Customizing the solver¶
The previous example Fitting experimental data is chosen again.
This time, noise and outliers are added to the data
# parameters
frequencies = numpy.logspace(0, 8, num=50)
Rct = 100.
Rs = 20.
Aw = 300.
C0 = 25e-6
When running the fit with the standard least-squares solver, we obtain
Instead, we can customize the solver:
df = pandas.DataFrame(data=data)
df.to_csv('test.csv', index=False)
# initialise fitter with verbose output
fitter = impedancefitter.Fitter('CSV', LogLevel='DEBUG')
and obtain
