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.
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.
the smoothed signal
numpy.hanning, numpy.hamming, numpy.bartlett, numpy.blackman, numpy.convolve
TODO: the window parameter could be the window itself if an array instead of a string
NOTE: length(output) != length(input), to correct this: return y[(window_len/2-1):-(window_len/2)] instead of just y.
Definition at line 8 of file smooth.py.
Author(s): Cressel Anderson, Travis Deyle, Advait Jain, Hai Nguyen, Advisor: Prof. Charlie Kemp, Lab: Healthcare Robotics Lab at Georgia Tech
autogenerated on Wed Nov 27 2013 11:34:06