def nxc_from_PC(self, filt=slice(None)):
corr_index = int(self.raw.shape[0] * 0.02)
V_PC_ill = self.data()['Voc'][filt]
V_PC_dark = np.mean(self.raw['Voc'][:corr_index])
#print(V_PC_dark)
del_sigma = self.A * (V_PC_ill - self.C)**2 + self.B * (
V_PC_ill - self.C) - (self.A * (V_PC_dark - self.C)**2 + self.B *
(V_PC_dark - self.C))
# Create an instance of a mobility object
MOB = Mobility()
"""
TEMPERATURE DEPENDENCE - OK
Here the mobility_sum is calculated using the temperature data input directly from the data file name.
"""
def nxc_PC(x):
return del_sigma / (C.e * self.W * MOB.mobility_sum(
Na=self.Na, Nd=self.Nd, temp=self.T, nxc=x))
nxc_guess = del_sigma / (C.e * self.W * MOB.mobility_sum(
Na=self.Na,
Nd=self.Nd,
temp=self.T,
nxc=np.ones_like(del_sigma) * 1e15))
nxc = self.find_iteratively(nxc_guess, nxc_PC)
return nxc
def nxc_from_PC(self, filt=slice(None)):
corr_index = int(self.raw.shape[0]*0.02)
V_PC_ill = self.data()['Voc'][filt]
V_PC_dark = np.mean(self.raw['Voc'][:corr_index])
#print(V_PC_dark)
del_sigma = self.A*(V_PC_ill-self.C)**2 + self.B*(V_PC_ill-self.C) - (self.A*(V_PC_dark-self.C)**2 + self.B*(V_PC_dark-self.C))
MOB = Mobility()
def nxc_PC(x):
return del_sigma / (C.e * self.W * MOB.mobility_sum(Na=self.Na, Nd=self.Nd, temp=self.T, nxc=x))
nxc_guess = del_sigma / (C.e * self.W * MOB.mobility_sum(Na=self.Na, Nd=self.Nd, temp=self.T, nxc=np.ones_like(del_sigma)*1e15))
nxc = self.find_iteratively(nxc_guess, nxc_PC)
return nxc
