Datasets:
8th: add reviewer verification PNGs (own DAS vs zea reconstruct.py)
Browse files
verification/coarse_pitch_0p3__point_z080_r4__own_das.png
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Git LFS Details
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verification/coarse_pitch_0p3__point_z080_r4__zea_reconstruct.png
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Git LFS Details
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verification/own_das_reconstruct.py
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#!/usr/bin/env python3
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"""
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own_das_reconstruct.py — reconstruct one rotation frame of an acquisition with
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the authors' ORIGINAL in-plane DAS beamformer (sim/beamform.py of the generation
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codebase, vendored below as `das_planewave`), independent of the zea pipeline.
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Produced for reviewer verification (Tristan, 2026-07-17): the same frame of the
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same acquisition is also reconstructed with the package's reconstruct.py (zea
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Pipeline: demodulate -> delay_and_sum -> envelope -> normalize -> log-compress);
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comparing the two PNGs confirms the raw RF channel data is correct and intended.
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Only numpy / scipy / h5py / matplotlib are needed (no zea).
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Usage:
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python own_das_reconstruct.py [--input ../data/coarse_pitch_0p3__point_z080_r4.hdf5]
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"""
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import argparse
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import os
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from pathlib import Path
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import h5py
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import matplotlib
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matplotlib.use("Agg")
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import matplotlib.pyplot as plt
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import numpy as np
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from scipy.signal import hilbert
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HERE = Path(__file__).parent
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DEFAULT_INPUT = HERE.parent / "data" / "coarse_pitch_0p3__point_z080_r4.hdf5"
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def das_planewave(rf, t0, xe, c, fs, z_mm, x_mm, apod=True):
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"""Vectorized in-plane DAS for one normal plane-wave transmit
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(verbatim logic of the authors' sim/beamform.py).
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rf: (n_ax, n_el) channel data; xe: (n_el,) element x positions [m].
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Returns beamformed image (nz, nx)."""
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n_ax, n_el = rf.shape
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xg = x_mm * 1e-3
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zg = z_mm * 1e-3
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win = np.hanning(n_el) if apod else np.ones(n_el)
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X = xg[None, :, None]
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Z = zg[:, None, None]
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XE = xe[None, None, :]
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delay = (Z + np.sqrt((X - XE) ** 2 + Z**2)) / c # (nz, nx, n_el)
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idx = np.round((delay - t0) * fs).astype(np.int64)
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ok = (idx >= 0) & (idx < n_ax)
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idx_c = np.clip(idx, 0, n_ax - 1)
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el = np.arange(n_el)[None, None, :]
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gathered = rf[idx_c, el] * ok * win[None, None, :]
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return gathered.sum(axis=2)
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def main():
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ap = argparse.ArgumentParser(description=__doc__)
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ap.add_argument("--input", type=Path, default=DEFAULT_INPUT)
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ap.add_argument("--output", type=Path, default=None)
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ap.add_argument("--frame-index", type=int, default=None)
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args = ap.parse_args()
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with h5py.File(args.input, "r") as f:
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scan = f["tracks/track_0/scan"]
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fs = float(scan["sampling_frequency"][()])
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c = float(scan["sound_speed"][()])
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t0 = float(np.asarray(scan["initial_times"]).ravel()[0])
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xe = np.asarray(f["probe/probe_geometry"])[:, 0] # element x [m]
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rot_deg = np.degrees(np.asarray(f["metadata/probe_pose/rotation"])[:, 2])
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raw = f["tracks/track_0/data/raw_data"] # (n_fr, 1, n_ax, n_el, 1)
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frame = args.frame_index
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if frame is None: # same default as reconstruct.py: ~90 deg rotation magnitude
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frame = int(np.argmin(np.abs(np.abs(rot_deg) - 90.0)))
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rf = np.asarray(raw[frame, 0, :, :, 0], dtype=np.float64)
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n_ax = rf.shape[0]
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dz = c / (2 * fs) * 1e3 # mm per RF sample
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z_max_mm = (t0 + n_ax / fs) * c / 2 * 1e3
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z_mm = np.arange(0.0, z_max_mm, dz)
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x_mm = xe * 1e3 # one beam per element
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img = das_planewave(rf, t0, xe, c, fs, z_mm, x_mm)
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env = np.abs(hilbert(img, axis=0))
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env /= env.max()
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bmode_db = 20 * np.log10(env + 1e-12)
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out = args.output or HERE / (args.input.stem + "__own_das.png")
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fig, ax = plt.subplots(figsize=(5, 8))
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im = ax.imshow(bmode_db, cmap="gray", vmin=-40, vmax=0, aspect="equal",
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extent=[x_mm[0], x_mm[-1], z_mm[-1], z_mm[0]])
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ax.set_xlabel("lateral [mm]")
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ax.set_ylabel("depth [mm]")
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ax.set_title(f"{args.input.name}\nauthors' own DAS (sim/beamform.py), "
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f"frame {frame} ({rot_deg[frame]:.0f}°), DR −40 dB")
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fig.colorbar(im, ax=ax, label="dB")
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fig.tight_layout()
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fig.savefig(out, dpi=150)
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print(f"frame {frame} @ {rot_deg[frame]:.1f} deg -> {out}")
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if __name__ == "__main__":
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main()
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