"""P4-M4: convergence-chart data source (search state points_history).

Run:  python scripts/test_p4_m4_convergence.py   (exit 0 = PASS)

Verifies get_state_summary() now exposes per-evaluated-point history
{id, batch_id, params, objective, feasible} consumed by the frontend
convergence chart, plus the API response model carries the field.
"""
import os
import sys

_ROOT = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
_BACKEND = os.path.join(_ROOT, "web", "backend")
sys.path.insert(0, _BACKEND)
sys.path.insert(0, _ROOT)  # l0 re-export resolves src.afmcore.l0.prescreening

from app.services.feasibility_search import (  # noqa: E402
    FeasibilityFirstSearch, ParameterRange,
)
from app.services.l0_prescreening import L0PreScreeningEngine  # noqa: E402

search = FeasibilityFirstSearch(
    parameters=[
        ParameterRange(name="airgap_mm", min_value=0.5, max_value=2.0),
        ParameterRange(name="magnet_thickness_mm", min_value=3.0, max_value=8.0),
    ],
    l0_engine=L0PreScreeningEngine(),
    total_budget=40,
    batch_size=4,
    initial_samples=8,
    objective_metric="tavg_nm",
    objective_direction="maximize",
    seed=42,
)
initial = search.generate_initial_batch()
assert len(initial) > 0, "initial batch empty"

# 1) empty points_history before results
sm = search.get_state_summary()
assert sm["points_history"] == [], sm["points_history"]
print("[1] points_history empty before any result OK")

# 2) report results for every initial point (batch 0 = initial LHS batch)
for p in initial:
    search.report_result(p.id, {"tavg_nm": float(p.id) * 0.1 + 1.0}, "ok")
sm = search.get_state_summary()
ph = sm["points_history"]
assert len(ph) == len(initial), (len(ph), len(initial))
assert all(h["objective"] is not None for h in ph)
assert all(h["batch_id"] == 0 for h in ph)
assert all(h["feasible"] is True for h in ph)
print("[2] points_history populated after initial-batch results OK (%d pts)" % len(ph))

# 3) second batch keeps history cumulative + batch_id increments to 1
batch2 = search.select_next_batch()
for p in batch2:
    search.report_result(p.id, {"tavg_nm": 3.0}, "ok")
sm = search.get_state_summary()
ph2 = sm["points_history"]
assert len(ph2) == len(initial) + len(batch2)
assert any(h["batch_id"] == 1 for h in ph2)
print("[3] history cumulative across batches OK (%d pts, batches=%s)"
      % (len(ph2), sorted({h["batch_id"] for h in ph2})))

# 4) infeasible point still listed with feasible=False
batch3 = search.select_next_batch()
for i, p in enumerate(batch3):
    search.report_result(p.id, {"tavg_nm": 0.0}, "infeasible")
sm = search.get_state_summary()
ph3 = sm["points_history"]
assert any(h["feasible"] is False for h in ph3), "infeasible point missing"
print("[4] infeasible points flagged OK")

# 5) response-model wiring carries the field (import validates schema)
from app.routers.search import SearchStateResponse  # noqa: E402
resp = SearchStateResponse(**sm)
assert isinstance(resp.points_history, list) and len(resp.points_history) == len(ph3)
print("[5] SearchStateResponse carries points_history OK")

print("\nALL P4-M4 CONVERGENCE-DATA TESTS PASSED")