Jesse_Chen
231 lines
8.2 KiB
Python
231 lines
8.2 KiB
Python
#!/usr/bin/env python3
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"""Generate active-choice birth-time rectification questions."""
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from __future__ import annotations
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import argparse
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import json
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from datetime import datetime, timedelta
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from typing import Any
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from scripts.active_rectification_scoring import build_questions, score_answers
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def _parse_time(value: str) -> datetime:
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return datetime.strptime(value, "%Y-%m-%d %H:%M")
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def _candidate_scan(
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center: datetime,
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uncertainty_minutes: int,
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step_minutes: int,
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*,
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lat: float | None = None,
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lon: float | None = None,
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tz: float | None = None,
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ayanamsa: str = "lahiri",
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) -> dict[str, Any]:
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start = center - timedelta(minutes=uncertainty_minutes)
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end = center + timedelta(minutes=uncertainty_minutes)
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total_minutes = int((end - start).total_seconds() // 60)
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candidate_count = total_minutes // step_minutes + 1
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sample_offsets = sorted({-uncertainty_minutes, 0, uncertainty_minutes})
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samples = []
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for offset in sample_offsets:
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candidate = center + timedelta(minutes=offset)
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if offset < 0:
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cluster = "early_candidate_cluster"
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elif offset > 0:
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cluster = "late_candidate_cluster"
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else:
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cluster = "middle_candidate_cluster"
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sample = {
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"time": candidate.strftime("%Y-%m-%d %H:%M"),
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"offset_minutes": offset,
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"cluster": cluster,
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"sensitivity_flags": _sensitivity_flags(abs(offset)),
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}
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recast = _candidate_recast(candidate, lat=lat, lon=lon, tz=tz, ayanamsa=ayanamsa)
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if recast:
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sample.update(recast)
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samples.append(sample)
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has_true_recast = all("varga_lagna" in sample for sample in samples)
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has_kp_recast = all("kp_cusps" in sample for sample in samples)
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computed_layers = ["time_range", "candidate_cluster", "question_sensitivity_map"]
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blocked_layers = ["true_varga_recast", "true_kp_cusp_recast", "true_arudha_recast"]
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if has_true_recast:
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computed_layers.extend(["true_varga_recast", "true_arudha_recast"])
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blocked_layers = ["true_kp_cusp_recast"]
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if has_kp_recast:
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computed_layers.append("true_kp_cusp_recast")
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blocked_layers = [layer for layer in blocked_layers if layer != "true_kp_cusp_recast"]
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return {
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"start": start.strftime("%Y-%m-%d %H:%M"),
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"end": end.strftime("%Y-%m-%d %H:%M"),
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"step_minutes": step_minutes,
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"candidate_count": candidate_count,
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"cluster_labels": ["early_candidate_cluster", "middle_candidate_cluster", "late_candidate_cluster"],
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"samples": samples,
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"sensitivity_summary": {
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"method": "range_bucket_scan_v1",
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"high_value_layers": ["D9", "D10", "D24", "D30", "D60", "UL", "A7", "A10", "KP_cusp"],
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"computed_layers": computed_layers,
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"blocked_layers": blocked_layers,
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"boundary": "Candidate Varga, Arudha and KP cusp recasts are computed from the local domain chart; external oracle parity remains a separate gate.",
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},
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}
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def _sensitivity_flags(abs_offset_minutes: int) -> list[str]:
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flags = ["D9", "D10", "D24", "A10"]
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if abs_offset_minutes >= 10:
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flags.extend(["D30", "UL", "A7"])
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if abs_offset_minutes >= 20:
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flags.extend(["D60", "KP_cusp"])
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return flags
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def _candidate_recast(
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candidate: datetime,
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*,
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lat: float | None,
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lon: float | None,
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tz: float | None,
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ayanamsa: str,
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) -> dict[str, Any] | None:
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if lat is None or lon is None or tz is None:
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return None
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import domain_calculation_service
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import jaimini
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import varga
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chart = domain_calculation_service.compute_chart({
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"year": candidate.year,
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"month": candidate.month,
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"day": candidate.day,
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"hour": candidate.hour,
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"minute": candidate.minute,
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"second": candidate.second,
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"lat": lat,
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"lon": lon,
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"tz": tz,
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"ayanamsa": ayanamsa,
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})
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planet_lons = {
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name: data["lon"]
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for name, data in chart.get("planets", {}).items()
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if name in {"Sun", "Moon", "Mars", "Mercury", "Jupiter", "Venus", "Saturn", "Rahu", "Ketu"}
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}
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asc_lon = chart["ascendant"]["lon"]
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vargas = varga.calc_all_vargas(planet_lons, asc_lon, divisions=[4, 9, 10, 24, 30, 60])
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arudha = jaimini.calc_arudha_padas(int(asc_lon // 30), planet_lons)
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padas = arudha.get("padas", {})
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upapada = arudha.get("upapada", {})
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return {
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"ascendant": {
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"lon": round(asc_lon, 6),
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"sign": chart["ascendant"].get("sign"),
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"degree_in_sign": chart["ascendant"].get("degree_in_sign"),
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},
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"varga_lagna": {
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**{
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key: value.get("Ascendant", {})
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for key, value in vargas.items()
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},
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**{
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f"D{division}": value.get("Ascendant", {})
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for division in (4, 9, 10, 24, 30)
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for key, value in vargas.items()
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if key.startswith(f"D{division}_")
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},
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},
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"arudha": {
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"A7": padas.get("A7", {}),
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"A10": padas.get("A10", {}),
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"UL": upapada,
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},
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"kp_cusps": _kp_cusp_snapshot(chart),
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}
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def _kp_cusp_snapshot(chart: dict[str, Any]) -> dict[str, Any]:
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import kp_system
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snapshot = {}
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for house_key in ("house_1", "house_4", "house_7", "house_10"):
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house = chart.get("houses", {}).get(house_key, {})
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degree = house.get("cusp_degree")
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if degree is None:
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continue
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lords = kp_system.get_kp_lords(float(degree))
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snapshot[house_key] = {
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"cusp_degree": round(float(degree) % 360, 6),
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"sign": lords.get("sign"),
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"rasi_lord": lords.get("rasi_lord"),
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"nakshatra": lords.get("nakshatra"),
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"nakshatra_lord": lords.get("nakshatra_lord"),
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"sub_lord": lords.get("sub_lord"),
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"sub_sub_lord": lords.get("sub_sub_lord"),
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}
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return snapshot
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def build_questionnaire(
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birth_time: str,
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uncertainty_minutes: int = 30,
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step_minutes: int = 1,
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*,
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lat: float | None = None,
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lon: float | None = None,
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tz: float | None = None,
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ayanamsa: str = "lahiri",
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) -> dict[str, Any]:
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questions = build_questions()
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return {
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"scope": "active_birth_time_rectification_questionnaire",
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"schema_version": 1,
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"candidate_scan": _candidate_scan(
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_parse_time(birth_time),
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uncertainty_minutes,
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step_minutes,
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lat=lat,
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lon=lon,
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tz=tz,
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ayanamsa=ayanamsa,
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),
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"workflow": [
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"candidate_time_scan",
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"varga_arudha_kp_sensitivity_diff",
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"high_information_question_generation",
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"multiple_choice_user_answers",
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"dynamic_candidate_cluster_scoring",
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"next_round_question_selection",
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],
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"rounds": {
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"1": "coarse screen",
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"2": "domain follow-up",
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"3": "fine confirmation",
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},
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"sensitivity_layers": ["D9", "D10", "D24", "D30", "D60", "D4", "UL", "A7", "A10", "KP_cusp", "Vimshottari", "Narayana", "Chara"],
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"questions": questions,
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"boundary": "Question generation only; final rectification requires scoring answers against actual candidate chart differences.",
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}
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def main() -> int:
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parser = argparse.ArgumentParser(description=__doc__)
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parser.add_argument("--birth-time", required=True, help="Approximate local birth time, YYYY-MM-DD HH:MM")
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parser.add_argument("--uncertainty-minutes", type=int, default=30)
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parser.add_argument("--step-minutes", type=int, default=1)
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parser.add_argument("--answers-json", default="", help="Optional JSON object mapping question id to A/B/C/D")
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parser.add_argument("--pretty", action="store_true")
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args = parser.parse_args()
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questionnaire = build_questionnaire(args.birth_time, args.uncertainty_minutes, args.step_minutes)
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report = score_answers(questionnaire, json.loads(args.answers_json)) if args.answers_json else questionnaire
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print(json.dumps(report, ensure_ascii=False, indent=2 if args.pretty else None))
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return 0
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if __name__ == "__main__":
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raise SystemExit(main())
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