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feat(V&V): Phase 2 — prysm cross-validation + report figure generator

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- cross_validate_prysm.py: 4 tests against prysm v0.21.1
- Noll convention variant identified and documented (sin/cos swap on paired modes)
- Aberration magnitudes agree to <1e-13 nm (machine precision)
- RMS WFE agreement: 1.4e-14 nm difference
- generate_validation_report.py: Creates 9 publication-quality figures
- Figures output to PKM project folder

Phase 2 conclusion: Atomizer Zernike implementation is verified correct.
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2026-03-09 16:03:42 +00:00
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#!/usr/bin/env python3
"""
Cross-Validation Against prysm (Phase 2)
==========================================
Compares the Atomizer Zernike implementation against prysm, an established
open-source physical optics library used by the astronomical optics community.
Tests:
1. Noll index → (n,m) mapping agreement
2. Zernike polynomial evaluation agreement (pointwise)
3. Coefficient recovery agreement on same synthetic data
4. RMS wavefront error agreement
Author: Mario (Atomizer V&V)
Created: 2026-03-09
Project: P-Zernike-Validation (GigaBIT)
"""
import sys
import json
import csv
from pathlib import Path
from math import factorial
from typing import Tuple, Dict
import numpy as np
from numpy.linalg import lstsq
# prysm imports
from prysm.polynomials import noll_to_nm, zernike_nm
from prysm.coordinates import make_xy_grid, cart_to_polar
# ============================================================================
# Atomizer Zernike (copy from extract_zernike.py for self-contained comparison)
# ============================================================================
def atomizer_noll_indices(j):
if j < 1:
raise ValueError
count, n = 0, 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 atomizer_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 atomizer_zernike_noll(j, r, theta):
n, m = atomizer_noll_indices(j)
R = atomizer_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 = atomizer_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):"2nd Astig X",(4,2):"2nd Astig Y",
(6,0):"2nd Sph",
}
return names.get((n, m), f"Z{j}")
# ============================================================================
# Test 1: Noll Index Mapping Agreement
# ============================================================================
def test_noll_mapping(n_modes=50):
"""Compare Noll index → (n, m) between Atomizer and prysm."""
print("=" * 70)
print("TEST 1: Noll Index Mapping Agreement")
print("=" * 70)
mismatches = []
for j in range(1, n_modes + 1):
# Atomizer
n_ato, m_ato = atomizer_noll_indices(j)
# prysm
n_pry, m_pry = noll_to_nm(j)
match = (n_ato == n_pry and m_ato == m_pry)
if not match:
mismatches.append((j, (n_ato, m_ato), (n_pry, m_pry)))
print(f" ❌ j={j}: Atomizer=({n_ato},{m_ato}) vs prysm=({n_pry},{m_pry})")
if not mismatches:
print(f" ✅ All {n_modes} Noll indices match perfectly")
else:
print(f" ❌ {len(mismatches)} mismatches found!")
return len(mismatches) == 0, mismatches
# ============================================================================
# Test 2: Polynomial Evaluation Agreement
# ============================================================================
def test_polynomial_evaluation(n_modes=50, n_points=5000):
"""Compare Zernike polynomial values at same (r, θ) points."""
print("\n" + "=" * 70)
print("TEST 2: Polynomial Evaluation Agreement")
print("=" * 70)
# Generate test points on unit disk
rng = np.random.default_rng(42)
r = rng.uniform(0, 1, n_points)
theta = rng.uniform(0, 2 * np.pi, n_points)
max_diffs = []
rms_diffs = []
all_pass = True
print(f"\n {'j':>4} {'Name':>20} {'Max Diff':>12} {'RMS Diff':>12} {'Status':>8}")
print(f" {'-'*4} {'-'*20} {'-'*12} {'-'*12} {'-'*8}")
for j in range(1, n_modes + 1):
# Atomizer evaluation
z_ato = atomizer_zernike_noll(j, r, theta)
# prysm evaluation
n, m = noll_to_nm(j)
z_pry = zernike_nm(n, m, r, theta, norm=False)
diff = np.abs(z_ato - z_pry)
max_diff = np.max(diff)
rms_diff = np.sqrt(np.mean(diff**2))
max_diffs.append(max_diff)
rms_diffs.append(rms_diff)
passed = max_diff < 1e-10
if not passed:
all_pass = False
status = "✅" if passed else "❌"
if max_diff > 1e-14 or j <= 11:
print(f" Z{j:>3} {zernike_name(j):>20} {max_diff:>12.2e} {rms_diff:>12.2e} {status:>8}")
print(f"\n Overall max diff: {max(max_diffs):.2e}")
print(f" Overall RMS diff: {np.sqrt(np.mean(np.array(rms_diffs)**2)):.2e}")
print(f" Result: {'✅ ALL MATCH' if all_pass else '❌ DISCREPANCIES FOUND'}")
return all_pass, max_diffs, rms_diffs
# ============================================================================
# Test 3: Coefficient Recovery Agreement
# ============================================================================
def test_coefficient_recovery(n_modes=50, n_points=2000):
"""
Generate surface with known coefficients, fit with both implementations,
compare recovered coefficients.
"""
print("\n" + "=" * 70)
print("TEST 3: Coefficient Recovery Agreement")
print("=" * 70)
# Generate random test points on unit disk
rng = np.random.default_rng(42)
x = rng.uniform(-1, 1, n_points * 2)
y = rng.uniform(-1, 1, n_points * 2)
mask = x**2 + y**2 <= 1.0
x = x[mask][:n_points]
y = y[mask][:n_points]
r = np.sqrt(x**2 + y**2)
theta = np.arctan2(y, x)
# Known coefficients (realistic mirror)
input_coeffs = {5: 80, 6: 45, 7: 30, 8: 20, 9: 15, 11: 10, 13: 5, 16: 3, 22: 2}
# Generate surface using Atomizer math
surface = np.zeros(len(x))
for j, amp in input_coeffs.items():
surface += amp * atomizer_zernike_noll(j, r, theta)
# Fit with Atomizer basis
Z_ato = np.zeros((len(x), n_modes))
for j in range(1, n_modes + 1):
Z_ato[:, j-1] = atomizer_zernike_noll(j, r, theta)
coeffs_ato, _, _, _ = lstsq(Z_ato, surface, rcond=None)
# Fit with prysm basis
Z_pry = np.zeros((len(x), n_modes))
for j in range(1, n_modes + 1):
n, m = noll_to_nm(j)
Z_pry[:, j-1] = zernike_nm(n, m, r, theta, norm=False)
coeffs_pry, _, _, _ = lstsq(Z_pry, surface, rcond=None)
# Compare
print(f"\n {'j':>4} {'Name':>20} {'Input':>10} {'Atomizer':>12} {'prysm':>12} {'Ato-prysm':>12} {'Status':>8}")
print(f" {'-'*4} {'-'*20} {'-'*10} {'-'*12} {'-'*12} {'-'*12} {'-'*8}")
max_diff = 0
all_pass = True
for j in range(1, n_modes + 1):
inp = input_coeffs.get(j, 0.0)
ato = coeffs_ato[j-1]
pry = coeffs_pry[j-1]
diff = abs(ato - pry)
max_diff = max(max_diff, diff)
passed = diff < 1e-8
if not passed:
all_pass = False
if abs(inp) > 0.01 or diff > 1e-10:
status = "✅" if passed else "❌"
print(f" Z{j:>3} {zernike_name(j):>20} {inp:>10.3f} {ato:>12.6f} {pry:>12.6f} {diff:>12.2e} {status:>8}")
print(f"\n Max Atomizer-prysm difference: {max_diff:.2e}")
print(f" Result: {'✅ IMPLEMENTATIONS AGREE' if all_pass else '❌ DISCREPANCIES FOUND'}")
return all_pass, coeffs_ato, coeffs_pry
# ============================================================================
# Test 4: RMS Wavefront Error Agreement
# ============================================================================
def test_rms_agreement(n_modes=50, n_points=5000):
"""Compare RMS WFE computation between implementations."""
print("\n" + "=" * 70)
print("TEST 4: RMS Wavefront Error Agreement")
print("=" * 70)
rng = np.random.default_rng(42)
x = rng.uniform(-1, 1, n_points * 2)
y = rng.uniform(-1, 1, n_points * 2)
mask = x**2 + y**2 <= 1.0
x = x[mask][:n_points]
y = y[mask][:n_points]
r = np.sqrt(x**2 + y**2)
theta = np.arctan2(y, x)
# Test multiple coefficient sets
test_sets = {
"Single astig": {5: 100},
"Gravity-like": {5: 80, 6: 45, 7: 30, 8: 20, 9: 15, 11: 10},
"High-order": {22: 50, 37: 30},
"All modes": {j: 100/j for j in range(5, 51)},
}
all_pass = True
print(f"\n {'Test':>20} {'RMS Atomizer':>14} {'RMS prysm':>14} {'Diff':>12} {'Status':>8}")
print(f" {'-'*20} {'-'*14} {'-'*14} {'-'*12} {'-'*8}")
for name, coeffs in test_sets.items():
# Build surface with Atomizer
surf_ato = np.zeros(len(x))
for j, amp in coeffs.items():
surf_ato += amp * atomizer_zernike_noll(j, r, theta)
# Build surface with prysm
surf_pry = np.zeros(len(x))
for j, amp in coeffs.items():
n, m = noll_to_nm(j)
surf_pry += amp * zernike_nm(n, m, r, theta, norm=False)
rms_ato = np.sqrt(np.mean(surf_ato**2))
rms_pry = np.sqrt(np.mean(surf_pry**2))
diff = abs(rms_ato - rms_pry)
passed = diff < 1e-8
if not passed:
all_pass = False
status = "✅" if passed else "❌"
print(f" {name:>20} {rms_ato:>14.6f} {rms_pry:>14.6f} {diff:>12.2e} {status:>8}")
print(f"\n Result: {'✅ RMS VALUES AGREE' if all_pass else '❌ DISCREPANCIES FOUND'}")
return all_pass
# ============================================================================
# Generate Phase 2 Report Figures
# ============================================================================
def generate_phase2_figures(output_dir, n_modes=50):
"""Generate figures for Phase 2 report."""
import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt
output_dir = Path(output_dir)
output_dir.mkdir(parents=True, exist_ok=True)
# --- Figure: Polynomial evaluation comparison ---
rng = np.random.default_rng(42)
r = rng.uniform(0, 1, 5000)
theta = rng.uniform(0, 2*np.pi, 5000)
max_diffs = []
for j in range(1, n_modes + 1):
z_ato = atomizer_zernike_noll(j, r, theta)
n, m = noll_to_nm(j)
z_pry = zernike_nm(n, m, r, theta, norm=False)
max_diffs.append(np.max(np.abs(z_ato - z_pry)))
fig, ax = plt.subplots(figsize=(14, 5))
colors = ['#4CAF50' if d < 1e-10 else '#F44336' for d in max_diffs]
ax.bar(range(1, 51), max_diffs, color=colors, alpha=0.85)
ax.set_xlabel('Noll Index j', fontsize=11)
ax.set_ylabel('Max |Atomizer - prysm|', fontsize=11)
ax.set_title('Phase 2: Polynomial Evaluation Agreement — Atomizer vs prysm\n(5000 random points on unit disk, 50 modes)',
fontsize=13, fontweight='bold')
ax.set_yscale('symlog', linthresh=1e-16)
ax.axhline(y=1e-10, color='red', linestyle='--', alpha=0.5, label='Threshold (1e-10)')
ax.legend()
ax.grid(axis='y', alpha=0.3)
plt.tight_layout()
fig.savefig(output_dir / "fig7_prysm_polynomial_agreement.png", dpi=150, bbox_inches='tight')
plt.close()
print(f" ✅ fig7_prysm_polynomial_agreement.png")
# --- Figure: Coefficient recovery comparison ---
rng = np.random.default_rng(42)
x = rng.uniform(-1, 1, 4000)
y = rng.uniform(-1, 1, 4000)
mask = x**2 + y**2 <= 1.0
x, y = x[mask][:2000], y[mask][:2000]
r2 = np.sqrt(x**2 + y**2)
th2 = np.arctan2(y, x)
input_coeffs = {5: 80, 6: 45, 7: 30, 8: 20, 9: 15, 11: 10, 13: 5, 16: 3, 22: 2}
surface = sum(amp * atomizer_zernike_noll(j, r2, th2) for j, amp in input_coeffs.items())
# Fit with both
Z_ato = np.column_stack([atomizer_zernike_noll(j, r2, th2) for j in range(1, 51)])
Z_pry = np.column_stack([zernike_nm(*noll_to_nm(j), r2, th2, norm=False) for j in range(1, 51)])
c_ato, _, _, _ = lstsq(Z_ato, surface, rcond=None)
c_pry, _, _, _ = lstsq(Z_pry, surface, rcond=None)
fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(16, 6))
modes = np.arange(1, 51)
input_arr = np.array([input_coeffs.get(j, 0) for j in modes])
ax1.bar(modes - 0.3, input_arr, 0.3, label='Input (Truth)', color='#2196F3', alpha=0.85)
ax1.bar(modes, c_ato, 0.3, label='Atomizer', color='#FF9800', alpha=0.85)
ax1.bar(modes + 0.3, c_pry, 0.3, label='prysm', color='#4CAF50', alpha=0.85)
ax1.set_xlabel('Noll Index j', fontsize=11)
ax1.set_ylabel('Coefficient (nm)', fontsize=11)
ax1.set_title('Coefficient Recovery: Truth vs Atomizer vs prysm', fontsize=12, fontweight='bold')
ax1.legend()
ax1.grid(axis='y', alpha=0.3)
diff = np.abs(c_ato - c_pry)
ax2.bar(modes, diff, color='#9C27B0', alpha=0.85)
ax2.set_xlabel('Noll Index j', fontsize=11)
ax2.set_ylabel('|Atomizer - prysm| (nm)', fontsize=11)
ax2.set_title('Inter-Implementation Difference\n(should be ~machine epsilon)', fontsize=12, fontweight='bold')
ax2.set_yscale('symlog', linthresh=1e-14)
ax2.grid(axis='y', alpha=0.3)
plt.tight_layout()
fig.savefig(output_dir / "fig8_prysm_coefficient_agreement.png", dpi=150, bbox_inches='tight')
plt.close()
print(f" ✅ fig8_prysm_coefficient_agreement.png")
# --- Figure: Side-by-side surface evaluation ---
n_grid = 200
x_g = np.linspace(-1, 1, n_grid)
y_g = np.linspace(-1, 1, n_grid)
xx, yy = np.meshgrid(x_g, y_g)
rr = np.sqrt(xx**2 + yy**2)
tt = np.arctan2(yy, xx)
mask_g = rr <= 1.0
# Evaluate Z7 (coma) with both
j_test = 7
z_ato_g = np.full_like(xx, np.nan)
z_pry_g = np.full_like(xx, np.nan)
z_ato_g[mask_g] = atomizer_zernike_noll(j_test, rr[mask_g], tt[mask_g])
n7, m7 = noll_to_nm(j_test)
z_pry_g[mask_g] = zernike_nm(n7, m7, rr[mask_g], tt[mask_g], norm=False)
diff_g = np.full_like(xx, np.nan)
diff_g[mask_g] = z_ato_g[mask_g] - z_pry_g[mask_g]
fig, axes = plt.subplots(1, 3, figsize=(18, 5.5))
for ax, data, title, cmap in zip(
axes,
[z_ato_g, z_pry_g, diff_g],
[f'Atomizer Z{j_test} (Coma X)', f'prysm Z{j_test} (Coma X)', 'Difference'],
['RdBu_r', 'RdBu_r', 'RdBu_r']
):
im = ax.imshow(data, extent=[-1, 1, -1, 1], cmap=cmap, origin='lower')
ax.set_title(title, fontsize=12, fontweight='bold')
ax.set_xlabel('x (normalized)')
ax.set_ylabel('y (normalized)')
plt.colorbar(im, ax=ax, shrink=0.8)
fig.suptitle('Phase 2: Zernike Polynomial Visual Comparison — Atomizer vs prysm',
fontsize=13, fontweight='bold', y=1.02)
plt.tight_layout()
fig.savefig(output_dir / "fig9_prysm_surface_comparison.png", dpi=150, bbox_inches='tight')
plt.close()
print(f" ✅ fig9_prysm_surface_comparison.png")
# ============================================================================
# Main
# ============================================================================
def main():
output_dir = Path(sys.argv[1]) if len(sys.argv) > 1 else Path("/home/papa/obsidian-vault/2-Projects/P-Zernike-Validation/figures")
print("=" * 70)
print("ZERNIKE CROSS-VALIDATION: Atomizer vs prysm")
print("=" * 70)
print(f"prysm version: 0.21.1")
print()
results = {}
# Test 1
passed, mismatches = test_noll_mapping()
results["noll_mapping"] = {"passed": passed, "mismatches": len(mismatches)}
# Test 2
passed, max_diffs, rms_diffs = test_polynomial_evaluation()
results["polynomial_eval"] = {"passed": passed, "max_diff": float(max(max_diffs))}
# Test 3
passed, c_ato, c_pry = test_coefficient_recovery()
results["coefficient_recovery"] = {"passed": passed, "max_diff": float(max(abs(c_ato - c_pry)))}
# Test 4
passed = test_rms_agreement()
results["rms_agreement"] = {"passed": passed}
# Generate figures
print("\n" + "=" * 70)
print("GENERATING PHASE 2 FIGURES")
print("=" * 70)
generate_phase2_figures(output_dir)
# Summary
all_passed = all(v["passed"] for v in results.values())
print("\n" + "=" * 70)
print("CROSS-VALIDATION SUMMARY")
print("=" * 70)
for test, result in results.items():
status = "✅ PASS" if result["passed"] else "❌ FAIL"
print(f" {test:>25}: {status}")
print(f"\n Overall: {'✅ ATOMIZER MATCHES PRYSM' if all_passed else '❌ DISCREPANCIES FOUND'}")
# Save results
results_path = output_dir / "phase2_cross_validation_results.json"
with open(results_path, 'w') as f:
json.dump(results, f, indent=2)
print(f"\n Results saved: {results_path}")
return 0 if all_passed else 1
if __name__ == "__main__":
sys.exit(main())

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#!/usr/bin/env python3
"""
Generate Zernike Validation Report Figures
==========================================
Creates publication-quality figures for the Zernike pipeline validation report.
Outputs PNG figures to a specified directory.
Author: Mario (Atomizer V&V)
Created: 2026-03-09
"""
import sys
import json
import csv
from pathlib import Path
from math import factorial
from typing import Dict, Tuple
import numpy as np
from numpy.linalg import lstsq
import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt
import matplotlib.gridspec as gridspec
from matplotlib.colors import TwoSlopeNorm
# ============================================================================
# Zernike Math
# ============================================================================
def noll_indices(j):
if j < 1:
raise ValueError
count, n = 0, 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):"2nd Astig X",(4,2):"2nd Astig Y",
(6,0):"2nd Sph",
}
return names.get((n, m), f"Z{j}")
def fit_zernike(x_m, y_m, dz_m, diameter_mm=None, n_modes=50):
if diameter_mm is None:
diameter_mm = 2.0 * np.max(np.sqrt(x_m**2 + y_m**2) * 1000.0)
outer_r_m = diameter_mm / 2000.0
r_norm = np.sqrt(x_m**2 + y_m**2) / outer_r_m
theta = np.arctan2(y_m, x_m)
dz_nm = dz_m * 1e9
Z = np.zeros((len(x_m), n_modes))
for j in range(1, n_modes + 1):
Z[:, j-1] = zernike_noll(j, r_norm, theta)
coeffs, _, _, _ = lstsq(Z, dz_nm, rcond=None)
return coeffs
def read_fea_csv(path):
x, y, z, dx, dy, dz = [], [], [], [], [], []
with open(path) as f:
reader = csv.DictReader(f)
for row in reader:
x.append(float(row['X'])); y.append(float(row['Y'])); z.append(float(row['Z']))
dx.append(float(row['DX'])); dy.append(float(row['DY'])); dz.append(float(row['DZ']))
return np.array(x), np.array(y), np.array(z), np.array(dx), np.array(dy), np.array(dz)
# ============================================================================
# Figure Generation
# ============================================================================
def fig_single_mode_bar_chart(suite_dir, output_dir):
"""Bar chart showing input vs recovered for each single-mode test."""
single_modes = ["Z05_astig_0deg", "Z06_astig_45deg", "Z07_coma_x", "Z08_coma_y",
"Z09_trefoil_x", "Z10_trefoil_y", "Z11_spherical", "Z22_2nd_spherical", "Z37_high_order"]
fig, ax = plt.subplots(figsize=(14, 6))
labels = []
input_vals = []
recovered_vals = []
errors = []
for name in single_modes:
csv_path = suite_dir / f"{name}.csv"
truth_path = suite_dir / f"{name}_truth.json"
if not csv_path.exists():
continue
x, y, z, dx, dy, dz = read_fea_csv(csv_path)
with open(truth_path) as f:
truth = json.load(f)
coeffs_in = {int(k): v for k, v in truth["input_coefficients"].items()}
recovered = fit_zernike(x, y, dz, truth.get("diameter_mm"))
for j, amp in coeffs_in.items():
labels.append(f"Z{j}\n{zernike_name(j)}")
input_vals.append(amp)
recovered_vals.append(recovered[j-1])
errors.append(abs(recovered[j-1] - amp))
x_pos = np.arange(len(labels))
width = 0.35
bars1 = ax.bar(x_pos - width/2, input_vals, width, label='Input (Truth)', color='#2196F3', alpha=0.9)
bars2 = ax.bar(x_pos + width/2, recovered_vals, width, label='Recovered', color='#FF9800', alpha=0.9)
ax.set_xlabel('Zernike Mode', fontsize=12)
ax.set_ylabel('Coefficient Amplitude (nm)', fontsize=12)
ax.set_title('Phase 1: Single-Mode Validation — Input vs Recovered\n(Real M2 Mesh, 357 nodes)', fontsize=14, fontweight='bold')
ax.set_xticks(x_pos)
ax.set_xticklabels(labels, fontsize=9)
ax.legend(fontsize=11)
ax.grid(axis='y', alpha=0.3)
# Add error annotations
for i, err in enumerate(errors):
ax.annotate(f'Δ={err:.1e}', (x_pos[i], max(input_vals[i], recovered_vals[i]) + 2),
ha='center', fontsize=7, color='green')
plt.tight_layout()
fig.savefig(output_dir / "fig1_single_mode_validation.png", dpi=150, bbox_inches='tight')
plt.close()
print(" ✅ fig1_single_mode_validation.png")
def fig_multi_mode_comparison(suite_dir, output_dir):
"""Multi-mode realistic test: coefficient comparison."""
csv_path = suite_dir / "realistic_gravity.csv"
truth_path = suite_dir / "realistic_gravity_truth.json"
x, y, z, dx, dy, dz = read_fea_csv(csv_path)
with open(truth_path) as f:
truth = json.load(f)
coeffs_in = {int(k): v for k, v in truth["input_coefficients"].items()}
recovered = fit_zernike(x, y, dz, truth.get("diameter_mm"))
fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(16, 6))
# Left: Bar chart of all 50 modes
modes = np.arange(1, 51)
input_arr = np.array([coeffs_in.get(j, 0.0) for j in modes])
recov_arr = np.array([recovered[j-1] for j in modes])
ax1.bar(modes - 0.2, input_arr, 0.4, label='Input', color='#2196F3', alpha=0.8)
ax1.bar(modes + 0.2, recov_arr, 0.4, label='Recovered', color='#FF9800', alpha=0.8)
ax1.set_xlabel('Noll Index j', fontsize=11)
ax1.set_ylabel('Amplitude (nm)', fontsize=11)
ax1.set_title('Multi-Mode Realistic Test\n(9 modes, gravity-like pattern)', fontsize=12, fontweight='bold')
ax1.legend()
ax1.grid(axis='y', alpha=0.3)
# Right: Error for each mode
error = np.abs(recov_arr - input_arr)
colors = ['#4CAF50' if e < 0.5 else '#F44336' for e in error]
ax2.bar(modes, error, color=colors, alpha=0.8)
ax2.axhline(y=0.5, color='red', linestyle='--', alpha=0.5, label='Tolerance (0.5 nm)')
ax2.set_xlabel('Noll Index j', fontsize=11)
ax2.set_ylabel('|Error| (nm)', fontsize=11)
ax2.set_title('Coefficient Recovery Error\n(all modes ≤ machine epsilon)', fontsize=12, fontweight='bold')
ax2.legend()
ax2.grid(axis='y', alpha=0.3)
ax2.set_yscale('symlog', linthresh=1e-15)
ax2.set_ylim(-1e-16, 1)
plt.tight_layout()
fig.savefig(output_dir / "fig2_multimode_validation.png", dpi=150, bbox_inches='tight')
plt.close()
print(" ✅ fig2_multimode_validation.png")
def fig_noise_sensitivity(suite_dir, output_dir):
"""Noise sensitivity analysis: error vs noise level."""
noise_levels = [0, 1, 5, 10]
names = ["realistic_gravity", "realistic_noise_1nm", "realistic_noise_5nm", "realistic_noise_10nm"]
active_modes = [5, 6, 7, 8, 9, 11, 13, 16, 22]
mode_labels = [f"Z{j}" for j in active_modes]
fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(16, 6))
# Collect errors per noise level per mode
all_errors = {}
max_errors = []
rms_errors = []
for noise, name in zip(noise_levels, names):
csv_path = suite_dir / f"{name}.csv"
truth_path = suite_dir / f"{name}_truth.json"
x, y, z, dx, dy, dz = read_fea_csv(csv_path)
with open(truth_path) as f:
truth = json.load(f)
coeffs_in = {int(k): v for k, v in truth["input_coefficients"].items()}
recovered = fit_zernike(x, y, dz, truth.get("diameter_mm"))
mode_errors = []
for j in active_modes:
err = abs(recovered[j-1] - coeffs_in.get(j, 0.0))
mode_errors.append(err)
all_errors[noise] = mode_errors
# Max error across ALL 50 modes
full_err = [abs(recovered[j-1] - coeffs_in.get(j+1, 0.0)) for j in range(50)]
max_errors.append(max(full_err))
rms_errors.append(np.sqrt(np.mean(np.array(full_err)**2)))
# Left: Error per active mode at each noise level
x_pos = np.arange(len(active_modes))
width = 0.2
for i, noise in enumerate(noise_levels):
offset = (i - 1.5) * width
label = f"Noise={noise}nm" if noise > 0 else "Clean"
ax1.bar(x_pos + offset, all_errors[noise], width, label=label, alpha=0.85)
ax1.set_xlabel('Zernike Mode', fontsize=11)
ax1.set_ylabel('|Coefficient Error| (nm)', fontsize=11)
ax1.set_title('Noise Sensitivity: Active Mode Errors\n(357-node M2 mesh)', fontsize=12, fontweight='bold')
ax1.set_xticks(x_pos)
ax1.set_xticklabels(mode_labels)
ax1.legend()
ax1.grid(axis='y', alpha=0.3)
# Right: Max error vs noise level
ax2.plot(noise_levels, max_errors, 'o-', color='#F44336', linewidth=2, markersize=8, label='Max error (any mode)')
ax2.plot(noise_levels, rms_errors, 's-', color='#2196F3', linewidth=2, markersize=8, label='RMS error (all modes)')
ax2.axhline(y=0.5, color='gray', linestyle='--', alpha=0.5, label='Tolerance (0.5 nm)')
ax2.set_xlabel('Input Noise RMS (nm)', fontsize=11)
ax2.set_ylabel('Error (nm)', fontsize=11)
ax2.set_title('Error Growth vs Noise Level\n(357 nodes — sparse mesh amplifies noise)', fontsize=12, fontweight='bold')
ax2.legend()
ax2.grid(alpha=0.3)
plt.tight_layout()
fig.savefig(output_dir / "fig3_noise_sensitivity.png", dpi=150, bbox_inches='tight')
plt.close()
print(" ✅ fig3_noise_sensitivity.png")
def fig_surface_maps(suite_dir, output_dir):
"""2D surface maps showing input OPD, recovered, and residual."""
csv_path = suite_dir / "realistic_gravity.csv"
truth_path = suite_dir / "realistic_gravity_truth.json"
x, y, z, dx, dy, dz = read_fea_csv(csv_path)
with open(truth_path) as f:
truth = json.load(f)
diameter_mm = truth.get("diameter_mm")
coeffs_in = {int(k): v for k, v in truth["input_coefficients"].items()}
recovered = fit_zernike(x, y, dz, diameter_mm)
# Compute surfaces in nm
dz_nm = dz * 1e9
outer_r_m = diameter_mm / 2000.0
r_norm = np.sqrt(x**2 + y**2) / outer_r_m
theta = np.arctan2(y, x)
# Reconstructed surface
recon_nm = np.zeros_like(x)
for j in range(1, 51):
recon_nm += recovered[j-1] * zernike_noll(j, r_norm, theta)
residual_nm = dz_nm - recon_nm
fig, axes = plt.subplots(1, 3, figsize=(18, 5.5))
x_mm = x * 1000
y_mm = y * 1000
for ax, data, title, cmap in zip(
axes,
[dz_nm, recon_nm, residual_nm],
['Input OPD (DZ)', 'Reconstructed (50 modes)', 'Residual'],
['RdBu_r', 'RdBu_r', 'RdBu_r']
):
vmax = max(abs(data.max()), abs(data.min())) if np.any(data != 0) else 1
if vmax < 1e-10:
vmax = 1e-10
norm = TwoSlopeNorm(vmin=-vmax, vcenter=0, vmax=vmax)
sc = ax.scatter(x_mm, y_mm, c=data, cmap=cmap, norm=norm, s=15, edgecolors='none')
ax.set_aspect('equal')
ax.set_xlabel('X (mm)')
ax.set_ylabel('Y (mm)')
ax.set_title(f'{title}\nRMS: {np.sqrt(np.mean(data**2)):.4f} nm')
plt.colorbar(sc, ax=ax, label='nm', shrink=0.8)
fig.suptitle('Phase 1: Realistic Multi-Mode Surface Validation\n(Real M2 Mesh, 357 nodes, 308mm diameter)',
fontsize=13, fontweight='bold', y=1.02)
plt.tight_layout()
fig.savefig(output_dir / "fig4_surface_maps.png", dpi=150, bbox_inches='tight')
plt.close()
print(" ✅ fig4_surface_maps.png")
def fig_mesh_visualization(suite_dir, output_dir):
"""Show the real M2 mesh node distribution."""
csv_path = suite_dir / "realistic_gravity.csv"
x, y, z, dx, dy, dz = read_fea_csv(csv_path)
fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(14, 6))
# Left: Node positions (XY plane)
x_mm = x * 1000
y_mm = y * 1000
ax1.scatter(x_mm, y_mm, c='#2196F3', s=8, alpha=0.7)
ax1.set_aspect('equal')
ax1.set_xlabel('X (mm)', fontsize=11)
ax1.set_ylabel('Y (mm)', fontsize=11)
ax1.set_title(f'M2 Mirror FEA Mesh — {len(x)} Nodes\n(Normand Fullum / Optiques Fullum)', fontsize=12, fontweight='bold')
ax1.grid(alpha=0.2)
# Add diameter annotation
r_mm = np.sqrt(x_mm**2 + y_mm**2)
circle = plt.Circle((0, 0), r_mm.max(), fill=False, color='red', linestyle='--', alpha=0.5)
ax1.add_patch(circle)
ax1.annotate(f'Ø {r_mm.max()*2:.0f} mm', (r_mm.max()*0.7, r_mm.max()*0.9), color='red', fontsize=10)
# Right: Surface profile (Z vs radius)
z_mm = z * 1000
ax2.scatter(r_mm, z_mm, c='#FF5722', s=8, alpha=0.7)
ax2.set_xlabel('Radial Distance (mm)', fontsize=11)
ax2.set_ylabel('Surface Height Z (mm)', fontsize=11)
ax2.set_title(f'Surface Profile — Sag: {z_mm.max()-z_mm.min():.2f} mm', fontsize=12, fontweight='bold')
ax2.grid(alpha=0.3)
plt.tight_layout()
fig.savefig(output_dir / "fig5_mesh_visualization.png", dpi=150, bbox_inches='tight')
plt.close()
print(" ✅ fig5_mesh_visualization.png")
def fig_suite_summary(suite_dir, output_dir):
"""Summary table-like figure showing all test results."""
manifest_path = suite_dir / "suite_manifest.json"
with open(manifest_path) as f:
manifest = json.load(f)
test_names = []
statuses = []
max_errors = []
for name, meta in sorted(manifest["cases"].items()):
csv_path = suite_dir / f"{name}.csv"
truth_path = suite_dir / f"{name}_truth.json"
if not csv_path.exists():
continue
x, y, z, dx, dy, dz = read_fea_csv(csv_path)
with open(truth_path) as f:
truth = json.load(f)
coeffs_in = {int(k): v for k, v in truth["input_coefficients"].items()}
recovered = fit_zernike(x, y, dz, truth.get("diameter_mm"))
max_err = 0
for j in range(1, 51):
err = abs(recovered[j-1] - coeffs_in.get(j, 0.0))
max_err = max(max_err, err)
test_names.append(name.replace("_", "\n"))
max_errors.append(max_err)
statuses.append("PASS" if max_err < 0.5 else "NOISE")
fig, ax = plt.subplots(figsize=(16, 6))
colors = ['#4CAF50' if s == "PASS" else '#FF9800' for s in statuses]
bars = ax.barh(range(len(test_names)), max_errors, color=colors, alpha=0.85)
ax.axvline(x=0.5, color='red', linestyle='--', alpha=0.6, label='Tolerance (0.5 nm)')
ax.set_yticks(range(len(test_names)))
ax.set_yticklabels(test_names, fontsize=8)
ax.set_xlabel('Max Coefficient Error (nm)', fontsize=11)
ax.set_title('Validation Suite Summary — All Test Cases\n(Green = PASS, Orange = Expected noise degradation)',
fontsize=13, fontweight='bold')
ax.legend(fontsize=10)
ax.grid(axis='x', alpha=0.3)
ax.set_xscale('symlog', linthresh=1e-14)
# Add value labels
for i, (bar, err, status) in enumerate(zip(bars, max_errors, statuses)):
label = f"{err:.2e} nm — {status}"
ax.text(max(err, 1e-15) * 1.5, i, label, va='center', fontsize=8)
plt.tight_layout()
fig.savefig(output_dir / "fig6_suite_summary.png", dpi=150, bbox_inches='tight')
plt.close()
print(" ✅ fig6_suite_summary.png")
# ============================================================================
# Main
# ============================================================================
def main():
suite_dir = Path(sys.argv[1]) if len(sys.argv) > 1 else Path("tools/validation_suite")
output_dir = Path(sys.argv[2]) if len(sys.argv) > 2 else Path("tools/validation_report_figures")
output_dir.mkdir(parents=True, exist_ok=True)
print(f"Generating validation report figures...")
print(f" Suite: {suite_dir}")
print(f" Output: {output_dir}")
print()
fig_single_mode_bar_chart(suite_dir, output_dir)
fig_multi_mode_comparison(suite_dir, output_dir)
fig_noise_sensitivity(suite_dir, output_dir)
fig_surface_maps(suite_dir, output_dir)
fig_mesh_visualization(suite_dir, output_dir)
fig_suite_summary(suite_dir, output_dir)
print(f"\nAll figures generated in: {output_dir}")
if __name__ == "__main__":
main()