def coherence_partial(self):
"""The partial coherence between data[i] and data[j], given data[k], as
a function of frequency band"""
tseries_length = self.input.data.shape[0]
spectrum_length = self.spectrum.shape[-1]
p_coherence=np.zeros((tseries_length,
tseries_length,
tseries_length,
spectrum_length))
for i in xrange(tseries_length):
for j in xrange(tseries_length):
for k in xrange(tseries_length):
if j==k or i==k:
pass
else:
p_coherence[i][j][k]=tsa.coherence_partial_calculate(
self.spectrum[i][j],
self.spectrum[i][i],
self.spectrum[j][j],
self.spectrum[i][k],
self.spectrum[j][k],
self.spectrum[k][k])
idx = tsu.tril_indices(tseries_length,-1)
p_coherence[idx[0],idx[1],...] = p_coherence[idx[1],idx[0],...].conj()
return p_coherence
def coherence_partial(self):
"""The partial coherence between data[i] and data[j], given data[k], as
a function of frequency band"""
tseries_length = self.data.shape[0]
spectrum_length = self.spectrum.shape[-1]
p_coherence=np.zeros((tseries_length,
tseries_length,
tseries_length,
spectrum_length),dtype=complex)
for i in xrange(tseries_length):
for j in xrange(tseries_length):
for k in xrange(t_series_length):
p_coherence[i][j][k]=tsa.coherence_partial_calculate(
self.spectrum[i][j],
self.spectrum[i][i],
self.spectrum[j][j],
self.spectrum[i][k],
self.spectrum[j][k],
self.spectrum[k][k])
return p_coherence
