Module wavelet.wavelets.sym9

Symlet 9 wavelet

""" Symlet 9 wavelet """


class Symlet9:
    """
    Properties
    ----------
     near symmetric, orthogonal, biorthogonal

    All values are from http://wavelets.pybytes.com/wavelet/sym9/
    """
    __name__ = "Symlet Wavelet 9"
    __motherWaveletLength__ = 18  # length of the mother wavelet
    __transformWaveletLength__ = 2  # minimum wavelength of input signal

    # decomposition filter
    # low-pass
    decompositionLowFilter = [
        0.0014009155259146807,
        0.0006197808889855868,
        -0.013271967781817119,
        -0.01152821020767923,
        0.03022487885827568,
        0.0005834627461258068,
        -0.05456895843083407,
        0.238760914607303,
        0.717897082764412,
        0.6173384491409358,
        0.035272488035271894,
        -0.19155083129728512,
        -0.018233770779395985,
        0.06207778930288603,
        0.008859267493400484,
        -0.010264064027633142,
        -0.0004731544986800831,
        0.0010694900329086053,
    ]

    # high-pass
    decompositionHighFilter = [
        -0.0010694900329086053,
        -0.0004731544986800831,
        0.010264064027633142,
        0.008859267493400484,
        -0.06207778930288603,
        -0.018233770779395985,
        0.19155083129728512,
        0.035272488035271894,
        -0.6173384491409358,
        0.717897082764412,
        -0.238760914607303,
        -0.05456895843083407,
        -0.0005834627461258068,
        0.03022487885827568,
        0.01152821020767923,
        -0.013271967781817119,
        -0.0006197808889855868,
        0.0014009155259146807,
    ]

    # reconstruction filters
    # low pass
    reconstructionLowFilter = [
        0.0010694900329086053,
        -0.0004731544986800831,
        -0.010264064027633142,
        0.008859267493400484,
        0.06207778930288603,
        -0.018233770779395985,
        -0.19155083129728512,
        0.035272488035271894,
        0.6173384491409358,
        0.717897082764412,
        0.238760914607303,
        -0.05456895843083407,
        0.0005834627461258068,
        0.03022487885827568,
        -0.01152821020767923,
        -0.013271967781817119,
        0.0006197808889855868,
        0.0014009155259146807,
    ]

    # high-pass
    reconstructionHighFilter = [
        0.0014009155259146807,
        -0.0006197808889855868,
        -0.013271967781817119,
        0.01152821020767923,
        0.03022487885827568,
        -0.0005834627461258068,
        -0.05456895843083407,
        -0.238760914607303,
        0.717897082764412,
        -0.6173384491409358,
        0.035272488035271894,
        0.19155083129728512,
        -0.018233770779395985,
        -0.06207778930288603,
        0.008859267493400484,
        0.010264064027633142,
        -0.0004731544986800831,
        -0.0010694900329086053,
    ]

Classes

class Symlet9

Properties

near symmetric, orthogonal, biorthogonal

All values are from http://wavelets.pybytes.com/wavelet/sym9/

Expand source code

class Symlet9:
    """
    Properties
    ----------
     near symmetric, orthogonal, biorthogonal

    All values are from http://wavelets.pybytes.com/wavelet/sym9/
    """
    __name__ = "Symlet Wavelet 9"
    __motherWaveletLength__ = 18  # length of the mother wavelet
    __transformWaveletLength__ = 2  # minimum wavelength of input signal

    # decomposition filter
    # low-pass
    decompositionLowFilter = [
        0.0014009155259146807,
        0.0006197808889855868,
        -0.013271967781817119,
        -0.01152821020767923,
        0.03022487885827568,
        0.0005834627461258068,
        -0.05456895843083407,
        0.238760914607303,
        0.717897082764412,
        0.6173384491409358,
        0.035272488035271894,
        -0.19155083129728512,
        -0.018233770779395985,
        0.06207778930288603,
        0.008859267493400484,
        -0.010264064027633142,
        -0.0004731544986800831,
        0.0010694900329086053,
    ]

    # high-pass
    decompositionHighFilter = [
        -0.0010694900329086053,
        -0.0004731544986800831,
        0.010264064027633142,
        0.008859267493400484,
        -0.06207778930288603,
        -0.018233770779395985,
        0.19155083129728512,
        0.035272488035271894,
        -0.6173384491409358,
        0.717897082764412,
        -0.238760914607303,
        -0.05456895843083407,
        -0.0005834627461258068,
        0.03022487885827568,
        0.01152821020767923,
        -0.013271967781817119,
        -0.0006197808889855868,
        0.0014009155259146807,
    ]

    # reconstruction filters
    # low pass
    reconstructionLowFilter = [
        0.0010694900329086053,
        -0.0004731544986800831,
        -0.010264064027633142,
        0.008859267493400484,
        0.06207778930288603,
        -0.018233770779395985,
        -0.19155083129728512,
        0.035272488035271894,
        0.6173384491409358,
        0.717897082764412,
        0.238760914607303,
        -0.05456895843083407,
        0.0005834627461258068,
        0.03022487885827568,
        -0.01152821020767923,
        -0.013271967781817119,
        0.0006197808889855868,
        0.0014009155259146807,
    ]

    # high-pass
    reconstructionHighFilter = [
        0.0014009155259146807,
        -0.0006197808889855868,
        -0.013271967781817119,
        0.01152821020767923,
        0.03022487885827568,
        -0.0005834627461258068,
        -0.05456895843083407,
        -0.238760914607303,
        0.717897082764412,
        -0.6173384491409358,
        0.035272488035271894,
        0.19155083129728512,
        -0.018233770779395985,
        -0.06207778930288603,
        0.008859267493400484,
        0.010264064027633142,
        -0.0004731544986800831,
        -0.0010694900329086053,
    ]

Class variables

var decompositionHighFilter

var decompositionLowFilter

var reconstructionHighFilter

var reconstructionLowFilter