marcopan
fc5fbe6b65
Thoth (tht) è il prodotto, PSD è il cliente. Nessun riferimento al contesto
clinico nel codice.
Rinomine:
- comando+package nsp→tht (dir nsp/→tht/, 46 import, pyproject entry point)
- gate nsp-gate.js→tht-gate.js (+ rewrite token, relayIfNspFails→relayIfThtFails)
- workspace chirone.{example,test}.yaml→tht.{example,test}.yaml (generici)
- env THOTH_→THT_ (19 var) + NSP_ stragglers (NSP_HARNESS_ROOT, NSP_SESSION)
- commenti/docstring chirone/psdwp3/policlinico neutralizzati ('the reference
implementation', 'the DWH')
Aggiunto [tool.setuptools.packages.find] include=['tht*'] (necessario: l'auto-
discovery rompeva con tht/ + workspaces/ come top-level multipli).
.env operatore aggiornato in-place (prefissi THT_, valori preservati, gitignored).
Verifica: pytest 109 passed, npm test 14 pass, tht phase meta --json OK, zero
residui nsp/THOTH_/NSP_/chirone nel package.
108 lines
3.4 KiB
Python
108 lines
3.4 KiB
Python
"""Reads workflow.yaml -- the SINGLE source of workflow truth (spec F2, §5.3).
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phase.py, the gate (tht-gate.js), and the skill all read from here.
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No more duplicated constants (the JS/Python drift bug in the reference implementation -- PHASE_NAMES
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truncated to 7 in JS -- is structurally impossible because there is one source).
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Edit workflow.yaml to change the workflow: add/reorder/merge/skip phases.
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"""
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from __future__ import annotations
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from dataclasses import dataclass, field
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from pathlib import Path
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from typing import Any
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import yaml
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_WF_PATH = Path(__file__).resolve().parent.parent / "workflow.yaml"
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@dataclass
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class PhaseSpec:
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id: str
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num: int
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name: str
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advance: str
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prerequisites: list[Any]
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artifacts_out: list[str] = field(default_factory=list)
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@dataclass
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class Workflow:
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schema_version: int
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phases: list[PhaseSpec]
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_decision_min_map: dict[str, int] = field(default_factory=dict)
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@property
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def max_phase(self) -> int:
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return len(self.phases)
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def phase_by_num(self, n: int) -> PhaseSpec:
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return self.phases[n - 1]
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def phase_name(self, n: int) -> str:
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if 1 <= n <= self.max_phase:
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return self.phase_by_num(n).name
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return "?"
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def decision_min_phase(self, decision_type: str) -> int:
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"""A decision type's min phase = the earliest phase whose prerequisites
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reference it (via decision_exists / decision_subject_exists). Defaults to 1."""
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return self._decision_min_map.get(decision_type, 1)
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def _collect_decision_mins(phases: list[PhaseSpec]) -> dict[str, int]:
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"""Scan prerequisites for decision_exists / decision_subject_exists mentions.
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Supports both forms:
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- decision_exists: <type> (scalar)
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- decision_exists: [<type>, ...] (list, first element is the type)
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- decision_subject_exists: [<type>, <subject>] (list, first element is the type)
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"""
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mins: dict[str, int] = {}
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def scan(node: Any, phase_num: int) -> None:
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if isinstance(node, dict):
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for key, value in node.items():
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if key in ("decision_exists", "decision_subject_exists"):
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if isinstance(value, list) and value:
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dtype = value[0]
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elif isinstance(value, str):
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dtype = value
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else:
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continue
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if isinstance(dtype, str):
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if dtype not in mins or phase_num < mins[dtype]:
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mins[dtype] = phase_num
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else:
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scan(value, phase_num)
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elif isinstance(node, list):
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for item in node:
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scan(item, phase_num)
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for p in phases:
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scan(p.prerequisites, p.num)
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return mins
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def load_workflow(path: Path | str = _WF_PATH) -> Workflow:
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path = Path(path)
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raw = yaml.safe_load(path.read_text())
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phases: list[PhaseSpec] = []
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for i, p in enumerate(raw["phases"], start=1):
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phases.append(
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PhaseSpec(
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id=p["id"],
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num=i,
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name=p["name"],
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advance=p["advance"],
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prerequisites=p.get("prerequisites", []),
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artifacts_out=p.get("artifacts_out", []),
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)
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)
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return Workflow(
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schema_version=raw.get("schema_version", 1),
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phases=phases,
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_decision_min_map=_collect_decision_mins(phases),
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)
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