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Wire CameraModel/CameraModelTolerance through BeamReconstructor

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BeamReconstructor now requires camera/camera_tolerance, threading them
into ModalFitter.fit_auto and PhaseRetriever.retrieve, and uses
GeometryCalibration.effective_pixel_scale for deconvolution instead of
the removed MeasurementPlane.pixel_scale.

Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com>
This commit is contained in:
Martino Ferrari
2026-07-03 12:26:18 +02:00
parent 1d399ccc9b
commit 57dc7d743e
2 changed files with 145 additions and 28 deletions
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tests/test_reconstruct.py
+126 -18
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@@ -4,6 +4,7 @@ import pytest
from he11lib.deconvolution import DiffusionDeconvolver
from he11lib.fitting import ModalFitter
from he11lib.geometry import CameraModel, CameraModelTolerance, GeometryCalibration
from he11lib.modes import LGBasis
from he11lib.reconstruct import BeamReconstructor
from he11lib.synthetic import SyntheticBeamGenerator
@@ -12,6 +13,7 @@ W0 = 5e-3
Z0 = 0.5
WAVELENGTH = 1.76e-3
PIXEL_SCALE = 4e-4
CAMERA_DISTANCE = 5.0
IMAGE_SHAPE = (61, 61)
Z_LIST = [0.35, 0.5, 0.65, 0.8]
@@ -20,16 +22,36 @@ def make_basis():
return LGBasis(w0=W0, z0=Z0, wavelength=WAVELENGTH)
def make_generator(basis):
return SyntheticBeamGenerator(basis=basis, image_shape=IMAGE_SHAPE, pixel_scale=PIXEL_SCALE)
def make_camera():
focal_length_px = (CAMERA_DISTANCE + Z0) / PIXEL_SCALE
return CameraModel(
focal_length_px=focal_length_px,
position=(0.0, 0.0, -CAMERA_DISTANCE),
orientation_deg=(0.0, 0.0, 0.0),
)
def zero_tolerance():
return CameraModelTolerance(
focal_length_px=0.0, position=(0.0, 0.0, 0.0), orientation_deg=(0.0, 0.0, 0.0)
)
def make_generator(basis, camera):
return SyntheticBeamGenerator(basis=basis, camera=camera)
def test_reconstruct_recovers_pure_mode_purity():
basis = make_basis()
gen = make_generator(basis)
planes = gen.generate(coefficients={(0, 0): 1.0 + 0j}, z_list=Z_LIST, noise_std=1e-4, seed=0)
camera = make_camera()
gen = make_generator(basis, camera)
planes = gen.generate(
coefficients={(0, 0): 1.0 + 0j}, z_list=Z_LIST, image_shape=IMAGE_SHAPE, noise_std=1e-4, seed=0
)
reconstructor = BeamReconstructor(w0=W0, z0=Z0, wavelength=WAVELENGTH, max_order=2)
reconstructor = BeamReconstructor(
w0=W0, z0=Z0, wavelength=WAVELENGTH, camera=camera, camera_tolerance=zero_tolerance(), max_order=2
)
result = reconstructor.reconstruct(planes)
power_fraction, _ = result.purity[(0, 0)]
@@ -39,13 +61,21 @@ def test_reconstruct_recovers_pure_mode_purity():
def test_reconstruct_recovers_center_offset():
basis = make_basis()
gen = make_generator(basis)
camera = make_camera()
gen = make_generator(basis, camera)
true_center = (10 * PIXEL_SCALE, -5 * PIXEL_SCALE)
planes = gen.generate(
coefficients={(0, 0): 1.0 + 0j}, z_list=Z_LIST, center=true_center, noise_std=1e-4, seed=1
coefficients={(0, 0): 1.0 + 0j},
z_list=Z_LIST,
image_shape=IMAGE_SHAPE,
center=true_center,
noise_std=1e-4,
seed=1,
)
reconstructor = BeamReconstructor(w0=W0, z0=Z0, wavelength=WAVELENGTH, max_order=2)
reconstructor = BeamReconstructor(
w0=W0, z0=Z0, wavelength=WAVELENGTH, camera=camera, camera_tolerance=zero_tolerance(), max_order=2
)
result = reconstructor.reconstruct(planes)
for cx, cy in result.centers:
@@ -55,21 +85,34 @@ def test_reconstruct_recovers_center_offset():
def test_reconstruct_with_deconvolution_corrects_blur():
basis = make_basis()
gen = make_generator(basis)
planes = gen.generate(coefficients={(0, 0): 1.0 + 0j}, z_list=Z_LIST, noise_std=1e-4, seed=2)
camera = make_camera()
gen = make_generator(basis, camera)
planes = gen.generate(
coefficients={(0, 0): 1.0 + 0j}, z_list=Z_LIST, image_shape=IMAGE_SHAPE, noise_std=1e-4, seed=2
)
deconvolver = DiffusionDeconvolver(thermal_diffusivity=1e-6, dwell_time=30.0)
calib = GeometryCalibration(camera)
blurred_planes = [
replace(p, flux=deconvolver.blur(p.flux, p.pixel_scale)) for p in planes
replace(p, flux=deconvolver.blur(p.flux, calib.effective_pixel_scale(p.flux.shape, p.z)))
for p in planes
]
# Without deconvolution, blur should measurably hurt purity recovery.
fitter = ModalFitter(basis)
result_no_deconv = fitter.fit(blurred_planes, modes=[(0, 0), (1, 0), (0, 1)])
result_no_deconv = fitter.fit(
blurred_planes, modes=[(0, 0), (1, 0), (0, 1)], camera=camera, camera_tolerance=zero_tolerance()
)
purity_no_deconv, _ = result_no_deconv.purity[(0, 0)]
reconstructor = BeamReconstructor(
w0=W0, z0=Z0, wavelength=WAVELENGTH, max_order=2, deconvolver=deconvolver
w0=W0,
z0=Z0,
wavelength=WAVELENGTH,
camera=camera,
camera_tolerance=zero_tolerance(),
max_order=2,
deconvolver=deconvolver,
)
result = reconstructor.reconstruct(blurred_planes)
purity_with_deconv, _ = result.purity[(0, 0)]
@@ -80,12 +123,21 @@ def test_reconstruct_with_deconvolution_corrects_blur():
def test_reconstruct_forces_phase_retrieval_fallback():
basis = make_basis()
gen = SyntheticBeamGenerator(basis=basis, image_shape=(121, 121), pixel_scale=3e-4)
camera = make_camera()
gen = SyntheticBeamGenerator(basis=basis, camera=camera)
z_list = [0.47, 0.5, 0.53]
planes = gen.generate(coefficients={(0, 0): 1.0 + 0j}, z_list=z_list, noise_std=1e-5, seed=3)
planes = gen.generate(
coefficients={(0, 0): 1.0 + 0j}, z_list=z_list, image_shape=IMAGE_SHAPE, noise_std=1e-5, seed=3
)
reconstructor = BeamReconstructor(
w0=W0, z0=Z0, wavelength=WAVELENGTH, max_order=2, force_phase_retrieval=True
w0=W0,
z0=Z0,
wavelength=WAVELENGTH,
camera=camera,
camera_tolerance=zero_tolerance(),
max_order=2,
force_phase_retrieval=True,
)
result = reconstructor.reconstruct(planes)
@@ -96,17 +148,73 @@ def test_reconstruct_forces_phase_retrieval_fallback():
def test_reconstruct_falls_back_automatically_on_high_residual():
basis = make_basis()
gen = SyntheticBeamGenerator(basis=basis, image_shape=(121, 121), pixel_scale=3e-4)
camera = make_camera()
gen = SyntheticBeamGenerator(basis=basis, camera=camera)
z_list = [0.47, 0.5, 0.53]
planes = gen.generate(coefficients={(0, 0): 1.0 + 0j}, z_list=z_list, noise_std=1e-5, seed=4)
planes = gen.generate(
coefficients={(0, 0): 1.0 + 0j}, z_list=z_list, image_shape=IMAGE_SHAPE, noise_std=1e-5, seed=4
)
reconstructor = BeamReconstructor(
w0=W0,
z0=Z0,
wavelength=WAVELENGTH,
camera=camera,
camera_tolerance=zero_tolerance(),
max_order=2,
phase_retrieval_residual_threshold=1e-8,
)
result = reconstructor.reconstruct(planes)
assert result.used_phase_retrieval is True
def test_reconstruct_recovers_camera_and_z_offset_from_nominal():
# End-to-end: ground truth is offset from the nominal camera/z inputs
# (within their tolerances), simulating realistic calibration error.
basis = make_basis()
true_camera = make_camera()
gen = make_generator(basis, true_camera)
true_z_list = Z_LIST
z_offsets = {z: 0.01 for z in true_z_list}
planes = gen.generate(
coefficients={(0, 0): 1.0 + 0j},
z_list=true_z_list,
image_shape=IMAGE_SHAPE,
nominal_z_offsets=z_offsets,
z_tolerance=0.03,
pointing_angle_horizontal_deg=0.2,
pointing_angle_vertical_deg=-0.1,
noise_std=1e-4,
seed=13,
)
nominal_focal_offset = true_camera.focal_length_px * 0.03
nominal_camera = CameraModel(
focal_length_px=true_camera.focal_length_px + nominal_focal_offset,
position=true_camera.position,
orientation_deg=true_camera.orientation_deg,
)
tolerance = CameraModelTolerance(
focal_length_px=true_camera.focal_length_px * 0.1,
position=(0.0, 0.0, 0.0),
orientation_deg=(0.0, 0.0, 0.0),
)
reconstructor = BeamReconstructor(
w0=W0,
z0=Z0,
wavelength=WAVELENGTH,
camera=nominal_camera,
camera_tolerance=tolerance,
max_order=1,
)
result = reconstructor.reconstruct(planes)
power_fraction, _ = result.purity[(0, 0)]
assert power_fraction > 0.95
assert result.pointing_angle_horizontal_deg == pytest.approx(0.2, abs=0.1)
assert result.pointing_angle_vertical_deg == pytest.approx(-0.1, abs=0.1)
assert result.geometry["focal_length_px"] == pytest.approx(true_camera.focal_length_px, rel=0.03)
for i, true_z in enumerate(true_z_list):
assert result.geometry[f"z_{i}"] == pytest.approx(true_z, abs=0.005)