"""Test analytics service logic without database."""
import sys
sys.path.insert(0, '.')

from app.services.analytics import (
    compute_experience_stats,
    find_similar_cases,
    compute_trend_data,
    compute_pareto_frontier,
    compute_sensitivity,
    get_metric_defs,
)

# Test data
mock_cases = [
    {
        "id": 1, "topology": "SSSR", "source_plan_id": "plan-001",
        "params": {"Airgap": 1.0, "RMSCurrent": 21, "MagnetThickness": 5},
        "metrics": {"tavg_nm": 2.5, "efficiency_pct": 85.2, "ripple_pct": 3.5},
        "conclusion": "Good baseline", "tags": ["baseline", "sssr"], "rating": 4,
    },
    {
        "id": 2, "topology": "SSSR", "source_plan_id": "plan-002",
        "params": {"Airgap": 1.2, "RMSCurrent": 21, "MagnetThickness": 5},
        "metrics": {"tavg_nm": 2.3, "efficiency_pct": 84.8, "ripple_pct": 3.2},
        "conclusion": "Larger airgap reduces torque", "tags": ["airgap-study"], "rating": 3,
    },
    {
        "id": 3, "topology": "DRSS", "source_plan_id": "plan-003",
        "params": {"Airgap": 1.0, "RMSCurrent": 25, "MagnetThickness": 6},
        "metrics": {"tavg_nm": 3.1, "efficiency_pct": 87.5, "ripple_pct": 2.8},
        "conclusion": "DRSS higher torque", "tags": ["drss", "high-torque"], "rating": 5,
    },
]

mock_results = [
    {"status": "OK", "params": {"Airgap": 0.8, "RMSCurrent": 20}, "metrics": {"tavg_nm": 2.8, "efficiency_pct": 86.0, "total_losses_w": 50}},
    {"status": "OK", "params": {"Airgap": 1.0, "RMSCurrent": 20}, "metrics": {"tavg_nm": 2.5, "efficiency_pct": 85.2, "total_losses_w": 48}},
    {"status": "OK", "params": {"Airgap": 1.2, "RMSCurrent": 20}, "metrics": {"tavg_nm": 2.3, "efficiency_pct": 84.8, "total_losses_w": 45}},
    {"status": "OK", "params": {"Airgap": 1.0, "RMSCurrent": 25}, "metrics": {"tavg_nm": 3.0, "efficiency_pct": 86.5, "total_losses_w": 55}},
    {"status": "FAILED", "params": {"Airgap": 1.5, "RMSCurrent": 20}, "metrics": {}, "error_message": "convergence failed"},
]

print("=== 1. Metric Definitions ===")
metrics = get_metric_defs()
print(f"Total metrics: {len(metrics)}")
for m in metrics[:3]:
    print(f"  {m['key']}: {m['label']} ({m['unit']})")

print("\n=== 2. Experience Stats ===")
stats = compute_experience_stats(mock_cases)
print(f"Total: {stats['total']}")
print(f"Topology: {stats['topology_distribution']}")
print(f"Avg rating: {stats['avg_rating']}")
print(f"Param coverage: {stats['param_coverage']}")
print(f"Metric ranges keys: {list(stats['metric_ranges'].keys())}")

print("\n=== 3. Similar Case Search ===")
target = {"Airgap": 1.0, "RMSCurrent": 21}
similar = find_similar_cases(target, mock_cases, top_k=3, tolerance=0.5)
print(f"Found {len(similar)} similar cases for {target}")
for s in similar:
    print(f"  Case #{s['id']}: similarity={s['similarity_score']}, shared={s['shared_params']}")

print("\n=== 4. Trend Data ===")
trend = compute_trend_data(mock_results, "Airgap", "tavg_nm")
print(f"X: {trend['x_key']}, Y: {trend['y_key']}")
print(f"Points: {trend['points']}")
print(f"Stats: {trend['stats']}")

print("\n=== 5. Pareto Frontier ===")
pareto = compute_pareto_frontier(mock_results, x_metric="total_losses_w", y_metric="efficiency_pct")
print(f"Total points: {pareto['total_count']}, Pareto points: {pareto['pareto_count']}")
for p in pareto['pareto_points']:
    print(f"  losses={p['x']}W, eff={p['y']}%")

print("\n=== 6. Parameter Sensitivity ===")
sens = compute_sensitivity(mock_results, "tavg_nm")
print(f"Params analyzed: {len(sens)}")
for s in sens:
    print(f"  {s['param']}: corr={s['correlation']} ({s['direction']})")

print("\n=== ALL TESTS PASSED ===")