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Stock block geometry — expected results and reader evidence

One closed faceted BREP with six planar faces. Triangulation yields 12 triangles, eight unique geometric positions, bounds 0,0,0 to 40,30,20 mm, surface area 5,200 mm² and volume 24,000 mm³. Hard-normal vertices may be duplicated.

Data import
Preview, first 49 linesjson
{
  "schemaVersion": 1,
  "units": "mm",
  "bounds": {
    "min": [
      0,
      0,
      0
    ],
    "max": [
      40,
      30,
      20
    ]
  },
  "closedSolids": 1,
  "planarFaces": 6,
  "uniqueGeometricVertices": 8,
  "expectedTriangulatedFaces": 12,
  "surfaceAreaSquareMm": 5200,
  "volumeCubicMm": 24000,
  "readerVerification": "occt-import-js 0.0.23 reader and independent triangle geometry passed",
  "note": "Readers may duplicate vertices at hard normals; compare geometric positions and bounds, not raw vertex buffer length.",
  "readerEvidence": {
    "schemaVersion": 1,
    "reader": "occt-import-js",
    "readerVersion": "0.0.23",
    "sourceSha256": "e771244dd8b734f9a36184aab59fa2c62f35da6eed116a9dd732eaaa077a286d",
    "triangles": 12,
    "planarFaces": 6,
    "bounds": {
      "min": [
        0,
        0,
        0
      ],
      "max": [
        40,
        30,
        20
      ]
    },
    "uniqueGeometricVertices": 8,
    "surfaceAreaSquareMm": 5200,
    "volumeCubicMm": 24000,
    "boundary": "Executed the installed CAD reader in Node and independently checked triangle geometry. Browser route verification is separate."
  }
}

Specifications

Units
mm
Closed Solids
1
Planar Faces
6
Vertices
8
Triangles
12
Synthetic
true
Reader
occt-import-js 0.0.23

Testing contract

Reference control
Scenario
Import the closed manufacturing stock block into a CAD reader and compare independently triangulated geometry with the companion expectations.
Expected result
One closed faceted BREP with six planar faces. Triangulation yields 12 triangles, eight unique geometric positions, bounds 0,0,0 to 40,30,20 mm, surface area 5,200 mm² and volume 24,000 mm³. Hard-normal vertices may be duplicated.

What is a .json file?

JSON (JavaScript Object Notation) is a lightweight, text-based data-interchange format representing objects, arrays, strings, numbers, booleans, and null. It is language-independent, human-readable, and the dominant format for web APIs and configuration. It requires a single well-formed root value.

How to use this file

Use an example JSON file to test parsers and serializers, schema validation, Unicode and number-precision handling, and API request or response processing.

How to use this file for testing

“Stock block geometry — expected results and reader evidence” is a deterministic Novus Examples fixture for Data import. Realistic faker-generated datasets with documented schemas for testing import and ETL flows.

Documented properties for this file: JSON · 1,170 bytes. Compare results against paired or grouped companions on this page when present (clean↔damaged, searchable↔scanned, or format twins) so scores stay reproducible across runs.

Download the file once, keep the path stable in CI or local scripts, and treat the spec table as the contract: dimensions, seeds, field lists, and roles are intentional. Corrupt or invalid samples are labelled as such, expect parsers to fail loudly rather than silently accept them.

3D and CAD fixtures carry one small, documented solid or scene. Convert and inspect against the known geometry, units, and structure; format twins let you diff interchange fidelity, and the 3D viewer previews the actual mesh.

Code examples

import json

with open("stock-block-expected.json") as f:
    data = json.load(f)
print(type(data), len(data))

Related files

Generated by generation/business_cad.py. Free for any use, no attribution required, license.