def sigmaHat(self, t, ColeColefunc=ColeCole):
etc = np.c_[self.curModel.eta, self.curModel.tau, self.curModel.c]
unqEtc, uETCind, invInd = uniqueRows(etc)
sigmaHat = []
for (ev, tv, cv) in unqEtc:
if ev == 0.:
sigmaHat.append(0.)
else:
key = '%.5e_%.5f_%.5e_%.5f' % (t, ev, tv, cv)
if self.sigmaHatDict.has_key(key):
val = self.sigmaHatDict[key]
else:
if t == 0:
# val = transFilt(F,1e-6)
val = ev / (tv * (1. - ev))
else:
F = lambda frq: (1j * 2 * np.pi * frq) * ColeColefunc(
frq, sigmaInf=1., eta=ev, tau=tv, c=cv)
val = transFilt(F, t)
self.sigmaHatDict[key] = val
sigmaHat.append(val)
sigmaHat = self.curModel.sigmaInf * np.array(sigmaHat)[invInd]
return sigmaHat
def sigmaHat(self, t, ColeColefunc=ColeCole):
etc = np.c_[self.curModel.eta, self.curModel.tau, self.curModel.c]
unqEtc, uETCind, invInd = uniqueRows(etc)
sigmaHat = []
for (ev, tv, cv) in unqEtc:
if ev == 0.:
sigmaHat.append(0.)
else:
key = '%.5e_%.5f_%.5e_%.5f' %(t, ev, tv, cv)
if self.sigmaHatDict.has_key(key):
val = self.sigmaHatDict[key]
else:
if t == 0:
# val = transFilt(F,1e-6)
val = ev/(tv*(1.-ev))
else:
F = lambda frq: (1j*2*np.pi*frq)*ColeColefunc(frq, sigmaInf=1., eta=ev, tau=tv, c=cv)
val = transFilt(F,t)
self.sigmaHatDict[key] = val
sigmaHat.append(val)
sigmaHat = self.curModel.sigmaInf*np.array(sigmaHat)[invInd]
return sigmaHat
def CCTF(t, sigmaInf, eta, tau, c):
F = lambda frq: (1j * 2 * np.pi * frq) * ColeCole(
frq, sigmaInf=sigmaInf, eta=eta, tau=tau, c=c)
return transFilt(F, t)
def CCTF(t, sigmaInf, eta, tau, c):
F = lambda frq: (1j*2*np.pi*frq)*ColeCole(frq, sigmaInf=sigmaInf, eta=eta, tau=tau, c=c)
return transFilt(F,t)
