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 getGamma(self, dt):
setc = np.c_[self.curModel.sigmaInf, self.curModel.eta, self.curModel.tau, self.curModel.c]
unqEtc, uETCind, invInd = uniqueRows(setc)
gammaVals = []
for (sv, ev, tv, cv) in unqEtc:
if ev == 0:
gammaVal = 0.
elif cv == 1.:
gammaVal = (dt/2.)*sv*ev/((1. - ev)*tv)
else:
m, d = self.getMD(dt, sv, ev, tv, cv)
gammaVal = (m*dt**cv)/(cv*(cv+1)) + d*dt/2.
gammaVals.append(gammaVal)
gamma = np.array(gammaVals)[invInd]
return gamma
def getGamma(self, dt):
setc = np.c_[self.curModel.sigmaInf, self.curModel.eta,
self.curModel.tau, self.curModel.c]
unqEtc, uETCind, invInd = uniqueRows(setc)
gammaVals = []
for (sv, ev, tv, cv) in unqEtc:
if ev == 0:
gammaVal = 0.
elif cv == 1.:
gammaVal = (dt / 2.) * sv * ev / ((1. - ev) * tv)
else:
m, d = self.getMD(dt, sv, ev, tv, cv)
gammaVal = (m * dt**cv) / (cv * (cv + 1)) + d * dt / 2.
gammaVals.append(gammaVal)
gamma = np.array(gammaVals)[invInd]
return gamma
