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wsimson commited on Sep 10

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us4us: keep README + pipeline.yaml only (no per-dataset LICENSE/reconstruct.py)

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us4us/reconstruct.py DELETED Viewed

@@ -1,421 +0,0 @@
-# SPDX-License-Identifier: CC-BY-4.0
-"""Reconstruct: transmission-mode attenuation FBP for the US4US ring-array USCT data.
-This is a full ring of point-source transmit elements with a receive sub-aperture
-on the far side of the ring (not surrounding the transmitter), so it's a
-transmission (amplitude-attenuation) acquisition, not reflection B-mode.
-`zea.ops.Beamform`/`USCTReflectivityDAS` don't apply, so this defines five custom
-operations (https://zea.readthedocs.io/en/latest/pipeline.html#custom-operations):
-    TransmissionAttenuation -> ApertureRearrange -> ResortSinogram
-        -> LinearizeDetectors -> FilteredBackprojection -> (built-in) GaussianBlur
-`LinearizeDetectors` mirrors the `linearize_detectors()` step us4us added to
-`reconstruction_example.py` on 2026-09-07: it resamples each projection from the
-ring's arc-spaced detectors onto the uniform chord axis that parallel-beam FBP
-assumes. Without it the reconstruction is radially distorted.
-`ApertureRearrange`'s `aperture_offset` (receive channel `k` of transmit `t` is
-ring element `(t + offset + k) mod 1024`, centered on the antipode) isn't
-documented anywhere in the file; confirmed via `validate_geometry` below, which
-predicts the direct-arrival sample from real element positions and compares it
-to the measured envelope peak.
-Usage:
-    python reconstruct.py --input yezitronix-b-rg-1.2.hdf5
-    python reconstruct.py --input s8_r.hdf5 --frame 0
-"""
-import argparse
-import re
-from pathlib import Path
-import matplotlib.pyplot as plt
-import numpy as np
-from keras import ops
-from mpl_toolkits.axes_grid1 import make_axes_locatable
-from zea.internal.registry import ops_registry
-from zea.ops import Cast, GaussianBlur, Operation
-import zea
-from zea import Config, File, Pipeline
-HERE = Path(__file__).parent
-CONFIG = HERE / "pipeline.yaml"
-N_EL = 1024  # ring elements
-N_RX = 512  # receive sub-aperture width
-APERTURE_OFFSET = (N_EL - N_RX) // 2  # 256
-PROBE_RADIUS = 13e-2  # m; matches us4us's reconstruction_example.py and the measured geometry
-@ops_registry("transmission_attenuation")
-class TransmissionAttenuation(Operation):
-    """Windowed peak RF amplitude per (tx, rx) pair, converted to attenuation via
-    ``-20 * log10(amp / reference)``. ``reference=1.0`` is arbitrary units (no
-    water-reference calibration)."""
-    # Default is None (resolved below) rather than 1.0 so that an explicitly
-    # passed value always differs from the signature default and is therefore
-    # written into pipeline.yaml - zea omits params left at their default.
-    def __init__(self, reference: float | None = None, **kwargs):
-        super().__init__(**kwargs)
-        self.reference = 1.0 if reference is None else reference
-    def call(self, **kwargs):
-        raw = kwargs[self.key]  # (n_tx, n_ax, n_rx, n_ch)
-        raw = ops.squeeze(raw, axis=-1)  # (n_tx, n_ax, n_rx)
-        n_ax = raw.shape[1]
-        # `keras.ops.bartlett` requires its length argument to be a concrete
-        # (non-traced) Python int even under jit, which a shape-derived value
-        # here isn't - so the triangular window is built directly instead.
-        i = ops.arange(n_ax, dtype="float32")
-        half = (n_ax - 1) / 2.0
-        window = (1.0 - ops.abs((i - half) / half))[None, :, None]
-        amp = ops.max(ops.abs(raw * window), axis=1)  # (n_tx, n_rx)
-        att = -20 * ops.log10(amp / self.reference)
-        return {self.output_key: att}
-@ops_registry("aperture_rearrange")
-class ApertureRearrange(Operation):
-    """Cyclically re-indexes each transmit's receive columns to a consistent
-    relative frame, so `ResortSinogram` can treat rows as consecutive projection
-    angles. `aperture_offset` is not documented in the file — see module
-    docstring."""
-    def __init__(self, aperture_offset: int | None = None, **kwargs):
-        super().__init__(**kwargs)
-        self.aperture_offset = APERTURE_OFFSET if aperture_offset is None else aperture_offset
-    def call(self, **kwargs):
-        att = kwargs[self.key]  # (n_tx, n_rx)
-        n_tx, n_rx = att.shape
-        rel_start = n_tx // 2 - self.aperture_offset
-        rel_end = n_tx // 2 + self.aperture_offset
-        tx = ops.arange(n_tx)
-        rx_end = ops.mod(tx + rel_end, n_tx)
-        rx_start = ops.mod(tx + rel_start, n_tx)
-        shift = ops.where(rx_end < rx_start, rx_end + 1, 0)  # (n_tx,)
-        col = ops.mod(ops.arange(n_rx)[None, :] + shift[:, None], n_rx)
-        rearranged = ops.take_along_axis(att, col, axis=1)
-        return {self.output_key: rearranged}
-@ops_registry("resort_sinogram")
-class ResortSinogram(Operation):
-    """Doubles angular sampling (n_tx -> 2*n_tx) and halves detector count
-    (n_rx -> n_rx // 2) by interleaving receive channels across transmits, into a
-    proper parallel-projection sinogram."""
-    def call(self, **kwargs):
-        att = kwargs[self.key]  # (n_tx, n_rx)
-        n_tx, n_rx = att.shape
-        rx = ops.arange(n_rx // 2)
-        tx = ops.arange(n_tx)
-        RX, TX = ops.meshgrid(rx, tx, indexing="xy")
-        src_tx = ops.mod(TX + n_rx // 4 - RX, n_tx)  # (n_tx, n_rx // 2)
-        # output[2t, r] = att[src_tx[t, r], 2r]; output[2t+1, r] = att[src_tx[t, r], 2r+1].
-        # Gathering the even/odd columns separately along axis 0 with the same
-        # src_tx index, then interleaving via stack+reshape, is equivalent to that
-        # scatter but keras.ops.take_along_axis-only (no fancy 2D indexing needed).
-        even = ops.take_along_axis(att[:, 0::2], src_tx, axis=0)
-        odd = ops.take_along_axis(att[:, 1::2], src_tx, axis=0)
-        sinogram = ops.reshape(ops.stack([even, odd], axis=1), (n_tx * 2, n_rx // 2))
-        return {self.output_key: sinogram}
-@ops_registry("linearize_detectors")
-class LinearizeDetectors(Operation):
-    """Resample each projection from the ring's arc positions onto a uniform
-    linear (chord) axis, which is what parallel-beam FBP assumes.
-    Detector `i` sits at angle `alpha_i` on the ring, so its perpendicular offset
-    from the ring centre is `R * sin(alpha_i)` — compressed towards the edges of
-    the aperture, not uniformly spaced. Backprojecting without correcting this
-    distorts the image radially. Matches `linearize_detectors()` in us4us's
-    `reconstruction_example.py` (added in their 2026-09-07 revision).
-    `keras.ops` has no `interp`, but both the source (`xp`) and target (`x`) axes
-    depend only on `n_detectors` and the radius, so the gather indices and
-    interpolation weights are constant and precomputed here in numpy. Endpoint
-    handling matches `np.interp` (clamp, no extrapolation).
-    """
-    # See the note on TransmissionAttenuation.__init__ for why this defaults to None.
-    def __init__(self, probe_radius: float | None = None, **kwargs):
-        super().__init__(**kwargs)
-        self.probe_radius = PROBE_RADIUS if probe_radius is None else probe_radius
-    def call(self, **kwargs):
-        sinogram = kwargs[self.key]  # (n_angles, n_detectors)
-        n_col = sinogram.shape[1]
-        i = np.arange(-(n_col // 2), n_col // 2)
-        xp = self.probe_radius * np.sin(i * np.pi / 2 / n_col)  # arc -> chord, non-uniform
-        x = i * np.sqrt(2) * self.probe_radius / n_col  # uniform target axis
-        lo = np.clip(np.searchsorted(xp, x) - 1, 0, n_col - 2)
-        w = np.clip((x - xp[lo]) / (xp[lo + 1] - xp[lo]), 0.0, 1.0).astype("float32")
-        left = ops.take(sinogram, ops.convert_to_tensor(lo), axis=1)
-        right = ops.take(sinogram, ops.convert_to_tensor(lo + 1), axis=1)
-        return {self.output_key: left * (1.0 - w) + right * w}
-@ops_registry("filtered_backprojection")
-class FilteredBackprojection(Operation):
-    """Ram-Lak (ramp) filter + backprojection onto a square grid sized to the
-    detector count, vectorized over all angles in one gather-and-sum."""
-    @staticmethod
-    def _ramp_filter_half(n_detectors: int):
-        """Ram-Lak filter, rfft-domain half. `sinogram` is real, so `rfft`/`irfft`
-        (real in, real out) are used below instead of a full complex `fft`/`ifft`
-        - there's no plain complex `ifft` in keras.ops anyway."""
-        half = n_detectors // 2
-        n = ops.concatenate([ops.arange(1.0, half + 1, 2.0), ops.arange(half - 1.0, 0.0, -2.0)])
-        odd = -1.0 / (np.pi * n) ** 2
-        even = ops.concatenate([ops.convert_to_tensor([0.25]), ops.zeros((half - 1,))])
-        f = ops.reshape(ops.stack([even, odd], axis=1), (n_detectors,))
-        real, _ = ops.rfft(f)
-        return 2 * real
-    def call(self, **kwargs):
-        sinogram = kwargs[self.key]  # (n_angles, n_detectors)
-        n_angles, n_detectors = sinogram.shape
-        ramp_half = self._ramp_filter_half(n_detectors)
-        real, imag = ops.rfft(sinogram)
-        filtered = ops.irfft((real * ramp_half, imag * ramp_half), fft_length=n_detectors)
-        # Backprojection geometry (which detector index each pixel/angle maps to)
-        # depends only on (n_angles, n_detectors).
-        half = n_detectors // 2
-        angles = ops.arange(n_angles, dtype="float32") * (2 * np.pi / n_angles)
-        x = ops.arange(n_detectors) - half
-        X, Y = ops.meshgrid(x, x)  # (n_detectors, n_detectors)
-        t = X[None] * ops.cos(angles)[:, None, None] + Y[None] * ops.sin(angles)[:, None, None]
-        idx_raw = ops.cast(ops.round(t + half), "int32")  # (n_angles, D, D)
-        # Skip out-of-range detector indices per angle rather than clamping them
-        # to the edge, which would smear a repeated edge value into the image.
-        valid = ops.logical_and(idx_raw >= 0, idx_raw < n_detectors)
-        idx_safe = ops.clip(idx_raw, 0, n_detectors - 1)
-        gathered = ops.take_along_axis(
-            ops.repeat(filtered[:, None, :], n_detectors, axis=1), idx_safe, axis=2
-        )  # (n_angles, D, D)
-        image = ops.sum(ops.where(valid, gathered, 0.0), axis=0)
-        return {self.output_key: image}
-def build_pipeline(
-    reference: float = 1.0,
-    aperture_offset: int = APERTURE_OFFSET,
-    probe_radius: float = PROBE_RADIUS,
-    smooth_sigma: float = 1.0,
-) -> Pipeline:
-    """Every acquisition-specific constant is passed explicitly, so it round-trips
-    into `pipeline.yaml` and the reconstruction is fully described by that file
-    rather than by constants in this script."""
-    return Pipeline(
-        operations=[
-            Cast(dtype="float32"),
-            TransmissionAttenuation(reference=reference),
-            ApertureRearrange(aperture_offset=aperture_offset),
-            ResortSinogram(),
-            LinearizeDetectors(probe_radius=probe_radius),
-            FilteredBackprojection(),
-            GaussianBlur(sigma=smooth_sigma, axes=(-2, -1)),
-        ],
-        with_batch_dim=False,
-    )
-def write_config(pipeline: Pipeline, path: Path) -> None:
-    config = pipeline.to_config()
-    config.to_yaml(str(path))
-def parse_dead_channels(description: str) -> np.ndarray:
-    """Dead-element indices, parsed from the file's root `description` text."""
-    match = re.search(r"turned off[^:]*:\s*([\d,\s]+?)\s*\(numbering", description)
-    if not match:
-        return np.array([], dtype=np.int64)
-    return np.array([int(x) for x in match.group(1).split(",") if x.strip()], dtype=np.int64)
-def ring_center_and_radius(probe_geometry: np.ndarray) -> tuple[np.ndarray, float]:
-    """Ring center [x, z] and radius [m], measured (this ring is centered at
-    z ~ 0.13 m, not the origin)."""
-    xz = probe_geometry[:, [0, 2]]
-    center = xz.mean(axis=0)
-    radius = float(np.linalg.norm(xz - center, axis=-1).mean())
-    return center, radius
-def rx_element_indices(aperture_offset: int, n_tx: int, n_rx: int) -> np.ndarray:
-    """Physical ring-element index for every (transmit, raw receive channel)
-    pair: `(n_tx, n_rx)`. Sizes come from the file, not from constants."""
-    k = np.arange(n_rx)
-    return (np.arange(n_tx)[:, None] + aperture_offset + k[None, :]) % n_tx
-def validate_geometry(rf, tx_position, rx_positions, sound_speed, initial_time, fs):
-    """Predicted vs. measured direct-arrival sample, per receive channel, for one
-    transmit. Agreement confirms the ring geometry, `initial_times`, and the
-    aperture offset at once. `rf` is `(n_ax, n_rx)` real RF for one transmit."""
-    distance = np.linalg.norm(rx_positions - tx_position, axis=-1)
-    predicted = (distance / sound_speed - initial_time) * fs
-    envelope = np.abs(rf)
-    measured = envelope.argmax(axis=0)
-    strong = envelope.max(axis=0) > 0.2 * envelope.max()
-    residual = float(np.median(np.abs(measured[strong] - predicted[strong])))
-    return predicted, measured, residual
-def main():
-    parser = argparse.ArgumentParser(description=__doc__)
-    parser.add_argument("--input", type=Path, required=True, help="a *.hdf5 file in this folder")
-    parser.add_argument("--frame", type=int, default=0)
-    parser.add_argument("--aperture_offset", type=int, default=APERTURE_OFFSET)
-    parser.add_argument(
-        "--probe_radius", type=float, default=PROBE_RADIUS, help="ring radius in metres"
-    )
-    parser.add_argument(
-        "--smooth_sigma", type=float, default=1.0, help="post-FBP Gaussian smoothing"
-    )
-    args = parser.parse_args()
-    input_path = args.input if args.input.exists() else HERE / args.input
-    if not input_path.exists():
-        raise FileNotFoundError(f"{args.input} not found (looked in cwd and {HERE}).")
-    output_path = input_path.with_name(f"{input_path.stem}_reconstruct.png")
-    # Build with every parameter explicit, save to pipeline.yaml, then reload and
-    # run from that file - so the YAML alone fully describes the reconstruction.
-    pipeline = build_pipeline(
-        aperture_offset=args.aperture_offset,
-        probe_radius=args.probe_radius,
-        smooth_sigma=args.smooth_sigma,
-    )
-    write_config(pipeline, CONFIG)
-    config = Config.from_path(str(CONFIG))
-    pipeline = Pipeline.from_config(config)
-    with File(str(input_path)) as f:
-        probe_geometry = f.probe.probe_geometry[:]
-        center, radius = ring_center_and_radius(probe_geometry)
-        sound_speed = float(f.scan.sound_speed)
-        fs = float(f.scan.sampling_frequency)
-        initial_times = f.scan.initial_times[:]
-        dead_elements = parse_dead_channels(str(f.attrs.get("description", "")))
-        zea.log.info(f"Dead elements parsed from description: {dead_elements.tolist()}")
-        # Cross-check the --probe_radius used by LinearizeDetectors against the
-        # radius actually implied by probe_geometry.
-        if abs(radius - args.probe_radius) > 1e-3:
-            zea.log.warning(
-                f"probe_radius={args.probe_radius:.4f} m differs from the measured ring "
-                f"radius {radius:.4f} m (centre {center[0]:.4f}, {center[1]:.4f} m)"
-            )
-        else:
-            zea.log.info(f"Ring radius: {radius:.4f} m (matches --probe_radius)")
-        raw = np.asarray(f.data.raw_data[args.frame])  # (n_tx, n_ax, n_rx, n_ch)
-        # As of the Sep-2026 resubmission these live in `data` as spatial maps
-        # (with coordinates); older files put them in the free-form `custom`
-        # group. Read `data` first, fall back to `custom` for the old layout.
-        # us4us ship their own FBP output and sinogram alongside the raw data, as
-        # spatial maps under `data` with `coordinates`.
-        stored_image = np.asarray(f.data.image.values[args.frame])
-        image_coords = np.asarray(f.data.image.coordinates)
-        stored_sinogram = np.asarray(f.data.sinogram.values[args.frame])
-        sinogram_coords = np.asarray(f.data.sinogram.coordinates)
-    rx_idx = rx_element_indices(
-        args.aperture_offset, n_tx=probe_geometry.shape[0], n_rx=raw.shape[2]
-    )  # (n_tx, n_rx)
-    ring_xz = probe_geometry[:, [0, 2]].astype(np.float32)
-    predicted, measured, residual = validate_geometry(
-        raw[0, :, :, 0].astype(np.float32),
-        ring_xz[0],
-        ring_xz[rx_idx[0]],
-        sound_speed,
-        float(initial_times[0]),
-        fs,
-    )
-    zea.log.info(f"Direct-arrival check: median |measured - predicted| = {residual:.2f} samples")
-    outputs = pipeline(data=raw, return_numpy=True)
-    recon = outputs["data"]  # (n_detectors, n_detectors)
-    zea.visualize.set_mpl_style()
-    fig, axes = plt.subplots(1, 4, figsize=(24, 5.5))
-    n_rx = raw.shape[2]
-    rf_db = 20 * np.log10(np.abs(raw[0, :, :, 0]) / (np.abs(raw[0, :, :, 0]).max() + 1e-12) + 1e-10)
-    axes[0].imshow(rf_db, aspect="auto", cmap="gray", vmin=-60, vmax=0)
-    axes[0].plot(np.arange(n_rx), predicted, "r--", lw=1.0, label="predicted direct arrival")
-    axes[0].plot(np.arange(n_rx), measured, "c:", lw=0.7, alpha=0.6, label="measured (argmax)")
-    axes[0].set_ylim(raw.shape[1], 0)
-    axes[0].legend(loc="lower left", fontsize=7)
-    axes[0].set_title(f"RF, transmit 0 [dB]\ngeometry check: residual {residual:.2f} samples")
-    axes[0].set_xlabel("Receive channel")
-    axes[0].set_ylabel("Axial sample")
-    # After LinearizeDetectors the backprojection grid spans the uniform chord
-    # axis, i.e. +-R*sqrt(2)/2 (~92 mm) about the ring centre - not +-R. Plotted
-    # in mm relative to the ring centre, matching the frame of the stored maps'
-    # own `coordinates` so the panels line up.
-    half_width = args.probe_radius * np.sqrt(2) / 2 * 1e3
-    extent = [-half_width, half_width, half_width, -half_width]
-    vmin, vmax = np.percentile(recon, [1, 99])
-    handle = axes[1].imshow(recon, cmap="magma", extent=extent, vmin=vmin, vmax=vmax)
-    axes[1].set_title(f"Attenuation FBP (zea.Pipeline)\n({input_path.name}, frame {args.frame})")
-    axes[1].set_xlabel("X (mm)")
-    axes[1].set_ylabel("Z (mm)")
-    cax = make_axes_locatable(axes[1]).append_axes("right", size="5%", pad=0.05)
-    fig.colorbar(handle, cax=cax)
-    # Stored sinogram: x is detector position in metres; the "z" slot carries the
-    # projection angle in radians (0..2*pi) rather than a Cartesian coordinate.
-    sino_extent = [
-        sinogram_coords[..., 0].min() * 1e3,
-        sinogram_coords[..., 0].max() * 1e3,
-        sinogram_coords[..., 2].max(),
-        sinogram_coords[..., 2].min(),
-    ]
-    axes[2].imshow(stored_sinogram, cmap="viridis", aspect="auto", extent=sino_extent)
-    axes[2].set_title("Stored sinogram (us4us)")
-    axes[2].set_xlabel("Detector position (mm)")
-    axes[2].set_ylabel("Projection angle (rad)")
-    img_extent = [
-        image_coords[..., 0].min() * 1e3,
-        image_coords[..., 0].max() * 1e3,
-        image_coords[..., 2].max() * 1e3,
-        image_coords[..., 2].min() * 1e3,
-    ]
-    svmin, svmax = np.percentile(stored_image, [1, 99])
-    h = axes[3].imshow(
-        stored_image, cmap="magma", extent=img_extent, vmin=svmin, vmax=svmax
-    )
-    axes[3].set_title("Stored FBP (us4us)")
-    axes[3].set_xlabel("X (mm)")
-    axes[3].set_ylabel("Z (mm)")
-    cax = make_axes_locatable(axes[3]).append_axes("right", size="5%", pad=0.05)
-    fig.colorbar(h, cax=cax)
-    fig.suptitle(f"{input_path.name} — frame {args.frame}")
-    fig.tight_layout()
-    plt.savefig(str(output_path), bbox_inches="tight", dpi=100)
-    print(f"Reconstructed  : {recon.shape}")
-    print(f"Saved          : {output_path}")
-if __name__ == "__main__":
-    main()