import numpy as np
import matplotlib.pyplot as plt

from scipy.interpolate import interp1d
from scipy.signal import savgol_filter, correlate, find_peaks

def numpy_to_native(data):
    if isinstance(data, (np.int64, np.int32)):
        return int(data)
    elif isinstance(data, (np.float64, np.float32)):
        return float(data)
    elif isinstance(data, np.ndarray):
        return data.tolist()
    elif isinstance(data, dict):
        return {k: numpy_to_native(v) for k, v in data.items()}
    elif isinstance(data, list):
        return [numpy_to_native(v) for v in data]
    else:
        return data

def process_signals(gz_signal, upsample_factor, window_size=40, poly_order=2, peak_distance=2, peak_prominence=1):
    smoothed_signal = savgol_filter(gz_signal, window_size, poly_order)
    upsampled_smoothed_signal = upsample_signal(smoothed_signal, upsample_factor)
    autocorr = correlate(upsampled_smoothed_signal, upsampled_smoothed_signal, mode='full')
    autocorr = autocorr[autocorr.size // 2:]
    peaks, _ = find_peaks(autocorr, distance=peak_distance, prominence=peak_prominence)
    return upsampled_smoothed_signal, peaks

def fill_missing_values(data, window_size=10):
    for col in data.columns:
        if data[col].isna().any():
            data[col] = data[col].rolling(window=window_size, min_periods=1, center=True).median()
    return data

def upsample_signal(signal, upsample_factor):
    x = np.arange(signal.size)
    interpolator = interp1d(x, signal, kind='quadratic')
    x_upsampled = np.linspace(0, signal.size - 1, signal.size * upsample_factor)
    return interpolator(x_upsampled)