import numpy as np
import scipy.signal as sps


def detect_audio_issues(spectral, time_stats):
    """Detect audio processing artifacts using forensic rules."""

    issues = []

    energy = spectral["energy_distribution"]
    freqs = spectral["freqs"]

    hf_env = spectral.get("hf_env", None)
    lf_env = spectral.get("lf_env", None)

    flatness = spectral.get("spectral_flatness", None)
    notches = spectral.get("spectral_notches", [])

    # ============================================================
    # 1️⃣ HF LOSS LOGIC
    # ============================================================

    hf_8_12 = energy["8k_12khz"]
    highest_freq = spectral["highest_freq_minus60db"]

    if hf_8_12 < 0.01 and highest_freq < 9000:
        issues.append((
            "HF_LOSS", "HIGH",
            f"Severe HF cutoff: {hf_8_12:.3f}% in 8–12k and rolloff at {highest_freq:.1f} Hz."
        ))
    elif hf_8_12 < 0.02:
        issues.append((
            "HF_LOSS", "LOW",
            f"Low HF energy ({hf_8_12:.3f}%). Normal for speech."
        ))

    # ============================================================
    # 2️⃣ LPF DETECTOR
    # ============================================================

    if hf_env is not None:
        hf_region = (freqs >= 5000) & (freqs <= 12000)
        hf_vals = hf_env[hf_region]
        hf_freq = freqs[hf_region]

        if len(hf_vals) > 10:
            coef = np.polyfit(hf_freq, hf_vals, 1)
            slope_per_hz = coef[0]
            slope_db_oct = slope_per_hz * np.log2(2) * 12000

            if highest_freq < 10000:
                issues.append((
                    "LPF_DETECTED", "HIGH",
                    f"Low-pass filter near {highest_freq:.0f} Hz."
                ))
            elif slope_db_oct < -6:
                issues.append((
                    "HF_EQ_SHELF", "LOW",
                    f"HF rolloff detected (~{slope_db_oct:.1f} dB/oct)."
                ))

    # ============================================================
    # 3️⃣ HPF DETECTOR
    # ============================================================

    if lf_env is not None:
        low_region = (freqs >= 20) & (freqs <= 300)
        min_len = min(len(low_region), len(lf_env))
        low_region = low_region[:min_len]
        lf_env_trim = lf_env[:min_len]
        freqs_trim = freqs[:min_len]

        lf_vals = lf_env_trim[low_region]
        lf_freq = freqs_trim[low_region]


        if len(lf_vals) > 10:
            coef_l = np.polyfit(lf_freq, lf_vals, 1)
            slope_l = coef_l[0]
            slope_db_oct_l = slope_l * np.log2(2) * 300

            if energy["below_100hz"] < 0.5:
                if slope_db_oct_l > 6:
                    issues.append((
                        "HPF_DETECTED", "HIGH",
                        f"High-pass filter detected (~{slope_db_oct_l:.1f} dB/oct)."
                    ))
                else:
                    issues.append((
                        "HPF_SUSPECTED", "LOW",
                        "Possible mild HPF (LF rolloff)."
                    ))

    # ============================================================
    # 4️⃣ NOISE REDUCTION DETECTOR
    # ============================================================

    if flatness is not None:
        hf_flat = flatness

        if hf_flat > 0.40 and len(notches) >= 3:
            issues.append((
                "NOISE_REDUCTION_ARTIFACTS", "HIGH",
                f"NR artifacts: HF flattening ({hf_flat:.2f}) + {len(notches)} notches."
            ))
        elif hf_flat > 0.35:
            issues.append((
                "NR_SOFT", "LOW",
                f"Mild noise reduction detected (HF flattening={hf_flat:.2f})."
            ))

    # ============================================================
    # 5️⃣ SPECTRAL NOTCHES
    # ============================================================

    if len(notches) > 0:
        issues.append((
            "SPECTRAL_NOTCHES", "MEDIUM",
            f"{len(notches)} spectral notches detected."
        ))

    # ============================================================
    # 6️⃣ BRICK-WALL DETECTOR
    # ============================================================

    if spectral["brick_wall_detected"]:
        issues.append((
            "BRICK_WALL", "HIGH",
            f"Brick-wall behavior at {spectral['brick_wall_freq']:.0f} Hz."
        ))

    # ============================================================
    # 7️⃣ COMPRESSION / DYNAMICS
    # ============================================================

    crest = time_stats["crest_factor_db"]

    if crest < 3:
        issues.append((
            "OVER_COMPRESSION", "HIGH",
            f"Very low crest factor ({crest:.1f} dB)."
        ))
    elif crest < 6:
        issues.append((
            "COMPRESSION", "MEDIUM",
            f"Moderate compression ({crest:.1f} dB)."
        ))

    # ============================================================
    # 8️⃣ CLIPPING
    # ============================================================

    if time_stats["peak"] >= 0.999:
        issues.append((
            "CLIPPING", "CRITICAL",
            f"Peak amplitude {time_stats['peak']:.6f}. Possible clipping."
        ))

    # ============================================================
    # 9️⃣ DE-ESSER DETECTION
    # ============================================================

    if hf_env is not None:
        band_3_6k = (freqs >= 3000) & (freqs <= 6000)
        band_6_10k = (freqs >= 6000) & (freqs <= 10000)

        presence_energy = np.mean(hf_env[band_3_6k])
        sibilance_energy = np.mean(hf_env[band_6_10k])

        if sibilance_energy < (presence_energy * 0.20):
            issues.append((
                "DE_ESSER_DETECTED", "MEDIUM",
                "Sibilance band (6–10 kHz) strongly reduced vs presence band (3–6 kHz)."
            ))

    # ============================================================
    # 🔟 MULTIBAND COMPRESSION
    # ============================================================

    if hf_env is not None:

        def band_crest(env, band):
            vals = env[band]
            if len(vals) == 0:
                return None
            return np.max(vals) - np.mean(vals)

        lf_band = (freqs >= 80) & (freqs <= 300)
        mf_band = (freqs >= 300) & (freqs <= 3000)
        hf_band = (freqs >= 3000) & (freqs <= 8000)

        cf_lf = band_crest(hf_env, lf_band)
        cf_mf = band_crest(hf_env, mf_band)
        cf_hf = band_crest(hf_env, hf_band)

        if cf_lf is not None and cf_mf is not None and cf_hf is not None:

            if cf_hf < (cf_lf * 0.4):
                issues.append((
                    "MULTIBAND_COMPRESSION", "MEDIUM",
                    "HF crest factor significantly lower than LF."
                ))

            if cf_mf < (cf_lf * 0.5):
                issues.append((
                    "MULTIBAND_COMPRESSION", "LOW",
                    "Mid-band crest factor compressed vs LF."
                ))

    # ============================================================
    # 1️⃣1️⃣ EQ CURVE CLASSIFIER
    # ============================================================

    if hf_env is not None:

        smooth = sps.medfilt(hf_env, kernel_size=9)

        coef_eq = np.polyfit(freqs, smooth, 1)
        tilt = coef_eq[0]

        curvature = np.polyfit(freqs, smooth, 2)[0]

        if tilt > 0.00002:
            issues.append((
                "EQ_HF_BOOST", "LOW",
                "HF shelf boost detected (positive tilt)."
            ))

        elif tilt < -0.00002:
            issues.append((
                "EQ_HF_CUT", "LOW",
                "HF shelf cut detected (negative tilt)."
            ))

        if curvature > 1e-12:
            issues.append((
                "EQ_PEAKING", "LOW",
                "Spectral curvature suggests midrange peaking EQ."
            ))

        if abs(tilt) > 0.00001 and abs(curvature) < 1e-12:
            issues.append((
                "EQ_TILT", "LOW",
                "Tilt EQ detected (linear spectral tilt)."
            ))

    return issues