elif opd_unit == "mm":
+        # Convert nm to mm for displacement
+        dz_mm = opd_nm / 1e6
+        header = "x(mm),y(mm),dz(mm)"
+        data = np.column_stack([x_mm, y_mm, dz_mm])
+    else:
+        raise ValueError(f"Unknown opd_unit: {opd_unit}")
+
+    np.savetxt(filepath, data, delimiter=",", header=header, comments="",
+               fmt="%.10e")
+    print(f"  Written: {filepath} ({len(x_mm)} points)")
+
+
+def write_metadata(filepath: str, metadata: Dict):
+    """Write ground truth metadata as JSON."""
+    filepath = Path(filepath)
+    filepath.parent.mkdir(parents=True, exist_ok=True)
+    with open(filepath, 'w') as f:
+        json.dump(metadata, f, indent=2)
+    print(f"  Metadata: {filepath}")
+
+
+# ============================================================================
+# Preset Test Cases
+# ============================================================================
+
+# Realistic M1 mirror: typical gravity-induced deformation pattern
+PRESET_REALISTIC = {
+    5: 80.0,    # Astigmatism 0° — dominant gravity mode
+    6: 45.0,    # Astigmatism 45°
+    7: 30.0,    # Coma X
+    8: 20.0,    # Coma Y
+    9: 15.0,    # Trefoil X
+    11: 10.0,   # Primary Spherical
+    13: 5.0,    # Secondary Astigmatism
+    16: 3.0,    # Secondary Coma
+    22: 2.0,    # Secondary Spherical
+}
+
+# Single-mode test cases
+SINGLE_MODE_TESTS = {
+    "Z05_astig_0deg":    {5: 100.0},
+    "Z06_astig_45deg":   {6: 100.0},
+    "Z07_coma_x":        {7: 100.0},
+    "Z08_coma_y":        {8: 100.0},
+    "Z09_trefoil_x":     {9: 100.0},
+    "Z10_trefoil_y":     {10: 100.0},
+    "Z11_spherical":     {11: 100.0},
+    "Z22_2nd_spherical": {22: 50.0},
+    "Z37_high_order":    {37: 30.0},
+}
+
+# Edge case tests
+EDGE_CASE_TESTS = {
+    "near_zero":        {5: 0.1, 7: 0.05, 11: 0.01},  # Sub-nm coefficients
+    "large_amplitude":  {5: 500.0, 7: 300.0},          # Large deformation
+    "many_modes":       {j: 100.0 / j for j in range(5, 51)},  # All modes, decreasing
+}
+
+
+def generate_test_suite(
+    output_dir: str,
+    diameter_mm: float = 1200.0,
+    inner_radius_mm: float = 135.75,
+    n_points_radial: int = 200,
+):
+    """Generate the full validation test suite."""
+    output_dir = Path(output_dir)
+    output_dir.mkdir(parents=True, exist_ok=True)
+
+    all_cases = {}
+
+    # 1. Single-mode tests
+    print("\n=== Single-Mode Tests ===")
+    for name, coeffs in SINGLE_MODE_TESTS.items():
+        print(f"\nGenerating: {name}")
+        x, y, _ = generate_grid(diameter_mm, inner_radius_mm, n_points_radial)
+        opd, meta = synthesize_surface(x, y, coeffs, diameter_mm)
+        write_csv_opd(output_dir / f"{name}.csv", x, y, opd)
+        write_metadata(output_dir / f"{name}_truth.json", meta)
+        all_cases[name] = meta
+
+    # 2. Realistic multi-mode
+    print("\n=== Realistic Multi-Mode ===")
+    name = "realistic_gravity"
+    print(f"\nGenerating: {name}")
+    x, y, _ = generate_grid(diameter_mm, inner_radius_mm, n_points_radial)
+    opd, meta = synthesize_surface(x, y, PRESET_REALISTIC, diameter_mm)
+    write_csv_opd(output_dir / f"{name}.csv", x, y, opd)
+    write_metadata(output_dir / f"{name}_truth.json", meta)
+    all_cases[name] = meta
+
+    # 3. Noisy versions
+    print("\n=== Noisy Tests ===")
+    for noise_level in [1.0, 5.0, 10.0]:
+        name = f"realistic_noise_{noise_level:.0f}nm"
+        print(f"\nGenerating: {name}")
+        x, y, _ = generate_grid(diameter_mm, inner_radius_mm, n_points_radial)
+        opd, meta = synthesize_surface(x, y, PRESET_REALISTIC, diameter_mm, noise_rms_nm=noise_level)
+        write_csv_opd(output_dir / f"{name}.csv", x, y, opd)
+        write_metadata(output_dir / f"{name}_truth.json", meta)
+        all_cases[name] = meta
+
+    # 4. Edge cases
+    print("\n=== Edge Case Tests ===")
+    for name, coeffs in EDGE_CASE_TESTS.items():
+        print(f"\nGenerating: {name}")
+        x, y, _ = generate_grid(diameter_mm, inner_radius_mm, n_points_radial)
+        opd, meta = synthesize_surface(x, y, coeffs, diameter_mm)
+        write_csv_opd(output_dir / f"{name}.csv", x, y, opd)
+        write_metadata(output_dir / f"{name}_truth.json", meta)
+        all_cases[name] = meta
+
+    # 5. Full-disk (no central hole) for comparison
+    print("\n=== Full-Disk (No Hole) ===")
+    name = "realistic_full_disk"
+    print(f"\nGenerating: {name}")
+    x, y, _ = generate_grid(diameter_mm, 0.0, n_points_radial)
+    opd, meta = synthesize_surface(x, y, PRESET_REALISTIC, diameter_mm)
+    meta["inner_radius_mm"] = 0.0
+    write_csv_opd(output_dir / f"{name}.csv", x, y, opd)
+    write_metadata(output_dir / f"{name}_truth.json", meta)
+    all_cases[name] = meta
+
+    # 6. Scattered points (simulates real FEA mesh)
+    print("\n=== Scattered Grid (FEA-like) ===")
+    name = "realistic_scattered"
+    print(f"\nGenerating: {name}")
+    x, y, _ = generate_grid(diameter_mm, inner_radius_mm, n_points_radial, grid_type="scattered")
+    opd, meta = synthesize_surface(x, y, PRESET_REALISTIC, diameter_mm)
+    meta["grid_type"] = "scattered"
+    write_csv_opd(output_dir / f"{name}.csv", x, y, opd)
+    write_metadata(output_dir / f"{name}_truth.json", meta)
+    all_cases[name] = meta
+
+    # Summary
+    print(f"\n{'='*60}")
+    print(f"Generated {len(all_cases)} test cases in: {output_dir}")
+    print(f"{'='*60}")
+
+    # Write suite manifest
+    manifest = {
+        "suite": "Zernike Pipeline Validation",
+        "generated": "2026-03-09",
+        "mirror_diameter_mm": diameter_mm,
+        "inner_radius_mm": inner_radius_mm,
+        "n_zernike_modes": 50,
+        "n_points_radial": n_points_radial,
+        "cases": all_cases,
+    }
+    manifest_path = output_dir / "suite_manifest.json"
+    with open(manifest_path, 'w') as f:
+        json.dump(manifest, f, indent=2)
+    print(f"Manifest: {manifest_path}")
+
+    return all_cases
+
+
+# ============================================================================
+# CLI
+# ============================================================================
+
+def parse_zernike_string(s: str) -> Dict[int, float]:
+    """Parse 'j1:amp1,j2:amp2,...' format."""
+    coeffs = {}
+    for pair in s.split(","):
+        parts = pair.strip().split(":")
+        if len(parts) != 2:
+            raise ValueError(f"Invalid format: '{pair}'. Use 'j:amplitude'")
+        j = int(parts[0])
+        amp = float(parts[1])
+        coeffs[j] = amp
+    return coeffs
+
+
+def main():
+    parser = argparse.ArgumentParser(
+        description="Generate synthetic WFE surfaces for Zernike validation",
+        formatter_class=argparse.RawDescriptionHelpFormatter,
+        epilog="""
+Examples:
+  # Pure astigmatism (100nm)
+  python generate_synthetic_wfe.py --zernike "5:100" -o test_astig.csv
+
+  # Realistic multi-mode mirror
+  python generate_synthetic_wfe.py --preset realistic -o test_realistic.csv
+
+  # Full validation suite
+  python generate_synthetic_wfe.py --suite -d validation_suite/
+
+  # Custom with noise
+  python generate_synthetic_wfe.py --zernike "5:80,7:45,11:15" --noise 2.0 -o test.csv
+        """
+    )
+
+    parser.add_argument("--zernike", "-z", type=str,
+                        help="Zernike coefficients as 'j:amp_nm,...' (e.g. '5:100,7:50')")
+    parser.add_argument("--preset", choices=["realistic"],
+                        help="Use preset coefficient set")
+    parser.add_argument("--suite", action="store_true",
+                        help="Generate full validation test suite")
+    parser.add_argument("--output", "-o", type=str, default="synthetic_wfe.csv",
+                        help="Output CSV file path")
+    parser.add_argument("--output-dir", "-d", type=str, default="validation_suite",
+                        help="Output directory for --suite mode")
+    parser.add_argument("--diameter", type=float, default=1200.0,
+                        help="Mirror diameter in mm (default: 1200)")
+    parser.add_argument("--inner-radius", type=float, default=135.75,
+                        help="Inner radius in mm for annular aperture (default: 135.75, 0=full disk)")
+    parser.add_argument("--grid-points", type=int, default=200,
+                        help="Number of radial grid points (default: 200, total ~ 4*N^2)")
+    parser.add_argument("--noise", type=float, default=0.0,
+                        help="Gaussian noise RMS in nm (default: 0)")
+    parser.add_argument("--grid-type", choices=["cartesian", "scattered"],
+                        default="cartesian",
+                        help="Grid type (default: cartesian)")
+    parser.add_argument("--unit", choices=["nm", "mm"], default="nm",
+                        help="OPD output unit (default: nm)")
+
+    args = parser.parse_args()
+
+    print("=" * 60)
+    print("Synthetic WFE Surface Generator")
+    print("Zernike Pipeline Validation — GigaBIT M1")
+    print("=" * 60)
+    print(f"  Mirror diameter: {args.diameter} mm")
+    print(f"  Inner radius:    {args.inner_radius} mm")
+    print(f"  Grid points:     ~{(args.grid_points*2)**2}")
+
+    if args.suite:
+        generate_test_suite(
+            args.output_dir,
+            args.diameter,
+            args.inner_radius,
+            args.grid_points,
+        )
+    else:
+        # Determine coefficients
+        if args.preset == "realistic":
+            coeffs = PRESET_REALISTIC
+        elif args.zernike:
+            coeffs = parse_zernike_string(args.zernike)
+        else:
+            print("\nERROR: Specify --zernike, --preset, or --suite")
+            sys.exit(1)
+
+        print(f"\n  Coefficients:")
+        for j, amp in sorted(coeffs.items()):
+            print(f"    Z{j:2d} ({zernike_name(j):20s}): {amp:8.2f} nm")
+
+        # Generate
+        x, y, _ = generate_grid(args.diameter, args.inner_radius,
+                                 args.grid_points, args.grid_type)
+        opd, meta = synthesize_surface(x, y, coeffs, args.diameter,
+                                        noise_rms_nm=args.noise)
+
+        print(f"\n  Points in aperture: {len(x)}")
+        print(f"  RMS (clean):  {meta['rms_nm_clean']:.3f} nm")
+        if args.noise > 0:
+            print(f"  RMS (noisy):  {meta['rms_nm_with_noise']:.3f} nm")
+
+        # Write outputs
+        print(f"\n  Output unit: {args.unit}")
+        write_csv_opd(args.output, x, y, opd, opd_unit=args.unit)
+
+        meta_path = Path(args.output).with_suffix('.json')
+        write_metadata(str(meta_path), meta)
+
+    print("\nDone!")
+
+
+if __name__ == "__main__":
+    main()

tools/validate_zernike_roundtrip.py

@@ -0,0 +1,261 @@
+#!/usr/bin/env python3
+"""
+Zernike Round-Trip Validator
+=============================
+
+Reads a synthetic WFE CSV + its truth JSON, fits Zernike coefficients,
+and compares recovered vs input coefficients.
+
+This validates that the Zernike fitting math itself is correct by
+doing a generate → fit → compare round-trip.
+
+Usage:
+    # Single file
+    python validate_zernike_roundtrip.py validation_suite/Z05_astig_0deg.csv
+
+    # Full suite
+    python validate_zernike_roundtrip.py --suite validation_suite/
+
+Author: Mario (Atomizer V&V)
+Created: 2026-03-09
+"""
+
+import sys
+import json
+import argparse
+from pathlib import Path
+from math import factorial
+from typing import Dict, Tuple
+import numpy as np
+from numpy.linalg import lstsq
+
+# ============================================================================
+# Zernike Math (same as generate_synthetic_wfe.py)
+# ============================================================================
+
+def noll_indices(j: int) -> Tuple[int, int]:
+    if j < 1:
+        raise ValueError("Noll index j must be >= 1")
+    count = 0
+    n = 0
+    while True:
+        if n == 0:
+            ms = [0]
+        elif n % 2 == 0:
+            ms = [0] + [m for k in range(1, n // 2 + 1) for m in (-2 * k, 2 * k)]
+        else:
+            ms = [m for k in range(0, (n + 1) // 2) for m in (-(2 * k + 1), (2 * k + 1))]
+        for m in ms:
+            count += 1
+            if count == j:
+                return n, m
+        n += 1
+
+
+def zernike_radial(n, m, r):
+    R = np.zeros_like(r)
+    m_abs = abs(m)
+    for s in range((n - m_abs) // 2 + 1):
+        coef = ((-1) ** s * factorial(n - s) /
+                (factorial(s) * factorial((n + m_abs) // 2 - s) * factorial((n - m_abs) // 2 - s)))
+        R += coef * r ** (n - 2 * s)
+    return R
+
+
+def zernike_noll(j, r, theta):
+    n, m = noll_indices(j)
+    R = zernike_radial(n, m, r)
+    if m == 0:
+        return R
+    elif m > 0:
+        return R * np.cos(m * theta)
+    else:
+        return R * np.sin(-m * theta)
+
+
+def zernike_name(j):
+    n, m = noll_indices(j)
+    names = {
+        (0, 0): "Piston", (1, -1): "Tilt X", (1, 1): "Tilt Y",
+        (2, 0): "Defocus", (2, -2): "Astig 45°", (2, 2): "Astig 0°",
+        (3, -1): "Coma X", (3, 1): "Coma Y",
+        (3, -3): "Trefoil X", (3, 3): "Trefoil Y",
+        (4, 0): "Spherical", (4, -2): "2ndAstig X", (4, 2): "2ndAstig Y",
+        (6, 0): "2nd Spherical",
+    }
+    return names.get((n, m), f"Z({n},{m:+d})")
+
+
+# ============================================================================
+# Zernike Fitting (Least Squares)
+# ============================================================================
+
+def fit_zernike(x_mm, y_mm, opd_nm, diameter_mm=1200.0, n_modes=50):
+    """
+    Fit Zernike coefficients to OPD data via least-squares.
+
+    Returns:
+        coefficients: array of shape (n_modes,), Noll-indexed from j=1
+    """
+    outer_radius = diameter_mm / 2.0
+    r_norm = np.sqrt(x_mm**2 + y_mm**2) / outer_radius
+    theta = np.arctan2(y_mm, x_mm)
+
+    # Build Zernike basis matrix
+    Z = np.zeros((len(x_mm), n_modes))
+    for j in range(1, n_modes + 1):
+        Z[:, j - 1] = zernike_noll(j, r_norm, theta)
+
+    # Least-squares fit
+    coeffs, residuals, rank, sv = lstsq(Z, opd_nm, rcond=None)
+
+    return coeffs
+
+
+# ============================================================================
+# Validation
+# ============================================================================
+
+def validate_file(csv_path: str, n_modes: int = 50, diameter_mm: float = 1200.0,
+                  tolerance_nm: float = 0.5, verbose: bool = True):
+    """
+    Validate a single synthetic WFE file.
+
+    Returns:
+        passed: bool
+        results: dict with comparison details
+    """
+    csv_path = Path(csv_path)
+    truth_path = csv_path.with_name(csv_path.stem + "_truth.json")
+
+    if not truth_path.exists():
+        # Try removing any suffix before _truth
+        base = csv_path.stem
+        truth_path = csv_path.with_name(base + "_truth.json")
+        if not truth_path.exists():
+            print(f"  WARNING: No truth file found for {csv_path.name}")
+            return None, None
+
+    # Load CSV
+    data = np.loadtxt(csv_path, delimiter=",", skiprows=1)
+    x_mm = data[:, 0]
+    y_mm = data[:, 1]
+    opd_nm = data[:, 2]
+
+    # Load truth
+    with open(truth_path) as f:
+        truth = json.load(f)
+
+    input_coeffs = {int(k): v for k, v in truth["input_coefficients"].items()}
+
+    # Fit Zernike
+    recovered = fit_zernike(x_mm, y_mm, opd_nm, diameter_mm, n_modes)
+
+    # Compare
+    max_error = 0.0
+    results = {"modes": {}}
+    all_passed = True
+
+    if verbose:
+        print(f"\n  {'Mode':>6} {'Name':>20} {'Input(nm)':>10} {'Recovered(nm)':>14} {'Error(nm)':>10} {'Status':>8}")
+        print(f"  {'-'*6} {'-'*20} {'-'*10} {'-'*14} {'-'*10} {'-'*8}")
+
+    for j in range(1, n_modes + 1):
+        input_val = input_coeffs.get(j, 0.0)
+        recovered_val = recovered[j - 1]
+        error = abs(recovered_val - input_val)
+        max_error = max(max_error, error)
+
+        mode_passed = error < tolerance_nm
+        if not mode_passed:
+            all_passed = False
+
+        results["modes"][j] = {
+            "input": input_val,
+            "recovered": float(recovered_val),
+            "error": float(error),
+            "passed": mode_passed,
+        }
+
+        # Only print modes with non-zero input or significant recovery
+        if verbose and (abs(input_val) > 0.01 or abs(recovered_val) > tolerance_nm):
+            status = "✅" if mode_passed else "❌"
+            print(f"  Z{j:>4d} {zernike_name(j):>20} {input_val:>10.3f} {recovered_val:>14.3f} {error:>10.6f} {status:>8}")
+
+    results["max_error_nm"] = float(max_error)
+    results["all_passed"] = all_passed
+    results["tolerance_nm"] = tolerance_nm
+    results["n_points"] = len(x_mm)
+
+    if verbose:
+        print(f"\n  Max error: {max_error:.6f} nm")
+        print(f"  Tolerance: {tolerance_nm:.3f} nm")
+        print(f"  Result: {'✅ PASS' if all_passed else '❌ FAIL'}")
+
+    return all_passed, results
+
+
+def validate_suite(suite_dir: str, n_modes: int = 50, tolerance_nm: float = 0.5):
+    """Validate all test cases in a suite directory."""
+    suite_dir = Path(suite_dir)
+    csv_files = sorted(suite_dir.glob("*.csv"))
+
+    print(f"\nValidating {len(csv_files)} test cases in: {suite_dir}")
+    print("=" * 70)
+
+    summary = {}
+    n_pass = 0
+    n_fail = 0
+    n_skip = 0
+
+    for csv_file in csv_files:
+        print(f"\n{'─'*70}")
+        print(f"Test: {csv_file.name}")
+
+        passed, results = validate_file(csv_file, n_modes, tolerance_nm=tolerance_nm)
+
+        if passed is None:
+            n_skip += 1
+            summary[csv_file.stem] = "SKIP"
+        elif passed:
+            n_pass += 1
+            summary[csv_file.stem] = "PASS"
+        else:
+            n_fail += 1
+            summary[csv_file.stem] = "FAIL"
+
+    print(f"\n{'='*70}")
+    print(f"SUITE SUMMARY")
+    print(f"{'='*70}")
+    print(f"  PASS: {n_pass}")
+    print(f"  FAIL: {n_fail}")
+    print(f"  SKIP: {n_skip}")
+    print(f"  Total: {len(csv_files)}")
+    print(f"\n  Overall: {'✅ ALL PASSED' if n_fail == 0 else '❌ FAILURES DETECTED'}")
+
+    return n_fail == 0, summary
+
+
+def main():
+    parser = argparse.ArgumentParser(description="Validate Zernike round-trip accuracy")
+    parser.add_argument("input", nargs="?", help="CSV file or --suite directory")
+    parser.add_argument("--suite", type=str, help="Validate all CSVs in directory")
+    parser.add_argument("--n-modes", type=int, default=50)
+    parser.add_argument("--tolerance", type=float, default=0.5,
+                        help="Max acceptable coefficient error in nm (default: 0.5)")
+    parser.add_argument("--diameter", type=float, default=1200.0)
+    args = parser.parse_args()
+
+    if args.suite:
+        passed, summary = validate_suite(args.suite, args.n_modes, args.tolerance)
+        sys.exit(0 if passed else 1)
+    elif args.input:
+        passed, results = validate_file(args.input, args.n_modes, args.diameter, args.tolerance)
+        sys.exit(0 if passed else 1)
+    else:
+        parser.print_help()
+        sys.exit(1)
+
+
+if __name__ == "__main__":
+    main()

tools/validation_suite/Z05_astig_0deg_truth.json

@@ -0,0 +1,14 @@
+{
+  "input_coefficients": {
+    "5": 100.0
+  },
+  "coefficient_names": {
+    "5": "Astigmatism 45\u00b0"
+  },
+  "n_points": 118572,
+  "diameter_mm": 1200.0,
+  "rms_nm_clean": 41.90787728635817,
+  "rms_nm_with_noise": 41.90787728635817,
+  "noise_rms_nm": 0.0,
+  "seed": 42
+}

tools/validation_suite/Z06_astig_45deg_truth.json

@@ -0,0 +1,14 @@
+{
+  "input_coefficients": {
+    "6": 100.0
+  },
+  "coefficient_names": {
+    "6": "Astigmatism 0\u00b0"
+  },
+  "n_points": 118572,
+  "diameter_mm": 1200.0,
+  "rms_nm_clean": 41.87578387312423,
+  "rms_nm_with_noise": 41.87578387312423,
+  "noise_rms_nm": 0.0,
+  "seed": 42
+}

tools/validation_suite/Z07_coma_x_truth.json

@@ -0,0 +1,14 @@
+{
+  "input_coefficients": {
+    "7": 100.0
+  },
+  "coefficient_names": {
+    "7": "Coma X"
+  },
+  "n_points": 118572,
+  "diameter_mm": 1200.0,
+  "rms_nm_clean": 35.928378983095506,
+  "rms_nm_with_noise": 35.928378983095506,
+  "noise_rms_nm": 0.0,
+  "seed": 42
+}

tools/validation_suite/Z08_coma_y_truth.json

@@ -0,0 +1,14 @@
+{
+  "input_coefficients": {
+    "8": 100.0
+  },
+  "coefficient_names": {
+    "8": "Coma Y"
+  },
+  "n_points": 118572,
+  "diameter_mm": 1200.0,
+  "rms_nm_clean": 35.928378983095506,
+  "rms_nm_with_noise": 35.928378983095506,
+  "noise_rms_nm": 0.0,
+  "seed": 42
+}

tools/validation_suite/Z09_trefoil_x_truth.json

@@ -0,0 +1,14 @@
+{
+  "input_coefficients": {
+    "9": 100.0
+  },
+  "coefficient_names": {
+    "9": "Trefoil X"
+  },
+  "n_points": 118572,
+  "diameter_mm": 1200.0,
+  "rms_nm_clean": 36.27358028530051,
+  "rms_nm_with_noise": 36.27358028530051,
+  "noise_rms_nm": 0.0,
+  "seed": 42
+}

tools/validation_suite/Z10_trefoil_y_truth.json

@@ -0,0 +1,14 @@
+{
+  "input_coefficients": {
+    "10": 100.0
+  },
+  "coefficient_names": {
+    "10": "Trefoil Y"
+  },
+  "n_points": 118572,
+  "diameter_mm": 1200.0,
+  "rms_nm_clean": 36.27358028530051,
+  "rms_nm_with_noise": 36.27358028530051,
+  "noise_rms_nm": 0.0,
+  "seed": 42
+}

tools/validation_suite/Z11_spherical_truth.json

@@ -0,0 +1,14 @@
+{
+  "input_coefficients": {
+    "11": 100.0
+  },
+  "coefficient_names": {
+    "11": "Primary Spherical"
+  },
+  "n_points": 118572,
+  "diameter_mm": 1200.0,
+  "rms_nm_clean": 41.33581266115739,
+  "rms_nm_with_noise": 41.33581266115739,
+  "noise_rms_nm": 0.0,
+  "seed": 42
+}

tools/validation_suite/Z22_2nd_spherical_truth.json

@@ -0,0 +1,14 @@
+{
+  "input_coefficients": {
+    "22": 50.0
+  },
+  "coefficient_names": {
+    "22": "2nd Spherical"
+  },
+  "n_points": 118572,
+  "diameter_mm": 1200.0,
+  "rms_nm_clean": 17.392678653363138,
+  "rms_nm_with_noise": 17.392678653363138,
+  "noise_rms_nm": 0.0,
+  "seed": 42
+}

tools/validation_suite/Z37_high_order_truth.json

@@ -0,0 +1,14 @@
+{
+  "input_coefficients": {
+    "37": 30.0
+  },
+  "coefficient_names": {
+    "37": "Z(8,+0)"
+  },
+  "n_points": 118572,
+  "diameter_mm": 1200.0,
+  "rms_nm_clean": 9.330123545845794,
+  "rms_nm_with_noise": 9.330123545845794,
+  "noise_rms_nm": 0.0,
+  "seed": 42
+}

tools/validation_suite/large_amplitude_truth.json

@@ -0,0 +1,16 @@
+{
+  "input_coefficients": {
+    "5": 500.0,
+    "7": 300.0
+  },
+  "coefficient_names": {
+    "5": "Astigmatism 45\u00b0",
+    "7": "Coma X"
+  },
+  "n_points": 118572,
+  "diameter_mm": 1200.0,
+  "rms_nm_clean": 235.6361394467916,
+  "rms_nm_with_noise": 235.6361394467916,
+  "noise_rms_nm": 0.0,
+  "seed": 42
+}

tools/validation_suite/many_modes_truth.json

@@ -0,0 +1,104 @@
+{
+  "input_coefficients": {
+    "5": 20.0,
+    "6": 16.666666666666668,
+    "7": 14.285714285714286,
+    "8": 12.5,
+    "9": 11.11111111111111,
+    "10": 10.0,
+    "11": 9.090909090909092,
+    "12": 8.333333333333334,
+    "13": 7.6923076923076925,
+    "14": 7.142857142857143,
+    "15": 6.666666666666667,
+    "16": 6.25,
+    "17": 5.882352941176471,
+    "18": 5.555555555555555,
+    "19": 5.2631578947368425,
+    "20": 5.0,
+    "21": 4.761904761904762,
+    "22": 4.545454545454546,
+    "23": 4.3478260869565215,
+    "24": 4.166666666666667,
+    "25": 4.0,
+    "26": 3.8461538461538463,
+    "27": 3.7037037037037037,
+    "28": 3.5714285714285716,
+    "29": 3.4482758620689653,
+    "30": 3.3333333333333335,
+    "31": 3.225806451612903,
+    "32": 3.125,
+    "33": 3.0303030303030303,
+    "34": 2.9411764705882355,
+    "35": 2.857142857142857,
+    "36": 2.7777777777777777,
+    "37": 2.7027027027027026,
+    "38": 2.6315789473684212,
+    "39": 2.5641025641025643,
+    "40": 2.5,
+    "41": 2.4390243902439024,
+    "42": 2.380952380952381,
+    "43": 2.3255813953488373,
+    "44": 2.272727272727273,
+    "45": 2.2222222222222223,
+    "46": 2.1739130434782608,
+    "47": 2.127659574468085,
+    "48": 2.0833333333333335,
+    "49": 2.0408163265306123,
+    "50": 2.0
+  },
+  "coefficient_names": {
+    "5": "Astigmatism 45\u00b0",
+    "6": "Astigmatism 0\u00b0",
+    "7": "Coma X",
+    "8": "Coma Y",
+    "9": "Trefoil X",
+    "10": "Trefoil Y",
+    "11": "Primary Spherical",
+    "12": "2nd Astig X",
+    "13": "2nd Astig Y",
+    "14": "Quadrafoil X",
+    "15": "Quadrafoil Y",
+    "16": "2nd Coma X",
+    "17": "2nd Coma Y",
+    "18": "2nd Trefoil X",
+    "19": "2nd Trefoil Y",
+    "20": "Pentafoil X",
+    "21": "Pentafoil Y",
+    "22": "2nd Spherical",
+    "23": "Z(6,-2)",
+    "24": "Z(6,+2)",
+    "25": "Z(6,-4)",
+    "26": "Z(6,+4)",
+    "27": "Z(6,-6)",
+    "28": "Z(6,+6)",
+    "29": "Z(7,-1)",
+    "30": "Z(7,+1)",
+    "31": "Z(7,-3)",
+    "32": "Z(7,+3)",
+    "33": "Z(7,-5)",
+    "34": "Z(7,+5)",
+    "35": "Z(7,-7)",
+    "36": "Z(7,+7)",
+    "37": "Z(8,+0)",
+    "38": "Z(8,-2)",
+    "39": "Z(8,+2)",
+    "40": "Z(8,-4)",
+    "41": "Z(8,+4)",
+    "42": "Z(8,-6)",
+    "43": "Z(8,+6)",
+    "44": "Z(8,-8)",
+    "45": "Z(8,+8)",
+    "46": "Z(9,-1)",
+    "47": "Z(9,+1)",
+    "48": "Z(9,-3)",
+    "49": "Z(9,+3)",
+    "50": "Z(9,-5)"
+  },
+  "n_points": 118572,
+  "diameter_mm": 1200.0,
+  "rms_nm_clean": 16.40426082558852,
+  "rms_nm_with_noise": 16.40426082558852,
+  "noise_rms_nm": 0.0,
+  "seed": 42
+}

tools/validation_suite/near_zero_truth.json

@@ -0,0 +1,18 @@
+{
+  "input_coefficients": {
+    "5": 0.1,
+    "7": 0.05,
+    "11": 0.01
+  },
+  "coefficient_names": {
+    "5": "Astigmatism 45\u00b0",
+    "7": "Coma X",
+    "11": "Primary Spherical"
+  },
+  "n_points": 118572,
+  "diameter_mm": 1200.0,
+  "rms_nm_clean": 0.04578284369521264,
+  "rms_nm_with_noise": 0.04578284369521264,
+  "noise_rms_nm": 0.0,
+  "seed": 42
+}

tools/validation_suite/realistic_full_disk_truth.json

@@ -0,0 +1,31 @@
+{
+  "input_coefficients": {
+    "5": 80.0,
+    "6": 45.0,
+    "7": 30.0,
+    "8": 20.0,
+    "9": 15.0,
+    "11": 10.0,
+    "13": 5.0,
+    "16": 3.0,
+    "22": 2.0
+  },
+  "coefficient_names": {
+    "5": "Astigmatism 45\u00b0",
+    "6": "Astigmatism 0\u00b0",
+    "7": "Coma X",
+    "8": "Coma Y",
+    "9": "Trefoil X",
+    "11": "Primary Spherical",
+    "13": "2nd Astig Y",
+    "16": "2nd Coma X",
+    "22": "2nd Spherical"
+  },
+  "n_points": 124980,
+  "diameter_mm": 1200.0,
+  "rms_nm_clean": 40.21839241800354,
+  "rms_nm_with_noise": 40.21839241800354,
+  "noise_rms_nm": 0.0,
+  "seed": 42,
+  "inner_radius_mm": 0.0
+}

tools/validation_suite/realistic_gravity_truth.json

@@ -0,0 +1,30 @@
+{
+  "input_coefficients": {
+    "5": 80.0,
+    "6": 45.0,
+    "7": 30.0,
+    "8": 20.0,
+    "9": 15.0,
+    "11": 10.0,
+    "13": 5.0,
+    "16": 3.0,
+    "22": 2.0
+  },
+  "coefficient_names": {
+    "5": "Astigmatism 45\u00b0",
+    "6": "Astigmatism 0\u00b0",
+    "7": "Coma X",
+    "8": "Coma Y",
+    "9": "Trefoil X",
+    "11": "Primary Spherical",
+    "13": "2nd Astig Y",
+    "16": "2nd Coma X",
+    "22": "2nd Spherical"
+  },
+  "n_points": 118572,
+  "diameter_mm": 1200.0,
+  "rms_nm_clean": 41.22340829508179,
+  "rms_nm_with_noise": 41.22340829508179,
+  "noise_rms_nm": 0.0,
+  "seed": 42
+}

tools/validation_suite/realistic_noise_10nm_truth.json

@@ -0,0 +1,30 @@
+{
+  "input_coefficients": {
+    "5": 80.0,
+    "6": 45.0,
+    "7": 30.0,
+    "8": 20.0,
+    "9": 15.0,
+    "11": 10.0,
+    "13": 5.0,
+    "16": 3.0,
+    "22": 2.0
+  },
+  "coefficient_names": {
+    "5": "Astigmatism 45\u00b0",
+    "6": "Astigmatism 0\u00b0",
+    "7": "Coma X",
+    "8": "Coma Y",
+    "9": "Trefoil X",
+    "11": "Primary Spherical",
+    "13": "2nd Astig Y",
+    "16": "2nd Coma X",
+    "22": "2nd Spherical"
+  },
+  "n_points": 118572,
+  "diameter_mm": 1200.0,
+  "rms_nm_clean": 41.22340829508179,
+  "rms_nm_with_noise": 42.39618932696218,
+  "noise_rms_nm": 10.0,
+  "seed": 42
+}

tools/validation_suite/realistic_noise_1nm_truth.json

@@ -0,0 +1,30 @@
+{
+  "input_coefficients": {
+    "5": 80.0,
+    "6": 45.0,
+    "7": 30.0,
+    "8": 20.0,
+    "9": 15.0,
+    "11": 10.0,
+    "13": 5.0,
+    "16": 3.0,
+    "22": 2.0
+  },
+  "coefficient_names": {
+    "5": "Astigmatism 45\u00b0",
+    "6": "Astigmatism 0\u00b0",
+    "7": "Coma X",
+    "8": "Coma Y",
+    "9": "Trefoil X",
+    "11": "Primary Spherical",
+    "13": "2nd Astig Y",
+    "16": "2nd Coma X",
+    "22": "2nd Spherical"
+  },
+  "n_points": 118572,
+  "diameter_mm": 1200.0,
+  "rms_nm_clean": 41.22340829508179,
+  "rms_nm_with_noise": 41.23235114137111,
+  "noise_rms_nm": 1.0,
+  "seed": 42
+}

tools/validation_suite/realistic_noise_5nm_truth.json

@@ -0,0 +1,30 @@
+{
+  "input_coefficients": {
+    "5": 80.0,
+    "6": 45.0,
+    "7": 30.0,
+    "8": 20.0,
+    "9": 15.0,
+    "11": 10.0,
+    "13": 5.0,
+    "16": 3.0,
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tools/validation_suite/realistic_scattered_truth.json

@@ -0,0 +1,31 @@
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tools/validation_suite/suite_manifest.json

@@ -0,0 +1,456 @@
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