# -*- coding: utf-8 -*-
"""
Erweiterte Audio-Effekte fuer Voice-Morphing.

Reverb, Lowpass, Highpass, Flanger, Distortion — rein numpy/scipy.
"""

from __future__ import annotations

import numpy as np


def apply_reverb(
    audio: np.ndarray,
    sample_rate: int,
    room_size: float = 0.5,
    damping: float = 0.5,
    wet: float = 0.3,
) -> np.ndarray:
    """
    Einfacher algorithmischer Reverb (Schroeder-Moorer).

    Nutzt parallele Comb-Filter + serielle Allpass-Filter.
    room_size: 0.0-1.0 (Nachhalllaenge).
    damping: 0.0-1.0 (Hoehen-Daempfung).
    wet: 0.0-1.0 (Dry/Wet-Mix).
    """
    if wet < 0.005:
        return audio

    audio = audio.astype(np.float64)

    # Comb-Filter Verzoegerungen (in Samples, skaliert mit room_size)
    base_delays = [1557, 1617, 1491, 1422, 1277, 1356, 1188, 1116]
    scale = 0.5 + room_size * 0.5  # 0.5-1.0 Skalierung
    delays = [int(d * scale * sample_rate / 44100) for d in base_delays]

    # Feedback-Koeffizienten
    feedback = 0.7 + room_size * 0.15  # 0.7-0.85
    damp = damping * 0.4  # 0.0-0.4

    # Parallele Comb-Filter
    comb_sum = np.zeros(len(audio) + max(delays), dtype=np.float64)

    for delay in delays:
        buf = np.zeros(len(comb_sum), dtype=np.float64)
        lp_state = 0.0  # Einfacher 1-Pol Lowpass fuer Damping

        for i in range(len(audio)):
            delayed = buf[i - delay] if i >= delay else 0.0

            # Damping (1-Pol Lowpass auf Feedback)
            lp_state = delayed * (1.0 - damp) + lp_state * damp
            buf[i] = audio[i] + lp_state * feedback

        comb_sum += buf

    comb_sum /= len(delays)

    # Serielle Allpass-Filter
    allpass_delays = [225, 556, 441, 341]
    allpass_delays = [int(d * sample_rate / 44100) for d in allpass_delays]
    allpass_gain = 0.5

    result = comb_sum[:len(audio)]
    for delay in allpass_delays:
        buf_in = result.copy()
        buf_out = np.zeros_like(result)

        for i in range(len(result)):
            delayed = buf_out[i - delay] if i >= delay else 0.0
            delayed_in = buf_in[i - delay] if i >= delay else 0.0
            buf_out[i] = -allpass_gain * buf_in[i] + delayed_in + allpass_gain * delayed

        result = buf_out

    # Dry/Wet Mix
    output = audio * (1.0 - wet) + result[:len(audio)] * wet

    # Clipping verhindern
    max_val = np.max(np.abs(output))
    if max_val > 1.0:
        output /= max_val

    return output


def apply_lowpass(
    audio: np.ndarray,
    sample_rate: int,
    cutoff: float = 4000.0,
    order: int = 2,
) -> np.ndarray:
    """
    Butterworth Lowpass-Filter.

    cutoff: Grenzfrequenz in Hz.
    order: Filterordnung (1-6).
    """
    from scipy.signal import butter, sosfilt

    nyq = sample_rate / 2.0
    if cutoff >= nyq * 0.99:
        return audio

    audio = audio.astype(np.float64)
    norm_cutoff = min(cutoff / nyq, 0.99)
    order = max(1, min(order, 6))
    sos = butter(order, norm_cutoff, btype="low", output="sos")

    return sosfilt(sos, audio)


def apply_highpass(
    audio: np.ndarray,
    sample_rate: int,
    cutoff: float = 200.0,
    order: int = 2,
) -> np.ndarray:
    """
    Butterworth Highpass-Filter.

    cutoff: Grenzfrequenz in Hz.
    order: Filterordnung (1-6).
    """
    from scipy.signal import butter, sosfilt

    if cutoff < 10.0:
        return audio

    audio = audio.astype(np.float64)
    nyq = sample_rate / 2.0
    norm_cutoff = min(cutoff / nyq, 0.99)
    order = max(1, min(order, 6))
    sos = butter(order, norm_cutoff, btype="high", output="sos")

    return sosfilt(sos, audio)


def apply_flanger(
    audio: np.ndarray,
    sample_rate: int,
    rate: float = 0.5,
    depth: float = 0.002,
    wet: float = 0.3,
) -> np.ndarray:
    """
    Flanger-Effekt via moduliertem Delay.

    rate: LFO-Frequenz in Hz (0.1-5.0).
    depth: Maximale Verzoegerung in Sekunden (0.001-0.01).
    wet: Dry/Wet-Mix (0.0-1.0).
    """
    if wet < 0.005 or depth < 0.0001:
        return audio

    audio = audio.astype(np.float64)
    max_delay_samples = int(depth * sample_rate)
    if max_delay_samples < 1:
        return audio

    # LFO (Sinus)
    t = np.arange(len(audio)) / sample_rate
    lfo = (1.0 + np.sin(2.0 * np.pi * rate * t)) * 0.5  # 0.0 - 1.0

    # Moduliertes Delay
    delayed = np.zeros_like(audio)
    for i in range(len(audio)):
        delay = int(lfo[i] * max_delay_samples)
        src_idx = i - delay
        if src_idx >= 0:
            delayed[i] = audio[src_idx]

    output = audio * (1.0 - wet) + delayed * wet

    return output


def apply_distortion(
    audio: np.ndarray,
    sample_rate: int,
    gain: float = 1.5,
    threshold: float = 0.8,
) -> np.ndarray:
    """
    Weiche Verzerrung (Soft-Clipping).

    gain: Verstaerkungsfaktor vor dem Clipping (1.0-10.0).
    threshold: Soft-Clip Schwelle (0.1-1.0).
    """
    if gain <= 1.001 and threshold >= 0.999:
        return audio

    audio = audio.astype(np.float64)

    # Verstaerken
    audio = audio * gain

    # Soft-Clipping via tanh
    audio = np.tanh(audio / threshold) * threshold

    return audio