# EVOLVE-BLOCK-START
"""
Real-Time Adaptive Signal Processing Algorithm for Non-Stationary Time Series

This algorithm implements a sliding window approach to filter volatile, non-stationary
time series data while minimizing noise and preserving signal dynamics.
"""
import numpy as np
from scipy import signal
from collections import deque


def adaptive_filter(x, window_size=20):
    """
    Adaptive signal processing algorithm using sliding window approach.

    Args:
        x: Input signal (1D array of real-valued samples)
        window_size: Size of the sliding window (W samples)

    Returns:
        y: Filtered output signal with length = len(x) - window_size + 1
    """
    if len(x) < window_size:
        raise ValueError(f"Input signal length ({len(x)}) must be >= window_size ({window_size})")

    # Initialize output array
    output_length = len(x) - window_size + 1
    y = np.zeros(output_length)

    # Simple moving average as baseline
    for i in range(output_length):
        window = x[i : i + window_size]

        # Basic moving average filter
        y[i] = np.mean(window)

    return y


def enhanced_filter_with_trend_preservation(x, window_size=20):
    """
    Enhanced version with trend preservation using weighted moving average.

    Args:
        x: Input signal (1D array of real-valued samples)
        window_size: Size of the sliding window

    Returns:
        y: Filtered output signal
    """
    if len(x) < window_size:
        raise ValueError(f"Input signal length ({len(x)}) must be >= window_size ({window_size})")

    output_length = len(x) - window_size + 1
    y = np.zeros(output_length)

    # Create weights that emphasize recent samples
    weights = np.exp(np.linspace(-2, 0, window_size))
    weights = weights / np.sum(weights)

    for i in range(output_length):
        window = x[i : i + window_size]

        # Weighted moving average with exponential weights
        y[i] = np.sum(window * weights)

    return y


def process_signal(input_signal, window_size=20, algorithm_type="enhanced"):
    """
    Main signal processing function that applies the selected algorithm.

    Args:
        input_signal: Input time series data
        window_size: Window size for processing
        algorithm_type: Type of algorithm to use ("basic" or "enhanced")

    Returns:
        Filtered signal
    """
    if algorithm_type == "enhanced":
        return enhanced_filter_with_trend_preservation(input_signal, window_size)
    else:
        return adaptive_filter(input_signal, window_size)


# EVOLVE-BLOCK-END


def generate_test_signal(length=1000, noise_level=0.3, seed=42):
    """
    Generate synthetic test signal with known characteristics.

    Args:
        length: Length of the signal
        noise_level: Standard deviation of noise to add
        seed: Random seed for reproducibility

    Returns:
        Tuple of (noisy_signal, clean_signal)
    """
    np.random.seed(seed)
    t = np.linspace(0, 10, length)

    # Create a complex signal with multiple components
    clean_signal = (
        2 * np.sin(2 * np.pi * 0.5 * t)  # Low frequency component
        + 1.5 * np.sin(2 * np.pi * 2 * t)  # Medium frequency component
        + 0.5 * np.sin(2 * np.pi * 5 * t)  # Higher frequency component
        + 0.8 * np.exp(-t / 5) * np.sin(2 * np.pi * 1.5 * t)  # Decaying oscillation
    )

    # Add non-stationary behavior
    trend = 0.1 * t * np.sin(0.2 * t)  # Slowly varying trend
    clean_signal += trend

    # Add random walk component for non-stationarity
    random_walk = np.cumsum(np.random.randn(length) * 0.05)
    clean_signal += random_walk

    # Add noise
    noise = np.random.normal(0, noise_level, length)
    noisy_signal = clean_signal + noise

    return noisy_signal, clean_signal


def run_signal_processing(signal_length=1000, noise_level=0.3, window_size=20):
    """
    Run the signal processing algorithm on a test signal.

    Returns:
        Dictionary containing results and metrics
    """
    # Generate test signal
    noisy_signal, clean_signal = generate_test_signal(signal_length, noise_level)

    # Process the signal
    filtered_signal = process_signal(noisy_signal, window_size, "enhanced")

    # Calculate basic metrics
    if len(filtered_signal) > 0:
        # Align signals for comparison (account for processing delay)
        delay = window_size - 1
        aligned_clean = clean_signal[delay:]
        aligned_noisy = noisy_signal[delay:]

        # Ensure same length
        min_length = min(len(filtered_signal), len(aligned_clean))
        filtered_signal = filtered_signal[:min_length]
        aligned_clean = aligned_clean[:min_length]
        aligned_noisy = aligned_noisy[:min_length]

        # Calculate correlation with clean signal
        correlation = np.corrcoef(filtered_signal, aligned_clean)[0, 1] if min_length > 1 else 0

        # Calculate noise reduction
        noise_before = np.var(aligned_noisy - aligned_clean)
        noise_after = np.var(filtered_signal - aligned_clean)
        noise_reduction = (noise_before - noise_after) / noise_before if noise_before > 0 else 0

        return {
            "filtered_signal": filtered_signal,
            "clean_signal": aligned_clean,
            "noisy_signal": aligned_noisy,
            "correlation": correlation,
            "noise_reduction": noise_reduction,
            "signal_length": min_length,
        }
    else:
        return {
            "filtered_signal": [],
            "clean_signal": [],
            "noisy_signal": [],
            "correlation": 0,
            "noise_reduction": 0,
            "signal_length": 0,
        }


if __name__ == "__main__":
    # Test the algorithm
    results = run_signal_processing()
    print(f"Signal processing completed!")
    print(f"Correlation with clean signal: {results['correlation']:.3f}")
    print(f"Noise reduction: {results['noise_reduction']:.3f}")
    print(f"Processed signal length: {results['signal_length']}")