"""Positive control for the angle-timing A1 pipeline (Claude acceptance 2026-10-01).

Plants three day-precision events per case on dates when transit Saturn sits within
0.15 deg of the TRUE natal ascendant, then ranks the truth minute with the same
ranks_for() used by A1. A working pipeline must rank it near 0; moving the same
dates by whole years must return it to chance (~0.5).

    PYTHONHASHSEED=0 python3 scripts/research/angle_timing_positive_control.py
"""
import sys, statistics, random
from datetime import date, timedelta
sys.path.insert(0, ".")
import swisseph as swe
from scripts.research import angle_timing_lib as at
from scripts.research.angle_timing_research import ranks_for
cases = at.load_cases()
res = {"real": [], "planted": [], "planted_shuffled_years": []}
rng = random.Random(7)
for case in cases:
    ch = at.CaseChart(case)
    asc_truth = float(ch.asc[at.RADIUS_MAX])
    # find Saturn conjunction dates with the TRUE natal ascendant (tropical, both sides)
    d = ch.local.date() + timedelta(days=400); hits = []
    prev = None
    while d < date(2026, 9, 1) and len(hits) < 3:
        jd = swe.julday(d.year, d.month, d.day, 12.0)
        s = float(at.aspect_sep(__import__("numpy").array([asc_truth]), at.planet_lon(jd, swe.SATURN))[0])
        if s < 0.15 and (not hits or (d - hits[-1]).days > 200): hits.append(d)
        d += timedelta(days=2)
    if not hits:
        continue
    planted = [{"id": f"p{i}", "domain": "career", "precision": "day", "date": h.isoformat()} for i, h in enumerate(hits)]
    # same dates moved by random whole years (keeps precision, breaks the alignment)
    moved = [{**e, "date": (date.fromisoformat(e["date"]) + timedelta(days=365 * rng.choice([-3, -2, 2, 3]))).isoformat()} for e in planted]
    moved = [e for e in moved if ch.local.date() < date.fromisoformat(e["date"]) < date(2026, 9, 1)]
    for key, evs in (("planted", planted), ("planted_shuffled_years", moved)):
        r = ranks_for(ch, evs)
        res[key].append(r["TR"]["1.0"]["30"])
    res["real"].append(ranks_for(ch, None)["TR"]["1.0"]["30"])
for k, v in res.items():
    print(f"{k:24s} n={len(v):2d} mean truth rank (TR, 1°, ±30) = {statistics.mean(v):.3f}   share with rank<0.15: {sum(1 for x in v if x < 0.15)/len(v):.2f}")