signalsmooth Namespace Reference

Functions
def	smooth
def	gauss_kern
def	blur_image
def	smooth_demo

Variables
tuple	Z = np.cos((X**2+Y**2)/200.)
tuple	Z2 = blur_image(Z, 3)

Detailed Description

cookb_signalsmooth.py

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

Function Documentation

def signalsmooth.blur_image	(		im,
			n,
			ny = None
	)

blurs the image by convolving with a gaussian kernel of typical
    size n. The optional keyword argument ny allows for a different
    size in the y direction.

def signalsmooth.gauss_kern	(		size,
			sizey = None
	)

Returns a normalized 2D gauss kernel array for convolutions 

def signalsmooth.smooth	(		x,
			window_len = 10,
			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.

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)

see also: 

numpy.hanning, numpy.hamming, numpy.bartlett, numpy.blackman, numpy.convolve
scipy.signal.lfilter
 
TODO: the window parameter could be the window itself if an array instead of a string   

def signalsmooth.smooth_demo

(

)

Variable Documentation

tuple signalsmooth.Z = np.cos((X**2+Y**2)/200.)

tuple signalsmooth.Z2 = blur_image(Z, 3)