Jesse_ChenandCursor
04ad3325d5
Venus/Jupiter/Moon periods no longer collapse into one marriage-opportunity line. Full reading reports the ten BPHS conditional dasha families; Skill 6.9.16. Co-authored-by: Cursor <cursoragent@cursor.com>
89 lines
3.6 KiB
Python
89 lines
3.6 KiB
Python
"""Optional child-period expanders for source-bounded Dasha producers."""
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from __future__ import annotations
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from copy import deepcopy
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from datetime import datetime, timedelta
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from typing import Any
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PYJHORA_PARENT_SEEDED_EQUAL_SPLIT_PROFILE = "pyjhora_parent_seeded_equal_split_observed_v1"
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def _parse_dt(value: Any) -> datetime:
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if isinstance(value, datetime):
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return value
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return datetime.fromisoformat(str(value))
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def _iso(value: datetime) -> str:
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return value.isoformat(timespec="seconds")
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def _sequence_from(lord: str, sequence: tuple[tuple[str, int], ...]) -> list[tuple[str, int]]:
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start = next(index for index, (item, _years) in enumerate(sequence) if item == lord)
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return list(sequence[start:] + sequence[:start])
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def expand_parent_seeded_equal_split_children(
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major_rows: list[dict[str, Any]],
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sequence: tuple[tuple[str, int], ...],
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*,
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profile: str | None,
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) -> tuple[list[dict[str, Any]], list[dict[str, Any]], list[dict[str, Any]], str]:
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"""Attach PyJHora-observed equal-split AD/PD rows when explicitly selected."""
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rows = deepcopy(major_rows)
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if profile in (None, "", "blocked"):
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return rows, [], [], "mahadasha_executed_ad_pd_blocked_pending_row_level_replay"
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if profile != PYJHORA_PARENT_SEEDED_EQUAL_SPLIT_PROFILE:
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return rows, [], [], "mahadasha_executed_child_period_profile_unknown_blocked"
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child_count = len(sequence)
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all_ad: list[dict[str, Any]] = []
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all_pd: list[dict[str, Any]] = []
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for md in rows:
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md_lord = str(md.get("lord") or md.get("planet"))
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md_start = _parse_dt(md["start_date"])
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md_end = _parse_dt(md["end_date"])
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md_seconds = (md_end - md_start).total_seconds()
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ad_seconds = md_seconds / child_count
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ad_rows: list[dict[str, Any]] = []
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ad_cursor = md_start
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for ad_index, (ad_lord, _ad_years) in enumerate(_sequence_from(md_lord, sequence)):
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ad_end = md_end if ad_index == child_count - 1 else ad_cursor + timedelta(seconds=ad_seconds)
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pd_seconds = (ad_end - ad_cursor).total_seconds() / child_count
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pd_rows: list[dict[str, Any]] = []
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pd_cursor = ad_cursor
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for pd_index, (pd_lord, _pd_years) in enumerate(_sequence_from(ad_lord, sequence)):
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pd_end = ad_end if pd_index == child_count - 1 else pd_cursor + timedelta(seconds=pd_seconds)
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pd_row = {
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"level": "pratyantardasha",
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"lord": pd_lord,
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"planet": pd_lord,
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"parent_mahadasha": md_lord,
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"parent_antardasha": ad_lord,
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"start_date": _iso(pd_cursor),
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"end_date": _iso(pd_end),
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"child_period_profile": profile,
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"derivation": "pyjhora_observed_parent_seeded_equal_split",
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}
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pd_rows.append(pd_row)
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all_pd.append(pd_row)
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pd_cursor = pd_end
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ad_row = {
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"level": "antardasha",
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"lord": ad_lord,
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"planet": ad_lord,
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"parent_mahadasha": md_lord,
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"start_date": _iso(ad_cursor),
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"end_date": _iso(ad_end),
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"child_period_profile": profile,
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"derivation": "pyjhora_observed_parent_seeded_equal_split",
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"pratyantardasha": pd_rows,
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}
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ad_rows.append(ad_row)
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all_ad.append(ad_row)
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ad_cursor = ad_end
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md["antardasha"] = ad_rows
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return rows, all_ad, all_pd, "mahadasha_antardasha_pratyantardasha_executed_pyjhora_observed_profile"
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