def calculate(row):
f, z = pp.cropFrequencies(FREQ, row.complex_z, 200)
f, z = pp.ignoreBelowX(f, z)
circuit = 'p(R1,C1)-p(R2,C2)'
guess = [5e+4, 1e-10, 5e+6, 1e-10]
model = modelling.model_impedance(circuit, guess, f, z)
return modelling.get_resistance(model)
def test_circuit_fit():
# Test example circuit from "Getting Started" page
circuit = 'R0-p(R1,C1)-p(R2-Wo1,C2)'
initial_guess = [.01, .01, 100, .01, .05, 100, 1]
results_local = np.array(
[1.65e-2, 8.68e-3, 3.32e0, 5.39e-3, 6.31e-2, 2.33e2, 2.20e-1])
results_global = np.array(
[1.65e-2, 5.34e-3, 0.22, 9.15e-3, 1.31e-1, 1.10e3, 2.78])
# Filter
example_frequencies_filtered, \
Z_correct_filtered = ignoreBelowX(example_frequencies, Z_correct)
# Test local fitting
assert np.isclose(circuit_fit(example_frequencies_filtered,
Z_correct_filtered,
circuit,
initial_guess,
constants={})[0],
results_local,
rtol=1e-2).all()
# Test global fitting on multiple seeds
# All seeds should converge to the same value
# seed = 0
assert np.isclose(circuit_fit(example_frequencies_filtered,
Z_correct_filtered,
circuit,
initial_guess,
constants={},
global_opt=True,
seed=0)[0],
results_global,
rtol=1e-1).all()
# seed = 1
assert np.isclose(circuit_fit(example_frequencies_filtered,
Z_correct_filtered,
circuit,
initial_guess,
constants={},
global_opt=True,
seed=1)[0],
results_global,
rtol=1e-1).all()
# seed = 42
assert np.isclose(circuit_fit(example_frequencies_filtered,
Z_correct_filtered,
circuit,
initial_guess,
constants={},
global_opt=True,
seed=42)[0],
results_global,
rtol=1e-1).all()
def model_conductivity(freq, complex_z, cutoff, circuit, guess):
"""Takes a row of the dataframe and return the model object, the resistance and the rmse.
Args:
row ([type]): [description]
Returns:
model: the model object
resistance: calculated resistance
rmse: the root mean square error on the resistance
"""
f, z = pp.cropFrequencies(np.array(freq), complex_z, cutoff)
f, z = pp.ignoreBelowX(f, z)
model = model_impedance(circuit, guess, f, z)
rmse = fitting.rmse(z, model.predict(f))
resistance = get_resistance(model)
return model, resistance, rmse
def cole(data,
freq,
temp,
freq_min=200,
freq_max=None,
fit=False,
ax=None,
**kwargs):
"""Creates a Cole-Cole plot (imaginary versus real impedance) at a given temperature. Finds the available data closest to the temperature specified by 'temp'. A linear least squares circle fit can be added by setting fit=True.
:param temp: temperature in degrees C
:type temp: float/int
"""
data = conductivity_only(data)
# for temp in temp_list:
[index, Tval] = index_temp(data.temp, temp)
f, z = pp.cropFrequencies(frequencies=np.array(freq),
Z=data.complex_z.iloc[index],
freqmin=freq_min,
freqmax=freq_max)
f, z = pp.ignoreBelowX(f, z)
p = ax.scatter(np.real(z) / 1000, np.abs(np.imag(z)) / 1000, **kwargs)
if fit:
model = data.model.iloc[index]
predicted = model.predict(np.geomspace(0.001, 2000000, 100))
ax.plot(np.real(predicted) / 1000, np.abs(np.imag(predicted)) / 1000)
ax.add_artist(AnchoredText(model.circuit, loc=2))
if not ax.xaxis.get_label().get_text():
ax.axis('equal')
# ax.axis('square')
ax.set_xlim(left=0)
ax.set_ylim(bottom=0)
ax.set_ylabel(r'$-Im(Z) [{}]$'.format(K_OHM))
ax.set_xlabel(r'$Re(Z) [{}]$'.format(K_OHM))
ax.ticklabel_format(style='sci', scilimits=(-3, 4), axis='both')
ax.set_title('Cole-Cole')
return ax
import matplotlib.pyplot as plt
from impedance import preprocessing
from impedance.models.circuits import CustomCircuit
from impedance.visualization import plot_nyquist
if __name__ == '__main__':
frequencies1, Z1 = preprocessing.readZPlot(
'C:\\Users\\luowe\\Desktop\\IRP_code\\30th\\1.z')
frequencies1, Z1 = preprocessing.ignoreBelowX(frequencies1, Z1)
frequencies2, Z2 = preprocessing.readZPlot(
'C:\\Users\\luowe\\Desktop\\IRP_code\\50th\\1.z')
frequencies2, Z2 = preprocessing.ignoreBelowX(frequencies2, Z2)
'''
frequencies3, Z3 = preprocessing.readZPlot(
'C:\\Users\\luowe\\Desktop\\IRP_code\\1st\\3.z')
frequencies3, Z3 = preprocessing.ignoreBelowX(frequencies3, Z3)
frequencies4, Z4 = preprocessing.readZPlot(
'C:\\Users\\luowe\\Desktop\\IRP_code\\1st\\4.z')
frequencies4, Z4 = preprocessing.ignoreBelowX(frequencies4, Z4)
frequencies5, Z5 = preprocessing.readZPlot(
'C:\\Users\\luowe\\Desktop\\IRP_code\\1st\\5.z')
frequencies5, Z5 = preprocessing.ignoreBelowX(frequencies5, Z5)
frequencies6, Z6 = preprocessing.readZPlot(
'C:\\Users\\luowe\\Desktop\\IRP_code\\1st\\6.z')
frequencies6, Z6 = preprocessing.ignoreBelowX(frequencies6, Z6)
frequencies7, Z7 = preprocessing.readZPlot(
def test_ignoreBelowX():
filtered_freq, filtered_Z = ignoreBelowX(frequencies, Z_correct)
assert (np.imag(filtered_Z) == Zi_np).all()
def test_circuit_fit():
# Test trivial model (10 Ohm resistor)
circuit = 'R0'
initial_guess = [10]
results_simple = [10]
frequencies = np.array([10, 100, 1000])
Z_data = np.array([10, 10, 10]) # impedance is constant
assert np.allclose(circuit_fit(frequencies,
Z_data,
circuit,
initial_guess,
constants={},
global_opt=True)[0],
results_simple,
rtol=1e-1)
# Test example circuit from "Getting Started" page
circuit = 'R0-p(R1,C1)-p(R2-Wo1,C2)'
initial_guess = [.01, .01, 100, .01, .05, 100, 1]
bounds = [(0, 0, 0, 0, 0, 0, 0), (10, 1, 1e3, 1, 1, 1e4, 100)]
# results change slightly using predefined bounds
results_local = np.array(
[1.65e-2, 8.68e-3, 3.32, 5.39e-3, 6.31e-2, 2.33e2, 2.20e-1])
results_local_bounds = results_local.copy()
results_local_bounds[5] = 2.38e2
results_local_weighted = np.array(
[1.64e-2, 9.06e-3, 3.06, 5.29e-3, 1.45e-1, 1.32e3, 2.02e-1])
results_global = np.array(
[1.65e-2, 5.34e-3, 0.22, 9.15e-3, 1.31e-1, 1.10e3, 2.78])
# Filter
example_frequencies_filtered, \
Z_correct_filtered = ignoreBelowX(example_frequencies, Z_correct)
# Test local fitting
assert np.allclose(circuit_fit(example_frequencies_filtered,
Z_correct_filtered,
circuit,
initial_guess,
constants={})[0],
results_local,
rtol=1e-2)
# Test local fitting with predefined bounds
assert np.allclose(circuit_fit(example_frequencies_filtered,
Z_correct_filtered,
circuit,
initial_guess,
bounds=bounds,
constants={})[0],
results_local_bounds,
rtol=1e-2)
# Test local fitting with predefined weights
# Use abs(Z), stacked in order of (Re, Im) components
sigma = np.hstack((np.abs(Z_correct_filtered), np.abs(Z_correct_filtered)))
assert np.allclose(circuit_fit(example_frequencies_filtered,
Z_correct_filtered,
circuit,
initial_guess,
sigma=sigma,
constants={})[0],
results_local_weighted,
rtol=1e-2)
# Test if using weight_by_modulus=True produces the same results
assert np.allclose(circuit_fit(example_frequencies_filtered,
Z_correct_filtered,
circuit,
initial_guess,
weight_by_modulus=True,
constants={})[0],
results_local_weighted,
rtol=1e-2)
# Test global fitting on multiple seeds
# All seeds should converge to the same parameter values
# seed = 0 (default)
assert np.allclose(circuit_fit(example_frequencies_filtered,
Z_correct_filtered,
circuit,
initial_guess,
constants={},
global_opt=True)[0],
results_global,
rtol=1e-1)
# seed = 0, with predefined bounds
assert np.allclose(circuit_fit(example_frequencies_filtered,
Z_correct_filtered,
circuit,
initial_guess,
constants={},
global_opt=True,
bounds=bounds,
seed=0)[0],
results_global,
rtol=1e-1)
# seed = 1
assert np.allclose(circuit_fit(example_frequencies_filtered,
Z_correct_filtered,
circuit,
initial_guess,
constants={},
global_opt=True,
seed=1)[0],
results_global,
rtol=1e-1)
# seed = 42
assert np.allclose(circuit_fit(example_frequencies_filtered,
Z_correct_filtered,
circuit,
initial_guess,
constants={},
global_opt=True,
seed=42)[0],
results_global,
rtol=1e-1)
import matplotlib.pyplot as plt
from impedance import preprocessing
from impedance.models.circuits import CustomCircuit
from impedance.visualization import plot_nyquist
circuit = 'R0-p(R1,C1)-p(R2-Wo1,C2)'
initial_guess = [.1, .01, .1, .05, .001, 20, 1]
circuit = CustomCircuit(circuit, initial_guess=initial_guess)
if __name__ == '__main__':
# Read and fit EIS data
frequencies, Z = preprocessing.readZPlot(
'C:\\Users\\luowe\\Desktop\\IRP_code\\Battery_Data\\50th\\10.z')
frequencies, Z = preprocessing.ignoreBelowX(frequencies, Z)
circuit.fit(frequencies, Z)
para = circuit.parameters_
# Generate training data
f = 'training data.txt'
with open(f, "a") as file:
file.write('50' + ',' + str(para[0]) + ',' + str(para[1]) + ',' +
str(para[3]) + '\n')
file.close()
# Plot data curve and fit curve
Z_fit = circuit.predict(frequencies)
fig, ax = plt.subplots()
plot_nyquist(ax, Z, fmt='o')
plot_nyquist(ax, Z_fit, fmt='-')
plt.legend(['Data', 'Fit'])
plt.show()
