Validation¶
EIS data fundamentally relies on the conditions of linearity,
Measurement Model¶
Testing your data with the measurement model is straightforward:
import matplotlib.pyplot as plt
import numpy as np
import sys
sys.path.append('../')
from eisfit import validation # noqa E402
data = np.genfromtxt('./data/exampleData.csv', delimiter=',')
f = data[:, 0]
Z = data[:, 1] + 1j*data[:, 2]
mask = np.imag(Z) < 0
model_list, error_list = validation.measurementModel(f, Z, max_k=25)
fig = plt.figure()
plt.plot(Z.real, -Z.imag, 'o')
for model in model_list:
Z_fit = model.predict(f)
plt.plot(Z_fit.real, -Z_fit.imag)
fig2, ax2 = plt.subplots()
ax2.plot(range(1, len(error_list)+1), error_list)
ax2.set_yscale('log')
ax2.set_ylabel('Root Mean Squared Error')
ax2.set_xlabel('Number of RC elements')
plt.show()
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eisfit.validation.measurementModel(frequencies, impedances, algorithm='SLSQP', max_k=7, R_guess=0.1, C_guess=10)[source]¶ Runs a measurement model test for validating impedance data
Iteratively add RC circuit elements until the error converges. If error does not converge, it indicates that the data doesn’t meet standards for linearity.
Notes
\[RMSE = R_0 + \sum_{0}^{k} R_i || C_i\]- frequencies: np.ndarray
- A list of frequencies to test
- impedances: np.ndarray of complex numbers
- A list of values to match to
- max_k: int
- The maximum number of RC elements to fit
- initial_guess: np.ndarray
- Initial guesses for R and C elements