Point data cards at pipeline.yaml and the merged reconstruct scripts

- weizmann-sampl: remove the pipeline YAML embedded in the card. It had
drifted from the real pipeline.yaml (grid 300x400 vs 377x525, and missing
apply_window plus the lens-correction parameters), so the card now points
at that file as the single source of truth.
- politorino: visualize_tracking_on_image.py has been merged into
reconstruct.py, so describe that script's constants instead.
- concordia: visualize.py has been merged into reconstruct.py, so describe
the one combined figure rather than two separate outputs.
- Spell the library "zea" rather than "Zea" throughout.

Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>

concordia/README.md

@@ -137,7 +137,7 @@ Fields from Arbitrarily Shaped, Apodized, and Excited Ultrasound Transducers,"
 
 The dataset is 2,000 individual zea HDF5 files (one acquisition per file) under
 `data/` (zea format; `zea_version` 0.1.6). Reference figures are in
-`examples/`; `reconstruct.py`, `visualize.py`, `pipeline.yaml`, and this card sit
+`examples/`; `reconstruct.py`, `pipeline.yaml`, and this card sit
 at the repository root. Per file:
 
 - `data/raw_data` — full FSA channel data, `int16`, quantized from the native
@@ -244,25 +244,23 @@ scatterers (amplitude 18–22) scattered at valid random positions, for realism.
 
 ## Data Validation
 
-**Setup:** `reconstruct.py` and `visualize.py` require `zea>=0.1.1`, `matplotlib`,
+**Setup:** `reconstruct.py` requires `zea>=0.1.1`, `matplotlib`,
 and `KERAS_BACKEND=jax` (or `torch`/`tensorflow`) set before import. This submission
 was built and verified against the [OpenH-RF repo](https://github.com/open-h/OpenH-RF)'s environment (`uv sync` inside a clone of that repo installs `zea` and every other
-dependency these scripts need — see that repo's README for the exact commands).
+dependency this script needs — see that repo's README for the exact commands).
 
 `reconstruct.py` (+ `pipeline.yaml`, zea's default DAS pipeline: Cast → ApplyWindow →
 Demodulate → Beamform → EnvelopeDetect → Normalize → LogCompress; each stage is
 explained in `reconstruct.py`'s own module docstring) reconstructs a synthetic
 transmit aperture (STA) B-mode from `data/raw_data` using all 128 transmits, over
-the same field of view as the stored `data/image` reference. A second helper,
-`visualize.py <file>.hdf5`, renders the stored `data/image`, its class-specific
-label, and the `data/scatterers` cloud side by side. `examples/` contains, for a
-representative capture of each of the five classes, an `<id>_bmode.png` (the STA
-reference reconstruction from `reconstruct.py`, physical mm axes, titled with the
-sample ID) and an `<id>_panels.png` (the `visualize.py` output: stored `data/image`,
-class label — segmentation foreground for anechoic/hypoechoic/hyperechoic,
+the same field of view as the stored `data/image` reference. It renders that
+reconstruction on physical mm axes next to the capture's class-specific label
+— segmentation foreground for anechoic/hypoechoic/hyperechoic,
 `data/diverse_source_image` for diverse, none for point-target — and the
-`data/scatterers` cloud coloured by |amplitude|, on shared equal-aspect mm axes),
-confirming the label, reconstruction, and scatterer field are spatially registered.
+`data/scatterers` cloud coloured by |amplitude|, all on shared equal-aspect mm
+axes, confirming the label, reconstruction, and scatterer field are spatially
+registered. `examples/` holds one such figure for a representative capture of
+each of the five classes.
 
 ## Known Issues
 

politorino/README.md

@@ -50,14 +50,13 @@ This data was employed for an initial study evaluating fascicle tracking algorit
 
 Submitted in the [`zea` file format](https://zea.readthedocs.io/en/latest/)
 (one HDF5 file per acquisition). 
-`visualize_tracking_on_image.py` maps the tracking data onto the reconstructed images. Options for running the file:
---file : .hdf5 file of the considered subjects 
---fps : frame rate of desired tracking (e.g., 25 fps) 
---tracking-frame-idx: index of the frame to visualize.
+`reconstruct.py` reconstructs a B-mode from the raw channel data and overlays
+the stored fascicle tracking on it, writing a `.png`. An example output is
+provided. The constants at the top of the script select what is drawn:
 
-The `reconstruct.py` code also implements the visualization of the tracking data and can be used stand-alone to view the tracking data. 
-
-The script automatically generates a .png file with the tracking data overlaid on the reconstructed image. An example output is provided. 
+- `FRAME` -- index of the acquisition frame to reconstruct
+- `FPS` -- which stored tracking rate to overlay (25, 50 or 125 fps)
+- `TRACK_INDEX` -- index of the tracking sample within that rate
 
 Per-sample contents of the HDF5:
 

twente-microbubblesim/README.md

@@ -57,7 +57,7 @@ deep-learning methods for microbubble super-resolution imaging.
 ## Dataset Characterization
 
 - **Data collection method:** Synthetic.
-- **Ground truth:** Simulated 3-D microbubble positions are stored as Zea custom
+- **Ground truth:** Simulated 3-D microbubble positions are stored as zea custom
   elements (`bubble_x`, `bubble_y`, and `bubble_z`).
 - **Bubble populations:** Monodisperse bubbles with a radius of 2.4 micrometres
   and 5% standard deviation, and a polydisperse SonoVue population.
@@ -79,7 +79,7 @@ deep-learning methods for microbubble super-resolution imaging.
 
 ## Dataset Format
 
-Each bubble distribution is stored in one Zea HDF5 file containing 12 pulse
+Each bubble distribution is stored in one zea HDF5 file containing 12 pulse
 tracks. The raw channel data in every `tracks/track_i/data/raw_data` dataset
 have shape `(1, 1, 8446, 96, 1)`: one frame, one transmit, 8,446 time samples,
 96 receive elements, and one real RF channel. RF values are `float32`; their
@@ -212,7 +212,7 @@ to one `.hdf5` acquisition file.
 
 ## Data Validation
 
-All 500 HDF5 files were checked for the expected Zea container structure, 12
+All 500 HDF5 files were checked for the expected zea container structure, 12
 ordered and labelled tracks, RF shape `(1, 1, 8446, 96, 1)`, and track-specific
 pulse metadata. The original submission passed zea 0.1.2 validation. Representative RF traces and pulse
 waveforms were also compared with their pulse-folder sources with no mismatch.

ubc/module_A/README.md

@@ -25,13 +25,13 @@ size_categories:
 This submission contains eight 3-D Shear-Wave Absolute Vibro-Elastography
 (S-WAVE) acquisitions of CIRS model 039 Shear Wave Liver Fibrosis Phantom
 samples. The source scanner export contains beamformed 64-line RF rather than
-measured pre-beamforming channel capture. Accordingly, every submitted Zea
+measured pre-beamforming channel capture. Accordingly, every submitted zea
 `raw_data` frame (stored at `/tracks/track_0/data/raw_data`) is explicitly
 labeled **in-silico/synthetic**: a dense
 point-scatterer field is estimated from one corrected source line-RF frame and
 forward-simulated through a documented 64-transmit, 128-receive-element model.
 
-The files are physically delay-consistent and reconstruct with Zea-native
+The files are physically delay-consistent and reconstruct with zea-native
 scanline delay-and-sum. They are not recovered or measured scanner channel
 data, and they must not be represented as such.
 
@@ -45,7 +45,7 @@ Chung Lee, Zongze Li, Patrick Boyan Chen, and Michael Frew.
 
 ## Dataset Creation Date
 
-09/01/2026 for this corrected Zea-beamformable synthetic-channel package. The source
+09/01/2026 for this corrected zea-beamformable synthetic-channel package. The source
 phantom acquisitions predate this packaging.
 
 ## License / Terms of Use
@@ -69,7 +69,7 @@ factory-rated nominal stiffness, not a derived S-WAVE estimate.
 - Acquisition System: Ultrasonix/Sonix with 4DEC9-5/10 end-firing 3-D probe.
 - Submitted Data Tier: phantom / simulation.
 - Simulation Framework: custom deterministic NumPy/SciPy forward simulator;
-  files and reconstruction were validated with Zea 0.1.4.
+  files and reconstruction were validated with zea 0.1.4.
 
 Acquisition and simulation details:
 
@@ -107,7 +107,7 @@ Acquisition and simulation details:
 ## Dataset Format
 
 There are 8 × 20 × 25 = **4,000** one-frame HDF5 files. One file per source
-frame keeps full Zea schema validation and reconstruction memory-bounded.
+frame keeps full zea schema validation and reconstruction memory-bounded.
 For motion analysis, group files by case and motor plane, then order `f00`
 through `f24`; `/custom/source_provenance/source_timing` retains the source
 clock index, within-plane temporal offset, and relative plane timestamp.
@@ -127,8 +127,8 @@ module_A/
 
 Each HDF5 file contains the following per-sample features:
 
-Numeric arrays use Zea 0.1.4's native frame-chunked Blosc/Zstd plus bitshuffle
-compression; reading these arrays requires `hdf5plugin`, which is imported by Zea.
+Numeric arrays use zea 0.1.4's native frame-chunked Blosc/Zstd plus bitshuffle
+compression; reading these arrays requires `hdf5plugin`, which is imported by zea.
 
 | Name | Shape | Dtype | Units | Description |
 |---|---:|---|---|---|
@@ -166,14 +166,14 @@ Phantom.
 
 ## Data Validation
 
-The contributor reports validation with Python 3.12.3 and Zea 0.1.4.
-The contributor's reconstruction reads only the Zea `raw_data` field and
+The contributor reports validation with Python 3.12.3 and zea 0.1.4.
+The contributor's reconstruction reads only the zea `raw_data` field and
 acquisition parameters, not the stored image or preserved source line RF:
 
 ```text
 Cast to float32
   -> RF demodulation
-  -> Zea delay_and_sum with enable_scanline=true
+  -> zea delay_and_sum with enable_scanline=true
      and enable_aligned_apodization=true
   -> Envelope detection
   -> 99.5th-percentile normalization
@@ -183,7 +183,7 @@ Cast to float32
 ```
 
 In scanline mode the output has 64 angular lines—one per stored focused
-transmit. Zea constructs the one-line-per-transmit grid and the one-hot aligned
+transmit. zea constructs the one-line-per-transmit grid and the one-hot aligned
 transmit mask internally; no custom `flat_pfield` or transmit-weight code is
 used.
 
@@ -191,7 +191,7 @@ used.
 `zlims: [0.0001, 0.100023]` are line depths from
 each transmit origin on the curved element surface.
 
-The contributor reports structural checks across all files, plus full Zea
+The contributor reports structural checks across all files, plus full zea
 schema validation and raw-only reconstruction on three distributed samples
 from each case (24 samples). For those samples, the preserved source line RF
 was also compared with the original E-scan events.

ubc/module_C/README.md

@@ -91,13 +91,13 @@ physical ground truth for the simulated channels.
 | Parameter | Value | Status |
 |---|---:|---|
 | Frames | 3,845 | Two sessions; one per matched BK/RealSense frame |
-| Focused transmit events per frame | 248 | Stored in each Zea file |
+| Focused transmit events per frame | 248 | Stored in each zea file |
 | Receive elements | 192 | Synthetic assumption |
 | Axial RF samples per trace | 3,153 | Includes 300 zero-tail samples |
 | RF components | 1 real component | Stored as `int16` |
-| Sampling frequency | 15 MHz | Stored Zea scan parameter |
-| Center/demodulation frequency | 3.75 MHz | Stored Zea scan parameter |
-| Assumed sound speed | 1,540 m/s | Stored Zea scan parameter |
+| Sampling frequency | 15 MHz | Stored zea scan parameter |
+| Center/demodulation frequency | 3.75 MHz | Stored zea scan parameter |
+| Assumed sound speed | 1,540 m/s | Stored zea scan parameter |
 | Assumed element pitch | 0.2 mm | Synthetic geometry assumption |
 | Assumed nominal aperture | 38.4 mm | 192 elements × 0.2 mm |
 | Element width / height | 0.18 mm / 5.0 mm | Synthetic model |
@@ -113,7 +113,7 @@ the proprietary scanner transmit law is unknown.
 
 ## Dataset Format
 
-Each Zea HDF5 file contains one frame and is written with **Zea 0.1.5**.
+Each zea HDF5 file contains one frame and is written with **zea 0.1.5**.
 The writer-version field describes serialization,
 not processing speed. Source IQ and its display reference are under
 `/custom/source_provenance`.
@@ -145,7 +145,7 @@ are intentionally withheld for de-identification.** IQ, BK viewport, NDI
 reference-tool, and RealSense host times are relative to the first **source** IQ
 frame of their own session, even when that frame is excluded from the release. RealSense device time is relative to its first camera frame.
 Negative host-relative camera times can precede the first IQ frame.
-Each one-frame Zea probe-pose timestamp is locally zero, with session-relative
+Each one-frame zea probe-pose timestamp is locally zero, with session-relative
 IQ time stored separately in `/custom/synchronization`.
 
 Use alignment indices and validity flags, not video frame rate alone, to link
@@ -215,7 +215,7 @@ this does not establish generalization to different phantoms or patients.
 `/custom/synchronization/source_valid_axial_samples`. The synthetic RF tensor
 has a fixed 3,153 axial samples regardless of source IQ length.
 
-Scalar Zea fields additionally store the 15-MHz sampling frequency, 3.75-MHz
+Scalar zea fields additionally store the 15-MHz sampling frequency, 3.75-MHz
 center and demodulation frequencies, and 1,540-m/s sound speed. Dataset-level
 attributes provide descriptions and units for individual fields.
 
@@ -235,13 +235,13 @@ this HF release.
 
 ### Reconstruction
 
-The contributor reports validation with Python 3.12, Zea 0.1.5, and a
+The contributor reports validation with Python 3.12, zea 0.1.5, and a
 CUDA 12.8 / PyTorch configuration. The retained `pipeline.yaml` records the
 shared reconstruction configuration; the standalone reconstruction entry
 point and its dependency file are not included in this HF release.
 
-The contributor's reconstruction reads synthetic `raw_data`, Zea scan/probe parameters, and
-imaging depth. Zea's native aligned scanline DAS uses exactly one transmit per
+The contributor's reconstruction reads synthetic `raw_data`, zea scan/probe parameters, and
+imaging depth. zea's native aligned scanline DAS uses exactly one transmit per
 scanline, followed by envelope detection, normalization, log compression, and
 the documented display conversion. Source IQ, stored B-mode, and videos are
 not reconstruction inputs. Prepared pipelines are reused only for matching
@@ -258,7 +258,7 @@ comparison. This does not imply that every RF file has been reconstructed.
 
 - Harris, C. R., et al. (2020). [Array programming with NumPy](https://doi.org/10.1038/s41586-020-2649-2). *Nature*, 585, 357–362.
 - Virtanen, P., et al. (2020). [SciPy 1.0: fundamental algorithms for scientific computing in Python](https://doi.org/10.1038/s41592-019-0686-2). *Nature Methods*, 17, 261–272.
-- Stevens, T. S. W., et al. (2026). [zea: A Toolbox for Cognitive Ultrasound Imaging](https://doi.org/10.21105/joss.09881). *Journal of Open Source Software*, 11(121), 9881. See also the [Zea operations documentation](https://zea.readthedocs.io/en/stable/_autosummary/zea.ops.html).
+- Stevens, T. S. W., et al. (2026). [zea: A Toolbox for Cognitive Ultrasound Imaging](https://doi.org/10.21105/joss.09881). *Journal of Open Source Software*, 11(121), 9881. See also the [zea operations documentation](https://zea.readthedocs.io/en/stable/_autosummary/zea.ops.html).
 
 ## Subject Metadata
 

ubc/module_C/pipeline.yaml

@@ -1,6 +1,6 @@
 # Raw-data-to-scanline pipeline used by reconstruct.py. The documented
 # display-only BK-like scan conversion and uncropped border are applied in
-# reconstruct.py after this Zea pipeline completes.
+# reconstruct.py after this zea pipeline completes.
 pipeline:
     name: module_c_native_scanline_delay_and_sum
     operations:

weizmann-sampl/README.md

@@ -104,37 +104,11 @@ the frames prior to workspace parameter freezing were removed from the raw chann
 
 ## Data Validation
 
-`reconstruct.py` runs the following `zea.Pipeline` (saved as `pipeline.yaml`,
-alongside this README) to reconstruct a B-mode image from the raw channel
-data: cast → demodulate → DAS beamform (with native, per-element/per-pixel
-lens correction) → envelope detect → normalize → log compress.
-
-```yaml
-# Beamforming config for the L11-5v thyroid scans (focused ray-line imaging).
-# RF data is real (not IQ/baseband, sampleMode=NS200BW) so it is demodulated
-# before beamforming. grid_size_x/z fix the output resolution; the actual
-# depth mapped onto it comes from this file's own N_AX (D1: 2048 samples,
-# ~50mm one-way in 1540 m/s tissue).
-
-parameters:
-  grid_size_x: 300
-  grid_size_z: 400
-  dynamic_range: [-60, 0]
-
-pipeline:
-  operations:
-    - name: keras.ops.cast
-      params:
-        dtype: float32
-    - demodulate
-    - name: beamform
-      params:
-        beamformer: delay_and_sum
-        num_patches: 200
-    - envelope_detect
-    - normalize
-    - log_compress
-```
+`reconstruct.py` runs the `zea.Pipeline` defined in `pipeline.yaml`, alongside
+this README, to reconstruct a B-mode image from the raw channel data: cast →
+apply window → demodulate → DAS beamform (with native, per-element/per-pixel
+lens correction) → envelope detect → normalize → log compress. See that file
+for the grid size, dynamic range and lens-correction parameters.
 
 Example reconstruction, run via `python reconstruct.py --data-dir subjects
 --n-scans 3 --n-frames 3 --seed 0 --output three_patients_grid.png`