fix(rectification): separate adjacent minutes with transition proximity
Day-level events now score Vimshottari/Narayana transition closeness so nearby candidate minutes can diverge, with gated quality probes and answer-prior ranking so high-base-rate existence questions stay out. Co-authored-by: Cursor <cursoragent@cursor.com>
This commit is contained in:
Jesse_Chen
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"""Deterministic dasha-transition proximity scoring for day/month events.
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Birth-time drift of about 1 minute moves Vimshottari/Narayana transition
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dates by a few days. A dated event near a candidate's AD/PD change is a
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bounded auxiliary signal, never larger than one day-level event body.
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"""
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from __future__ import annotations
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from collections.abc import Callable, Sequence
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from datetime import date
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from typing import Any
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from scripts.rectification.event_probes import _narayana_start_dates, _vim_start_dates
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PROXIMITY_WINDOW_DAYS = 45
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DAY_KERNEL_DAYS = 15
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MONTH_KERNEL_DAYS = 45
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DAY_MAX_POINTS = 1.0
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MONTH_MAX_POINTS = 0.35
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VIM_SHARE = 0.6
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NARAYANA_SHARE = 0.4
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def representative_event_date(event: dict[str, Any]) -> date | None:
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precision = str(event.get("precision") or "")
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if precision not in {"day", "month"}:
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return None
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raw_start = event.get("date_start") or event.get("date")
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raw_end = event.get("date_end") or raw_start
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try:
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start = date.fromisoformat(str(raw_start)[:10])
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end = date.fromisoformat(str(raw_end)[:10])
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except ValueError:
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return None
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if precision == "day" or start == end:
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return start
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mid_day = min(15, end.day)
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try:
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return start.replace(day=mid_day)
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except ValueError:
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return start
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def _nearest_delta(starts: Sequence[date], event_date: date) -> tuple[date | None, int | None]:
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eligible = [
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item for item in starts
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if abs((item - event_date).days) <= PROXIMITY_WINDOW_DAYS
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]
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if not eligible:
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return None, None
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nearest = min(eligible, key=lambda item: (abs((item - event_date).days), item.toordinal()))
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return nearest, abs((nearest - event_date).days)
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def _kernel(delta_days: int | None, width: float) -> float:
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if delta_days is None or width <= 0:
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return 0.0
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return max(0.0, 1.0 - (delta_days / width))
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def score_transition_proximity(
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*,
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event_date: date,
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precision: str,
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vim_starts: Sequence[date],
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narayana_starts: Sequence[date] | None = None,
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vim_pd_starts: Sequence[date] | None = None,
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) -> dict[str, Any]:
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if precision not in {"day", "month"}:
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return {
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"points": 0.0,
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"rule_ids": [],
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"nearest_vim_delta_days": None,
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"nearest_narayana_delta_days": None,
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}
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kernel_width = float(DAY_KERNEL_DAYS if precision == "day" else MONTH_KERNEL_DAYS)
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cap = DAY_MAX_POINTS if precision == "day" else MONTH_MAX_POINTS
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ad_starts = list(vim_starts)
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pd_starts = list(vim_pd_starts or ())
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ad_date, ad_delta = _nearest_delta(ad_starts, event_date)
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pd_date, pd_delta = _nearest_delta(pd_starts, event_date)
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if pd_delta is not None and (ad_delta is None or pd_delta < ad_delta):
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vim_delta = pd_delta
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vim_kind = "pd"
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vim_date = pd_date
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else:
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vim_delta = ad_delta
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vim_kind = "ad"
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vim_date = ad_date
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_, narayana_delta = _nearest_delta(list(narayana_starts or ()), event_date)
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vim_kernel = _kernel(vim_delta, kernel_width)
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narayana_kernel = _kernel(narayana_delta, kernel_width)
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points = round(cap * (VIM_SHARE * vim_kernel + NARAYANA_SHARE * narayana_kernel), 4)
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rules: list[str] = []
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if vim_kernel > 0:
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rules.append(f"vim_transition_proximity_{vim_kind}")
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if narayana_kernel > 0:
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rules.append("narayana_transition_proximity_ad")
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return {
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"points": points,
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"rule_ids": rules,
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"nearest_vim_delta_days": vim_delta,
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"nearest_narayana_delta_days": narayana_delta,
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"nearest_vim_date": vim_date,
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}
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def _context_time(context: dict[str, Any]) -> str | None:
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feature = context.get("feature") if isinstance(context.get("feature"), dict) else {}
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raw = feature.get("time")
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if isinstance(raw, str) and len(raw) >= 5:
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return raw[:5]
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at = context.get("candidate_at")
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if hasattr(at, "strftime"):
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return at.strftime("%H:%M")
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return None
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def merge_transition_proximity(
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matrix: dict[str, dict[str, dict[str, Any]]],
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events: Sequence[dict[str, Any]],
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static_contexts: Sequence[dict[str, Any]],
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birth_date: str,
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*,
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public_technique_layers: Callable[[str, Sequence[str]], list[str]],
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) -> None:
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by_time = {
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time: context
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for context in static_contexts
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if isinstance(context, dict) and (time := _context_time(context))
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}
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vim_cache: dict[tuple[Any, ...], list[date]] = {}
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pd_cache: dict[tuple[Any, ...], list[date]] = {}
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narayana_cache: dict[tuple[Any, ...], list[date] | None] = {}
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for event in events:
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if not isinstance(event, dict):
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continue
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event_id = str(event.get("id") or "")
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cells = matrix.get(event_id)
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if not event_id or not isinstance(cells, dict):
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continue
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event_date = representative_event_date(event)
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if event_date is None:
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continue
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precision = str(event.get("precision") or "")
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lo, hi = event_date.year - 1, event_date.year + 1
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for time, cell in cells.items():
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context = by_time.get(str(time)[:5])
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if not isinstance(cell, dict) or not isinstance(context, dict):
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continue
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moon = (context.get("planet_longitudes") or {}).get("Moon")
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if not isinstance(moon, (int, float)):
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continue
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vim_key = (birth_date, round(float(moon), 6), lo, hi)
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if vim_key not in vim_cache:
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vim_cache[vim_key] = _vim_start_dates(birth_date, float(moon), lo, hi)
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pd_cache[vim_key] = _vim_start_dates(
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birth_date,
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float(moon),
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lo,
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hi,
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include_pratyantar=True,
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)
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planets = context.get("planet_longitudes") or {}
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asc = context.get("ascendant_index")
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narayana_key = (
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birth_date,
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int(asc) if isinstance(asc, int) else None,
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lo,
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hi,
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round(float(moon), 6),
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)
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if narayana_key not in narayana_cache:
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narayana_cache[narayana_key] = (
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_narayana_start_dates(int(asc), planets, birth_date, lo, hi)
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if isinstance(asc, int) and isinstance(planets, dict)
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else None
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)
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ad_starts = vim_cache[vim_key]
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ad_set = set(ad_starts)
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pd_only = [item for item in pd_cache[vim_key] if item not in ad_set]
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scored = score_transition_proximity(
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event_date=event_date,
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precision=precision,
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vim_starts=ad_starts,
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vim_pd_starts=pd_only,
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narayana_starts=narayana_cache[narayana_key] or [],
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)
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if scored["points"] <= 0 and not scored["rule_ids"]:
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continue
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cell["points"] = round(float(cell.get("points") or 0) + float(scored["points"]), 4)
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cell["rule_ids"] = sorted({
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*list(cell.get("rule_ids") or []),
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*scored["rule_ids"],
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})
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domain = str(event.get("domain") or cell.get("domain") or "")
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cell["technique_layers"] = public_technique_layers(domain, cell["rule_ids"])
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