tip modelling and deconvolution

tests/node_tests/tip_blind_estimate.py

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+import numpy as np
+import pytest
+from tests.node_tests._shared import make_field
+from backend.data_types import DataField
+
+
+def run_blind(field, n_pixels=17, threshold=0.0, method="partial", use_edges=False):
+    from backend.nodes.tip_blind_estimate import BlindTipEstimate
+    node = BlindTipEstimate()
+    tip, certainty = node.process(
+        field=field, n_pixels=n_pixels, threshold=threshold,
+        method=method, use_edges=use_edges,
+    )
+    return tip, certainty
+
+
+# ── Output types and dimensions ──────────────────────────────────────────────
+
+def test_outputs_are_data_fields():
+    field = make_field(shape=(32, 32), xreal=32e-9, yreal=32e-9)
+    tip, certainty = run_blind(field, n_pixels=9)
+    assert isinstance(tip, DataField)
+    assert isinstance(certainty, DataField)
+
+
+def test_tip_output_shape():
+    """Tip must be (n_pixels, n_pixels) — odd, bumped if even."""
+    field = make_field(shape=(32, 32), xreal=32e-9, yreal=32e-9)
+    for n in (7, 11, 17):
+        tip, _ = run_blind(field, n_pixels=n)
+        assert tip.data.shape == (n, n), f"Expected ({n},{n}), got {tip.data.shape}"
+
+
+def test_tip_n_pixels_even_bumped():
+    field = make_field(shape=(32, 32), xreal=32e-9, yreal=32e-9)
+    tip, _ = run_blind(field, n_pixels=16)
+    assert tip.data.shape[0] == 17
+
+
+def test_certainty_output_matches_field_shape():
+    field = make_field(shape=(48, 64))
+    _, certainty = run_blind(field, n_pixels=9)
+    assert certainty.data.shape == field.data.shape
+
+
+def test_certainty_is_binary():
+    """Certainty map values must all be 0.0 or 1.0."""
+    field = make_field(shape=(32, 32), xreal=32e-9, yreal=32e-9)
+    _, certainty = run_blind(field, n_pixels=9)
+    vals = np.unique(certainty.data)
+    for v in vals:
+        assert v in (0.0, 1.0), f"Non-binary certainty value: {v}"
+
+
+# ── Tip conventions ───────────────────────────────────────────────────────────
+
+def test_tip_min_is_zero():
+    field = make_field(shape=(32, 32), xreal=32e-9, yreal=32e-9)
+    tip, _ = run_blind(field, n_pixels=9)
+    assert tip.data.min() >= -1e-15
+
+
+def test_tip_max_at_centre():
+    """Apex (centre pixel) must be the maximum of the estimated tip."""
+    field = make_field(shape=(32, 32), xreal=32e-9, yreal=32e-9)
+    tip, _ = run_blind(field, n_pixels=9)
+    ci = tip.data.shape[0] // 2
+    assert tip.data[ci, ci] == pytest.approx(tip.data.max(), abs=1e-20)
+
+
+def test_tip_units_inherited():
+    field = make_field(xreal=1e-6, yreal=1e-6)
+    field.si_unit_xy = "nm"
+    field.si_unit_z = "V"
+    tip, _ = run_blind(field, n_pixels=9)
+    assert tip.si_unit_xy == "nm"
+    assert tip.si_unit_z == "V"
+
+
+# ── Flat field gives flat (zero) tip ─────────────────────────────────────────
+
+def test_flat_field_gives_zero_tip():
+    """A flat image has no features → blind estimation cannot refine the tip → stays flat."""
+    flat = make_field(data=np.full((32, 32), 3.14))
+    tip, _ = run_blind(flat, n_pixels=7)
+    # Tip should be all zeros (no refinement possible)
+    assert np.allclose(tip.data, 0.0, atol=1e-12)
+
+
+# ── Single spike → estimated tip matches expected shape ──────────────────────
+
+def test_spike_gives_sharp_tip():
+    """
+    A single sharp spike dilated by a known paraboloid tip gives a broadened image.
+    Blind estimation on the broadened image should recover a tip that is ≤ the true tip
+    everywhere (blind estimation is an upper bound on the true tip shape).
+    """
+    from scipy.ndimage import grey_dilation
+    from backend.nodes.tip_model import TipModel
+
+    pixel_size = 1e-9
+    n = 64
+    field_ref = make_field(shape=(n, n), xreal=n * pixel_size, yreal=n * pixel_size)
+
+    # Create true parabolic tip (33×33, radius=20nm)
+    true_tip_node = TipModel()
+    true_tip, = true_tip_node.process(
+        field=field_ref, shape="parabola", radius=20e-9,
+        half_angle=20.0, n_pixels=33,
+    )
+
+    # Spike surface
+    surface = np.zeros((n, n))
+    surface[n // 2, n // 2] = 1e-9   # 1 nm spike
+
+    # Dilated (measured) image
+    dil_struct = true_tip.data - true_tip.data.max()
+    measured_data = grey_dilation(surface, structure=dil_struct)
+    measured = make_field(data=measured_data, xreal=n * pixel_size, yreal=n * pixel_size)
+
+    # Blind estimation
+    est_tip, certainty = run_blind(measured, n_pixels=33, threshold=0.0, method="partial")
+
+    # The estimated tip must be ≤ the true tip everywhere (blind est. is upper bound)
+    # Both are min=0, max=apex. Compare normalised shapes.
+    true_norm = true_tip.data / true_tip.data.max() if true_tip.data.max() > 0 else true_tip.data
+    est_norm = est_tip.data / est_tip.data.max() if est_tip.data.max() > 0 else est_tip.data
+    # After normalisation: est ≤ true everywhere (blind is conservative)
+    assert np.all(est_norm <= true_norm + 1e-6), \
+        "Blind estimate exceeded true tip (not a valid upper bound)"
+
+
+# ── Partial vs full ───────────────────────────────────────────────────────────
+
+def test_full_method_runs():
+    """Full estimation should run without error on a small image."""
+    field = make_field(shape=(24, 24), xreal=24e-9, yreal=24e-9)
+    tip, certainty = run_blind(field, n_pixels=7, method="full")
+    assert isinstance(tip, DataField)
+    assert isinstance(certainty, DataField)
+
+
+# ── Certainty increases with sharp features ───────────────────────────────────
+
+def test_certainty_nonzero_for_sharp_image():
+    """An image with distinct features should produce some certain pixels."""
+    from scipy.ndimage import grey_dilation
+    from backend.nodes.tip_model import TipModel
+
+    pixel_size = 1e-9
+    n = 64
+    field_ref = make_field(shape=(n, n), xreal=n * pixel_size, yreal=n * pixel_size)
+    true_tip_node = TipModel()
+    true_tip, = true_tip_node.process(
+        field=field_ref, shape="parabola", radius=20e-9,
+        half_angle=20.0, n_pixels=17,
+    )
+
+    # Create a sharp pyramid surface
+    cx, cy = n // 2, n // 2
+    ys, xs = np.mgrid[0:n, 0:n]
+    surface = np.maximum(0, 5e-9 - 0.3e-9 * np.maximum(np.abs(xs - cx), np.abs(ys - cy)))
+    dil_struct = true_tip.data - true_tip.data.max()
+    measured_data = grey_dilation(surface, structure=dil_struct)
+    measured = make_field(data=measured_data, xreal=n * pixel_size, yreal=n * pixel_size)
+
+    _, certainty = run_blind(measured, n_pixels=17, method="partial")
+    assert certainty.data.sum() > 0, "No certain pixels found for a sharp image"