"""Shared plotting primitives for signal visualization."""

from __future__ import annotations

import gc
import json
from typing import Optional

import matplotlib
import matplotlib.pyplot as plt
import numpy as np
from scipy.fft import fft, fftshift
from scipy.signal.windows import hann

from ria_toolkit_oss.datatypes.recording import Recording
from ria_toolkit_oss.view.tools import (
    COLORS,
    decimate,
    extract_metadata_fields,
    set_path,
)


def _add_annotations(annotations, compact_mode, show_labels, sample_rate_hz, center_freq_hz, ax2):
    if annotations and not compact_mode:
        for annotation in annotations:
            start_idx = annotation.get("core:sample_start", 0)
            length = annotation.get("core:sample_count", 0)
            start_time = start_idx / sample_rate_hz
            end_time = (start_idx + length) / sample_rate_hz
            freq_low = annotation.get("core:freq_lower_edge", center_freq_hz - sample_rate_hz / 4)
            freq_high = annotation.get("core:freq_upper_edge", center_freq_hz + sample_rate_hz / 4)
            comment = annotation.get("core:comment", "{}")

            try:
                comment_data = json.loads(comment) if isinstance(comment, str) else comment
                ann_type = comment_data.get("type", "unknown")
                if ann_type == "intersection":
                    color = COLORS["success"]
                elif ann_type == "parallel":
                    color = COLORS["primary"]
                elif ann_type == "standalone":
                    color = COLORS["warning"]
                else:
                    color = COLORS["error"]
            except Exception:
                color = COLORS["error"]

            rect = plt.Rectangle(
                (start_time, freq_low),
                end_time - start_time,
                freq_high - freq_low,
                color=color,
                alpha=0.4,
                linewidth=2,
            )
            ax2.add_patch(rect)
            if show_labels:
                label = annotation.get("core:label", "Signal")
                ax2.text(
                    start_time,
                    freq_high,
                    label,
                    color=COLORS["light"],
                    fontsize=10,
                    bbox=dict(boxstyle="round,pad=0.2", facecolor=color, alpha=0.7),
                )


def _get_nfft_size(signal, fast_mode):
    if len(signal) < 1000:
        nfft = 128
    elif len(signal) < 10_000:
        nfft = 256
    elif len(signal) < 100_000:
        nfft = 512
    elif len(signal) < 1_000_000:
        nfft = 1024
    else:
        nfft = 2048

    if fast_mode:
        nfft = min(nfft, 512)
    overlap = nfft // 8 if fast_mode else nfft // 4
    return nfft, overlap


def _get_plot_samples(signal, fast_mode, slow_max, fast_max):
    max_samples = fast_max if fast_mode else slow_max
    if len(signal) > max_samples:
        start_idx = len(signal) // 2 - max_samples // 2
        return signal[start_idx : start_idx + max_samples]
    else:
        return signal


def _set_dpi(fast_mode, labels_mode, extension):
    if fast_mode:
        dpi = 75
    elif labels_mode:
        dpi = 200
    else:
        dpi = 150
    return dpi if extension == "png" else None


def setup_style(*, labels_mode: bool = False, compact_mode: bool = False) -> None:
    """Configure matplotlib with the signal-testbed styling."""

    plt.style.use("dark_background")

    if compact_mode:
        base_font = 8
        title_font = 10
        label_font = 8
    elif labels_mode:
        base_font = 12
        title_font = 16
        label_font = 14
    else:
        base_font = 10
        title_font = 12
        label_font = 10

    matplotlib.rcParams.update(
        {
            "figure.facecolor": "#0f172a",
            "axes.facecolor": "#1e293b",
            "axes.edgecolor": COLORS["muted"],
            "axes.labelcolor": COLORS["light"],
            "text.color": COLORS["light"],
            "xtick.color": COLORS["muted"],
            "ytick.color": COLORS["muted"],
            "grid.color": COLORS["muted"],
            "grid.alpha": 0.3,
            "font.size": base_font,
            "axes.titlesize": title_font,
            "axes.labelsize": label_font,
            "figure.titlesize": title_font + 2,
            "legend.frameon": False,
            "legend.facecolor": "none",
            "xtick.labelsize": base_font,
            "ytick.labelsize": base_font,
        }
    )


def detect_constellation_symbols(signal: np.ndarray, method: str = "differential") -> np.ndarray:
    """Heuristic symbol detector used for constellation highlighting."""

    if len(signal) < 100:
        return np.ones(len(signal), dtype=bool)

    if method == "differential":
        di = np.diff(signal.imag)
        dq = np.diff(signal.real)
        derivative_magnitude = np.sqrt(di**2 + dq**2)
        derivative_magnitude = np.append(derivative_magnitude, 0)
        threshold = np.percentile(derivative_magnitude, 15)
        return derivative_magnitude < threshold

    if method == "amplitude":
        amplitude = np.abs(signal)
        amplitude_change = np.abs(np.diff(amplitude))
        amplitude_change = np.append(amplitude_change, 0)
        threshold = np.percentile(amplitude_change, 20)
        return amplitude_change < threshold

    if method == "phase":
        phase = np.angle(signal)
        phase_diff = np.diff(np.unwrap(phase))
        phase_diff = np.append(phase_diff, 0)
        threshold = np.percentile(np.abs(phase_diff), 20)
        return np.abs(phase_diff) < threshold

    if method == "combined":
        diff_stable = detect_constellation_symbols(signal, "differential")
        amp_stable = detect_constellation_symbols(signal, "amplitude")
        phase_stable = detect_constellation_symbols(signal, "phase")
        stability_count = diff_stable.astype(int) + amp_stable.astype(int) + phase_stable.astype(int)
        return stability_count >= 2

    raise ValueError(f"Unknown method: {method}")


def view_simple_sig(
    recording: Recording,
    annotations: Optional[list] = None,
    output_path: Optional[str] = "images/signal.png",
    saveplot: Optional[bool] = True,
    fast_mode: Optional[bool] = False,
    compact_mode: Optional[bool] = False,
    horizontal_mode: Optional[bool] = False,
    constellation_mode: Optional[bool] = False,
    labels_mode: Optional[bool] = False,
    slice: Optional[tuple] = None,
    title: Optional[str] = "Signal",
):
    """
    Create a simple plot of various signal visualizations as a png or svg image.

    :param recording: The recording object to plot.
    :type recording: Recording
    :param output_path: The output image path. Defaults to "images/signal.png"
    :type output_path: str, optional
    :param saveplot: Whether or not to save the plot. Defaults to True.
    :type saveplot: bool, optional
    :param fast_mode: Use fast mode for faster render. Defaults to False.
    :type fast_mode: bool, optional
    :param compact_mode: Use compact mode for compact plot. Defaults to False.
    :type compact_mode: bool, optional
    :param horizontal_mode: Display plots horizontally. Defaults to False.
    :type horizontal_mode: bool, optional
    :param constellation_mode: Display constellation plot and PSD if not using compact mode. Defaults to False.
    :type constellation_mode: bool, optional
    :param labels_mode: Display more thorough labels. Defaults to False.
    :type labels_mode: bool, optional
    :param slice: Slice of signal to display. Defaults to None.
    :type slice: tuple[int, int], optional
    :param title: Title of plot. Defaults to "Signal".
    :type title: str, optional

    """

    signal = recording.data[0]
    sample_rate_hz, center_freq_hz, sdr = extract_metadata_fields(recording.metadata)

    setup_style(labels_mode=labels_mode, compact_mode=compact_mode)

    if slice:
        start_idx, end_idx = slice
        signal = signal[start_idx:end_idx]
        print(f"Using slice: samples {start_idx} to {end_idx} ({len(signal):,} samples)")

    max_display_pixels = 100_000 if fast_mode else 250_000
    display_signal = decimate(signal, max_display_pixels) if len(signal) > max_display_pixels else signal
    spec_signal = signal

    if compact_mode:
        fig, (ax1, ax2) = plt.subplots(2, 1, figsize=(12, 6), gridspec_kw={"height_ratios": [1, 5]})
        show_title = False
        show_labels = False
        ax_constellation = ax_psd = None
    elif horizontal_mode:
        if constellation_mode:
            fig, (ax1, ax2, ax3) = plt.subplots(1, 3, figsize=(18, 6))
            ax_constellation = ax3
        else:
            fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(16, 6))
            ax_constellation = None
        show_title = True
        show_labels = labels_mode
        ax_psd = None
    else:
        if constellation_mode:
            fig, ((ax1, ax2), (ax3, ax4)) = plt.subplots(2, 2, figsize=(16, 12))
            ax_constellation, ax_psd = ax3, ax4
        else:
            fig, (ax1, ax2) = plt.subplots(2, 1, figsize=(14, 10))
            ax_constellation = ax_psd = None
        show_title = True
        show_labels = labels_mode

    if show_title:
        fig.suptitle(title, fontsize=16, color=COLORS["light"], y=0.96)
    fig.patch.set_facecolor("#0f172a")

    total_duration_s = len(signal) / sample_rate_hz if sample_rate_hz else 0.0
    t_s = np.linspace(0, total_duration_s, len(display_signal)) if len(display_signal) else np.array([])

    ax1.plot(t_s, display_signal.real, color=COLORS["purple"], linewidth=0.8, alpha=0.8, label="I")
    ax1.plot(t_s, display_signal.imag, color=COLORS["magenta"], linewidth=0.8, alpha=0.8, label="Q")
    ax1.set_xlim(0, total_duration_s)
    ax1.grid(True, alpha=0.3)

    nfft, overlap = _get_nfft_size(signal=signal, fast_mode=fast_mode)

    _, freqs, _, _ = ax2.specgram(
        spec_signal,
        NFFT=nfft,
        Fc=center_freq_hz,
        Fs=sample_rate_hz,
        noverlap=overlap,
        cmap="twilight",
    )

    ax2.set_ylim(center_freq_hz - sample_rate_hz / 2, center_freq_hz + sample_rate_hz / 2)
    ax2.set_xlim(0, total_duration_s)

    if show_labels:
        if horizontal_mode:
            ax1.set_xlabel("Time (s)")
        else:
            ax2.set_xlabel("Time (s)")

        ax1.set_ylabel("Amplitude")
        ax1.set_title(f"Time Series - {sdr} SDR", loc="left", pad=10)
        ax1.legend(loc="upper right")

        ax2.set_ylabel("Frequency (Hz)")
        ax2.set_title(
            f"Spectrogram - {center_freq_hz / 1e6:.1f} MHz ± {sample_rate_hz / 2e6:.1f} MHz", loc="left", pad=10
        )
        yticks = ax2.get_yticks()
        ax2.set_yticklabels([f"{y / 1e6:.1f}" for y in yticks])
    elif not compact_mode:
        ax1.set_title("Time Series", loc="left", pad=10)
        ax1.legend(loc="upper right", fontsize=8)

        ax2.set_title("Spectrogram", loc="left", pad=10)

    _add_annotations(
        annotations=annotations,
        compact_mode=compact_mode,
        show_labels=show_labels,
        sample_rate_hz=sample_rate_hz,
        center_freq_hz=center_freq_hz,
        ax2=ax2,
    )

    if ax_constellation is not None:
        constellation_samples = _get_plot_samples(signal=signal, fast_mode=fast_mode, slow_max=50_000, fast_max=20_000)
        method = "differential" if fast_mode else "combined"
        stable_points = detect_constellation_symbols(constellation_samples, method=method)

        ax_constellation.scatter(
            constellation_samples.real[~stable_points],
            constellation_samples.imag[~stable_points],
            c=COLORS["muted"],
            s=0.5,
            alpha=0.2,
        )
        ax_constellation.scatter(
            constellation_samples.real[stable_points],
            constellation_samples.imag[stable_points],
            c=COLORS["purple"],
            s=3,
            alpha=0.8,
        )
        ax_constellation.set_xlabel("In-phase (I)")
        ax_constellation.set_ylabel("Quadrature (Q)")
        ax_constellation.set_title("Constellation")
        ax_constellation.grid(True, alpha=0.3)
        ax_constellation.set_aspect("equal")

    if ax_psd is not None:
        psd_samples = _get_plot_samples(signal=signal, fast_mode=fast_mode, slow_max=65_536, fast_max=16_384)
        window = hann(len(psd_samples))
        spectrum = np.abs(fftshift(fft(psd_samples * window))) ** 2
        freqs = np.linspace(-sample_rate_hz / 2, sample_rate_hz / 2, len(psd_samples))
        freqs = freqs + center_freq_hz
        spectrum_db = 10 * np.log10(spectrum + 1e-12)

        ax_psd.plot(freqs / 1e6, spectrum_db, color=COLORS["accent"], linewidth=1.0)
        ax_psd.set_xlabel("Frequency (MHz)")
        ax_psd.set_ylabel("Power (dB)")
        ax_psd.set_title("Power Spectral Density")
        ax_psd.grid(True, alpha=0.3)

    if compact_mode:
        ax1.set_xticks([])
        ax1.set_yticks([])
        ax2.set_xticks([])
        ax2.set_yticks([])

        plt.subplots_adjust(left=0, right=1, top=1, bottom=0, hspace=0)
    else:
        plt.tight_layout()
        if show_title:
            plt.subplots_adjust(top=0.92)

    if saveplot:
        output_path, extension = set_path(output_path=output_path)
        dpi_value = _set_dpi(fast_mode=fast_mode, labels_mode=labels_mode, extension=extension)

        plt.savefig(output_path, dpi=dpi_value, bbox_inches="tight", facecolor="#0f172a", edgecolor="none")
        print(f"Saved signal plot to {output_path}")
        return output_path

    plt.show()

    # Garbage collection and clean up to prevent memory overloading
    plt.close("all")
    gc.collect()
    return None


__all__ = [
    "setup_style",
    "detect_constellation_symbols",
    "view_simple_sig",
]