orisys-sensor-design/blender/scripts/test_probe_falloff.py

"""Tests for the bpy-free probe falloff and basis helpers.

The Xynova regression fixture below is the measured set of nearest-probe-vertex
distances (mm) for the 2502 non-probe vertices of ``sensing_surface`` in
``sensors/finger-xynova/r01/source/geometry.blend`` (sha256
``1C8A050B8490B6832D3A9F269DFEF3DAFBF94DBFE3E943B3956BE1104A50D9C9``). It was
dumped once from Blender and is embedded as base64 float32 because pytest cannot
import bpy: the measured distribution has to travel with the test. The values
are sorted ascending, so the first entries are the ones the radius can reach.
"""

from __future__ import annotations

import base64

import numpy as np
import pytest

from probe_falloff import (
    FALLOFF_MODES,
    affected_bands,
    edge_lengths_mm,
    falloff_weight,
    falloff_weights,
    flatness_mm,
    linear_weight,
    orthonormal_basis,
    orthonormality_error,
    percentile,
    sharp_weight,
    smooth_weight,
)

#: Sorted nearest-probe distances, mm, for the 2502 non-probe vertices.
_NONPROBE_DISTANCES_F32_B64 = (
    "SRaSPkIO1T5RDtU+nmLVPrFi1T44xks/DpdUPxiXVD8n71Q/pu9UPzvEYj8SxmI/h6BoP4egaD+vtWg/cAh7P40Iez8uv4E/"
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#: Signed offsets of the 6 measured probe vertices from their centroid along the
#: basis z axis (mm). Peak-to-valley is the contract's flatness_mm = 0.007611.
_MEASURED_PROBE_OFFSETS_Z = (
    -0.0006562,
    0.0042672,
    -0.0033438,
    0.0036863,
    -0.0033205,
    -0.0006329,
)


def _measured_nonprobe_distances() -> np.ndarray:
    raw = base64.b64decode(_NONPROBE_DISTANCES_F32_B64)
    return np.frombuffer(raw, dtype="<f4").astype(np.float64)


def _measured_probe_cloud() -> tuple[np.ndarray, np.ndarray]:
    """Return (coords, member_indices) reproducing the measured radii exactly.

    The 6 probe members sit at the origin; each non-probe vertex sits on the +x
    axis at its measured nearest-probe distance, so the minimum distance to a
    member equals the measured value.
    """
    distances = _measured_nonprobe_distances()
    members = np.zeros((6, 3), dtype=np.float64)
    others = np.stack(
        [distances, np.zeros_like(distances), np.zeros_like(distances)], axis=1
    )
    coords = np.vstack([members, others])
    return coords, np.arange(6, dtype=np.int64)


# ------------------------------------------------------------- curves


def test_curve_endpoints() -> None:
    assert smooth_weight(0.0) == 1.0
    assert smooth_weight(1.0) == 0.0
    assert linear_weight(0.0) == 1.0
    assert linear_weight(1.0) == 0.0
    assert sharp_weight(0.0) == 1.0
    assert sharp_weight(1.0) == 0.0
    assert smooth_weight(0.25) == pytest.approx(1.0 - (3 * 0.25**2 - 2 * 0.25**3))


def test_curve_monotonic_decreasing() -> None:
    samples = np.linspace(0.0, 1.0, 101)
    smooth = np.array([smooth_weight(t) for t in samples])
    linear = np.array([linear_weight(t) for t in samples])
    sharp = np.array([sharp_weight(t) for t in samples])
    for curve in (smooth, linear, sharp):
        assert np.all(np.diff(curve) <= 0.0)
        assert curve.min() == pytest.approx(0.0)
        assert curve.max() == pytest.approx(1.0)


def test_falloff_weight_thresholds_and_dispatch() -> None:
    assert falloff_weight(0.0, "smooth") == 1.0
    assert falloff_weight(-5.0, "sharp") == 1.0
    assert falloff_weight(1.0, "constant") == 0.0
    assert falloff_weight(1.5, "smooth") == 0.0
    assert falloff_weight(0.4, "linear") == pytest.approx(0.6)
    assert falloff_weight(0.4, "sharp") == pytest.approx(0.36)
    assert falloff_weight(0.4, "constant") == 1.0
    with pytest.raises(ValueError):
        falloff_weight(0.5, "bounce")


def test_falloff_modes_list_matches_the_contract() -> None:
    assert FALLOFF_MODES == ("smooth", "linear", "sharp", "constant")


# -------------------------------------------------------------- basis


def _mixed_normals() -> np.ndarray:
    """Six unit normals whose mean is tilted away from +z (mixed, not symmetric)."""
    angle_a = np.radians(2.5533)
    angle_b = np.radians(0.5)
    tilted_a = np.array([np.sin(angle_a), 0.0, np.cos(angle_a)])
    tilted_b = np.array([-np.sin(angle_b), 0.0, np.cos(angle_b)])
    return np.array([tilted_a] * 3 + [tilted_b] * 3, dtype=np.float64)


def _tilted_normals(angle_deg: float) -> np.ndarray:
    """Six unit normals with mean exactly +z and max deviation angle_deg."""
    angle = np.radians(angle_deg)
    return np.array(
        [[np.sin(angle), 0.0, np.cos(angle)]] * 3
        + [[-np.sin(angle), 0.0, np.cos(angle)]] * 3,
        dtype=np.float64,
    )


def test_orthonormal_basis_rows_are_x_y_z_and_right_handed() -> None:
    normal = np.array([0.2, -0.3, 0.9])
    normals = np.array([normal / np.linalg.norm(normal)] * 6)
    origin, basis, report = orthonormal_basis(
        normals, origin=[74.76961, 16.2155, 120.80202]
    )
    x, y, z = basis
    assert report["member_count"] == 6
    assert report["origin_given"] is True
    assert origin == pytest.approx([74.76961, 16.2155, 120.80202])
    assert z == pytest.approx(normals[0])
    assert x == pytest.approx(
        np.cross([0.0, 0.0, 1.0], normals[0])
        / np.linalg.norm(np.cross([0.0, 0.0, 1.0], normals[0]))
    )
    assert y == pytest.approx(np.cross(z, x))
    assert np.allclose(np.cross(z, x), y, atol=1e-12)
    assert orthonormality_error(basis) < 1e-12
    assert report["orthonormal"] is True
    assert report["x_fallback"] is False
    assert report["normal_angle_max_deg"] == pytest.approx(0.0, abs=1e-9)


def test_orthonormal_basis_reports_max_normal_angle() -> None:
    normals = _mixed_normals()
    _, basis, report = orthonormal_basis(normals, origin=[0.0, 0.0, 0.0])
    unit = normals / np.linalg.norm(normals, axis=1)[:, None]
    expected = float(
        np.degrees(np.arccos(np.clip(unit @ basis[2], -1.0, 1.0))).max()
    )
    assert report["normal_angle_max_deg"] == pytest.approx(expected, abs=1e-9)
    assert 1.5 < report["normal_angle_max_deg"] < 1.6
    assert orthonormality_error(basis) < 1e-12


def test_orthonormal_basis_origin_is_optional() -> None:
    origin, basis, report = orthonormal_basis(_mixed_normals())
    assert origin is None
    assert report["origin_given"] is False
    assert basis.shape == (3, 3)


def test_orthonormal_basis_degenerate_x_falls_back_to_plus_x() -> None:
    origin, basis, report = orthonormal_basis(
        np.array([[0.0, 0.0, 1.0]] * 4), origin=[0.0, 0.0, 0.0]
    )
    assert report["x_fallback"] is True
    assert basis[0] == pytest.approx([1.0, 0.0, 0.0])
    assert basis[1] == pytest.approx([0.0, 1.0, 0.0])
    assert basis[2] == pytest.approx([0.0, 0.0, 1.0])
    assert report["orthonormal"] is True


def test_orthonormal_basis_rejects_degenerate_input() -> None:
    with pytest.raises(ValueError):
        orthonormal_basis(np.zeros((0, 3)))
    with pytest.raises(ValueError):
        orthonormal_basis(np.zeros((2, 2)))
    with pytest.raises(ValueError):
        orthonormal_basis(np.array([[0.0, 0.0, 0.0]]))
    with pytest.raises(ValueError):
        # perfectly cancelling normals leave no surface normal
        orthonormal_basis(
            np.array([[1.0, 0.0, 0.0], [-1.0, 0.0, 0.0]])
        )


def test_orthonormality_error_detects_a_bad_basis() -> None:
    assert orthonormality_error(np.eye(3)) == 0.0
    assert orthonormality_error(np.diag([2.0, 1.0, 1.0])) == pytest.approx(3.0)
    with pytest.raises(ValueError):
        orthonormality_error(np.eye(2))


# ----------------------------------------------------- falloff_weights


def test_falloff_weights_are_one_for_members_and_zero_at_radius() -> None:
    coords = np.array(
        [
            [0.0, 0.0, 0.0],  # member
            [0.5, 0.0, 0.0],  # member
            [0.25, 0.0, 0.0],  # inside (d = 0.25 from the first member)
            [1.0, 0.0, 0.0],  # exactly radius from the 0.5 member -> d = 0.5
            [0.0, 0.9, 0.0],  # outside
        ]
    )
    weights = falloff_weights(coords, [0, 1], 0.5, "smooth")
    assert weights.dtype == np.float64
    assert weights.shape == (5,)
    assert weights[0] == 1.0
    assert weights[1] == 1.0
    assert weights[2] == pytest.approx(smooth_weight(0.5))
    assert weights[3] == 0.0
    assert weights[4] == 0.0


def test_falloff_weights_match_the_scalar_curve() -> None:
    rng = np.random.default_rng(7)
    coords = rng.uniform(-1.0, 1.0, size=(64, 3))
    members = np.array([0, 1, 2])
    for mode in FALLOFF_MODES:
        weights = falloff_weights(coords, members, 0.75, mode)
        for index in range(coords.shape[0]):
            distance = float(
                np.linalg.norm(coords[index] - coords[members], axis=1).min()
            )
            assert weights[index] == pytest.approx(
                falloff_weight(distance / 0.75, mode), abs=1e-15
            )


def test_falloff_weights_are_symmetric_in_offset_direction() -> None:
    coords = np.array(
        [
            [0.0, 0.0, 0.0],
            [0.3, 0.0, 0.0],
            [-0.3, 0.0, 0.0],
            [0.0, 0.3, 0.0],
            [0.0, 0.0, 0.3],
            [0.3 / np.sqrt(3)] * 3,
        ]
    )
    weights = falloff_weights(coords, [0], 0.5, "smooth")
    for index in (2, 3, 4, 5):
        assert weights[index] == pytest.approx(weights[1], abs=1e-15)
    assert weights[1] == pytest.approx(smooth_weight(0.6))


def test_falloff_weights_constant_mode() -> None:
    coords = np.array([[0.0, 0.0, 0.0], [0.4, 0.0, 0.0], [0.6, 0.0, 0.0]])
    weights = falloff_weights(coords, [0], 0.5, "constant")
    assert list(weights) == [1.0, 1.0, 0.0]


@pytest.mark.parametrize(
    "kwargs",
    [
        {"radius_mm": 0.0},
        {"radius_mm": -1.0},
        {"mode": "bounce"},
    ],
)
def test_falloff_weights_reject_bad_settings(kwargs: dict) -> None:
    coords = np.array([[0.0, 0.0, 0.0], [0.1, 0.0, 0.0]])
    settings = {"radius_mm": 0.5}
    settings.update(kwargs)
    with pytest.raises(ValueError):
        falloff_weights(coords, [0], **settings)


def test_falloff_weights_reject_bad_geometry() -> None:
    with pytest.raises(ValueError):
        falloff_weights(np.zeros((3, 2)), [0], 0.5)
    with pytest.raises(ValueError):
        falloff_weights(np.zeros((3, 3)), [], 0.5)
    with pytest.raises(ValueError):
        falloff_weights(np.zeros((3, 3)), [5], 0.5)


# -------------------------------------------------------- affected_bands


def test_affected_bands_omits_empty_bands() -> None:
    weights = np.array([1.0, 1.0, 0.75, 0.4, 0.2, 0.07, 0.01, 0.0])
    assert affected_bands(weights) == {
        "1.0": 2,
        "0.5-1.0": 1,
        "0.3-0.5": 1,
        "0.1-0.3": 1,
        "0.05-0.1": 1,
        "0.0-0.05": 1,
    }
    assert affected_bands(np.array([0.0, 0.0, 1.0])) == {"1.0": 1}


# ------------------------------------------- measured Xynova regression


def test_measured_xynova_radius_half_mm_reaches_exactly_five_vertices() -> None:
    coords, members = _measured_probe_cloud()
    assert coords.shape == (2508, 3)
    assert len(members) == 6

    weights = falloff_weights(coords, members, 0.5, "smooth")
    inside = weights[(weights > 0.0) & (weights < 1.0)]

    assert int((weights == 0.0).sum()) == 2497
    assert int((weights >= 1.0).sum()) == 6
    assert inside.size == 5, "radius 0.5 mm must touch exactly 5 non-probe vertices"

    distances = _measured_nonprobe_distances()
    assert float(distances[0]) == pytest.approx(0.2853262745311897, abs=1e-6)
    assert float(distances[1]) == pytest.approx(0.41612439347389385, abs=1e-6)
    assert float(distances[4]) == pytest.approx(0.41676858076285744, abs=1e-6)
    assert float(distances[5]) == pytest.approx(0.795993, abs=1e-6)
    # The gap between the 5th and 6th neighbour is what makes the count 5, not 11.
    assert int((distances < 0.5).sum()) == 5
    assert int((distances < 0.6).sum()) == 5
    assert int((distances < 0.79).sum()) == 5


def test_measured_xynova_weight_bands_match_the_contract_report() -> None:
    coords, members = _measured_probe_cloud()
    weights = falloff_weights(coords, members, 0.5, "smooth")
    inside = np.sort(weights[(weights > 0.0) & (weights < 1.0)])

    assert affected_bands(weights) == {"1.0": 6, "0.3-0.5": 1, "0.05-0.1": 4}
    assert inside[4] == pytest.approx(0.3947265, abs=1e-6)  # d = 0.28533
    assert inside[:4] == pytest.approx(
        [0.0739043, 0.0739052, 0.0749794, 0.0749802], abs=1e-6
    )
    # One vertex in 0.3-0.5, four in 0.05-0.1, none anywhere else.
    assert int(((inside >= 0.3) & (inside < 0.5)).sum()) == 1
    assert int(((inside >= 0.05) & (inside < 0.1)).sum()) == 4


def test_measured_xynova_percentile_utility() -> None:
    distances = _measured_nonprobe_distances()
    assert percentile(distances, 0.0) == pytest.approx(float(distances.min()))
    assert percentile(distances, 50.0) == pytest.approx(
        float(np.percentile(distances, 50)), rel=1e-6
    )
    assert percentile(distances, 100.0) == pytest.approx(float(distances.max()))


# ----------------------------------------------------------- statistics


def test_edge_lengths_mm() -> None:
    coords = np.array(
        [[0.0, 0.0, 0.0], [3.0, 0.0, 0.0], [3.0, 4.0, 0.0], [0.0, 0.0, 2.0]]
    )
    edges = np.array([[0, 1], [1, 2], [0, 3]])
    assert edge_lengths_mm(coords, edges) == pytest.approx([3.0, 4.0, 2.0])
    assert edge_lengths_mm(coords, np.zeros((0, 2), dtype=np.int64)).size == 0


def test_edge_percentiles_match_the_measured_contract_row() -> None:
    # Contract: mesh edges min 0.0504, p50 0.6285, p90 0.991, max 1.816 mm.
    edges = np.array([0.0504, 0.628501, 0.991, 1.816])
    assert percentile(edges, 0.0) == pytest.approx(0.0504)
    assert percentile(edges, 100.0) == pytest.approx(1.816)
    assert percentile(edges, 50.0) == pytest.approx(np.percentile(edges, 50))


def test_flatness_mm_is_peak_to_valley_along_the_normal() -> None:
    offsets = np.array(_MEASURED_PROBE_OFFSETS_Z)
    coords = np.stack([np.zeros_like(offsets), np.zeros_like(offsets), offsets], axis=1)
    assert flatness_mm(coords, [0.0, 0.0, 0.0], [0.0, 0.0, 1.0]) == pytest.approx(
        0.007611, abs=1e-6
    )
    assert flatness_mm(coords, [0.0, 0.0, 0.0], [0.0, 0.0, 2.0]) == pytest.approx(
        0.007611, abs=1e-6
    )  # non-unit normals are normalised
    with pytest.raises(ValueError):
        flatness_mm(coords, [0.0, 0.0, 0.0], [0.0, 0.0, 0.0])


def test_percentile_edges() -> None:
    assert percentile([3.0, 1.0, 2.0], 0.0) == 1.0
    assert percentile([3.0, 1.0, 2.0], 100.0) == 3.0
    assert percentile([3.0, 1.0, 2.0], 50.0) == 2.0
    with pytest.raises(ValueError):
        percentile([], 50.0)
    with pytest.raises(ValueError):
        percentile([1.0], 101.0)