"""Realism planner — picks the next ``(decky, persona, class, action)`` tuple. Stage 3: returns ``create``-only plans (the edit branch lands in stage 3b). Pure-function, deterministic given the same inputs: caller passes deckies (with personas pre-resolved on each row), ``now``, and an RNG. The persona resolution split — topology-pool vs. global-pool — is the orchestrator's job, not the planner's. Each decky dict reaching :func:`pick` carries a ``_realism_personas`` key with the resolved :class:`~decnet.realism.personas.EmailPersona` list. Keeps the planner test-isolated and avoids forcing it to know about the :class:`~decnet.web.db.repository.BaseRepository` / topology pool / global pool. Diurnal gating uses :func:`decnet.realism.diurnal.in_work_hours` per persona; we filter the (decky, persona) pairs *before* picking, so a persona outside its window is never considered. """ from __future__ import annotations import secrets from datetime import datetime from typing import Any, Optional, Sequence from decnet.realism import bodies, naming from decnet.realism.diurnal import in_work_hours, sample_mtime from decnet.realism.personas import EmailPersona from decnet.realism.taxonomy import ContentClass, Plan, PlanAction # noqa: F401 # Stage-3 weighted sampling: # * User content (notes/todo/draft/script) gets the bulk — those are # the realism win when a persona "looks busy." # * System content (cron/daemon/cache) is plausible filler. # * Email + canary are owned by other paths and not picked here. _USER_CLASS_WEIGHTS: tuple[tuple[ContentClass, int], ...] = ( (ContentClass.NOTE, 30), (ContentClass.TODO, 20), (ContentClass.DRAFT, 15), (ContentClass.SCRIPT, 10), ) _SYSTEM_CLASS_WEIGHTS: tuple[tuple[ContentClass, int], ...] = ( (ContentClass.LOG_CRON, 12), (ContentClass.LOG_DAEMON, 8), (ContentClass.CACHE_TMP, 5), ) # Canary classes are picked rarely. Each plant materialises a real # CanaryToken row + DNS slug + HTTP URL — flooding the fleet with # canaries makes the dashboard noisy and the per-decky alert surface # explode. ~3% of file picks land here. _CANARY_CLASS_WEIGHTS: tuple[tuple[ContentClass, int], ...] = ( (ContentClass.CANARY_AWS_CREDS, 1), (ContentClass.CANARY_ENV_FILE, 1), (ContentClass.CANARY_GIT_CONFIG, 1), (ContentClass.CANARY_SSH_KEY, 1), (ContentClass.CANARY_HONEYDOC, 1), (ContentClass.CANARY_HONEYDOC_DOCX, 1), (ContentClass.CANARY_HONEYDOC_PDF, 1), (ContentClass.CANARY_MYSQL_DUMP, 1), ) _CANARY_PROBABILITY = 0.03 def _weighted_pick( weights: tuple[tuple[ContentClass, int], ...], rng: secrets.SystemRandom, ) -> ContentClass: total = sum(w for _, w in weights) target = rng.randint(1, total) running = 0 for cls, w in weights: running += w if target <= running: return cls return weights[-1][0] # unreachable, satisfy mypy def _eligible_pairs( deckies: Sequence[dict[str, Any]], now: datetime, ) -> list[tuple[dict[str, Any], EmailPersona]]: """Cross-product of deckies × resolved personas, diurnal-filtered. A decky with no personas (empty ``_realism_personas``) is skipped entirely; same fail-quiet semantics as the emailgen scheduler. """ out: list[tuple[dict[str, Any], EmailPersona]] = [] for decky in deckies: personas: list[EmailPersona] = decky.get("_realism_personas") or [] for persona in personas: if in_work_hours(persona.active_hours, now): out.append((decky, persona)) return out def pick( deckies: Sequence[dict[str, Any]], now: datetime, *, edit_candidate: Optional[dict[str, Any]] = None, rand: Optional[secrets.SystemRandom] = None, ) -> Optional[Plan]: """Return a single :class:`Plan` for the orchestrator's tick. Stage-3b policy: weighted action roll — 60% create, 30% edit, 10% "leave alone" (planner returns ``None`` to skip). When the roll is "edit" and *edit_candidate* is set (a row from :meth:`BaseRepository.pick_random_synthetic_file_for_edit`), we return an edit Plan; otherwise we fall through to create. The orchestrator scheduler is responsible for fetching the edit candidate before calling — keeps this function pure-of-DB and test-friendly. Returns ``None`` when no eligible (decky, persona) pair exists or when the action roll lands on "leave alone." """ rng = rand or secrets.SystemRandom() eligible = _eligible_pairs(deckies, now) if not eligible: return None # Action roll. Edit only fires when there's a candidate from the # repo — otherwise we either re-roll to create or skip. roll = rng.random() if roll < 0.10: return None # "leave alone" — quiet tick is realism too if roll < 0.40 and edit_candidate is not None: return _edit_plan(edit_candidate, now, rng) decky, persona = rng.choice(eligible) # Canary first — they're rare (~3% of file picks), uniformly # weighted across generators. Falling here means the orchestrator # plants a callback-bearing artifact this tick instead of an # inert one. if rng.random() < _CANARY_PROBABILITY: content_class = _weighted_pick(_CANARY_CLASS_WEIGHTS, rng) # Canary placement is the cultivator's job — plan.target_path # is advisory; a "" lets the cultivator override entirely. target_path = "" body_hint = None mtime = sample_mtime(persona.active_hours, now, rand=rng) return Plan( decky_uuid=decky["uuid"], decky_name=decky["name"], persona=persona.name, content_class=content_class, action="create", target_path=target_path, mtime=mtime, body_hint=body_hint, notes=( f"persona={persona.name}", f"class={content_class.value}", "kind=canary", ), ) # User vs system content — biased toward user (realism wins are # bigger there). if rng.random() < 0.7: content_class = _weighted_pick(_USER_CLASS_WEIGHTS, rng) else: content_class = _weighted_pick(_SYSTEM_CLASS_WEIGHTS, rng) target_path = naming.make_path(content_class, persona.name, rand=rng) body_hint = bodies.make_body(content_class, persona.name, rand=rng) mtime = sample_mtime(persona.active_hours, now, rand=rng) return Plan( decky_uuid=decky["uuid"], decky_name=decky["name"], persona=persona.name, content_class=content_class, action="create", target_path=target_path, mtime=mtime, body_hint=body_hint, notes=( f"persona={persona.name}", f"class={content_class.value}", f"window={persona.active_hours}", ), ) def _edit_plan( candidate: dict[str, Any], now: datetime, rng: secrets.SystemRandom, ) -> Optional[Plan]: """Build an edit-action :class:`Plan` from a synthetic_files row. The candidate dict is the shape :meth:`BaseRepository.list_synthetic_files` returns — we only need ``decky_uuid``, ``path``, ``persona``, ``content_class``, ``last_body``, ``uuid``. Returns ``None`` if the candidate's content_class is somehow not editable (defensive — the repo query already filters those out). """ try: cls = ContentClass(candidate["content_class"]) except (KeyError, ValueError): return None if cls.is_canary() or cls == ContentClass.CACHE_TMP: return None # mtime: edits bump forward by ~hours-to-days, but never past now. # We model as "the file was edited some time after creation but # before now" — sample_mtime with a tighter cap keeps it recent. edit_mtime = sample_mtime( "00:00-00:00", now, rand=rng, backdate_min_hours=1.0, backdate_max_days=2.0, ) return Plan( decky_uuid=candidate["decky_uuid"], decky_name=candidate.get("decky_name", ""), persona=candidate.get("persona", ""), content_class=cls, action="edit", target_path=candidate["path"], mtime=edit_mtime, body_hint=None, # edit uses previous_body, not a fresh hint previous_body=candidate.get("last_body", ""), notes=( f"persona={candidate.get('persona', '')}", f"class={cls.value}", "action=edit", f"synthetic_file_uuid={candidate.get('uuid', '')}", ), )