ax=plt.gca()
ax.set_axis_off()
plt.show()"""
frequency_powers = np.arange(0, 6, 0.2)
frequencies = [10**x for x in frequency_powers]
omegas = np.multiply(frequencies, 2 * math.pi)
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
ax=plt.gca()
ax.set_axis_off()
plt.show()"""
frequency_powers=np.arange(1, 2, 0.2)
frequencies=[10**x for x in frequency_powers]
omegas=np.multiply(frequencies, 2*math.pi)
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":0.5}
param_names=["R1", "Q1", "alpha1"]
sim=EIS_genetics()
fig, ax=plt.subplots()
#sim.plot_data(sim.dict_simulation(ZARC, frequencies, [], [true_params[x] for x in param_names], param_names),ax)
num_oscillations=5
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
from EIS_class import EIS
import numpy as np
import matplotlib.pyplot as plt
from EIS_optimiser import EIS_optimiser, EIS_genetics
from circuit_drawer import circuit_artist
frequency_powers = np.arange(1, 6, 0.1)
frequencies = [10**x for x in frequency_powers]
for i in range(1, 6):
file_name = "Best_candidates/round_2/{1}/Best_candidates_dict_{0}_12_gen.npy".format(
i, "AIC")
results_dict = np.load(file_name, allow_pickle=True).item()
#print(results_dict[0]["scores"])
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)
#translated_circuit={'z1': {'p1': {'p1': [[{'p1': {'p1': ['C1', 'R1'], 'p2': {'p1': 'R2', 'p2': 'C2'}}, 'p2': {'p1': 'W1', 'p2': 'R3'}}, {'p1': 'W2', 'p2': 'C3'}], ["C12", "C13"]], 'p2': ['W3', 'C4']}, 'p2': {'p1': ('Q1', 'alpha1'), 'p2': 'R4'}}, 'z2': {'p1': 'R5', 'p2': {'p1': "C14", 'p2': 'W4'}}, 'z0': 'R0'}
print(len(results_dict["parameters"][j]))
print(len(params))
circuit_artist(translated_circuit)
netlist = EIS(circuit=translated_circuit, construct_netlist=True)
netlist_keys = list(netlist.netlist_dict.keys())
net_dict = netlist.netlist_dict
cpe_num = 0
for key in netlist_keys:
#plt.plot(noisy_data[:,0], -noisy_data[:,1])
#plt.show()
for methods in ["None"]:
results_list = []
results_circuit = []
true_score = []
for i in range(0, 5):
noisy_data = np.column_stack(
(optim.add_noise(sim[:, 0],
sigma), optim.add_noise(sim[:, 1], sigma)))
#print(sigma, optim.get_std(noisy_data, sim), optim.optimise(noisy_data, sigma,"minimisation", [true_params[x] for x in param_names]))
#gene_test=EIS_genetics(generation_size=8, generation_test=True, selection="bayes_factors")#, generation_test_save="Generation_images/Bayes_factor_randles_test/round_1/")
#gene_test.evolve(frequencies, noisy_data)
gene_test = EIS_genetics(generation_size=12,
generation_test=False,
selection=methods,
initial_tree_size=1,
best_record=True,
num_top_circuits=6)
value, _ = gene_test.assess_score(randles,
param_names,
frequencies,
noisy_data,
score_func=methods)
print(value, "value")
true_score.append(value)
num_generations = 5
gene_test.evolve(frequencies,
noisy_data,
num_generations=num_generations)
np.save(
"Best_candidates/round_3/{0}/Best_candidates_dict_{1}_12_gen.npy".
file_name = "Best_candidates/round_3/{1}/Best_candidates_dict_{0}_12_gen.npy".format(
i, "None")
results_dict = np.load(file_name, allow_pickle=True).item()
#print(results_dict[0]["scores"])
fig = plt.figure()
ax = [[0 for x in range(0, 6)] for y in range(0, 3)]
gs = fig.add_gridspec(3, 6)
for m in range(0, 2):
for j in range(0, 6):
ax[m][j] = fig.add_subplot(gs[m, j])
td_ax = fig.add_subplot(gs[2, :])
#plt.show()
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)
#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"],
ax=plt.gca()
ax.set_axis_off()
plt.show()"""
frequency_powers = np.arange(0, 6, 0.2)
frequencies = [10**x for x in frequency_powers]
omegas = np.multiply(frequencies, 2 * math.pi)
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[0], label="Analytic frequency simulation")
num_oscillations = 3
time_start = 0
sampling_rate = 200
def cpe1(denom, i, charge_array, time_array, dt):
total = 0