"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)
total = np.sum(np.multiply(time_array, charge_array))
return dt * total / denom
circuit_artist(translator.translate_tree(scores["circuits"][i][0]))
plt.show()
for i in range(1, 6):
file_name = "Best_candidates/AIC_best_candidates_dict_{0}_8_gen_3.npy".format(
i)
results_dict = np.load(file_name, allow_pickle=True).item()
#print(results_dict[0]["scores"])
fig, ax = plt.subplots(2, 5)
for j in range(0, len(results_dict["scores"])):
translator = EIS()
simulator = EIS_genetics()
translated_circuit, params = translator.translate_tree(
results_dict["models"][j], get_param_list=True)
print(len(results_dict["parameters"][j]))
print(len(params))
circuit_artist(translated_circuit, ax[0, j])
try:
sim_data = simulator.tree_simulation(results_dict["models"][j],
frequencies,
results_dict["data"],
results_dict["parameters"][j])
simulator.plot_data(results_dict["data"], ax[1, j])
simulator.plot_data(sim_data, ax[1, j])
except:
print(file_name)
ax[0, j].set_title(results_dict["scores"][j])
plt.show()
results_dict["models"][j], get_param_list=True)
#translated_circuit={'z1': {'p1': [['C1', ("Q1", "alpha1")], ['R1', "C6"]],
# 'p2': {'p1': [{'p1': "C7", 'p2': 'R2'}, [{'p1': 'C8', 'p2': 'R3'}, {'p1': 'C2', 'p2': ("Q2", "alpha2")}]], 'p2': ["C5", 'C3']}},
# 'z2': 'C4', 'z0': 'R0'}
print(translated_circuit)
#print(translated_circuit)
#print(len(results_dict["parameters"][j]))
#print(len(params))
circuit_artist(translated_circuit, ax[0][j])
ax[0][j].set_axis_off()
sim_data = simulator.tree_simulation(results_dict["models"][j],
frequencies, results_dict["data"],
results_dict["parameters"][j])
simulator.plot_data(results_dict["data"], ax[1][j], label="Target")
simulator.plot_data(sim_data, ax[1][j], label="Sim")
ax[1][j].set_xlabel("$Z_r$")
ax[1][j].set_ylabel("-$Z_i$")
sim_dict = dict(zip(params, results_dict["parameters"][j]))
#for i in range(5, 9):
# sim_dict["C"+str(i)]=1
td = time_domain(translated_circuit, params=sim_dict)
c, t, p = td.simulate()
plt.plot(p, c)
plt.show()
ax[0][j].set_title(round(results_dict["scores"][j], 2))
ax[1][2].legend()
plt.show()
potential_period = np.where((ss_time > 0.5 * k * (interval))
& (ss_time < (k + 1) * interval * 0.5))
ax[1].plot(ss_time[potential_period], subbed[potential_period])
subbed_interval = subbed[potential_period]
cross_times[k -
2] = ss_time[np.where(subbed == min(subbed_interval))][0]
if k - 2 == (cross_loc):
break
ax[1].axvline(cross_times[cross_loc])
ax[0].axvline(cross_times[cross_loc])
plt.show()
t_cross = cross_times[cross_loc]
magnitude = max(ss_potential) / max(ss_current)
phase_numerator = 2 * math.pi * t_cross - t_end - t0
phase_denom = t_end - t0
phase = phase_numerator / phase_denom
calculated_impede = magnitude * np.exp(1j * phase)
impede[j, 0] = np.real(calculated_impede)
impede[j, 1] = np.imag(calculated_impede)
impede[j, 2] = np.real(z_freq)
impede[j, 3] = np.imag(z_freq)
ax[0].set_xlabel("$Z_r$")
ax[0].set_ylabel("$-Z_i$")
ax[1].set_xlabel("$Z_r$")
ax[1].set_ylabel("$-Z_i$")
sim.plot_data(impede[:, 2:], ax[1], label="FT method")
sim.plot_data(impede[:, :2], ax[1], label="Amp+phase method")
ax[1].legend()
ax[0].legend()
plt.show()