kind string | outputs dict | procedure dict | schema_version int64 | scope dict | source dict |
|---|---|---|---|---|---|
scitomo_walnut_edip_sparse20_preparation | {
"cone_vec": {
"angle_label_basis": {
"angular_increment_deg": 0.3,
"kind": "nominal_relative_orbit_angle",
"pose_authority": "cone_vec",
"relative_zero_geometry_row": 0
},
"angles_deg": [
0,
18,
36,
54,
72,
90,
108,
126,
144,
... | {
"angular_sub_sampling": 60,
"calibration": "-log((scan-dark)/(flat-dark))",
"calibration_clipping": null,
"corrected_geometry_member": "Walnut1/Projections/tubeV2/scan_geom_corrected.geom",
"detector_sub_sampling": {
"col_indices": [
1,
4,
7,
10,
13,
16,
19,
... | 1 | {
"excludes": [
"FDK initialization",
"DIP or EDIP reconstruction",
"pretrained network parameters",
"CUDA execution",
"blessing metadata"
],
"includes": "measured EDIP Walnut-1/orbit-2 Sparse-20 projections, exact corrected vector geometry, and published AGD-50 evaluation reference"
} | {
"catalog_source": "walnut_1_zenodo",
"dataset_paper_doi": "10.1038/s41597-019-0235-y",
"doi": "10.5281/zenodo.2686726",
"provider_file": "Walnut1.zip",
"provider_md5": "0ecd525f245e3029788538a44f5491e3",
"provider_sha256": "ae47f2b2cf19b34191ee0605e3436451637779e5d41b2d178df385f524064392"
} |
Scitomo Walnut EDIP Sparse-20 prepared dataset
This is a derived Scitomo Sparse-20 preparation of the public Walnut-1 cone-beam X-ray CT acquisition. It is not the original Walnut archive, not a published EDIP reconstruction, and not a blessed Scitomo result. The package contains measured projections, corrected vector-cone geometry, and the published AGD-50 evaluation reference used by the maintained Scitomo Walnut EDIP evidence workflow.
Provenance and attribution
The source authority is the Scitomo catalog entry walnut_1_zenodo, pinned to
Zenodo record 2686726, DOI
10.5281/zenodo.2686726, and the
single provider member Walnut1.zip.
The original dataset is described in the Scientific Data publication
10.1038/s41597-019-0235-y. The
official reconstruction-code reference is
cicwi/WalnutReconstructionCodes.
The source record declares CC BY 4.0
licensing. Please retain this attribution when using or redistributing the
derived preparation.
The exact provider identity used for preparation is:
provider file: Walnut1.zip
byte size: 6058580169
MD5: 0ecd525f245e3029788538a44f5491e3
SHA-256: ae47f2b2cf19b34191ee0605e3436451637779e5d41b2d178df385f524064392
The preparation was rerun from that pinned source with Scitomo commit
e568717442680afd45437dd713feda04e73a244f and
tools/datasets/prepare_walnut_edip.py blob
ac1a71bfa14358a492fc637f95e84dd6a4334584. The fresh replay was compared
byte-for-byte with the reviewed four-file package before publication staging.
Exact Scitomo preparation semantics
The preparation is the existing G9/0091-0094 Scitomo implementation. No alternate preprocessing, geometry reconstruction, angle convention, or fallback backend is used.
- Walnut-1, orbit 2, with 1200 unique views.
- The 20 measured views use corrected geometry rows
0,60,...,1140and scanner TIFF members1200,1140,...,60. - Each detector image uses the existing CWI orientation
transpose(flipud(raw)). - The dark field is
di000000.tif; the flat field is the float32 mean ofio000000.tifandio000001.tif. - Calibration is exactly
-log((scan-dark)/(flat-dark)), with no clipping or epsilon substitution. Invalid non-positive calibration values fail closed. - Detector reduction is centered stride-3 decimation, using zero-based rows
1,4,...,970and columns1,4,...,766. Projections are canonical Scitomo(A,V,U)=(20,324,256)float32 arrays. cone_veccontains the exact 20 corrected vector-cone rows as float64 millimetre geometry. Its detector basis vectors are scaled by 3 for the centered stride-3 detector grid.cone_vecis the pose authority.- Nominal labels are
0,18,...,342degrees from the documented 0.3-degree increment. They are ordered labels only and are not used to rebuild or reinterpret vector poses. - The reference is
full_AGD_50_{slice:06d}.tifffor slices 000000 through- It is independently reduced from
(501,501,501)to(167,167,167)by exact centered 3x3x3 mean pooling, producing float32 data on the reviewed centered 0.3 mm isotropic grid.
- It is independently reduced from
This package stops at prepared measured inputs and evaluation reference. It contains no DIP/EDIP reconstruction, pretrained parameters, CUDA execution, acceptance threshold, visual review, or blessing metadata.
Files and identities
The filenames below are the manifest-owned names; they are intentionally not renamed to generic aliases.
| File | Shape | Dtype | SHA-256 |
|---|---|---|---|
walnut1-orbit2-sparse20-preparation.json |
manifest | JSON | f87b462bd50f4fec7cce1097aacf7b661951891c3d86202e37d5d81836999da3 |
walnut1-orbit2-sparse20-projections.npy |
(20,324,256) |
float32 |
e65da23610d85919033314cae6411227aa3f7965449f3b243c63188f599539e6 |
walnut1-orbit2-sparse20-reference.npy |
(167,167,167) |
float32 |
0c79a1f2d80586c64b9dc5c822186a56a4b81316275c40c128a0d4f4c9addaf3 |
walnut1-orbit2-sparse20-cone-vec.npy |
(20,12) |
float64 |
c775f397ae264cd647f78112f56041afedccffac22685743010f965bb05ab775 |
The manifest is the package contract. It records the source authority, procedure, shapes, dtypes, nominal labels, pose-authority declaration, and artifact SHA-256 values.
Loading in Scitomo
Use the repository-owned preparation loader against a directory containing the four files. It verifies the manifest authority and every array's path, hash, shape, and dtype before constructing the measured Problem:
from pathlib import Path
from tools.datasets.prepare_walnut_edip import load_walnut_edip_sparse20_problem
problem = load_walnut_edip_sparse20_problem(
prepared_dir=Path("/path/to/this/dataset"),
)
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