Equivalent Circuit Fitting

pypalmsens.fitting.CircuitModel fits the equivalent circuit specified with the CDC descriptor code. Optional settings are fixing the value of a parameter, setting the min/max bounds for a parameter, specifying the frequency range to fit, limitting the number of iterations, delta error term or delta parameter term.

Example usage for fitting an equivalent circuit:

>>> import pypalmsens as ps

>>> measurements = ps.load_session_file('eis_data.pssession')
>>> eis_data = measurements[0].eis_data[0]

>>> cdc = 'R(RC)'
>>> model = ps.fitting.CircuitModel(cdc=cdc)
>>> result = model.fit(eis_data)
>>> result
FitResult(cdc='R(RC)', parameters=[564.65, 10077.11, 3.327e-08], chisq=0.00040, exit_code='MinimumDeltaErrorTerm', n_iter=9, error=[1.47, 1.54, 1.92])

CircuitModel takes a single parameter, the circuit description code (CDC). Note that the code must be in all caps. For more information, see this link.

result is an instance of pypalmsens.fitting.FitResult, a dataclass with fit values, errors, and other fitting data. You can pass result.parameters back to pypalmsens.fitting.CircuitModel.fit) to redo the fit:

>>> result = model.fit(eis_data, parameters=result.parameters)

Parameters

If you want to tune the parameters, like fixing values or setting bounds, you can use set them using the pypalmsens.fitting.Parameters class. model.default_parameters grabs the default parameters for the CDC. These can be modified, for example:

>>> parameters = model.default_parameters

>>> parameters[0].value = 123  # set starting value to 123
>>> parameters[0].fixed = True # fix this value
>>> parameters[1].min = 12  # set lower bound
>>> parameters[1].max = 34  # set upper bound

>>> result = model.fit(eis_data, parameters=parameters)

Re-fit EIS data

If you already fitted your data in PSTrace, you can redo the fit or use the values as a starting parameters:

>>> model = ps.fitting.CircuitModel(cdc=eisdata.cdc)
>>> result = model.fit(eisdata, parameters=eisdata.cdc_values)

Plotting

If you have matplotlib installed, you can generate the plots from the result:

>>> result.plot_nyquist(eis_data)
>>> result.plot_bode(eis_data)