feat(dtypes): complex- und floatlist-dtypes (typisierte Float-Liste)#84
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Zwei weitere Attribut-dtypes, strikt additiv:
- complex -> complex (sdata:complex) "1+2j"/"(1+2j)"/Zahl; bool abgelehnt
- floatlist -> list[float] (sdata:floatlist) aus CSV-String, list/tuple oder
numpy-Array (.tolist); Alias dtype="list[float]"/"float[]"
Beide haben keinen Standard-XSD-Typ -> eigene Datentyp-CURIEs (sdata:complex /
sdata:floatlist), damit der JSON-LD-Round-Trip verlustfrei bleibt:
- semantic._DTYPE_FROM_XSD: sdata:complex -> complex
- semantic._set_from_node: list mit @type sdata:floatlist -> floatlist (statt str-list)
Anbindung: DTYPES_INV (complex), json_default (complex -> str), DtypeSpec.to_json
(complex -> "(1+2j)"; floatlist JSON-nativ), resolve-Alias list[float]. floatlist-
Leer-Check skalar-sicher (kein elementweiser numpy-Vergleich).
Tests: Coercion (gueltig/leer/ungueltig/strict, numpy-Array, bool abgelehnt), to_json,
json_default, resolve/Alias, xsd-Map, JSON- und JSON-LD-Round-Trip (floatlist bleibt
floatlist, nicht str-list). dtypes.py 275/275 100%, semantic.py 100%.
Doku + CHANGELOG [1.3.0] ergaenzt.
Kanonische CI: 616 passed, 7 skipped, TOTAL 100%; mkdocs --strict gruen.
Not up to standards ⛔🔴 Issues
|
| Category | Results |
|---|---|
| CodeStyle | 1 minor |
| Complexity | 1 medium |
🟢 Metrics 29 complexity · 0 duplication
Metric Results Complexity 29 Duplication 0
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Was
Zwei weitere Attribut-dtypes (reine stdlib + numpy-Core), strikt additiv:
complexcomplexsdata:complex"1+2j"/"(1+2j)"/Zahl;boolabgelehntfloatlistlist[float]sdata:floatlistlist/tupleoder numpy-Array (.tolist)floatlistakzeptiert auch das Aliasdtype="list[float]"(und"float[]").Verlustfreier JSON-LD-Round-Trip
Beide haben keinen Standard-XSD-Typ → eigene Datentyp-CURIEs:
semantic._DTYPE_FROM_XSD:sdata:complex→complexsemantic._set_from_node: eine Liste mit@type sdata:floatlist→floatlist(statt der generischen str-
list)Anbindung
DTYPES_INV(complex),json_default(complex→str),DtypeSpec.to_json(
complex→"(1+2j)";floatlistJSON-nativ),resolve-Aliaslist[float].Der
floatlist-Leer-Check ist skalar-sicher (kein elementweiser numpy-Vergleich).Tests / CI
tests/test_dtypes.py: Coercion (gültig/leer/ungültig/strict, numpy-Array,boolabgelehnt),
to_json,json_default,resolve/Alias, XSD-Map, JSON- undJSON-LD-Round-Trip (floatlist bleibt floatlist, nicht str-list).
dtypes.py275/275 100 %,semantic.py100 %.Kanonische CI: 616 passed, 7 skipped, TOTAL 100 %;
mkdocs build --strictgrün.ns/context.jsonld-Sync-Guard unberührt. Doku + CHANGELOG [1.3.0] ergänzt.