"""Before/after waveform and spectrum comparison plots."""

import numpy as np
import matplotlib
matplotlib.use("Agg")
import matplotlib.pyplot as plt


def _to_mono(audio):
    """Collapse to mono for plotting."""
    if audio.ndim > 1 and audio.shape[1] > 1:
        return audio.mean(axis=1)
    return audio.ravel()


def _downsample_for_plot(signal, time, max_points=500_000):
    """Reduce sample count so matplotlib stays responsive."""
    if len(signal) > max_points:
        step = len(signal) // max_points
        return signal[::step], time[::step]
    return signal, time


def plot_waveform_comparison(original, mastered, sample_rate):
    """Create a stacked before/after waveform plot.

    Returns a matplotlib Figure.
    """
    fig, axes = plt.subplots(2, 1, figsize=(8, 4), sharex=True)

    duration = len(original) / sample_rate
    time_o = np.linspace(0, duration, len(original))
    time_m = np.linspace(0, duration, len(mastered))

    orig_mono = _to_mono(original)
    mast_mono = _to_mono(mastered)

    orig_mono, time_o = _downsample_for_plot(orig_mono, time_o)
    mast_mono, time_m = _downsample_for_plot(mast_mono, time_m)

    axes[0].plot(time_o, orig_mono, color="#4a90d9", linewidth=0.3)
    axes[0].set_ylabel("Amplitude")
    axes[0].set_title("Original")
    axes[0].set_ylim(-1.05, 1.05)

    axes[1].plot(time_m, mast_mono, color="#d94a4a", linewidth=0.3)
    axes[1].set_ylabel("Amplitude")
    axes[1].set_title("Mastered")
    axes[1].set_xlabel("Time (seconds)")
    axes[1].set_ylim(-1.05, 1.05)

    plt.tight_layout()
    return fig


def plot_spectrum_comparison(original, mastered, sample_rate):
    """Create a frequency spectrum comparison with shape-normalized overlay
    and a difference trace showing the processing's spectral impact.

    The mastered spectrum is level-aligned to the original so the plot
    compares spectral *shape*, not overall loudness (LUFS stats handle that).

    Returns a matplotlib Figure.
    """
    fig, (ax_spec, ax_diff) = plt.subplots(
        2, 1, figsize=(8, 5), height_ratios=[3, 1], sharex=True,
    )

    orig_mono = _to_mono(original)
    mast_mono = _to_mono(mastered)

    n_fft = 8192

    def avg_spectrum(signal, n_fft, sr):
        hop = n_fft // 2
        n_windows = max(1, (len(signal) - n_fft) // hop)
        spectra = []
        for i in range(min(n_windows, 100)):
            start = i * hop
            window = signal[start : start + n_fft] * np.hanning(n_fft)
            spectrum = np.abs(np.fft.rfft(window))
            spectra.append(spectrum)
        avg = np.mean(spectra, axis=0)
        freqs = np.fft.rfftfreq(n_fft, 1.0 / sr)
        avg_db = 20.0 * np.log10(avg + 1e-10)
        return freqs, avg_db

    freqs_o, spec_o = avg_spectrum(orig_mono, n_fft, sample_rate)
    freqs_m, spec_m = avg_spectrum(mast_mono, n_fft, sample_rate)

    # --- Level-align mastered to original (remove overall loudness diff) ---
    # Use only the passband (100 Hz – 10 kHz) for alignment so the HPF/LPF
    # roll-offs at the extremes don't skew the offset.
    passband = (freqs_o >= 100) & (freqs_o <= 10000)
    level_offset = np.mean(spec_o[passband]) - np.mean(spec_m[passband])
    spec_m_aligned = spec_m + level_offset

    # --- Top: overlaid spectra (shape comparison) ---
    ax_spec.plot(freqs_o, spec_o, color="#4a90d9", alpha=0.7, linewidth=1,
                 label="Original")
    ax_spec.plot(freqs_m, spec_m_aligned, color="#d94a4a", alpha=0.7,
                 linewidth=1, label="Mastered (level-aligned)")
    ax_spec.set_ylabel("Magnitude (dB)")
    ax_spec.set_title("Spectral Shape Comparison")
    ax_spec.legend(loc="upper right", fontsize=8)
    ax_spec.grid(True, alpha=0.3)

    # --- Bottom: difference (mastered − original) ---
    diff = spec_m_aligned - spec_o
    ax_diff.plot(freqs_o, diff, color="#2ca02c", linewidth=1)
    ax_diff.axhline(0, color="gray", linewidth=0.5, linestyle="--")
    ax_diff.set_ylabel("Δ dB")
    ax_diff.set_xlabel("Frequency")
    ax_diff.set_title("Processing Difference (Mastered − Original)", fontsize=9)
    ax_diff.set_ylim(-6, 6)
    ax_diff.grid(True, alpha=0.3)

    # Shared x-axis settings
    ax_diff.set_xscale("log")
    ax_diff.set_xlim(20, sample_rate / 2)
    ax_diff.set_xticks([10, 100, 1000, 10000])
    ax_diff.set_xticklabels(["10 Hz", "100 Hz", "1 kHz", "10 kHz"])

    plt.tight_layout()
    return fig