grapa.shared.maths.smooth

grapa.shared.maths.smooth(x, window_len=11, window='hanning')

smooth the data using a window with requested size. This method is based on the convolution of a scaled window with the signal. The signal is prepared by introducing reflected copies of the signal (with the window size) in both ends so that transient parts are minimized in the begining and end part of the output signal.

Parameters:

  • x – the input signal

  • window_len – the dimension of the smoothing window; should be an odd integer

  • window – the type of window from ‘flat’, ‘hanning’, ‘hamming’, ‘bartlett’, ‘blackman’. flat window will produce a moving average smoothing.

Returns:

the smoothed signal as np.array

Example:

t = linspace(-2, 2, 0.1)
x = sin(t) + randn(len(t)) * 0.1
y = smooth(x)

See also: numpy.hanning, numpy.hamming, numpy.bartlett, numpy.blackman, numpy.convolve, scipy.signal.lfilter

TODO: check the following was done: length(output) != length(input) return y[(window_len/2-1):-(window_len/2)] instead of just y.