44 lines
1.7 KiB
Markdown
44 lines
1.7 KiB
Markdown
# JSON to CDSL
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This program converts SolidWorks export JSON into parameterized Learning IR
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(`*.cdsl.json`).
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Conversion responsibilities:
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- normalize SolidWorks JSON into an intermediate representation;
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- classify sketches into named parameterized profiles;
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- reuse equivalent profiles through semantic references;
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- derive deterministic geometry through the sketch solver;
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- remove avoidable floating-point noise;
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- report unsupported or non-self-sufficient shapes.
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The converter must not hide missing geometry behind an external context file
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when producing a training-ready CDSL artifact.
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## Evidence v2 batch converter
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`evidence_v2_to_cdsl.py` converts the newer
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`solidworks.cad_evidence.v2` extraction format into semantic CDSL v1.1. It
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does not modify the current rebuild engine: features without a runtime
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implementation are emitted with `execution_status: "deferred"` instead.
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```bash
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PYTHONPATH=backend/engine python json_to_cdsl/evidence_v2_to_cdsl.py \
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/path/to/evidence_v2 \
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--truth-dir /path/to/evidence_v2/truth \
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--out json_to_cdsl/output \
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--workers 1
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```
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For every `*.solidworks_evidence_v2.json` input it writes:
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- `<part_id>.cdsl.json`: self-contained semantic CDSL in millimetres;
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- `<part_id>.diagnostic.json`: source blockers, deferred features, optional
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`inferred_from_step` selectors, semantic validation, and STEP-versus-source
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bounding-box/volume/area/topology metrics;
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- `manifest.json`: batch-level counts and paths.
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The optional STEP directory is used only to infer a host face when exactly one
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candidate is compatible with a Hole Wizard's captured locations. Ambiguous
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geometry stays in `unresolved`; the converter never invents a selector.
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