Add card, checksums, index and previews

PHerc0172-lasagnetta-meta.tar

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+version https://git-lfs.github.com/spec/v1
+oid sha256:470c02db6d4d39bb732f39ff458e41aa35196a022bba9b19de590f8aae915007
+size 10240

README.md

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+---
+license: cc-by-nc-4.0
+tags: [vesuvius-challenge, herculaneum, ct, lasagna, lasagnetta, ome-zarr]
+pretty_name: PHerc0172 Lasagnetta field (a preview of villa's lasagna field)
+---
+
+# PHerc0172 Lasagnetta field
+
+A Lasagnetta preview of villa's lasagna field for the whole PHerc0172 scan, in villa's own layout, so you can point
+lasagna or the spiral fit at it without running a model yourself. The open data has no lasagna field for this scroll.
+
+![cos and surface normals, a slice through the middle of the scroll](preview-xy.png)
+
+## Get it
+
+```bash
+hf download SharpMeow/pherc0172-lasagnetta --repo-type dataset --local-dir pherc0172-lasagnetta
+cd pherc0172-lasagnetta && sha256sum -c SHA256SUMS
+for f in parts/*.tar; do tar -xf "$f"; done
+tar -xf PHerc0172-lasagnetta-meta.tar   # last: the whole-scan PHerc0172.lasagna.json and preview.json
+```
+
+You end up with `PHerc0172.lasagna.json` and `PHerc0172_{cos,grad_mag,nx,ny}.ome.zarr`, 136.4 GB in all.
+
+## Load it
+
+```python
+import json
+import numpy as np
+import zarr
+
+meta = json.load(open("PHerc0172.lasagna.json"))
+cos = zarr.open(meta["groups"]["cos"]["zarr"], mode="r")     # cos, 2x down from the CT level read
+nx = zarr.open(meta["groups"]["nx"]["zarr"], mode="r")       # normals, 4x down; stored as v * 127 + 128
+ny = zarr.open(meta["groups"]["ny"]["zarr"], mode="r")
+z, y, x = 5176, 725, 1125                                      # a voxel on the normals' grid that holds data
+a, b = (float(nx[z, y, x]) - 128) / 127, (float(ny[z, y, x]) - 128) / 127
+c = np.sqrt(max(0.0, 1 - a * a - b * b))                     # nz is not stored
+patch = cos[2 * z, 2 * y:2 * y + 64, 2 * x:2 * x + 64]       # the same place on the cos grid
+print("normal", round(a, 3), round(b, 3), round(c, 3), "cos patch mean", float(np.mean(patch)))
+```
+
+`load_example.py` is this snippet; it was run on an unpacked part before upload.
+
+## What it is
+
+- Scan: PHerc0172, `20241024131838-7.910um-53keV-masked.zarr`, read at level 0 (7.91 um voxels), Vesuvius Challenge open data
+- Model: [Lasagnetta](https://huggingface.co/SharpMeow/lasagnetta) 0.1.1, weights at revision 0f9ad8cd: the
+  release recipe (three students averaged, cos blur 0.5, cos offset 0.04)
+- Code: [ChaseHendrick/Scrolls](https://github.com/ChaseHendrick/Scrolls) `scripts/lasagnetta` at a011d5e4, tile 320 x 576 x 576
+- Channels: cos at OME-Zarr level 1 to 4 (2x down from the level read); grad_mag, nx, ny at level 2 to 4 (4x down)
+- Run: 57 parts on Hugging Face Jobs (RTX PRO 6000), one x, y, z box each. Every part edge sits on a
+  multiple of 512 scan voxels, so every chunk of every level was written by exactly one part, and the parts together
+  are the whole field. Values are kept only where the masked scan holds CT; everywhere else is fill 0.
+- Previews: xy at z about 10,480 and xz at y about 3,344 (full-resolution voxels), from OME-Zarr level 4.
+
+## How close to villa
+
+Lasagnetta copies villa's lasagna net and runs about 5 times faster. On 30 crops of this scan that no student had
+trained on, it matched villa's sheet crossings at peak F1 0.852 (crossings matched within one cos voxel; 25
+Oct 2026). The [model card](https://huggingface.co/SharpMeow/lasagnetta) has the method and the pooled numbers. The release's self-check, run on this whole volume before this field was made, pointed to villa's net here (median peak F1 over its spots under 0.82), so use this field as a quick look and run villa's `predict3d` where exact values matter.
+
+![the same field from the side](preview-xz.png)
+
+## Notes
+
+Model output: a preview of another model's output, not a reading of any text, and nobody has checked it by eye.
+villa's own field stays the reference where exact values matter.
+
+## Related
+
+The [Lasagnetta guide](https://github.com/ChaseHendrick/Scrolls/blob/main/docs/tools/lasagnetta.md) shows how to run it on your own region, with a self-check that tells you where to
+use villa's net instead. villa lasagna fields for ten more scans are on the [Lasagna Fields](https://huggingface.co/spaces/SharpMeow/lasagna-fields) page.
+
+## Credit and terms
+
+The scan is Vesuvius Challenge open data, CC BY-NC 4.0; this derived field is shared under the same terms. Lasagnetta
+copies villa's lasagna ([ScrollPrize/villa](https://github.com/ScrollPrize/villa), weights
+[scrollprize/lasagna](https://huggingface.co/scrollprize/lasagna), MIT); credit to its authors. Shared by Chase Hendrick.

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+  },
+  {
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+   "bytes": 2528358400,
+   "sha256": "faf7e300b6fb79e89d70857a21923a60251eb6d3c0c756c7e084fd9572b5666c"
+  },
+  {
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+   "bytes": 3283005440,
+   "sha256": "b638bce5024fbe2f75d41f059f2967a2af5b24d2661f33ff18ef4c9f0b7a2636"
+  },
+  {
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+   "bytes": 700170240,
+   "sha256": "5cc4c851875dbf81625399b1c4e07e0c7647ed487dac046623bce1dc0f432f4f"
+  }
+ ],
+ "bytes": 136444723200,
+ "note": "model output: a Lasagnetta preview of villa's lasagna field; never ground truth"
+}

load_example.py

@@ -0,0 +1,13 @@
+import json
+import numpy as np
+import zarr
+
+meta = json.load(open("PHerc0172.lasagna.json"))
+cos = zarr.open(meta["groups"]["cos"]["zarr"], mode="r")     # cos, 2x down from the CT level read
+nx = zarr.open(meta["groups"]["nx"]["zarr"], mode="r")       # normals, 4x down; stored as v * 127 + 128
+ny = zarr.open(meta["groups"]["ny"]["zarr"], mode="r")
+z, y, x = 5176, 725, 1125                                      # a voxel on the normals' grid that holds data
+a, b = (float(nx[z, y, x]) - 128) / 127, (float(ny[z, y, x]) - 128) / 127
+c = np.sqrt(max(0.0, 1 - a * a - b * b))                     # nz is not stored
+patch = cos[2 * z, 2 * y:2 * y + 64, 2 * x:2 * x + 64]       # the same place on the cos grid
+print("normal", round(a, 3), round(b, 3), round(c, 3), "cos patch mean", float(np.mean(patch)))