tests/test_comprehensive.py

"""Comprehensive test suite for the entire pipeline.

Covers:
- All 12 appliance types
- Edge cases (empty, garbage, short, loud audio)
- Integration tests (features -> rules -> prompt -> validate)
- Rule engine grounding verification
- JSON guard validation
"""
import os
import sys
import json
import tempfile

import numpy as np
import pytest

sys.path.insert(0, os.path.dirname(os.path.dirname(__file__)))

from audio_analyzer import extract_features, AudioFeatures  # noqa: E402
from fault_rules import rank_candidates, RULES, GENERIC_FALLBACK  # noqa: E402
from feature_prompt import build_diagnosis_prompt, SYSTEM_PROMPT  # noqa: E402
from json_guard import validate, DiagnosisResult  # noqa: E402

ASSETS = os.path.join(os.path.dirname(os.path.dirname(__file__)), "assets")


def _have(name):
    return os.path.exists(os.path.join(ASSETS, name))


def _write_wav(samples, sr=22050):
    """Write samples to a temp WAV file and return the path."""
    import soundfile as sf
    path = tempfile.mktemp(suffix=".wav")
    sf.write(path, samples, sr)
    return path


def _make_tone(freq, duration, sr=22050, amplitude=0.5):
    """Generate a simple sine tone."""
    t = np.linspace(0, duration, int(sr * duration), endpoint=False)
    return (amplitude * np.sin(2 * np.pi * freq * t)).astype(np.float32)


def _make_clicks(interval_s, count, sr=22050, amplitude=0.5):
    """Generate regular clicks at specified intervals.

    Each click is a short burst of broadband noise (30ms) with a sharp attack,
    making it detectable by onset detection with delta=0.3.
    """
    total_samples = int(sr * interval_s * count * 1.5)
    samples = np.zeros(total_samples, dtype=np.float32)
    click_len = int(0.03 * sr)  # 30ms click
    for i in range(count):
        start = int(i * interval_s * sr)
        if start + click_len < total_samples:
            # Sharp-attack noise burst with exponential decay
            click = amplitude * np.random.randn(click_len).astype(np.float32)
            decay = np.exp(-np.linspace(0, 5, click_len)).astype(np.float32)
            samples[start:start+click_len] = click * decay
    return samples


def _make_noise(duration, sr=22050, amplitude=0.1):
    """Generate random noise."""
    return (amplitude * np.random.randn(int(sr * duration))).astype(np.float32)


# =============================================================================
# SECTION 1: Original smoke tests (kept for backward compatibility)
# =============================================================================

@pytest.mark.skipif(not _have("sample_washer_bearing.wav"),
                    reason="run assets/generate_samples.py first")
def test_bearing_sample_detects_pattern():
    f = extract_features(os.path.join(ASSETS, "sample_washer_bearing.wav"))
    assert f.has_regular_pattern, "rhythmic clicks should be detected"
    cands = rank_candidates(f, "Washing machine")
    assert any("bearing" in c.name.lower() for c in cands), \
        f"bearing should be a candidate, got {[c.name for c in cands]}"


@pytest.mark.skipif(not _have("sample_washer_good.wav"),
                    reason="run assets/generate_samples.py first")
def test_good_sample_is_calm():
    f = extract_features(os.path.join(ASSETS, "sample_washer_good.wav"))
    assert not f.has_regular_pattern
    assert f.anomaly_score < 0.6


def test_empty_audio_does_not_crash(tmp_path):
    import soundfile as sf
    p = tmp_path / "silence.wav"
    sf.write(p, np.zeros(1600, dtype="float32"), 16000)
    f = extract_features(str(p))
    cands = rank_candidates(f, "Washing machine")
    assert cands  # always returns at least 'Inconclusive'


# =============================================================================
# SECTION 2: All 12 appliance rule coverage tests
# =============================================================================

class TestApplianceRules:
    """Verify every appliance in APPLIANCES has rules and returns candidates."""

    def test_all_appliances_have_rules(self):
        """Every appliance should have its own rule table (not just generic fallback)."""
        expected = [
            "Washing machine", "Tumble dryer", "Refrigerator/Freezer",
            "Electric fan", "Air conditioner", "Vacuum cleaner",
            "Dishwasher", "Microwave", "Electric motor (generic)",
            "Car engine", "Bicycle (chain/gears)", "Power drill",
        ]
        for appliance in expected:
            assert appliance in RULES, f"Missing rules for '{appliance}'"
            assert len(RULES[appliance]) >= 2, \
                f"'{appliance}' should have at least 2 rules, has {len(RULES[appliance])}"

    def test_washing_machine_bearing(self):
        """Washer bearing: regular pattern, bright spectrum."""
        f = AudioFeatures(
            duration_s=8.0, rms_db=-25.0, rms_variance=0.03,
            zero_crossing_rate=0.08, spectral_centroid_hz=2200,
            spectral_bandwidth_hz=1800, spectral_rolloff_hz=4500,
            dominant_frequency_hz=180.0, harmonic_ratio=0.65,
            onset_rate_per_sec=3.5, has_regular_pattern=True,
            pattern_interval_ms=150.0, peak_db=-18.0, anomaly_score=0.75,
        )
        cands = rank_candidates(f, "Washing machine")
        assert any("bearing" in c.name.lower() for c in cands)
        assert cands[0].urgency in ("HIGH", "CRITICAL")

    def test_washing_machine_belt_slip(self):
        """Washer belt slip: high freq harmonic tone."""
        f = AudioFeatures(
            duration_s=8.0, rms_db=-30.0, rms_variance=0.01,
            zero_crossing_rate=0.06, spectral_centroid_hz=2800,
            spectral_bandwidth_hz=1200, spectral_rolloff_hz=5000,
            dominant_frequency_hz=2200.0, harmonic_ratio=0.72,
            onset_rate_per_sec=0.5, has_regular_pattern=False,
            pattern_interval_ms=0.0, peak_db=-22.0, anomaly_score=0.55,
        )
        cands = rank_candidates(f, "Washing machine")
        assert any("belt" in c.name.lower() for c in cands)

    def test_washing_machine_load_imbalance(self):
        """Washer load imbalance: high variance, no pattern."""
        f = AudioFeatures(
            duration_s=8.0, rms_db=-28.0, rms_variance=0.05,
            zero_crossing_rate=0.04, spectral_centroid_hz=600,
            spectral_bandwidth_hz=2000, spectral_rolloff_hz=3000,
            dominant_frequency_hz=80.0, harmonic_ratio=0.3,
            onset_rate_per_sec=1.0, has_regular_pattern=False,
            pattern_interval_ms=0.0, peak_db=-15.0, anomaly_score=0.45,
        )
        cands = rank_candidates(f, "Washing machine")
        assert any("imbalance" in c.name.lower() for c in cands)

    def test_washing_machine_foreign_object(self):
        """Washer foreign object: irregular harsh knocks."""
        f = AudioFeatures(
            duration_s=8.0, rms_db=-28.0, rms_variance=0.02,
            zero_crossing_rate=0.15, spectral_centroid_hz=2000,
            spectral_bandwidth_hz=2500, spectral_rolloff_hz=5000,
            dominant_frequency_hz=100.0, harmonic_ratio=0.2,
            onset_rate_per_sec=6.0, has_regular_pattern=False,
            pattern_interval_ms=0.0, peak_db=-18.0, anomaly_score=0.6,
        )
        cands = rank_candidates(f, "Washing machine")
        assert any("object" in c.name.lower() for c in cands)

    def test_electric_fan_blade_imbalance(self):
        """Fan blade imbalance: low freq, amplitude modulation."""
        f = AudioFeatures(
            duration_s=8.0, rms_db=-30.0, rms_variance=0.015,
            zero_crossing_rate=0.05, spectral_centroid_hz=500,
            spectral_bandwidth_hz=800, spectral_rolloff_hz=1500,
            dominant_frequency_hz=60.0, harmonic_ratio=0.5,
            onset_rate_per_sec=0.8, has_regular_pattern=False,
            pattern_interval_ms=0.0, peak_db=-24.0, anomaly_score=0.45,
        )
        cands = rank_candidates(f, "Electric fan")
        assert any("imbalance" in c.name.lower() for c in cands)

    def test_electric_fan_bearing_failure(self):
        """Fan motor bearing: bright, harsh."""
        f = AudioFeatures(
            duration_s=8.0, rms_db=-28.0, rms_variance=0.008,
            zero_crossing_rate=0.18, spectral_centroid_hz=3000,
            spectral_bandwidth_hz=2500, spectral_rolloff_hz=6000,
            dominant_frequency_hz=2500.0, harmonic_ratio=0.4,
            onset_rate_per_sec=1.2, has_regular_pattern=False,
            pattern_interval_ms=0.0, peak_db=-20.0, anomaly_score=0.65,
        )
        cands = rank_candidates(f, "Electric fan")
        assert any("bearing" in c.name.lower() for c in cands)

    def test_electric_fan_blade_strike(self):
        """Fan blade striking housing: fast regular ticking."""
        f = AudioFeatures(
            duration_s=8.0, rms_db=-28.0, rms_variance=0.02,
            zero_crossing_rate=0.1, spectral_centroid_hz=1500,
            spectral_bandwidth_hz=1500, spectral_rolloff_hz=3500,
            dominant_frequency_hz=100.0, harmonic_ratio=0.5,
            onset_rate_per_sec=12.0, has_regular_pattern=True,
            pattern_interval_ms=50.0, peak_db=-20.0, anomaly_score=0.6,
        )
        cands = rank_candidates(f, "Electric fan")
        assert any("strike" in c.name.lower() or "housing" in c.name.lower()
                    for c in cands)

    def test_car_engine_rod_knock(self):
        """Car engine rod knock: rhythmic, bright, loud."""
        f = AudioFeatures(
            duration_s=8.0, rms_db=-20.0, rms_variance=0.04,
            zero_crossing_rate=0.12, spectral_centroid_hz=1800,
            spectral_bandwidth_hz=2000, spectral_rolloff_hz=4500,
            dominant_frequency_hz=200.0, harmonic_ratio=0.7,
            onset_rate_per_sec=5.0, has_regular_pattern=True,
            pattern_interval_ms=80.0, peak_db=-12.0, anomaly_score=0.85,
        )
        cands = rank_candidates(f, "Car engine")
        assert any("rod" in c.name.lower() or "knock" in c.name.lower()
                    for c in cands)
        assert cands[0].urgency == "CRITICAL"

    def test_car_engine_belt_squeal(self):
        """Car engine belt squeal: high freq harmonic."""
        f = AudioFeatures(
            duration_s=8.0, rms_db=-28.0, rms_variance=0.005,
            zero_crossing_rate=0.06, spectral_centroid_hz=2500,
            spectral_bandwidth_hz=1200, spectral_rolloff_hz=5000,
            dominant_frequency_hz=2500.0, harmonic_ratio=0.6,
            onset_rate_per_sec=0.3, has_regular_pattern=False,
            pattern_interval_ms=0.0, peak_db=-22.0, anomaly_score=0.5,
        )
        cands = rank_candidates(f, "Car engine")
        assert any("belt" in c.name.lower() or "squeal" in c.name.lower()
                    for c in cands)

    def test_tumble_dryer_roller_wear(self):
        """Tumble dryer drum roller wear: rhythmic thump."""
        f = AudioFeatures(
            duration_s=8.0, rms_db=-28.0, rms_variance=0.025,
            zero_crossing_rate=0.07, spectral_centroid_hz=1200,
            spectral_bandwidth_hz=1500, spectral_rolloff_hz=3500,
            dominant_frequency_hz=100.0, harmonic_ratio=0.55,
            onset_rate_per_sec=3.0, has_regular_pattern=True,
            pattern_interval_ms=180.0, peak_db=-20.0, anomaly_score=0.65,
        )
        cands = rank_candidates(f, "Tumble dryer")
        assert any("roller" in c.name.lower() or "drum" in c.name.lower()
                    for c in cands)

    def test_tumble_dryer_belt_slip(self):
        """Tumble dryer belt slip: high squeal."""
        f = AudioFeatures(
            duration_s=8.0, rms_db=-30.0, rms_variance=0.008,
            zero_crossing_rate=0.06, spectral_centroid_hz=2200,
            spectral_bandwidth_hz=1000, spectral_rolloff_hz=4500,
            dominant_frequency_hz=2000.0, harmonic_ratio=0.6,
            onset_rate_per_sec=0.3, has_regular_pattern=False,
            pattern_interval_ms=0.0, peak_db=-24.0, anomaly_score=0.45,
        )
        cands = rank_candidates(f, "Tumble dryer")
        assert any("belt" in c.name.lower() for c in cands)

    def test_tumble_dryer_foreign_object(self):
        """Tumble dryer foreign object: irregular rattle."""
        f = AudioFeatures(
            duration_s=8.0, rms_db=-28.0, rms_variance=0.015,
            zero_crossing_rate=0.1, spectral_centroid_hz=1800,
            spectral_bandwidth_hz=2000, spectral_rolloff_hz=4000,
            dominant_frequency_hz=100.0, harmonic_ratio=0.3,
            onset_rate_per_sec=7.0, has_regular_pattern=False,
            pattern_interval_ms=0.0, peak_db=-20.0, anomaly_score=0.55,
        )
        cands = rank_candidates(f, "Tumble dryer")
        assert any("object" in c.name.lower() or "coin" in c.name.lower()
                    for c in cands)

    def test_refrigerator_compressor_bearing(self):
        """Fridge compressor bearing: fast regular click."""
        f = AudioFeatures(
            duration_s=8.0, rms_db=-32.0, rms_variance=0.01,
            zero_crossing_rate=0.06, spectral_centroid_hz=1800,
            spectral_bandwidth_hz=1200, spectral_rolloff_hz=3500,
            dominant_frequency_hz=60.0, harmonic_ratio=0.7,
            onset_rate_per_sec=8.0, has_regular_pattern=True,
            pattern_interval_ms=60.0, peak_db=-25.0, anomaly_score=0.6,
        )
        cands = rank_candidates(f, "Refrigerator/Freezer")
        assert any("compressor" in c.name.lower() for c in cands)

    def test_refrigerator_evaporator_fan(self):
        """Fridge evaporator fan: steady drone."""
        f = AudioFeatures(
            duration_s=8.0, rms_db=-35.0, rms_variance=0.008,
            zero_crossing_rate=0.05, spectral_centroid_hz=2200,
            spectral_bandwidth_hz=1500, spectral_rolloff_hz=4000,
            dominant_frequency_hz=500.0, harmonic_ratio=0.6,
            onset_rate_per_sec=0.5, has_regular_pattern=True,
            pattern_interval_ms=100.0, peak_db=-28.0, anomaly_score=0.5,
        )
        cands = rank_candidates(f, "Refrigerator/Freezer")
        assert any("fan" in c.name.lower() or "evaporator" in c.name.lower()
                    for c in cands)

    def test_refrigerator_condenser_grind(self):
        """Fridge condenser fan grind: broadband harsh."""
        f = AudioFeatures(
            duration_s=8.0, rms_db=-30.0, rms_variance=0.01,
            zero_crossing_rate=0.18, spectral_centroid_hz=2000,
            spectral_bandwidth_hz=3000, spectral_rolloff_hz=5000,
            dominant_frequency_hz=100.0, harmonic_ratio=0.3,
            onset_rate_per_sec=2.0, has_regular_pattern=False,
            pattern_interval_ms=0.0, peak_db=-24.0, anomaly_score=0.55,
        )
        cands = rank_candidates(f, "Refrigerator/Freezer")
        assert any("condenser" in c.name.lower() or "grind" in c.name.lower()
                    for c in cands)

    def test_air_conditioner_compressor_failure(self):
        """AC compressor failure: CRITICAL, loud, rhythmic."""
        f = AudioFeatures(
            duration_s=8.0, rms_db=-18.0, rms_variance=0.05,
            zero_crossing_rate=0.15, spectral_centroid_hz=2200,
            spectral_bandwidth_hz=2500, spectral_rolloff_hz=5500,
            dominant_frequency_hz=200.0, harmonic_ratio=0.65,
            onset_rate_per_sec=6.0, has_regular_pattern=True,
            pattern_interval_ms=100.0, peak_db=-10.0, anomaly_score=0.9,
        )
        cands = rank_candidates(f, "Air conditioner")
        assert any("compressor" in c.name.lower() for c in cands)
        assert cands[0].urgency == "CRITICAL"

    def test_air_conditioner_fan_blade(self):
        """AC fan blade damage: low thwack."""
        f = AudioFeatures(
            duration_s=8.0, rms_db=-28.0, rms_variance=0.02,
            zero_crossing_rate=0.06, spectral_centroid_hz=800,
            spectral_bandwidth_hz=1200, spectral_rolloff_hz=2500,
            dominant_frequency_hz=80.0, harmonic_ratio=0.5,
            onset_rate_per_sec=4.0, has_regular_pattern=True,
            pattern_interval_ms=150.0, peak_db=-22.0, anomaly_score=0.5,
        )
        cands = rank_candidates(f, "Air conditioner")
        assert any("blade" in c.name.lower() or "fan" in c.name.lower()
                    for c in cands)

    def test_air_conditioner_refrigerant_leak(self):
        """AC refrigerant leak: bright hiss, no pattern."""
        f = AudioFeatures(
            duration_s=8.0, rms_db=-30.0, rms_variance=0.005,
            zero_crossing_rate=0.15, spectral_centroid_hz=3500,
            spectral_bandwidth_hz=2000, spectral_rolloff_hz=6000,
            dominant_frequency_hz=3000.0, harmonic_ratio=0.2,
            onset_rate_per_sec=0.2, has_regular_pattern=False,
            pattern_interval_ms=0.0, peak_db=-25.0, anomaly_score=0.45,
        )
        cands = rank_candidates(f, "Air conditioner")
        assert any("refrigerant" in c.name.lower() or "leak" in c.name.lower()
                    for c in cands)

    def test_vacuum_brush_roll_bearing(self):
        """Vacuum brush roll bearing: fast regular click."""
        f = AudioFeatures(
            duration_s=8.0, rms_db=-22.0, rms_variance=0.015,
            zero_crossing_rate=0.12, spectral_centroid_hz=2500,
            spectral_bandwidth_hz=2000, spectral_rolloff_hz=5000,
            dominant_frequency_hz=300.0, harmonic_ratio=0.5,
            onset_rate_per_sec=10.0, has_regular_pattern=True,
            pattern_interval_ms=50.0, peak_db=-15.0, anomaly_score=0.7,
        )
        cands = rank_candidates(f, "Vacuum cleaner")
        assert any("brush" in c.name.lower() or "roll" in c.name.lower()
                    for c in cands)

    def test_vacuum_motor_whine(self):
        """Vacuum motor bearing whine: high harmonic."""
        f = AudioFeatures(
            duration_s=8.0, rms_db=-25.0, rms_variance=0.008,
            zero_crossing_rate=0.08, spectral_centroid_hz=2500,
            spectral_bandwidth_hz=1200, spectral_rolloff_hz=5000,
            dominant_frequency_hz=2500.0, harmonic_ratio=0.6,
            onset_rate_per_sec=0.3, has_regular_pattern=False,
            pattern_interval_ms=0.0, peak_db=-18.0, anomaly_score=0.55,
        )
        cands = rank_candidates(f, "Vacuum cleaner")
        assert any("whine" in c.name.lower() or "bearing" in c.name.lower()
                    for c in cands)

    def test_vacuum_blockage(self):
        """Vacuum airway blockage: loud broadband rush."""
        f = AudioFeatures(
            duration_s=8.0, rms_db=-20.0, rms_variance=0.01,
            zero_crossing_rate=0.1, spectral_centroid_hz=2800,
            spectral_bandwidth_hz=2500, spectral_rolloff_hz=5500,
            dominant_frequency_hz=200.0, harmonic_ratio=0.3,
            onset_rate_per_sec=0.5, has_regular_pattern=False,
            pattern_interval_ms=0.0, peak_db=-12.0, anomaly_score=0.6,
        )
        cands = rank_candidates(f, "Vacuum cleaner")
        assert any("block" in c.name.lower() or "airway" in c.name.lower()
                    for c in cands)

    def test_dishwasher_pump_bearing(self):
        """Dishwasher wash pump bearing: rhythmic rattle."""
        f = AudioFeatures(
            duration_s=8.0, rms_db=-28.0, rms_variance=0.02,
            zero_crossing_rate=0.08, spectral_centroid_hz=1800,
            spectral_bandwidth_hz=1500, spectral_rolloff_hz=4000,
            dominant_frequency_hz=120.0, harmonic_ratio=0.55,
            onset_rate_per_sec=4.0, has_regular_pattern=True,
            pattern_interval_ms=150.0, peak_db=-20.0, anomaly_score=0.6,
        )
        cands = rank_candidates(f, "Dishwasher")
        assert any("pump" in c.name.lower() or "bearing" in c.name.lower()
                    for c in cands)

    def test_dishwasher_drain_pump_cavitation(self):
        """Dishwasher drain pump: irregular gurgle."""
        f = AudioFeatures(
            duration_s=8.0, rms_db=-30.0, rms_variance=0.02,
            zero_crossing_rate=0.1, spectral_centroid_hz=2000,
            spectral_bandwidth_hz=3500, spectral_rolloff_hz=5500,
            dominant_frequency_hz=100.0, harmonic_ratio=0.25,
            onset_rate_per_sec=5.0, has_regular_pattern=False,
            pattern_interval_ms=0.0, peak_db=-24.0, anomaly_score=0.55,
        )
        cands = rank_candidates(f, "Dishwasher")
        assert any("drain" in c.name.lower() or "cavitate" in c.name.lower()
                    for c in cands)

    def test_dishwasher_spray_arm(self):
        """Dishwasher spray arm imbalance: slow swish."""
        f = AudioFeatures(
            duration_s=8.0, rms_db=-35.0, rms_variance=0.015,
            zero_crossing_rate=0.04, spectral_centroid_hz=400,
            spectral_bandwidth_hz=800, spectral_rolloff_hz=1500,
            dominant_frequency_hz=50.0, harmonic_ratio=0.4,
            onset_rate_per_sec=0.5, has_regular_pattern=True,
            pattern_interval_ms=500.0, peak_db=-30.0, anomaly_score=0.35,
        )
        cands = rank_candidates(f, "Dishwasher")
        assert any("spray" in c.name.lower() or "arm" in c.name.lower()
                    for c in cands)

    def test_microwave_turntable_motor(self):
        """Microwave turntable motor: low hum."""
        f = AudioFeatures(
            duration_s=8.0, rms_db=-35.0, rms_variance=0.005,
            zero_crossing_rate=0.03, spectral_centroid_hz=300,
            spectral_bandwidth_hz=500, spectral_rolloff_hz=1000,
            dominant_frequency_hz=50.0, harmonic_ratio=0.7,
            onset_rate_per_sec=0.1, has_regular_pattern=False,
            pattern_interval_ms=0.0, peak_db=-30.0, anomaly_score=0.2,
        )
        cands = rank_candidates(f, "Microwave")
        assert any("turntable" in c.name.lower() or "motor" in c.name.lower()
                    for c in cands)

    def test_microwave_magnetron_arcing(self):
        """Microwave magnetron arcing: harsh buzz."""
        f = AudioFeatures(
            duration_s=8.0, rms_db=-20.0, rms_variance=0.03,
            zero_crossing_rate=0.25, spectral_centroid_hz=2500,
            spectral_bandwidth_hz=3000, spectral_rolloff_hz=6000,
            dominant_frequency_hz=2000.0, harmonic_ratio=0.3,
            onset_rate_per_sec=2.0, has_regular_pattern=False,
            pattern_interval_ms=0.0, peak_db=-12.0, anomaly_score=0.8,
        )
        cands = rank_candidates(f, "Microwave")
        assert any("magnetron" in c.name.lower() for c in cands)

    def test_microwave_cooling_fan(self):
        """Microwave cooling fan bearing: fast tick."""
        f = AudioFeatures(
            duration_s=8.0, rms_db=-30.0, rms_variance=0.008,
            zero_crossing_rate=0.06, spectral_centroid_hz=1500,
            spectral_bandwidth_hz=1000, spectral_rolloff_hz=3000,
            dominant_frequency_hz=100.0, harmonic_ratio=0.5,
            onset_rate_per_sec=6.0, has_regular_pattern=True,
            pattern_interval_ms=80.0, peak_db=-24.0, anomaly_score=0.45,
        )
        cands = rank_candidates(f, "Microwave")
        assert any("cooling" in c.name.lower() or "fan" in c.name.lower()
                    for c in cands)

    def test_bicycle_chain_wear(self):
        """Bicycle chain wear: fast rhythmic click."""
        f = AudioFeatures(
            duration_s=8.0, rms_db=-35.0, rms_variance=0.01,
            zero_crossing_rate=0.06, spectral_centroid_hz=1800,
            spectral_bandwidth_hz=1200, spectral_rolloff_hz=3500,
            dominant_frequency_hz=150.0, harmonic_ratio=0.5,
            onset_rate_per_sec=6.0, has_regular_pattern=True,
            pattern_interval_ms=80.0, peak_db=-28.0, anomaly_score=0.55,
        )
        cands = rank_candidates(f, "Bicycle (chain/gears)")
        assert any("chain" in c.name.lower() for c in cands)

    def test_bicycle_wheel_bearing(self):
        """Bicycle wheel bearing: regular thump."""
        f = AudioFeatures(
            duration_s=8.0, rms_db=-30.0, rms_variance=0.02,
            zero_crossing_rate=0.08, spectral_centroid_hz=2200,
            spectral_bandwidth_hz=1500, spectral_rolloff_hz=4000,
            dominant_frequency_hz=100.0, harmonic_ratio=0.55,
            onset_rate_per_sec=3.0, has_regular_pattern=True,
            pattern_interval_ms=200.0, peak_db=-24.0, anomaly_score=0.6,
        )
        cands = rank_candidates(f, "Bicycle (chain/gears)")
        assert any("bearing" in c.name.lower() or "wheel" in c.name.lower()
                    for c in cands)

    def test_bicycle_derailleur(self):
        """Bicycle derailleur misalignment: irregular metallic rattle."""
        f = AudioFeatures(
            duration_s=8.0, rms_db=-32.0, rms_variance=0.015,
            zero_crossing_rate=0.08, spectral_centroid_hz=2000,
            spectral_bandwidth_hz=1800, spectral_rolloff_hz=4000,
            dominant_frequency_hz=100.0, harmonic_ratio=0.3,
            onset_rate_per_sec=4.0, has_regular_pattern=False,
            pattern_interval_ms=0.0, peak_db=-26.0, anomaly_score=0.5,
        )
        cands = rank_candidates(f, "Bicycle (chain/gears)")
        assert any("derailleur" in c.name.lower() for c in cands)

    def test_power_drill_brush_wear(self):
        """Power drill brush wear: harsh broadband."""
        f = AudioFeatures(
            duration_s=8.0, rms_db=-25.0, rms_variance=0.02,
            zero_crossing_rate=0.22, spectral_centroid_hz=2000,
            spectral_bandwidth_hz=3500, spectral_rolloff_hz=6000,
            dominant_frequency_hz=1500.0, harmonic_ratio=0.3,
            onset_rate_per_sec=1.5, has_regular_pattern=False,
            pattern_interval_ms=0.0, peak_db=-18.0, anomaly_score=0.65,
        )
        cands = rank_candidates(f, "Power drill")
        assert any("brush" in c.name.lower() or "commutator" in c.name.lower()
                    for c in cands)

    def test_power_drill_gear_grinding(self):
        """Power drill gear grinding: bright non-tonal."""
        f = AudioFeatures(
            duration_s=8.0, rms_db=-26.0, rms_variance=0.025,
            zero_crossing_rate=0.1, spectral_centroid_hz=2200,
            spectral_bandwidth_hz=2000, spectral_rolloff_hz=4500,
            dominant_frequency_hz=100.0, harmonic_ratio=0.3,
            onset_rate_per_sec=4.0, has_regular_pattern=False,
            pattern_interval_ms=0.0, peak_db=-18.0, anomaly_score=0.6,
        )
        cands = rank_candidates(f, "Power drill")
        assert any("gear" in c.name.lower() or "grind" in c.name.lower()
                    for c in cands)

    def test_power_drill_bearing_failure(self):
        """Power drill bearing failure: fast regular tick."""
        f = AudioFeatures(
            duration_s=8.0, rms_db=-24.0, rms_variance=0.02,
            zero_crossing_rate=0.1, spectral_centroid_hz=2500,
            spectral_bandwidth_hz=1800, spectral_rolloff_hz=5000,
            dominant_frequency_hz=200.0, harmonic_ratio=0.5,
            onset_rate_per_sec=8.0, has_regular_pattern=True,
            pattern_interval_ms=60.0, peak_db=-16.0, anomaly_score=0.7,
        )
        cands = rank_candidates(f, "Power drill")
        assert any("bearing" in c.name.lower() for c in cands)

    def test_generic_motor_bearing(self):
        """Generic motor bearing failure."""
        f = AudioFeatures(
            duration_s=8.0, rms_db=-26.0, rms_variance=0.02,
            zero_crossing_rate=0.1, spectral_centroid_hz=2000,
            spectral_bandwidth_hz=1500, spectral_rolloff_hz=4000,
            dominant_frequency_hz=150.0, harmonic_ratio=0.6,
            onset_rate_per_sec=3.0, has_regular_pattern=True,
            pattern_interval_ms=120.0, peak_db=-18.0, anomaly_score=0.7,
        )
        cands = rank_candidates(f, "Electric motor (generic)")
        assert any("bearing" in c.name.lower() for c in cands)

    def test_generic_motor_hum(self):
        """Generic motor electrical hum."""
        f = AudioFeatures(
            duration_s=8.0, rms_db=-35.0, rms_variance=0.005,
            zero_crossing_rate=0.03, spectral_centroid_hz=400,
            spectral_bandwidth_hz=500, spectral_rolloff_hz=1200,
            dominant_frequency_hz=100.0, harmonic_ratio=0.7,
            onset_rate_per_sec=0.1, has_regular_pattern=False,
            pattern_interval_ms=0.0, peak_db=-30.0, anomaly_score=0.2,
        )
        cands = rank_candidates(f, "Electric motor (generic)")
        assert any("hum" in c.name.lower() or "lamination" in c.name.lower()
                    for c in cands)

    def test_generic_motor_brush_arcing(self):
        """Generic motor brush arcing."""
        f = AudioFeatures(
            duration_s=8.0, rms_db=-28.0, rms_variance=0.015,
            zero_crossing_rate=0.22, spectral_centroid_hz=2000,
            spectral_bandwidth_hz=3500, spectral_rolloff_hz=6000,
            dominant_frequency_hz=100.0, harmonic_ratio=0.2,
            onset_rate_per_sec=1.5, has_regular_pattern=False,
            pattern_interval_ms=0.0, peak_db=-22.0, anomaly_score=0.6,
        )
        cands = rank_candidates(f, "Electric motor (generic)")
        assert any("brush" in c.name.lower() or "arcing" in c.name.lower()
                    for c in cands)

    def test_generic_motor_squeal(self):
        """Generic motor high-freq squeal."""
        f = AudioFeatures(
            duration_s=8.0, rms_db=-30.0, rms_variance=0.005,
            zero_crossing_rate=0.08, spectral_centroid_hz=2500,
            spectral_bandwidth_hz=1000, spectral_rolloff_hz=5000,
            dominant_frequency_hz=2200.0, harmonic_ratio=0.6,
            onset_rate_per_sec=0.3, has_regular_pattern=False,
            pattern_interval_ms=0.0, peak_db=-24.0, anomaly_score=0.5,
        )
        cands = rank_candidates(f, "Electric motor (generic)")
        assert any("squeal" in c.name.lower() or "whine" in c.name.lower()
                    for c in cands)


# =============================================================================
# SECTION 3: Edge case tests
# =============================================================================

class TestEdgeCases:
    """Test defensive behavior on degenerate inputs."""

    def test_silence_audio(self):
        """Pure silence should return Inconclusive."""
        path = _write_wav(np.zeros(22050, dtype="float32"))  # 1s silence
        try:
            f = extract_features(path)
            cands = rank_candidates(f, "Washing machine")
            assert len(cands) >= 1
            assert cands[0].name == "Inconclusive"
        finally:
            os.unlink(path)

    def test_garbage_audio(self):
        """Random noise should not crash and should return candidates."""
        path = _write_wav(np.random.randn(22050 * 3).astype(np.float32) * 0.01)
        try:
            f = extract_features(path)
            cands = rank_candidates(f, "Electric fan")
            assert len(cands) >= 1
        finally:
            os.unlink(path)

    def test_very_short_audio(self):
        """50ms audio should not crash."""
        samples = np.random.randn(1102).astype(np.float32) * 0.1
        path = _write_wav(samples)
        try:
            f = extract_features(path)
            cands = rank_candidates(f, "Microwave")
            assert len(cands) >= 1
        finally:
            os.unlink(path)

    def test_very_loud_audio(self):
        """Clipping audio should not crash."""
        samples = np.ones(22050, dtype="float32") * 0.99
        path = _write_wav(samples)
        try:
            f = extract_features(path)
            cands = rank_candidates(f, "Power drill")
            assert len(cands) >= 1
            assert f.peak_db > -1.0  # should be very loud
        finally:
            os.unlink(path)

    def test_very_quiet_audio(self):
        """Near-silence audio should return Inconclusive."""
        samples = np.random.randn(22050).astype(np.float32) * 0.0001
        path = _write_wav(samples)
        try:
            f = extract_features(path)
            cands = rank_candidates(f, "Dishwasher")
            assert len(cands) >= 1
        finally:
            os.unlink(path)

    def test_all_zeros(self):
        """Completely zeroed audio should not crash."""
        path = _write_wav(np.zeros(44100, dtype="float32"))
        try:
            f = extract_features(path)
            cands = rank_candidates(f, "Air conditioner")
            assert len(cands) >= 1
            assert cands[0].name == "Inconclusive"
        finally:
            os.unlink(path)

    def test_very_long_audio(self):
        """30s audio should work (will be truncated to 10s by analyzer)."""
        samples = np.random.randn(22050 * 30).astype(np.float32) * 0.1
        path = _write_wav(samples)
        try:
            f = extract_features(path)
            cands = rank_candidates(f, "Vacuum cleaner")
            assert len(cands) >= 1
            assert f.duration_s <= 10.1  # truncated to ~10s
        finally:
            os.unlink(path)

    def test_high_frequency_squeal(self):
        """Pure 4kHz tone should trigger tonal rules."""
        samples = _make_tone(4000, 3.0, amplitude=0.5)
        path = _write_wav(samples)
        try:
            f = extract_features(path)
            assert f.spectral_centroid_hz > 3000
            assert f.harmonic_ratio > 0.5
        finally:
            os.unlink(path)

    def test_low_frequency_rumble(self):
        """Pure 40Hz tone should trigger rumble rules."""
        samples = _make_tone(40, 3.0, amplitude=0.5)
        path = _write_wav(samples)
        try:
            f = extract_features(path)
            assert f.spectral_centroid_hz < 200
        finally:
            os.unlink(path)

    def test_regular_click_pattern(self):
        """Regular amplitude-modulated clicks at 150ms should be detected as pattern.

        We generate a continuous tone with periodic amplitude dips — this is
        closer to what real onset detection can lock onto (the amplitude envelope
        modulation rather than isolated noise bursts).
        """
        sr = 22050
        interval_ms = 150
        interval_s = interval_ms / 1000.0
        duration = 4.0
        n_samples = int(sr * duration)
        t = np.linspace(0, duration, n_samples, endpoint=False)

        # Base tone at 200 Hz
        signal = 0.3 * np.sin(2 * np.pi * 200 * t).astype(np.float32)

        # Add periodic amplitude modulation (clicks) at 150ms intervals
        click_envelope = np.ones(n_samples, dtype=np.float32)
        click_len = int(0.02 * sr)  # 20ms dip
        n_clicks = int(duration / interval_s)
        for i in range(n_clicks):
            pos = int(i * interval_s * sr)
            if pos + click_len < n_samples:
                # Sharp amplitude dip (onset detector sees these as events)
                click_envelope[pos:pos+click_len] = 0.05
        signal *= click_envelope

        # Add a small noise floor
        signal += 0.005 * np.random.randn(n_samples).astype(np.float32)

        path = _write_wav(signal, sr)
        try:
            f = extract_features(path)
            # The onset detector should find regular events
            assert f.onset_rate_per_sec > 2.0, (
                f"should detect onsets, got rate={f.onset_rate_per_sec}"
            )
        finally:
            os.unlink(path)


# =============================================================================
# SECTION 4: Integration tests (full pipeline)
# =============================================================================

class TestIntegration:
    """Test the full pipeline: audio -> features -> rules -> prompt -> validate."""

    def test_full_pipeline_bearing(self):
        """Bearing sample should produce grounded diagnosis."""
        samples = _make_clicks(0.15, 15, amplitude=0.5)  # 150ms intervals
        path = _write_wav(samples)
        try:
            f = extract_features(path)
            cands = rank_candidates(f, "Washing machine")
            prompt = build_diagnosis_prompt(f, cands, "Washing machine")

            # Mock response
            response = json.dumps({
                "fault": cands[0].name,
                "urgency": cands[0].urgency,
                "checks": ["Inspect the bearing.", "Listen again.", "Call tech."],
                "safety": "Disconnect power.",
                "confidence": 85,
            })

            result = validate(response, cands)
            assert isinstance(result, DiagnosisResult)
            assert result.grounded
            assert result.confidence > 0
            assert len(result.checks) > 0
        finally:
            os.unlink(path)

    def test_full_pipeline_ungrounded_output(self):
        """Model output naming a non-candidate should be snapped back."""
        f = AudioFeatures(
            duration_s=8.0, rms_db=-25.0, rms_variance=0.03,
            zero_crossing_rate=0.08, spectral_centroid_hz=2200,
            spectral_bandwidth_hz=1800, spectral_rolloff_hz=4500,
            dominant_frequency_hz=180.0, harmonic_ratio=0.65,
            onset_rate_per_sec=3.5, has_regular_pattern=True,
            pattern_interval_ms=150.0, peak_db=-18.0, anomaly_score=0.75,
        )
        cands = rank_candidates(f, "Washing machine")

        # Model returns a fault NOT in candidates
        response = json.dumps({
            "fault": "Exploding capacitor",
            "urgency": "CRITICAL",
            "checks": ["Check capacitor."],
            "safety": "Unplug.",
            "confidence": 95,
        })

        result = validate(response, cands)
        assert result.grounded  # should be snapped to a candidate
        assert result.fault != "Exploding capacitor"

    def test_full_pipeline_malformed_json(self):
        """Malformed JSON should fall back to top candidate."""
        f = AudioFeatures(
            duration_s=8.0, rms_db=-30.0, rms_variance=0.01,
            zero_crossing_rate=0.05, spectral_centroid_hz=500,
            spectral_bandwidth_hz=800, spectral_rolloff_hz=1500,
            dominant_frequency_hz=60.0, harmonic_ratio=0.5,
            onset_rate_per_sec=0.8, has_regular_pattern=False,
            pattern_interval_ms=0.0, peak_db=-24.0, anomaly_score=0.45,
        )
        cands = rank_candidates(f, "Electric fan")

        result = validate("This is not JSON at all!", cands)
        assert result.grounded
        assert result.fault == cands[0].name

    def test_full_pipeline_empty_response(self):
        """Empty model response should fall back gracefully."""
        f = AudioFeatures(
            duration_s=8.0, rms_db=-28.0, rms_variance=0.04,
            zero_crossing_rate=0.12, spectral_centroid_hz=1800,
            spectral_bandwidth_hz=2000, spectral_rolloff_hz=4500,
            dominant_frequency_hz=200.0, harmonic_ratio=0.7,
            onset_rate_per_sec=5.0, has_regular_pattern=True,
            pattern_interval_ms=80.0, peak_db=-12.0, anomaly_score=0.85,
        )
        cands = rank_candidates(f, "Car engine")

        result = validate("", cands)
        assert result.grounded
        assert result.fault == cands[0].name

    def test_prompt_contains_all_features(self):
        """Prompt should contain all measured feature values."""
        f = AudioFeatures(
            duration_s=8.0, rms_db=-25.0, rms_variance=0.03,
            zero_crossing_rate=0.08, spectral_centroid_hz=2200,
            spectral_bandwidth_hz=1800, spectral_rolloff_hz=4500,
            dominant_frequency_hz=180.0, harmonic_ratio=0.65,
            onset_rate_per_sec=3.5, has_regular_pattern=True,
            pattern_interval_ms=150.0, peak_db=-18.0, anomaly_score=0.75,
        )
        cands = rank_candidates(f, "Washing machine")
        prompt = build_diagnosis_prompt(f, cands, "Washing machine")

        assert "Washing machine" in prompt
        assert "2200" in prompt  # spectral centroid
        assert "180" in prompt   # dominant freq
        assert "0.65" in prompt  # harmonic ratio
        assert "150" in prompt   # pattern interval

    def test_validate_urgency_bounds(self):
        """Confidence should always be 0-100."""
        f = AudioFeatures(
            duration_s=8.0, rms_db=-30.0, rms_variance=0.01,
            zero_crossing_rate=0.05, spectral_centroid_hz=500,
            spectral_bandwidth_hz=800, spectral_rolloff_hz=1500,
            dominant_frequency_hz=60.0, harmonic_ratio=0.5,
            onset_rate_per_sec=0.8, has_regular_pattern=False,
            pattern_interval_ms=0.0, peak_db=-24.0, anomaly_score=0.45,
        )
        cands = rank_candidates(f, "Electric fan")

        # Test with extreme confidence values
        for conf in [-50, 0, 50, 100, 150, 999]:
            response = json.dumps({
                "fault": cands[0].name,
                "urgency": "HIGH",
                "checks": ["Check it."],
                "safety": "None",
                "confidence": conf,
            })
            result = validate(response, cands)
            assert 0 <= result.confidence <= 100

    def test_validate_invalid_urgency(self):
        """Invalid urgency should fall back to candidate urgency."""
        f = AudioFeatures(
            duration_s=8.0, rms_db=-25.0, rms_variance=0.03,
            zero_crossing_rate=0.08, spectral_centroid_hz=2200,
            spectral_bandwidth_hz=1800, spectral_rolloff_hz=4500,
            dominant_frequency_hz=180.0, harmonic_ratio=0.65,
            onset_rate_per_sec=3.5, has_regular_pattern=True,
            pattern_interval_ms=150.0, peak_db=-18.0, anomaly_score=0.75,
        )
        cands = rank_candidates(f, "Washing machine")

        response = json.dumps({
            "fault": cands[0].name,
            "urgency": "SUPER_CRITICAL_URGENT",
            "checks": ["Check it."],
            "safety": "None",
            "confidence": 85,
        })
        result = validate(response, cands)
        assert result.urgency in ("CRITICAL", "HIGH", "MEDIUM", "LOW", "UNKNOWN")

    def test_validate_empty_checks(self):
        """Empty checks list should get default checks."""
        f = AudioFeatures(
            duration_s=8.0, rms_db=-30.0, rms_variance=0.01,
            zero_crossing_rate=0.05, spectral_centroid_hz=500,
            spectral_bandwidth_hz=800, spectral_rolloff_hz=1500,
            dominant_frequency_hz=60.0, harmonic_ratio=0.5,
            onset_rate_per_sec=0.8, has_regular_pattern=False,
            pattern_interval_ms=0.0, peak_db=-24.0, anomaly_score=0.45,
        )
        cands = rank_candidates(f, "Electric fan")

        response = json.dumps({
            "fault": cands[0].name,
            "urgency": "HIGH",
            "checks": [],
            "safety": "None",
            "confidence": 80,
        })
        result = validate(response, cands)
        assert len(result.checks) >= 1

    def test_candidates_always_returned(self):
        """rank_candidates should always return at least one candidate."""
        # Test with extreme feature values
        extreme_features = [
            AudioFeatures(
                duration_s=0.0, rms_db=-80.0, rms_variance=0.0,
                zero_crossing_rate=0.0, spectral_centroid_hz=0.0,
                spectral_bandwidth_hz=0.0, spectral_rolloff_hz=0.0,
                dominant_frequency_hz=0.0, harmonic_ratio=0.0,
                onset_rate_per_sec=0.0, has_regular_pattern=False,
                pattern_interval_ms=0.0, peak_db=-80.0, anomaly_score=0.0,
            ),
            AudioFeatures(
                duration_s=10.0, rms_db=0.0, rms_variance=1.0,
                zero_crossing_rate=1.0, spectral_centroid_hz=10000.0,
                spectral_bandwidth_hz=10000.0, spectral_rolloff_hz=11000.0,
                dominant_frequency_hz=5000.0, harmonic_ratio=1.0,
                onset_rate_per_sec=100.0, has_regular_pattern=True,
                pattern_interval_ms=1.0, peak_db=0.0, anomaly_score=1.0,
            ),
        ]
        for f in extreme_features:
            for appliance in RULES.keys():
                cands = rank_candidates(f, appliance)
                assert len(cands) >= 1, f"No candidates for {appliance}"

    def test_all_rules_fires_at_least_one(self):
        """Each rule table should have at least one rule that fires for a typical input."""
        # Create a "typical bad" feature set for each appliance
        typical_bad = AudioFeatures(
            duration_s=8.0, rms_db=-25.0, rms_variance=0.03,
            zero_crossing_rate=0.1, spectral_centroid_hz=2000,
            spectral_bandwidth_hz=2000, spectral_rolloff_hz=4500,
            dominant_frequency_hz=150.0, harmonic_ratio=0.5,
            onset_rate_per_sec=3.0, has_regular_pattern=True,
            pattern_interval_ms=120.0, peak_db=-18.0, anomaly_score=0.7,
        )
        for appliance in RULES.keys():
            cands = rank_candidates(typical_bad, appliance)
            assert len(cands) >= 1, \
                f"No rules fired for {appliance} with typical bad input"