Downstream came back with concrete requirements: don't pre-compute Std
shape[] tilde strings, just dump the raw EPRO2 `objects: {id: payload}`
dict and they'll write a ~100-LoC adapter on their side. Pulling the
tilde-mapping work back saves us from second-guessing positional fields
without their parser to verify against, and shortens our pcb_writer
from ~500 lines to ~40.
Output shape (Std envelope intact, just no `shape[]`):
{
"success": true, "code": 0,
"result": {
"uuid", "puuid", "title",
"docType": 3 | 1,
"components": {},
"dataStr": {
"head": {
"docType": "3" | "1",
"editorVersion": "facere-epro2/0.1 (epro2 <X.Y.Z>)",
"units": "mil",
"epro2_doc_uuid": ...,
"epro2_editor_version": ...,
},
"BBox": {x, y, width, height}, # mil
"layers": [...], # Std layer-string array
"objects": dict(doc.objects), # raw EPRO2, 1:1
"preference": {}, "netColors": [], "DRCRULE": {},
}
}
}
Per-doc spec downstream gave us:
- shape[] dropped (empty placeholder misleads adapter)
- all units mil (no mm conversion — Std canvas already declares mil)
- head.units="mil" so adapter doesn't have to guess
- BBox min/max across known x/y/startX/endX/centerX fields; adapter
can refine by walking path arrays itself
- layers[] keeps Std's 17-line default + inner SIGNAL layers actually
used (21~Inner1.., 22~Inner2..)
- empty stubs preference/netColors/DRCRULE for grep-based triage
New: docs/sources/epro2_to_std_mapping.md with the full EPRO2 OPTYPE →
Std verb table that downstream's adapter authors will copy from. Tables
include the layer-id remapping (the 5↔7 paste/mask flip, 11→10 outline,
12→11 multi, SIGNAL 15+→21+), PCB op mappings, SCH op mappings (marked
best-effort: no Std SCH samples in our corpus), and the 5-Voltage
placeholder COMPONENT → extra net flag trick. Extracted from the
previous Option-3 writer (commit fe6971f) so adapter writers don't
have to reverse-engineer it from source.
ESP-VoCat smoke: 6 PCB + 9 SCH = 15 JSON files, head.units=mil
preserved, no shape[] field present. 82 → 84 unit tests pass.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
