"""
domain/interfaces/services/cardiogan_preprocessor.py
────────────────────────────────────────────────────
Abstract interface for CardioGAN signal preprocessing.
"""
from __future__ import annotations

from abc import ABC, abstractmethod
import numpy as np


class CardioGANSignalPreprocessor(ABC):
    """
    Contract for preprocessing signals for CardioGAN.

    Responsible for:
        1. PPG Resampling: 125 Hz -> 128 Hz
        2. Filtering: Butterworth 1-8 Hz
        3. Normalization: Z-score (per-subject)
        4. Segmentation: 512-sample sliding windows (10% overlap)
        5. Scaling: Min-max normalize to [-1, 1] per window
    """

    @abstractmethod
    def preprocess_ppg(self, ppg_raw: np.ndarray) -> np.ndarray:
        """
        Ingest raw continuous PPG signals @ 125 Hz and output segmented, filtered
        windows of shape (N_windows, 512) @ 128 Hz in range [-1, 1].

        Args:
            ppg_raw: 1-D array of raw PPG values (usually 125 Hz).

        Returns:
            2-D NumPy array of shape (N_windows, 512) ready for CardioGAN inference.
            
        Raises:
            PreprocessingError: If preprocessing fails (e.g. signal too short).
        """
        ...

    @abstractmethod
    def postprocess_ecg(self, ecg_windows_128: np.ndarray) -> np.ndarray:
        """
        Ingest the synthetic ECG windows (N_windows, 512) @ 128 Hz produced by CardioGAN
        and downsample them back to 125 Hz and align/clip/pad them to VGTL-Net format
        (224 samples per window).

        Args:
            ecg_windows_128: 2-D array of synthetic ECG windows @ 128 Hz.

        Returns:
            2-D NumPy array of shape (N_windows, 224) aligned for VGTL-Net.

        Raises:
            PreprocessingError: If postprocessing fails.
        """
        ...