Fre = float(fields[0])
if Fre >= FreqLB and Fre <= FreqUB:
F=F+[float(fields[0])]
R=R+[float(fields[1])]
Im=Im+[float(fields[2])]
thetas=thetas+[numpy.arctan(float(fields[2])/float(fields[1]))]
#Form data into complex array
FArr=numpy.array(F)
RArr=numpy.array(R)
ImArr=numpy.array(Im)*1j
TotArr=RArr+ImArr
#Fit the data
fit_result, ParamNames = fit.custom_fitting(F, TotArr, params)
Fitted_variables = fit_result.x
#Obtain the residuals
residuals = fit.res_vec( Fitted_variables, FArr, TotArr)
#Bootstrap to get error and generate final model
boot_params, corr = boot.strap(residuals, FArr, TotArr, Fitted_variables, ParamNames)
result = cir.Z(boot_params, FArr, modelname)
boot_generation = result.z
Real_Boot_Fit = boot_generation.real
Imag_Boot_Fit = boot_generation.imag
Thetas_Fit = []
i=0
for x in Real_Boot_Fit:
theta = numpy.arctan(Imag_Boot_Fit[i]/Real_Boot_Fit[i])
def strap(residuals, FArr, ZArr, params, ParamNames):
#Break apart complex array of original residuals
length = int(len(residuals) / 2)
Real_residuals = residuals[0:length]
Imag_residuals = residuals[length:]
#Break apart original complex array
RArr = ZArr.real
ImArr = ZArr.imag
Modulus = numpy.sqrt(RArr**2 + ImArr**2)
#Obtain the Statistical parameters of the Residuals from original fitting (Needed input = Real + Imaginary Residuals)
Real_res_error = statistics.stdev(Real_residuals)
Imag_res_error = statistics.stdev(Imag_residuals)
Real_res_mean = statistics.mean(Real_residuals)
Imag_res_mean = statistics.mean(Imag_residuals)
#Initialize iterator
i = 0
#Initilize Matrix to carry disributions of fitted variables
Fitted_Variables_Matrix = []
#Initialize Parameters list
global Param_names
#Main Bootstrap. Fitting to be performed x times in i<x
while i < 50:
#Generate a new set of residuals based on the statistics acquired above.
Boot_R_residuals = numpy.random.normal(
Real_res_mean, Real_res_error, size=length) * Modulus
Boot_i_residuals = numpy.random.normal(
Imag_res_mean, Imag_res_error, size=length) * Modulus
#Generate a new data set based on these residuals (Requires original impedance data)
new_r_boot = RArr + Boot_R_residuals
new_i_boot = ImArr + Boot_i_residuals
total_boot = new_r_boot + 1j * new_i_boot
#Check simulated impedance (uncomment to check boots)
#plt.plot(new_r_boot, -new_i_boot)
#plt.savefig("boots.png")
#Perform a fit on newly generated data (Requires parameter guesss)
fit_result, pn = fit.custom_fitting(FArr, total_boot, params)
#Generate a list of variables from fit
Fitted_variables = list(fit_result.x)
Fitted_Variables_Matrix.append(Fitted_variables)
i = i + 1
Param_names = pn
Fitted_Variables_Matrix = pd.DataFrame(Fitted_Variables_Matrix,
columns=ParamNames)
Correlation_Matrix = Fitted_Variables_Matrix.corr()
#Take the standard deviation of each of the fitted variables distributions
Stds = Fitted_Variables_Matrix.std(axis=0)
#Take the average fit
means = Fitted_Variables_Matrix.mean(axis=0)
#Compile all of the fitted variables errors into a single list
evz = list(Stds)
ms = list(means)
#Compile and save these results to a csv
Params = list(zip(Param_names, ms, evz))
Paramsdf = pd.DataFrame(data=Params,
columns=['Parameters', 'Value', 'Error'])
Paramsdf.to_csv('Fitted_Parameters.csv', index=False)
#Clear figure at end of program
plt.clf()
#Return Fitted Params
return ms, Correlation_Matrix
