Datasets:
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55b0cc0 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 | #!/usr/bin/env python3
"""
own_das_reconstruct.py — reconstruct one rotation frame of an acquisition with
the authors' ORIGINAL in-plane DAS beamformer (sim/beamform.py of the generation
codebase, vendored below as `das_planewave`), independent of the zea pipeline.
Produced for reviewer verification (Tristan, 2026-07-17): the same frame of the
same acquisition is also reconstructed with the package's reconstruct.py (zea
Pipeline: demodulate -> delay_and_sum -> envelope -> normalize -> log-compress);
comparing the two PNGs confirms the raw RF channel data is correct and intended.
Only numpy / scipy / h5py / matplotlib are needed (no zea).
Usage:
python own_das_reconstruct.py [--input ../data/coarse_pitch_0p3__point_z080_r4.hdf5]
"""
import argparse
import os
from pathlib import Path
import h5py
import matplotlib
matplotlib.use("Agg")
import matplotlib.pyplot as plt
import numpy as np
from scipy.signal import hilbert
HERE = Path(__file__).parent
DEFAULT_INPUT = HERE.parent / "data" / "coarse_pitch_0p3__point_z080_r4.hdf5"
def das_planewave(rf, t0, xe, c, fs, z_mm, x_mm, apod=True):
"""Vectorized in-plane DAS for one normal plane-wave transmit
(verbatim logic of the authors' sim/beamform.py).
rf: (n_ax, n_el) channel data; xe: (n_el,) element x positions [m].
Returns beamformed image (nz, nx)."""
n_ax, n_el = rf.shape
xg = x_mm * 1e-3
zg = z_mm * 1e-3
win = np.hanning(n_el) if apod else np.ones(n_el)
X = xg[None, :, None]
Z = zg[:, None, None]
XE = xe[None, None, :]
delay = (Z + np.sqrt((X - XE) ** 2 + Z**2)) / c # (nz, nx, n_el)
idx = np.round((delay - t0) * fs).astype(np.int64)
ok = (idx >= 0) & (idx < n_ax)
idx_c = np.clip(idx, 0, n_ax - 1)
el = np.arange(n_el)[None, None, :]
gathered = rf[idx_c, el] * ok * win[None, None, :]
return gathered.sum(axis=2)
def main():
ap = argparse.ArgumentParser(description=__doc__)
ap.add_argument("--input", type=Path, default=DEFAULT_INPUT)
ap.add_argument("--output", type=Path, default=None)
ap.add_argument("--frame-index", type=int, default=None)
args = ap.parse_args()
with h5py.File(args.input, "r") as f:
scan = f["tracks/track_0/scan"]
fs = float(scan["sampling_frequency"][()])
c = float(scan["sound_speed"][()])
t0 = float(np.asarray(scan["initial_times"]).ravel()[0])
xe = np.asarray(f["probe/probe_geometry"])[:, 0] # element x [m]
rot_deg = np.degrees(np.asarray(f["metadata/probe_pose/rotation"])[:, 2])
raw = f["tracks/track_0/data/raw_data"] # (n_fr, 1, n_ax, n_el, 1)
frame = args.frame_index
if frame is None: # same default as reconstruct.py: ~90 deg rotation magnitude
frame = int(np.argmin(np.abs(np.abs(rot_deg) - 90.0)))
rf = np.asarray(raw[frame, 0, :, :, 0], dtype=np.float64)
n_ax = rf.shape[0]
dz = c / (2 * fs) * 1e3 # mm per RF sample
z_max_mm = (t0 + n_ax / fs) * c / 2 * 1e3
z_mm = np.arange(0.0, z_max_mm, dz)
x_mm = xe * 1e3 # one beam per element
img = das_planewave(rf, t0, xe, c, fs, z_mm, x_mm)
env = np.abs(hilbert(img, axis=0))
env /= env.max()
bmode_db = 20 * np.log10(env + 1e-12)
out = args.output or HERE / (args.input.stem + "__own_das.png")
fig, ax = plt.subplots(figsize=(5, 8))
im = ax.imshow(bmode_db, cmap="gray", vmin=-40, vmax=0, aspect="equal",
extent=[x_mm[0], x_mm[-1], z_mm[-1], z_mm[0]])
ax.set_xlabel("lateral [mm]")
ax.set_ylabel("depth [mm]")
ax.set_title(f"{args.input.name}\nauthors' own DAS (sim/beamform.py), "
f"frame {frame} ({rot_deg[frame]:.0f}°), DR −40 dB")
fig.colorbar(im, ax=ax, label="dB")
fig.tight_layout()
fig.savefig(out, dpi=150)
print(f"frame {frame} @ {rot_deg[frame]:.1f} deg -> {out}")
if __name__ == "__main__":
main()
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