Add Case A ground truth; reorganise ground_truth into clean/ and noisy/ subdirs; update README for Case A

.gitattributes

@@ -24059,3 +24059,5 @@ stereo_noisy/camera2/B03999_A.tif filter=lfs diff=lfs merge=lfs -text
 stereo_noisy/camera2/B03999_B.tif filter=lfs diff=lfs merge=lfs -text
 stereo_noisy/camera2/B04000_A.tif filter=lfs diff=lfs merge=lfs -text
 stereo_noisy/camera2/B04000_B.tif filter=lfs diff=lfs merge=lfs -text
+ground_truth/clean/direct_stats.mat filter=lfs diff=lfs merge=lfs -text
+ground_truth/noisy/direct_stats.mat filter=lfs diff=lfs merge=lfs -text

README.md

@@ -20,7 +20,12 @@ size_categories:
 
 Synthetic particle-image-velocimetry (PIV) images of a turbulent channel flow at Re_τ ≈ 1000, paired with DNS-derived ground-truth statistics. Designed to benchmark PIV algorithms end-to-end against a reference dataset of known answer.
 
-Case B (noisy): 22 000 particles per 2048×2048 image, Gaussian sensor noise (SNR ≈ 8), 4000 image pairs, planar and stereo (±45°) geometries. Clean (Case A, 85 000 particles) will follow as a separate upload once storage allows.
+Two cases are provided:
+
+- **Case A (clean)**: 85 000 particles per 2048 × 2048 image (≈ 5.2 ppw at 16 × 16 windows), no noise. Sets an upper bound on PIV accuracy.
+- **Case B (noisy)**: 22 000 particles per image (≈ 1.3 ppw), Gaussian sensor noise (mean 80, std 16, SNR ≈ 8). Realistic experimental conditions.
+
+Each case contains 4 000 image pairs in both planar and stereo (± 45° forward-scatter) geometries. Ground truth is provided separately for each case — both derive from the same underlying JHTDB channel snapshots but with their respective particle counts, so finite-sample statistics are self-consistent.
 
 Companion to the PIVtools software paper (SoftwareX, submitted). The dataset is self-contained: drop it next to a [PIVtools](https://github.com/MTT69/python-PIVtools) install and the benchmark scripts reproduce every validation figure in the paper.
 
@@ -38,10 +43,10 @@ pivtools-cli init --output ./work/config.yaml
 # edit config.yaml to point sources at ./tc/planar_noisy
 pivtools-cli ensemble --config ./work/config.yaml
 
-# 4. Benchmark against DNS
+# 4. Benchmark against DNS  (use ground_truth/clean for Case A, ground_truth/noisy for Case B)
 python ./tc/scripts/benchmark_comparison.py \
     --mode ensemble \
-    --gt-dir ./tc/ground_truth \
+    --gt-dir ./tc/ground_truth/noisy \
     --ensemble-dir ./work/calibrated_piv/4000/Cam1/ensemble \
     --num-frames 4000 \
     --output-dir ./work/validation
@@ -54,27 +59,39 @@ MTT69/TurbulentChannel/
 ├── README.md                         (this file)
 ├── LICENSE                           (CC-BY-4.0)
 ├── ground_truth/
-│   └── direct_stats_noisy.mat        DNS statistics for Case B (22k particles)
-├── planar_noisy/                     Case B planar images (4000 pairs)
-│   ├── B00001_A.tif … B04000_B.tif   2048×2048 16-bit TIFF, flat at root
+│   ├── clean/direct_stats.mat        DNS statistics for Case A (85k particles)
+│   └── noisy/direct_stats.mat        DNS statistics for Case B (22k particles)
+├── planar_clean/                     Case A planar images (4000 pairs, 85k particles)
+│   └── B00001_A.tif … B04000_B.tif   2048 × 2048 TIFF, flat at root
+├── planar_noisy/                     Case B planar images (4000 pairs, 22k particles)
+│   ├── B00001_A.tif … B04000_B.tif
 │   └── calibration_boards/           20 synthetic dotboard calibration images
-├── stereo_noisy/                     Case B stereo images (4000 pairs × 2 cameras)
-│   ├── camera1/                      cam 1 TIFFs (±45° forward-scatter)
+│                                     (shared between Case A and Case B)
+├── stereo_clean/                     Case A stereo images (4000 pairs × 2 cameras)
+│   ├── camera1/                      cam 1 TIFFs
 │   ├── camera2/                      cam 2 TIFFs
+│   ├── mask_Cam1.mat                 pixel-space masks
+│   └── mask_Cam2.mat
+├── stereo_noisy/                     Case B stereo images (4000 pairs × 2 cameras)
+│   ├── camera1/
+│   ├── camera2/
 │   ├── calibration/
 │   │   ├── cam1/                     20 stereo dotboard images, cam 1
 │   │   └── cam2/                     20 stereo dotboard images, cam 2
-│   ├── mask_Cam1.mat                 pixel-space masks
+│   │                                 (shared between Case A and Case B)
+│   ├── mask_Cam1.mat
 │   └── mask_Cam2.mat
 └── scripts/
     ├── benchmark_comparison.py       Planar + ensemble vs DNS
     ├── stereo_benchmark_comparison.py Stereo 3-component + 6 stresses vs DNS
     ├── cross_method_comparison.py    Multi-method overlay figures
-    ├── paper_figures.py              Combined clean+noisy paper figures
+    ├── paper_figures.py              Combined clean + noisy paper figures
     ├── tcf_direct_stats.py           Recompute ground truth from JHTDB particles
     └── sig_configs/                  EUROSIG configuration files (.cdl)
 ```
 
+**Calibration boards are shared.** To avoid duplicate uploads, the calibration images for both Case A and Case B live at `planar_noisy/calibration_boards/` (planar) and `stereo_noisy/calibration/{cam1,cam2}/` (stereo). When processing Case A, point your PIVtools config at those same calibration paths.
+
 ## Image specifications
 
 | Parameter | Value |
@@ -83,14 +100,20 @@ MTT69/TurbulentChannel/
 | Particle diameter | 3 px |
 | Laser sheet thickness | 16 px (1.2 mm physical) |
 | Number of pairs | 4000 |
-| Case B particle count | 22 000 per image (≈ 1.3 ppw at 16×16 windows) |
+| Case A particle count | 85 000 per image (≈ 5.2 ppw at 16 × 16 windows), no noise |
+| Case B particle count | 22 000 per image (≈ 1.3 ppw at 16 × 16 windows) |
 | Case B noise | Gaussian, mean = 80, std = 16, SNR ≈ 8 |
 | Stereo geometry | Two cameras at ±45° forward-scatter |
 | dt | Matches JHTDB snapshot spacing (see CDL configs) |
 
 ## Ground truth
 
-`ground_truth/direct_stats_noisy.mat` is computed directly from the JHTDB particle position snapshots used to render the images. Contents:
+Two ground-truth files are provided, one per case, each in its own subdirectory so the benchmark scripts can point at them directly via `--gt-dir`:
+
+- `ground_truth/clean/direct_stats.mat` — Case A reference (85 000 particle trajectories)
+- `ground_truth/noisy/direct_stats.mat` — Case B reference (22 000 trajectories)
+
+Both are computed directly from the JHTDB particle position snapshots used to render the corresponding images. Benchmark Case A PIV against the clean file and Case B against the noisy one — finite-sample statistics are self-consistent within each case. Both share this schema:
 
 | Key | Shape | Description |
 |-----|-------|-------------|
@@ -115,7 +138,7 @@ Synthetic images are rendered from JHTDB turbulent-channel particle trajectories
 | `sigconf_planar_noisy_B.cdl` | Planar frame B, 22k particles, noise pattern B |
 | `SIGconf_Stereo_cam1_noisy_A.cdl`, `..._B.cdl` | Stereo cam 1, frames A and B |
 | `SIGconf_Stereo_cam2_noisy_A.cdl`, `..._B.cdl` | Stereo cam 2, frames A and B |
-| `sigconf_planar.cdl`, `SIGconf_Stereo_cam{1,2}.cdl` | Case A (clean, 85k particles) — for reference, Case A images not yet in this dataset |
+| `sigconf_planar.cdl`, `SIGconf_Stereo_cam{1,2}.cdl` | Case A planar + stereo (85k particles, no noise) |
 
 To regenerate images bit-for-bit, install EUROSIG and invoke each `.cdl` with its associated particle-position files from JHTDB. See the SIG documentation for build instructions.
 
@@ -128,7 +151,7 @@ Compares planar or ensemble PIV against the DNS ground truth; produces U+, Reyno
 ```bash
 python scripts/benchmark_comparison.py \
     --mode ensemble \
-    --gt-dir ./ground_truth \
+    --gt-dir ./ground_truth/noisy \
     --ensemble-dir <path/to/your/ensemble_result_directory> \
     --num-frames 4000 \
     --output-dir ./out
@@ -139,7 +162,7 @@ python scripts/benchmark_comparison.py \
 | `--mode` / `-m` | `instantaneous` or `ensemble` |
 | `--runs` / `-r` | Comma-separated 0-based pass indices (e.g. `2,3`) |
 | `--windows` / `-w` | Labels for those passes (e.g. `32,16`) |
-| `--gt-dir` / `-g` | Directory containing `direct_stats_noisy.mat` (required) |
+| `--gt-dir` / `-g` | Directory containing `direct_stats.mat` — e.g. `./ground_truth/noisy` or `./ground_truth/clean` (required) |
 | `--base-dir` / `-b` | PIV results base (instantaneous mode) |
 | `--ensemble-dir` / `-e` | Direct path to ensemble result directory |
 | `--num-frames` / `-n` | Frame count subdirectory (default 1000; use 4000 for this dataset) |
@@ -153,7 +176,7 @@ Uses LaTeX for labels (`text.usetex=True`); requires MiKTeX / TeXLive.
 
 ```bash
 python scripts/stereo_benchmark_comparison.py \
-    --gt-dir ./ground_truth \
+    --gt-dir ./ground_truth/noisy \
     --stereo-base <path/to/your/stereo_results> \
     --num-frames 4000 \
     --output-dir ./out
@@ -165,7 +188,7 @@ Publication-quality plots comparing one pass from each of instantaneous, ensembl
 
 ```bash
 python scripts/cross_method_comparison.py \
-    --gt-dir ./ground_truth \
+    --gt-dir ./ground_truth/noisy \
     --output-dir ./out \
     --inst-stats <path/to/instantaneous/mean_stats.mat> \
     --ens-dir <path/to/ensemble_dir> \

ground_truth/clean/direct_stats.mat

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