wavelet¶
Module: missions.nodes.feature_generation.wavelet
¶
Generate features based on wavelet transformation
Note
Currently there is only a simple wrapper around pywt http://www.pybytes.com/pywavelets/ So one has to install this package. Scipy does not seem to give a real support for wavelets.
Inheritance diagram for pySPACE.missions.nodes.feature_generation.wavelet
:
PywtWaveletNode
¶

class
pySPACE.missions.nodes.feature_generation.wavelet.
PywtWaveletNode
(wavelet=None, mode='sym', *args, **kwargs)[source]¶ Bases:
pySPACE.missions.nodes.base_node.BaseNode
Extract features based on the discrete wavelet transform from pywavelets
The components of the wavelet transform are returned as new features.
Note
This node is only a wrapper around the pywavelet package (http://www.pybytes.com/pywavelets/)
Parameters
wavelet: Name of the wanted wavelet, including the number of taps
The pywt documentation currently names the following possibilities:
:haar family: haar :db family: db1, db2, db3, db4, db5, db6, db7, db8, db9, db10, db11, db12, db13, db14, db15, db16, db17, db18, db19, db20 :sym family: sym2, sym3, sym4, sym5, sym6, sym7, sym8, sym9, sym10, sym11, sym12, sym13, sym14, sym15, sym16, sym17, sym18, sym19, sym20 :coif family: coif1, coif2, coif3, coif4, coif5 :bior family: bior1.1, bior1.3, bior1.5, bior2.2, bior2.4, bior2.6, bior2.8, bior3.1, bior3.3, bior3.5, bior3.7, bior3.9, bior4.4, bior5.5, bior6.8 :rbio family: rbio1.1, rbio1.3, rbio1.5, rbio2.2, rbio2.4, rbio2.6, rbio2.8, rbio3.1, rbio3.3, rbio3.5, rbio3.7, rbio3.9, rbio4.4, rbio5.5, rbio6.8 :dmey family: dmey
which refer to Haar, Daubechies, Symlets, Coiflets, Biorthogonal, Reverse biorthogonal and Discrete Meyer wavelets being used to transform the time series.
(recommended, default: ‘haar’)
mode: One of the pywt extension modes:
['zpd', 'cpd', 'sym', 'ppd', 'sp1', 'per']
which mean zero, constant, symmetric, periodic and smooth padding and periodization. The latter uses the minimum number of coefficients in comparison to periodic padding.
(optional, default: ‘sym’)
Exemplary Call
 node : DWT parameters : mode : 'cpd' wavelet: 'haar'
Input: TimeSeries
Output: FeatureVector
Requires: pywavelets
Author: Mario Michael Krell (mario.krell@dfki.de)
Created: 2012/12/04
POSSIBLE NODE NAMES:  PywtWaveletNode
 PywtWavelet
 DWT
POSSIBLE INPUT TYPES:  TimeSeries
Class Components Summary
_execute
(x)Extract the wavelet features from the given data x input_types

_execute
(x)[source]¶ Extract the wavelet features from the given data x
The feature names will get an A for approximation and D for details coefficients. So on example name is: Waveletname_Channelname_DetailsIndex, where index is the position of the coefficient in the transformed list. Details is A or D. Each channel is processed separately and its name is used in channelname.

input_types
= ['TimeSeries']¶