def plot_generation(self, windex, loser_idx, scores, sim_array, generation,
data, axes, plot_count):
idxes = [windex, loser_idx]
titles = ["Winner", "Loser"]
dict_constructor = EIS()
for i in range(0, len(idxes)):
circuit_ax = axes[0][plot_count + i]
idx = idxes[i]
circuit_dict = dict_constructor.translate_tree(generation[idx])
circuit_artist(circuit_dict, circuit_ax)
circuit_ax.set_title(titles[i])
circuit_ax.set_axis_off()
data_ax = axes[1][plot_count + i]
data_ax.plot(data[:, 0], -data[:, 1])
sim_data = sim_array[idx]
data_ax.plot(sim_data[:, 0], -sim_data[:, 1])
data_ax.set_title(round(scores[idx], 3))
import matplotlib.pyplot as plt
import numpy as np
from EIS_class import EIS
from EIS_optimiser import EIS_genetics, EIS_optimiser
from circuit_drawer import circuit_artist
import math
from scipy import special
from scipy import fft as syft
ZARC = {"z1": ("Q1", "alpha1"), "z2": "R1"}
def zarc(frequency, q, alpha, r):
return r + (1 / (q * (1j * frequency)**alpha))
"""translator=EIS()
circuit_artist(ZARC)
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}
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:
if "Q" in key:
cpe_num += 1
if "gamma1" in netlist_keys:
continue
f = open("temp_netlist.net", "w")
f.write("V1 0 1 1\n")
sim_dict = dict(zip(params, results_dict["parameters"][j]))
for key in netlist_keys:
if "R" in key or "C" in key:
import os
import sys
dir = os.getcwd()
dir_list = dir.split("/")
source_list = dir_list[:-1] + ["src"]
source_loc = ("/").join(source_list)
sys.path.append(source_loc)
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
randles = {"z1": "R0", "z2": {"p1": ("Q1", "alpha1"), "p2": ["R2", "W1"]}}
translator = EIS()
circuit_artist(randles)
ax = plt.gca()
ax.set_axis_off()
plt.show()
frequency_powers = np.arange(1, 6, 0.1)
frequencies = [10**x for x in frequency_powers]
bounds = {
"R1": [0, 1000],
"Q1": [0, 1e-2],
"alpha1": [0.1, 0.9],
"R2": [0, 1000],
"W1": [1, 200]
}
true_params = {
"R1": 100,
"Q1": 1e-3,
"alpha1": 0.5,
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"],
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()
'p2': ('Q1', 'alpha1')
},
'p2': ["R4", {
'p1': 'R3',
'p2': 'W2'
}]
},
'z0': 'R0'
}
"""distances_dict={"z1":{"x_pos":1,"y_pos":0.5,
"p1":
{"name":"R1", "y_pos":0.5+0.25, "x_pos":1},
"p2":
{"name":"R2", "y_pos":0.5-0.25, "x_pos":1}},
'z2':{"x_pos": 2,"y_pos":0.5,
"p1":
[{"name":"R4", "y_pos":0.5+0.25, "x_pos":2-0.25}, {'p1':
{"name":"W1", "y_pos":0.5+0.25+(0.25/2), "x_pos":2+0.25},
'p2':
{"name":"CPE1", "y_pos":0.5+0.25-(0.25/2), "x_pos":2+0.25},
}],
'p2':
{'p1':
{"name":"R3", "y_pos":0.5-0.25+(0.25/2), "x_pos":2},
'p2':
{"name":"W2", "y_pos":0.5-0.25-(0.25/2), "x_pos":2}
}
}
}"""
test = circuit_artist(test_dict)
"left_series":{"left_element":"R", "right_element":"C"},
"right_paralell":{"left_series":{"right_element":"R", "left_element":"R"}, "right_element":"R"}},"right_element":"C"},
}
triple_randles={"root_series":{"left_paralell":{"left_element":"R", "right_element":"R"},
"right_series":
{"left_paralell":{"left_element":"R", "right_element":"R"}, "right_series":
{"left_paralell":{"left_element":"R", "right_element":"R"}, "right_paralell":{"left_element":"R", "right_element":"R"}}}}}
z=test.construct_dict_from_tree(triple_randles["root_series"], "root_series")
#print(test.random_circuit_tree(3))
random_tree=test.random_circuit_tree(4)
#top_key=list(random_tree.keys())[0]
#random_dict=test.construct_dict_from_tree(random_tree[top_key], top_key)
circuit_artist(test.translate_tree(random_tree))
top_key=test.get_top_key(random_tree)
#print(random_tree)
#print(random_dict)
#print(test.construct_circuit_from_tree_dict(random_dict))
import random
get_node=test.find_random_node(random_tree[top_key], random.randint(1, 4), random_tree)
new_tree=test.random_circuit_tree(3)
crossed_tree=test.crossover(random_tree, new_tree)
circuit_artist(test.translate_tree(crossed_tree))
#print("\n")
#print(new_tree)
#print(replace_tree)
#print(get_node)
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]
file_name = "AIC_best_scores.npy"
scores = np.load(file_name, allow_pickle=True).item()
for i in range(0, len(scores["scores"])):
#print(scores["scores"][i])
plt.plot(scores["scores"][i])
#plt.axhline(scores["true_score"][i])
plt.show()
translator = EIS()
print(scores["circuits"][i])
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))
#plt.show()
for i in range(1, 6):
file_name = "Best_candidates/round_2/BIC/Best_candidates_dict_{0}_12_gen.npy".format(
i)
results_dict = np.load(file_name, allow_pickle=True).item()
#print(results_dict[0]["scores"])
fig, ax = plt.subplots(2, 6)
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], dpi=500)
ax[0, j].set_axis_off()
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],
label="Target")
simulator.plot_data(sim_data, ax[1, j], label="Sim")
ax[1, j].set_xlabel("$Z_r$")
ax[1, j].set_ylabel("-$Z_i$")
except:
print(file_name)