msf.py 文件源码

def demoMSF():
    t = np.linspace(0.0, 30*np.pi, 1000)

    s1 = spsig.sawtooth(t) #+ 3.0
    s2 = np.cos(5.0*t)
    s3 = np.random.uniform(-1.0, 1.0, size=t.size)
    s = np.vstack((s1,s2,s3)).T

    #m = np.array([ [0.5, 0.5, 0.0], [0.5, 0.0, 0.5], [0.0, 0.5, 0.5] ])
    m = np.random.random((3,3))
    m /= m.sum(axis=0)

    sMixed = s.dot(m)

    msfFilt = MSF(sMixed, lags=0)
    fig = plt.figure()

    axOrig = fig.add_subplot(4,1, 1)
    axOrig.plot(s+util.colsep(s))
    axOrig.set_title('Unmixed Signal')
    axOrig.autoscale(tight=True)

    axMixed = fig.add_subplot(4,1, 2)
    axMixed.plot(sMixed+util.colsep(sMixed))
    axMixed.set_title('Mixed Signal (random transform)')
    axMixed.autoscale(tight=True)

    axUnmixed = fig.add_subplot(4,1, 3)
    msfFilt.plotTransform(sMixed, ax=axUnmixed)
    axUnmixed.set_title('MSF Components')
    axUnmixed.autoscale(tight=True)

    axCleaned = fig.add_subplot(4,1, 4)
    msfFilt.plotFilter(sMixed, comp=(2,), remove=True, ax=axCleaned)
    axCleaned.set_title('Cleaned Signal (Last Component Removed)')
    axCleaned.autoscale(tight=True)

    fig.tight_layout()