def calcWiener2(ds, dpathInput="/", dpathEnsemble='/evtcond', newpath='/kernel'):
'''Calculates the 0, 1, and 2 order wiener kernels between the Input and Event channelis.'''
dat=ds.getSubData(dpathEnsemble).getData()
sd = ds.getSubData(dpathInput)
inp = sd.getData()
h0=float(dat.shape[1])/inp.shape[0]
sigma2=(inp**2).sum()/inp.shape[0]
sigma4=(inp**4).sum()/inp.shape[0]
h1=dat.mean(1)/sigma2
h1=h1-h1.mean()
h2=dot(dat, transpose(dat))/(dat.shape[1]-1)
h2=h2/(sigma4)
h2=h2-identity(h2.shape[0])*h0/sigma2
ds.createSubData(newpath, data=None, head={"SampleType":"group", "h0":h0}, delete=True)
ds.createSubData(newpath+"/h1", data=h1, head={"SampleType":"timeseries", "SamplesPerSecond":sd.fs()}, delete=True)
ds.createSubData(newpath+"/h2", data=h2, head={"SampleType":"timeseries", "SamplesPerSecond":sd.fs()}, delete=True)
def combineWiener(ds, dpathPred="/dp", ratio=.02): sd = ds.getSubData(dpathPred) dat = sd.getData() dat = dat[:,0] + ratio*dat[:,1] sd.datinit(dat)
