bounds = {
"R1": [0, 1000],
"Q1": [0, 1e-2],
"alpha1": [0.1, 0.9],
"R2": [0, 1000],
"W1": [1, 200]
}
true_params = {"R1": 10, "Q1": 0.001, "alpha1": 0.8}
param_names = ["R1", "Q1", "alpha1"]
sim = EIS_genetics()
normaliser = EIS(circuit=ZARC,
parameter_bounds=bounds,
parameter_names=param_names,
test=True,
fitting=True)
sim1 = normaliser.simulate([true_params[x] for x in param_names], omegas)
fig, ax = plt.subplots(1, 2)
sim.plot_data(sim.dict_simulation(ZARC, omegas, [],
[true_params[x] for x in param_names],
param_names),
ax[1],
label="Analytic frequency simulation")
num_oscillations = 3
time_start = 0
sampling_rate = 200
def cpe1(denom, i, charge_array, time_array, dt):
total = 0
if i != 0:
time_array = np.flip(time_array)
for R3 in [10, 50, 100, 150, 200, 250]:
for i in range(0, len(frequencies)):
params = {
"R1": 10,
"R3": R3,
"C2": 1e-3,
"R2": 250,
"Q1": 2e-3,
"omega": frequencies[i],
"C1": 1e-6,
"W1": 40,
"alpha1": 1
}
#spectra[0][i]=gamry_randles(**params)
start = time.time()
spectra[1][i] = test.simulate(**params)
times[1] = time.time() - start
print(times)
#plt.plot(np.real(spectra[0,:]), -np.imag(spectra[0,:]), label="hard")
ax[0, -1].plot(np.real(spectra[1, :]), -np.imag(spectra[1, :]), label=R3)
ax[0, -1].scatter(
np.real(spectra[1, :])[0::8], -np.imag(spectra[1, :])[0::8])
plt.legend()
ax[0, -1].set_title("R3")
for C2 in [1e-6, 1e-5, 1e-4, 1e-3, 1e-2, 1e-1]:
for i in range(0, len(frequencies)):
params = {
"R1": 10,
"R3": 100,
"C2": C2,
"R2": 250,