Sync data card: fix stale Data Validation section (zea API reconstruct.py), target description, rotation_angles_deg path

README.md

@@ -23,7 +23,7 @@ size_categories:
 > + `../pipeline.yaml` for the reference reconstruction.
 
 ## Dataset Description
-Synthetic rotational 3D ultrasound acquisitions of point and cyst targets, captured
+Synthetic rotational 3D ultrasound acquisitions of point, pair, and off-axis targets, captured
 with an **elevation-focused 1D linear array** that is rotated 180° about its axial
 axis (1° steps, 180 frames). Each acquisition stores the **raw per-element channel
 RF** for a single normal plane-wave transmit at every rotation angle — i.e. the data
@@ -122,7 +122,7 @@ reproducible from source and kept on lab storage.
     (`experiment__acq_exp_*.hdf5`), centre plane wave, ~182 measured rotation angles
     over ~180°.
 - **Frames per acquisition:** simulated 180 (one per 1° step); measured ~182 (the
-  actual encoder angles are stored in `scan/rotation_angles_deg`, not necessarily
+  actual encoder angles are stored in `custom/scan/rotation_angles_deg`, not necessarily
   uniform).
 - **Total size on disk:** simulated ~0.6–5 MB per case (zea gzip; point-target RF is
   sparse), measured ~80–92 MB per case (dense tissue RF); **~1.5 GB** for the full set
@@ -150,19 +150,23 @@ reproducible from source and kept on lab storage.
 N/A (synthetic phantom; no subjects/PHI).
 
 ## Data Validation
-`../reconstruct.py` (**runnable, verified** — numpy/h5py/scipy/matplotlib) loads a zea
-acquisition (`data/raw_data` + `scan/*`), runs in-plane plane-wave **DAS → envelope →
-log-compress** per rotation frame, and writes a B-mode PNG (representative angle +
-MAX-over-angle), auto-zoomed to the target depth band:
+`../reconstruct.py` (**runnable, verified** — official `zea` API, no fallback code)
+loads one zea acquisition, reads its acquisition parameters via
+`zea.Config.from_path('../pipeline.yaml')` + `File.load_parameters`, beamforms the
+rotation frame closest to 90° (the frame where an off-axis target lies in-plane) with
+the native `zea.Pipeline` op chain **Cast → Demodulate → Beamform(delay_and_sum) →
+EnvelopeDetect → Normalize → LogCompress** defined in `../pipeline.yaml`, and writes a
+two-panel PNG: the B-mode image, and the per-frame probe **rotation angle**
+(`custom/scan/rotation_angles_deg`) so downstream users know how to interpret the
+frame axis — the special data this dataset adds, per reviewer feedback:
 ```
-python python/reconstruct.py data/<probe>__<target>.hdf5 --out out.png
+python ../reconstruct.py --input data/<probe>__<target>.hdf5 --output out.png
 ```
-The equivalent native `zea.Pipeline` op chain (TOFCorrection → DelayAndSum →
-EnvelopeDetect → Normalize → LogCompress) is documented in `../pipeline.yaml`. The
-rotational **eSAF** across frames — the contribution of this dataset — is implemented in
+The rotational **eSAF** across frames — the contribution of this dataset — is implemented in
 `../matlab/saf` (`recon_3d` → `safrot_backproj`); per-probe before/after eSAF reference
 images and a FWHM-vs-depth overview accompany the MATLAB `.mat` release
-(`../sim/dataset_overview.m`).
+(`../sim/dataset_overview.m`), and the resulting paired `custom/labels/saf_bmode_volume`
+is stored in every `.hdf5` (see Dataset Format above).
 
 ## Known Issues
 - **Paired SAF label — on-axis targets (r0 = 0) do not narrow, by design.** eSAF