def one_side_moving_average(signal: np.array,
window_size: int = 5) -> np.array:
"""
Сaclulate moving average of signal without zeros,
replaces zero values with the moving average
Args:
signal: (np.array) 1D signal
window size: (int) takes 2*window_size points for moving average; 5 by default (10 points)
Output: (np.array) filtered signal
"""
n = window_size
#calculate baseline without zeros
signal_mean = signal[signal != 0].mean()
#create masked array for zero values
signal = np.ma.masked_array(signal, signal == 0)
#calculted moving average with window size = n
ave = np.cumsum(signal.filled(0))
ave[n:] = ave[n:] - ave[:-n]
counts = np.cumsum(~signal.mask)
counts[n:] = counts[n:] - counts[:-n]
ave[~signal.mask] /= counts[~signal.mask]
ave[signal.
mask] = signal_mean # replces nan with baseline mean taken without nan
return ave
def moving_average(signal: np.array, window_size: int = 5) -> np.array:
"""
Сaclulate moving average for signal with zeros,
replaces zero values with the moving average
Args:
signal: (np.array) 1D signal
window size: (int) takes 2*window_size points for moving average; 5 by default (10 points)
Output: (np.array) corrected by moving mean signal
"""
n = window_size
#calculate baseline without zeros
signal_mean = signal[signal != 0].mean()
#create masked array for nan values
signal = np.ma.masked_array(signal, signal == 0)
#calculted moving average with window size = n
ave = np.cumsum(signal.filled(0))
ave[n:] = ave[n:] - ave[:-n]
counts = np.cumsum(~signal.mask)
counts[n:] = counts[n:] - counts[:-n]
ave[~signal.mask] /= counts[~signal.mask]
ave[signal.
mask] = signal_mean # replces nan with baseline mean taken without nan
signal = np.flip(
ave, 0) #reverces signal to make a symmetrical moving averaging
#calculate baseline without zeros
signal_mean = signal[signal != 0].mean()
signal = np.ma.masked_array(signal, signal == 0)
#calculted moving average with window size = n
ave = np.cumsum(signal.filled(0))
ave[n:] = ave[n:] - ave[:-n]
counts = np.cumsum(~signal.mask)
counts[n:] = counts[n:] - counts[:-n]
ave[~signal.mask] /= counts[~signal.mask]
ave[signal.
mask] = signal_mean # replaces zeros with baseline mean taken without nan
#reverse back
ave = np.flip(ave, 0) #reverces signal back
np.nan_to_num(ave)
return ave
