Publish DeepDeWedge tutorial package revision 4 with explicit zero center

ATTRIBUTION.md

@@ -1,31 +1,33 @@
-# Attribution and modification notice
-
-## Original material
-
-**DeepDeWedge Tutorial Data**
-Creator: Simon Wiedemann
-DOI: <https://doi.org/10.6084/m9.figshare.25043435.v1>
-Figshare file id: `45582309`
-Archive member: `tutorial_data/fitted_model.ckpt`
-License: Creative Commons Attribution 4.0 International
-
-The method is described by Simon Wiedemann and Reinhard Heckel, *A deep
-learning method for simultaneous denoising and missing wedge reconstruction in
-cryogenic electron tomography*, Nature Communications 15, 8255 (2024),
-<https://doi.org/10.1038/s41467-024-51438-y>.
-
-Pinned upstream code: <https://github.com/MLI-lab/DeepDeWedge/tree/072075692a44a8f17394214369e6e762abe52bc3>
-(BSD-2-Clause).
-
-## Changes in this package
-
-On 2026-09-04 Scitomo freshly converted only the authoritative checkpoint
-`official/fitted_model.ckpt`, after byte-size and SHA-256 verification, through
-the exact pinned upstream source and current generic FORMAT 2 exporter. The
-54 U-Net state tensors were explicitly mapped into canonical Network state.
-The two fitted affine quantities were preserved as external DeepDeWedge
-inference-profile state; they are not Network state. No old Hugging Face
-Safetensors or format-1 package artifact was conversion input.
-
-No endorsement by the cited authors, the Machine Learning and Information
-Processing Laboratory, Figshare, or the rights holders is implied.
+# Attribution and modification notice
+
+## Original material
+
+**DeepDeWedge Tutorial Data**
+Creator: Simon Wiedemann
+DOI: <https://doi.org/10.6084/m9.figshare.25043435.v1>
+Figshare file id: `45582309`
+Archive member: `tutorial_data/fitted_model.ckpt`
+License: Creative Commons Attribution 4.0 International
+
+The method is described by Simon Wiedemann and Reinhard Heckel, *A deep
+learning method for simultaneous denoising and missing wedge reconstruction in
+cryogenic electron tomography*, Nature Communications 15, 8255 (2024),
+<https://doi.org/10.1038/s41467-024-51438-y>.
+
+Pinned upstream code: <https://github.com/MLI-lab/DeepDeWedge/tree/072075692a44a8f17394214369e6e762abe52bc3>
+(BSD-2-Clause).
+
+## Changes in this package
+
+On 2026-09-04 Scitomo freshly converted only the authoritative checkpoint
+`official/fitted_model.ckpt`, after byte-size and SHA-256 verification, through
+the exact pinned upstream source and current generic FORMAT 2 exporter. The
+54 U-Net state tensors were explicitly mapped into canonical Network state.
+The two fitted affine quantities were preserved as external DeepDeWedge
+inference-profile state; they are not Network state. No old Hugging Face
+Safetensors or format-1 package artifact was conversion input.
+
+No endorsement by the cited authors, the Machine Learning and Information
+Processing Laboratory, Figshare, or the rights holders is implied.
+
+On 2026-09-23 Scitomo prepared revision 4 as a metadata-only update: the zero-degree missing-wedge support center became explicit in the inference contract. The verified revision-3 Safetensors bytes and original source attribution remain unchanged.

METADATA_REVISION.md

@@ -0,0 +1,9 @@
+# Revision 4 metadata change
+
+Source: pinned `scitomo/deepdewedge-tutorial` revision `876366091006d5206dfc7929bdf253d9f0390a13` (package revision 3), verified against the Scitomo learned-weight catalog.
+
+The inference contract now serializes `missing_wedge_support_center_deg: 0.0`. The numerical interpretation is unchanged. Construction and every byte of `weights.safetensors` are preserved from the verified source package. The linked validation and conversion records were rebound to revision 4 and the new inference fingerprint; original conversion evidence remains historical evidence for the unchanged tensor mapping.
+
+Previous inference fingerprint: `sha256:dd4d9b190f9e3a0c94a0da2d383f5dc42aaa0ac888dcec760d57b4cf5362aabd`.
+Current inference fingerprint: `sha256:e532deefbd56a419a90c04a519cf49e1bfc9e2484b9c28fc17494efa23462036`.
+Weights SHA-256: `e8f51c499d6e5c14ddce45d5cf9935511220de161ce62deb50a2c3a8cae5c731`.

README-revision3.md

@@ -0,0 +1,82 @@
+---
+license: cc-by-4.0
+library_name: scitomo
+tags: [cryo-electron-tomography, deepdewedge, safetensors, scitomo, format-2]
+---
+
+# DeepDeWedge tutorial checkpoint — fresh Scitomo FORMAT 2 package
+
+This is a fresh FORMAT 2 export from the authoritative original Lightning
+checkpoint, not a migration of any earlier Hugging Face package. Normal runtime
+uses Scitomo's generic FORMAT 2 loader and Safetensors only; it does not require
+PyTorch Lightning or the upstream DeepDeWedge source checkout.
+
+## Package identity
+
+- package id: `deepdewedge_tutorial`; package revision: `3`
+- learned-checkpoint format: `2`; manifest schema: `4`
+- Scitomo conversion checkout: `2832957f69daff0d7baec5df17a7c54954623eed`
+- minimum Scitomo version: `0.7.3`
+- previous Hugging Face commit: `87db06570dd874a99af1289e62b79ea99f87f006` — **HISTORICAL ONLY; NOT CONVERSION INPUT**
+
+## Authoritative provenance
+
+- upstream repository: <https://github.com/MLI-lab/DeepDeWedge>
+- upstream revision: `072075692a44a8f17394214369e6e762abe52bc3`
+- Figshare DOI: <https://doi.org/10.6084/m9.figshare.25043435.v1>; file id: `45582309`
+- original archive SHA-256: `7c871342e51f5a66a773fe427d72944b5d2cc8ff41c5b7415ab38dbfc9ac6d58`
+- original checkpoint member: `tutorial_data/fitted_model.ckpt`
+- original checkpoint size: `327952642` bytes
+- original checkpoint SHA-256: `5262f6c11e85fd662b02e59efe936fa7b69913758e841235be2683f7bd03ec76`
+
+DeepDeWedge Tutorial Data is attributed to Simon Wiedemann and is distributed
+under CC BY 4.0. The pinned DeepDeWedge implementation is BSD-2-Clause; its
+license text is included below `LICENSES/`. See `ATTRIBUTION.md`.
+
+## Scientific inference semantics
+
+The pure persisted Network owns only the lowered U-Net architecture and its 54
+canonical tensors. The fitted affine values remain outside Network state in the
+typed `deepdewedge_inference` profile:
+
+- `network_affine_loc`: `-0.14898751676082611`
+- `network_affine_scale`: `1.3237642049789429`
+- input layout: `(..., Z, Y, X)`; Network layout: `(..., C, Z, Y, X)`
+- paired halves are refined independently then averaged; full-width missing wedge: 50 degrees
+- 96³ patches, 32³ overlap, trailing-reflection coverage, linear-ramp reassembly
+- preconditioning recomputes patch statistics; output uses the checkpoint-fitted affine
+
+## Fresh conversion and validation
+
+`refresh_format2.py` is the exact one-off implementation and records
+the verified source, explicit 54-tensor mapping, strict Network lowering, and
+generic export. It was run with Python `3.12.13`, Torch
+`2.12.1+cpu`, Lightning `2.6.5`, Safetensors
+`0.8.0`, and Scitomo `0.7.3.dev0` on `Windows-11-10.0.22631-SP0`.
+
+The generic exporter freshly serializes `weights.safetensors`; no previous
+Hugging Face Safetensors, manifest, construction, or inference record is read.
+The conversion record lists every source checkpoint tensor to canonical target
+mapping. The validation record binds package state closure, generic loader
+reload, external-affine semantics, and deterministic forward parity.
+
+For a deterministic directional, non-symmetric CPU float32 input of 4,096 elements,
+authoritative upstream output versus FORMAT 2 pure-Network-plus-profile output
+passed `rtol=1e-5`, `atol=1e-6`: maximum absolute error
+`0`, relative L2 error `0`.
+
+## Files and closure
+
+`manifest.json` is the authoritative, closed inventory of every package file,
+with each fresh size and SHA-256. It declares only FORMAT 2 construction,
+inference, Safetensors, conversion, validation, and documentation/license
+resources; there is no format-1 or migration artifact. Validate and load with:
+
+```python
+import scitomo as st
+loaded = st.api.load_learned_network("/path/to/package")
+```
+
+This operation uses the generic Scitomo FORMAT 2 loader and does not import
+Lightning or DeepDeWedge. It is a checkpoint package, not a claim of scientific
+approval for a new dataset or acquisition protocol.

README.md

@@ -1,82 +1,13 @@
----
-license: cc-by-4.0
-library_name: scitomo
-tags: [cryo-electron-tomography, deepdewedge, safetensors, scitomo, format-2]
----
-
-# DeepDeWedge tutorial checkpoint — fresh Scitomo FORMAT 2 package
-
-This is a fresh FORMAT 2 export from the authoritative original Lightning
-checkpoint, not a migration of any earlier Hugging Face package. Normal runtime
-uses Scitomo's generic FORMAT 2 loader and Safetensors only; it does not require
-PyTorch Lightning or the upstream DeepDeWedge source checkout.
-
-## Package identity
-
-- package id: `deepdewedge_tutorial`; package revision: `3`
-- learned-checkpoint format: `2`; manifest schema: `4`
-- Scitomo conversion checkout: `2832957f69daff0d7baec5df17a7c54954623eed`
-- minimum Scitomo version: `0.7.3`
-- previous Hugging Face commit: `87db06570dd874a99af1289e62b79ea99f87f006` — **HISTORICAL ONLY; NOT CONVERSION INPUT**
-
-## Authoritative provenance
-
-- upstream repository: <https://github.com/MLI-lab/DeepDeWedge>
-- upstream revision: `072075692a44a8f17394214369e6e762abe52bc3`
-- Figshare DOI: <https://doi.org/10.6084/m9.figshare.25043435.v1>; file id: `45582309`
-- original archive SHA-256: `7c871342e51f5a66a773fe427d72944b5d2cc8ff41c5b7415ab38dbfc9ac6d58`
-- original checkpoint member: `tutorial_data/fitted_model.ckpt`
-- original checkpoint size: `327952642` bytes
-- original checkpoint SHA-256: `5262f6c11e85fd662b02e59efe936fa7b69913758e841235be2683f7bd03ec76`
-
-DeepDeWedge Tutorial Data is attributed to Simon Wiedemann and is distributed
-under CC BY 4.0. The pinned DeepDeWedge implementation is BSD-2-Clause; its
-license text is included below `LICENSES/`. See `ATTRIBUTION.md`.
-
-## Scientific inference semantics
-
-The pure persisted Network owns only the lowered U-Net architecture and its 54
-canonical tensors. The fitted affine values remain outside Network state in the
-typed `deepdewedge_inference` profile:
-
-- `network_affine_loc`: `-0.14898751676082611`
-- `network_affine_scale`: `1.3237642049789429`
-- input layout: `(..., Z, Y, X)`; Network layout: `(..., C, Z, Y, X)`
-- paired halves are refined independently then averaged; full-width missing wedge: 50 degrees
-- 96³ patches, 32³ overlap, trailing-reflection coverage, linear-ramp reassembly
-- preconditioning recomputes patch statistics; output uses the checkpoint-fitted affine
-
-## Fresh conversion and validation
-
-`refresh_format2.py` is the exact one-off implementation and records
-the verified source, explicit 54-tensor mapping, strict Network lowering, and
-generic export. It was run with Python `3.12.13`, Torch
-`2.12.1+cpu`, Lightning `2.6.5`, Safetensors
-`0.8.0`, and Scitomo `0.7.3.dev0` on `Windows-11-10.0.22631-SP0`.
-
-The generic exporter freshly serializes `weights.safetensors`; no previous
-Hugging Face Safetensors, manifest, construction, or inference record is read.
-The conversion record lists every source checkpoint tensor to canonical target
-mapping. The validation record binds package state closure, generic loader
-reload, external-affine semantics, and deterministic forward parity.
-
-For a deterministic directional, non-symmetric CPU float32 input of 4,096 elements,
-authoritative upstream output versus FORMAT 2 pure-Network-plus-profile output
-passed `rtol=1e-5`, `atol=1e-6`: maximum absolute error
-`0`, relative L2 error `0`.
-
-## Files and closure
-
-`manifest.json` is the authoritative, closed inventory of every package file,
-with each fresh size and SHA-256. It declares only FORMAT 2 construction,
-inference, Safetensors, conversion, validation, and documentation/license
-resources; there is no format-1 or migration artifact. Validate and load with:
-
-```python
-import scitomo as st
-loaded = st.api.load_learned_network("/path/to/package")
-```
-
-This operation uses the generic Scitomo FORMAT 2 loader and does not import
-Lightning or DeepDeWedge. It is a checkpoint package, not a claim of scientific
-approval for a new dataset or acquisition protocol.
+---
+license: cc-by-4.0
+library_name: scitomo
+tags: [cryo-electron-tomography, deepdewedge, safetensors, scitomo, format-2]
+---
+
+# DeepDeWedge tutorial checkpoint — metadata revision 4
+
+Revision 4 records the explicit zero-degree center of the ideal missing-wedge support in the inference contract. This is the same scientific inference behavior as revision 3. The canonical Network construction and all `weights.safetensors` bytes are unchanged.
+
+The immutable revision-3 source is Hugging Face commit `876366091006d5206dfc7929bdf253d9f0390a13`. Its verified original conversion and validation are described in `README-revision3.md`, `refresh_format2.py`, and the conversion and validation records. Revision 4 rebinds those records to the explicit inference contract without converting or serializing weights again. See `METADATA_REVISION.md` for the exact change.
+
+The original DeepDeWedge tutorial data is CC BY 4.0, and the upstream code is BSD-2-Clause. See `ATTRIBUTION.md` and `LICENSES/`. This package is not a new scientific qualification of a dataset or acquisition protocol.

conversion/conversion-record.json

@@ -1,468 +1,475 @@
-{
-  "construction_fingerprint": "sha256:644b162de70d014f9273c992fd583ee4fdfb8ae425a35fba26b3fbd41d560f28",
-  "environment": {
-    "platform": "Windows-11-10.0.22631-SP0",
-    "python": "3.12.13",
-    "pytorch_lightning": "2.6.5",
-    "safetensors": "0.8.0",
-    "scitomo": "0.7.3.dev0",
-    "scitomo_commit": "2832957f69daff0d7baec5df17a7c54954623eed",
-    "torch": "2.12.1+cpu"
-  },
-  "inference_fingerprint": "sha256:dd4d9b190f9e3a0c94a0da2d383f5dc42aaa0ac888dcec760d57b4cf5362aabd",
-  "kind": "scitomo_checkpoint_conversion",
-  "package_id": "deepdewedge_tutorial",
-  "package_revision": 3,
-  "schema_version": 2,
-  "source": {
-    "identifier": "45582309/tutorial_data/fitted_model.ckpt",
-    "kind": "figshare_checkpoint",
-    "metadata": {
-      "archive_member": "tutorial_data/fitted_model.ckpt",
-      "archive_sha256": "7c871342e51f5a66a773fe427d72944b5d2cc8ff41c5b7415ab38dbfc9ac6d58",
-      "checkpoint_size_bytes": 327952642,
-      "upstream_repository": "https://github.com/MLI-lab/DeepDeWedge"
-    },
-    "project": "DeepDeWedge Tutorial Data",
-    "revision": "072075692a44a8f17394214369e6e762abe52bc3",
-    "sha256": "5262f6c11e85fd662b02e59efe936fa7b69913758e841235be2683f7bd03ec76",
-    "url": "https://doi.org/10.6084/m9.figshare.25043435.v1"
-  },
-  "tensor_mappings": [
-    {
-      "details": {
-        "source_checkpoint": "tutorial_data/fitted_model.ckpt"
-      },
-      "kind": "identity",
-      "source": "state_dict.unet.bottleneck.0.bias",
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-    },
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-        "source_checkpoint": "tutorial_data/fitted_model.ckpt"
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-      "target": "bottleneck_conv_1.weight"
-    },
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-      "target": "down_0_conv_0.bias"
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-        "source_checkpoint": "tutorial_data/fitted_model.ckpt"
-      },
-      "kind": "identity",
-      "source": "state_dict.unet.down_blocks.0.layers.4.weight",
-      "target": "down_0_conv_1.weight"
-    },
-    {
-      "details": {
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-      "kind": "identity",
-      "source": "state_dict.unet.down_blocks.0.layers.8.bias",
-      "target": "down_0_conv_2.bias"
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-    {
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-      "kind": "identity",
-      "source": "state_dict.unet.down_blocks.0.layers.8.weight",
-      "target": "down_0_conv_2.weight"
-    },
-    {
-      "details": {
-        "source_checkpoint": "tutorial_data/fitted_model.ckpt"
-      },
-      "kind": "identity",
-      "source": "state_dict.unet.down_blocks.1.layers.0.bias",
-      "target": "down_1_conv_0.bias"
-    },
-    {
-      "details": {
-        "source_checkpoint": "tutorial_data/fitted_model.ckpt"
-      },
-      "kind": "identity",
-      "source": "state_dict.unet.down_blocks.1.layers.0.weight",
-      "target": "down_1_conv_0.weight"
-    },
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-      "source": "state_dict.unet.down_blocks.1.layers.4.bias",
-      "target": "down_1_conv_1.bias"
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-      "target": "down_1_conv_1.weight"
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-      },
-      "kind": "identity",
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-      "target": "down_1_conv_2.bias"
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-      },
-      "kind": "identity",
-      "source": "state_dict.unet.down_blocks.1.layers.8.weight",
-      "target": "down_1_conv_2.weight"
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-      "target": "down_2_conv_0.bias"
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-      "target": "down_2_conv_0.weight"
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-      "kind": "identity",
-      "source": "state_dict.unet.down_blocks.2.layers.4.bias",
-      "target": "down_2_conv_1.bias"
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-      "kind": "identity",
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-      "target": "down_2_conv_1.weight"
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-      "target": "downsample_0.bias"
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-      "source": "state_dict.unet.down_samplers.0.layers.0.weight",
-      "target": "downsample_0.weight"
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-      "source": "state_dict.unet.down_samplers.1.layers.0.bias",
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-      "kind": "identity",
-      "source": "state_dict.unet.up_blocks.0.layers.0.bias",
-      "target": "up_0_conv_0.bias"
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