suncasa.utils.signalsmooth

cookb_signalsmooth.py

from: http://scipy.org/Cookbook/SignalSmooth

Attributes

Z

Functions

`smooth`(x[, window_len, window])	smooth the data using a window with requested size.
`gauss_kern`(size[, sizey])	Returns a normalized 2D gauss kernel array for convolutions
`blur_image`(im, n[, ny])	blurs the image by convolving with a gaussian kernel of typical
`smooth_demo`()

Module Contents

suncasa.utils.signalsmooth.smooth(x, window_len=10, window='hanning')[source]

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.

input:: x: the input signal window_len: the dimension of the smoothing window window: the type of window from ‘flat’, ‘hanning’, ‘hamming’, ‘bartlett’, ‘blackman’

flat window will produce a moving average smoothing.
output:: the smoothed signal

example:

import numpy as np t = np.linspace(-2,2,0.1) x = np.sin(t)+np.random.randn(len(t))*0.1 y = smooth(x)