"""
infrastructure/processing/scipy_signal_processor.py
─────────────────────────────────────────────────────
ScipySignalProcessor — implements SignalProcessor using SciPy.

Pipeline (Template Method from SignalProcessor.process()):
    1. filter_signal()  — Butterworth bandpass (0.5–8 Hz, 4th order)
    2. normalize()      — Z-score normalisation (mean=0, std=1)
    3. segment()        — sliding 8-second windows (no overlap)

All constants are read from src/shared/constants.py — no magic numbers here.
"""
from __future__ import annotations

import numpy as np
from scipy import signal as scipy_signal

from src.domain.interfaces.services.signal_processor import SignalProcessor
from src.shared.constants import (
    PPG_BANDPASS_HIGH,
    PPG_BANDPASS_LOW,
    PPG_FILTER_ORDER,
    PPG_SEGMENT_DURATION_SEC,
)
from src.shared.logger import get_logger

logger = get_logger(__name__)


class ScipySignalProcessor(SignalProcessor):
    """
    PPG signal preprocessor using SciPy's digital signal processing tools.

    Implements the three abstract steps of SignalProcessor:
        filter_signal → Butterworth bandpass IIR filter
        normalize     → Z-score (subtract mean, divide by std)
        segment       → Fixed-length windows (non-overlapping)
    """

    # ── Step 1: Filter ────────────────────────────────────────────────────────

    def filter_signal(
        self,
        signal: np.ndarray,
        sampling_rate: float,
    ) -> np.ndarray:
        """
        Apply a 4th-order Butterworth bandpass filter (0.5–8 Hz).

        Removes:
          • Baseline wander (< 0.5 Hz — motion artefacts, breathing)
          • High-frequency noise (> 8 Hz — electronics, EMI)

        Uses ``sosfiltfilt`` (zero-phase, forward-backward) to avoid
        phase distortion in the filtered signal.

        Args:
            signal:        1-D raw PPG amplitude array.
            sampling_rate: Sampling rate in Hz.

        Returns:
            Filtered 1-D NumPy array (same length as input).
        """
        nyquist = sampling_rate / 2.0
        low = PPG_BANDPASS_LOW / nyquist
        high = PPG_BANDPASS_HIGH / nyquist

        # Clamp to valid range (< 1.0) to avoid ValueError from scipy
        low = max(1e-4, min(low, 0.999))
        high = max(low + 1e-4, min(high, 0.999))

        sos = scipy_signal.butter(
            N=PPG_FILTER_ORDER,
            Wn=[low, high],
            btype="bandpass",
            output="sos",
        )
        filtered = scipy_signal.sosfiltfilt(sos, signal)
        logger.debug(
            "filter_signal() — fs=%.1f Hz, band=[%.2f, %.2f] Hz",
            sampling_rate,
            PPG_BANDPASS_LOW,
            PPG_BANDPASS_HIGH,
        )
        return filtered

    # ── Step 2: Normalize ─────────────────────────────────────────────────────

    def normalize(self, signal: np.ndarray) -> np.ndarray:
        """
        Z-score normalise the signal to mean=0, std=1.

        Handles edge cases:
          • All-zero or constant signal → return zeros (avoid division by zero).

        Args:
            signal: 1-D filtered PPG signal.

        Returns:
            Z-score normalised 1-D array.
        """
        mean = np.mean(signal)
        std = np.std(signal)

        if std < 1e-8:
            logger.warning(
                "normalize() — signal std ≈ 0 (constant signal). "
                "Returning zero array."
            )
            return np.zeros_like(signal)

        normalised = (signal - mean) / std
        logger.debug(
            "normalize() — mean=%.4f, std=%.4f → Z-score applied", mean, std
        )
        return normalised

    # ── Step 3: Segment ───────────────────────────────────────────────────────

    def segment(
        self,
        signal: np.ndarray,
        sampling_rate: float,
    ) -> np.ndarray:
        """
        Split the signal into non-overlapping windows of PPG_SEGMENT_DURATION_SEC seconds.

        Any trailing samples that don't fill a complete window are discarded.

        Args:
            signal:        1-D normalised PPG signal.
            sampling_rate: Sampling rate in Hz.

        Returns:
            2-D NumPy array of shape ``(n_segments, window_size)``.
            Returns shape ``(0, window_size)`` if the signal is too short.
        """
        window_size = int(PPG_SEGMENT_DURATION_SEC * sampling_rate)

        if len(signal) < window_size:
            logger.warning(
                "segment() — signal length %d < window_size %d. "
                "Returning empty segments array.",
                len(signal),
                window_size,
            )
            return np.empty((0, window_size), dtype=np.float64)

        n_segments = len(signal) // window_size
        trimmed = signal[: n_segments * window_size]
        segments = trimmed.reshape(n_segments, window_size)

        logger.debug(
            "segment() — %d segments of %d samples (%.1f s each)",
            n_segments,
            window_size,
            PPG_SEGMENT_DURATION_SEC,
        )
        return segments