"""
LectureLens — Tests: Audio Analyzer
Run with: pytest tests/test_audio.py -v
"""

from __future__ import annotations

import struct
import wave
from pathlib import Path

import numpy as np
import pytest

SAMPLE_FILES = Path(__file__).parent / "sample_files"


# ── Helpers ────────────────────────────────────────────────────────────────────

def make_wav(path: Path, duration: float = 3.0, sr: int = 44100, amplitude: float = 0.3) -> Path:
    """Generate a simple sine-wave WAV file for testing."""
    t = np.linspace(0, duration, int(sr * duration), endpoint=False)
    samples = (np.sin(2 * np.pi * 440 * t) * amplitude * 32767).astype(np.int16)
    with wave.open(str(path), "w") as wf:
        wf.setnchannels(1)
        wf.setsampwidth(2)
        wf.setframerate(sr)
        wf.writeframes(samples.tobytes())
    return path


def make_clipped_wav(path: Path, duration: float = 2.0, sr: int = 44100) -> Path:
    """Generate a WAV that clips (amplitude > 1.0 before clamping)."""
    t = np.linspace(0, duration, int(sr * duration), endpoint=False)
    # amplitude = 1.5 → clips after int16 conversion
    raw = np.sin(2 * np.pi * 440 * t) * 1.5
    samples = np.clip(raw * 32767, -32768, 32767).astype(np.int16)
    with wave.open(str(path), "w") as wf:
        wf.setnchannels(1)
        wf.setsampwidth(2)
        wf.setframerate(sr)
        wf.writeframes(samples.tobytes())
    return path


# ── Setup ──────────────────────────────────────────────────────────────────────

@pytest.fixture(scope="module")
def clean_wav(tmp_path_factory) -> Path:
    p = tmp_path_factory.mktemp("audio") / "clean.wav"
    return make_wav(p, duration=5.0, amplitude=0.3)


@pytest.fixture(scope="module")
def clipped_wav(tmp_path_factory) -> Path:
    p = tmp_path_factory.mktemp("audio") / "clipped.wav"
    return make_clipped_wav(p)


# ── Clipping ───────────────────────────────────────────────────────────────────

def test_no_clipping_in_clean_audio(clean_wav):
    from app.analyzers.audio_analyzer import count_clipped_samples
    clips = count_clipped_samples(clean_wav)
    assert clips == 0, f"Expected 0 clipped samples, got {clips}"


def test_clipping_detected_in_hot_audio(clipped_wav):
    from app.analyzers.audio_analyzer import count_clipped_samples
    clips = count_clipped_samples(clipped_wav)
    assert clips > 0, "Expected clipped samples to be detected"


# ── SNR ────────────────────────────────────────────────────────────────────────

def test_snr_returns_positive_value(clean_wav):
    from app.analyzers.audio_analyzer import calculate_snr
    snr = calculate_snr(clean_wav)
    assert snr is not None
    assert snr >= 0, f"SNR should be non-negative, got {snr}"


# ── Silence detection ──────────────────────────────────────────────────────────

def test_no_silence_in_continuous_audio(clean_wav):
    from app.analyzers.audio_analyzer import detect_silence
    segs = detect_silence(clean_wav, noise_db=-40, min_duration=1.0)
    # A sine wave has no silence
    assert isinstance(segs, list)


def test_silence_detected_in_padded_audio(tmp_path):
    """WAV with 3 seconds of silence in the middle should trigger detection."""
    import soundfile as sf

    sr = 16_000
    tone = np.sin(2 * np.pi * 440 * np.linspace(0, 2, sr * 2)).astype(np.float32) * 0.4
    silence = np.zeros(sr * 4, dtype=np.float32)
    audio = np.concatenate([tone, silence, tone])
    path = tmp_path / "silence_test.wav"
    sf.write(str(path), audio, sr)

    from app.analyzers.audio_analyzer import detect_silence
    segs = detect_silence(path, noise_db=-40, min_duration=1.0)
    assert len(segs) >= 1, "Expected at least one silence segment"
    durations = [s.end - s.start for s in segs]
    assert max(durations) > 3.0, f"Expected silence > 3s, got max {max(durations):.1f}s"


# ── Alert engine (audio) ───────────────────────────────────────────────────────

def test_alert_generated_for_low_loudness():
    from app.schemas import AudioMetrics
    from app.alert_engine import generate_alerts

    metrics = AudioMetrics(integrated_loudness_lufs=-28.0)
    alerts = generate_alerts(metrics, "audio", thresholds_path="thresholds.yaml")
    kpis = [a.kpi for a in alerts]
    assert "integrated_loudness_lufs" in kpis


def test_no_alerts_for_good_audio():
    from app.schemas import AudioMetrics
    from app.alert_engine import generate_alerts

    metrics = AudioMetrics(
        integrated_loudness_lufs=-14.0,
        true_peak_dbtp=-2.0,
        clipped_samples_count=0,
        snr_db=30.0,
        loudness_range_lu=8.0,
        dnsmos_ovrl=4.0,
    )
    alerts = generate_alerts(metrics, "audio", thresholds_path="thresholds.yaml")
    assert len(alerts) == 0, f"Expected no alerts, got: {alerts}"


def test_critical_alert_for_clipping():
    from app.schemas import AudioMetrics
    from app.alert_engine import generate_alerts

    metrics = AudioMetrics(clipped_samples_count=500)
    alerts = generate_alerts(metrics, "audio", thresholds_path="thresholds.yaml")
    critical = [a for a in alerts if a.kpi == "clipped_samples_count"]
    assert len(critical) >= 1


# ── Score ──────────────────────────────────────────────────────────────────────

def test_audio_score_range():
    from app.schemas import AudioMetrics
    from app.alert_engine import compute_audio_score

    m = AudioMetrics(
        integrated_loudness_lufs=-14.0,
        true_peak_dbtp=-2.0,
        clipped_samples_count=0,
        snr_db=30.0,
        dnsmos_ovrl=4.2,
    )
    score = compute_audio_score(m)
    assert 0.0 <= score <= 1.0, f"Score out of range: {score}"
    assert score > 0.7, f"Expected high score for good audio, got {score}"