def multi_fit(data_dir: str, plot: str = "best") -> FittedMultiData:
"""Fits all files in a folder.
if plot = "best", plots best fitted data
if plot = "all", plots all
if plot = "", plot none
"""
group_fitted = {}
group_fitted_list = []
for filepath in reversed(list(Path(data_dir).glob('*.csv'))):
filename = str(filepath.name)
array = open_file_numpy(data_dir, filename)
data = get_data_numpy(array)
(freq_log, imped_log, phase, imag_imped, real_imped) = data
single_data = SingleData(data=data, params=get_params(data_dir, filename))
# print(single_data)
fitted_dict = fit(data_dir, filename, plot=plot)
best_fit, chi2, f, Z, Z_fit, circuit_str, voltage = list(fitted_dict.values())[0]
R0, R1, R2, C2, CPP2, C1, CPP = best_fit
print(chi2, R0, R1, R2, C2, C1, CPP, CPP2)
fit_dc = fit_single_data(stored_data=single_data, fit_params=best_fit,
chi2=chi2, circuit=circuit_str, Z_fit=Z_fit)
assert fit_dc.voltage == voltage
if plot == "best" or "both":
import matplotlib.pyplot as plt
fig, ax = plt.subplots()
plot_nyquist(ax, Z, fmt='o')
plot_nyquist(ax, Z_fit, fmt='-')
plt.legend(['Data', 'Fit'])
plt.title(f"{voltage}, chi2={chi2:.3f}") # , R2={best_fit[1]}, C2={best_fit[0]}, R3={R3}, C3={C3} ")
# plt.show()
fig.set_size_inches(18.5, 10.5, forward=True)
plt.savefig(Rf'{data_dir}\{fit_dc.voltage}_bestfit2Rp.png',
bbox_inches='tight', dpi=500, transparent=True)
with open(R".\res.csv", 'a') as f:
f.write(f"{fit_dc.voltage}, \t {R0}, {R1}, {C1}, {CPP}, {R2}, {C2}, {CPP2} \n")
group_fitted[fit_dc.voltage] = {
'circuit_str': circuit_str,
'fit': best_fit,
'chi2': chi2,
'data': (f, Z, Z_fit),
'voltage': voltage,
'invsqc1': 1 / (C1 ** 2 * 1000000000000),
'invsqc2': 1 / (1 / C2 ** 2 * 1000000000000)
}
group_fitted_list.append(fit_dc)
group_fitted_dc = FittedMultiData(group_fitted_list)
# group_fitted_json = json.dumps(group_fitted, sort_keys=True, indent=4, separators=(',', ': '))
# with open(Rf'{data_dir}\fitted_data.json', 'w') as f:
# f.write(group_fitted_json)
return group_fitted_dc
def main() -> None:
data_dir = str(Path.cwd())
settings = get_json_settings('settings')
both_plot_exists = False
phase_plot_exists = False
gain_plot_exists = False
nyquist_exists = False
voltages = []
for filepath in Path(data_dir).glob('*.csv'):
filename = filepath.name
params = get_params(data_dir,
filename) # get parameters from file name
pprint.pprint(params.voltage)
df = open_file_numpy(
data_dir, filename) # open file if csv and read into numpy array
if df is not None and df.any():
data = get_data_numpy(
df) # return impedance (5 columns) or cv data (4 columns)
else:
data = None
if data and len(data) == 5:
write_imp_data(data, params) # writes to csv
write_zview_data(data, params)
(freq_log, imped_log, phase, imag_imped, real_imped) = data
if not both_plot_exists:
bode_both = multi_bode_axes(settings, y_axes="both") # create axes
both_plot_exists = True # make truthy
multi_bode_add_data(bode_both, freq_log, imped_log, phase, params)
if not phase_plot_exists:
bode_phase = multi_bode_axes(settings,
y_axes="phase") # create axes
phase_plot_exists = True # make truthy
# multi_bode_title(bode_phase, params, settings)
multi_bode_add_data(bode_phase, freq_log, imped_log, phase, params)
if not gain_plot_exists:
bode_gain = multi_bode_axes(settings, y_axes="gain") # create axes
gain_plot_exists = True # make truthy
multi_bode_add_data(bode_gain, freq_log, imped_log, phase, params)
if not nyquist_exists:
nyquist = multi_nyquist_axes(settings)
nyquist_exists = True
multi_nyquist_add_data(nyquist, imag_imped, real_imped, params)
voltages.append(params.voltage)
multi_bode_plot_save(bode_both, params)
multi_bode_plot_save(bode_phase, params)
multi_bode_plot_save(bode_gain, params)
multi_nyquist_plot_save(nyquist, params) # , set_colors='twilight',)
def multi_fit(data_dir: str,
plot: str = "both",
output_dir: str = "") -> FittedMultiData:
"""Fits all files in a folder.
if plot = "best", plots best fitted data
if plot = "all", plots all
if plot = "", plot none
"""
data_path = Path(data_dir)
output_path = Path(
output_dir) if output_dir else data_path.parent / "output"
group_fitted = {}
group_fitted_list = []
for filepath in list(data_path.glob('*.csv'))[1:]:
filename = str(filepath.name)
array = open_file_numpy(data_dir, filename)
data = get_data_numpy(array)
(freq_log, imped_log, phase, imag_imped, real_imped) = data
single_data = SingleData(data=data,
params=get_params(data_dir, filename))
# print(single_data)
fitted_dict = fit(data_dir, filename, plot=plot)
if plot in ("all", "both"):
number_printed = 21
elif plot == "best":
number_printed = 1
for i in range(number_printed):
loop_fit, chi2_loop, f_array, Z_loop, Z_fit_loop, circuit_str, voltage = list(
fitted_dict.values())[i]
fit_dc = FittedSingleData(stored_data=single_data,
fit_params=loop_fit,
chi2=chi2_loop,
circuit=circuit_str,
Z_fit=Z_fit_loop)
R0, R1, C1, CPP, R2, C2, CPP2, R3, C3, CPP3 = loop_fit
import matplotlib.pyplot as plt
fig, ax = plt.subplots()
plot_nyquist(ax, Z_loop, fmt='o')
plot_nyquist(ax, Z_fit_loop, fmt='-')
plt.legend(['Data', 'Fit'])
plt.title(f"{voltage}_{chi2_loop:.4f}")
fig.set_size_inches(8.5, 5.5, forward=True)
plt.gcf().text(
1.1,
0.1,
f"R0={R0: .0f}\n\n R1={R1: .0f}\n C1={C1}\n CPE1={CPP}\n\n R2={R2: .0f}\n C2={C2}\n CPE2={CPP2}\n\n R3={R3:.0f}\n C3={C3}\n CPE3={CPP3}\n\n {circuit_str}\n\n chi2v={chi2_loop}",
fontsize=12)
chi_str = f"{chi2_loop: .8f}".replace('.', '_')
plt.savefig(
Rf'{output_path}\{voltage}_chi_{chi_str}_{circuit_str}.png',
bbox_inches='tight',
dpi=500,
transparent=True)
if i == 0 and plot in ("best", "both"):
plt.savefig(
Rf'{output_path}\best\{voltage}_chi_{chi_str}_{circuit_str}_b.png',
bbox_inches='tight',
dpi=500,
transparent=True)
with open(R".\res_best.csv", 'a') as data_f:
data_f.write(
f"""{fit_dc.voltage}, \t \n {chi2_loop} {R0}, {R1}, {C1}, {CPP}, {R2}, {C2},
{CPP2}, {R3}, {C3}, {CPP3}, \n""")
plt.close()
# plt.show()
group_fitted[fit_dc.voltage] = {
'circuit_str': circuit_str,
'fit': loop_fit,
'chi2': chi2_loop,
'data': (f_array, Z_loop, Z_fit_loop),
'voltage': voltage,
'invsqc1': 1 / (C1**2),
'invsqc2': 1 / (C2**2)
}
# group_fitted_json = json.dumps(group_fitted, sort_keys=True, indent=4, separators=(',', ': '))
# with open(Rf'{data_dir}\fitted_data.json', 'w') as f:
# f.write(group_fitted_json)
group_fitted_list.append(fit_dc)
group_fitted_dc = FittedMultiData(group_fitted_list)
return group_fitted_dc
def fit(data_dir, filename, plot: str = ""):
array = open_file_numpy(data_dir, filename)
params = get_params(data_dir, filename)
print(array.shape)
data = get_data_numpy(array)
(freq_log, imped_log, phase, imag_imped, real_imped) = data
freq = np.power(10, freq_log)
# np.savetxt("zzz.csv", np.concatenate((freq, real_imped, -imag_imped), axis=1), delimiter=",")
# f, Z = preprocessing.readCSV("zzz.csv")
f = freq[:, 0]
Z = np.array(real_imped + 1j * -imag_imped, dtype=np.complex)[:, 0]
circuit_str_5a = 'R0-p(R1,CPE1)-p(R2,CPE2)-p(R3,CPE3)'
R0_guesses = [6850]
R1_guesses = [1E7, 1E6]
C1_guesses = [5E-8, 1E-8, 5E-9]
CPP_guesses = [0.8, 0.9, 1.0]
R2_guesses = [1E7, 1E5, 1E6, 1E4]
C2_guesses = [1E-6, 1E-7, 1E-8]
CPP2_guesses = [0.9, 0.8, 1.0]
R3_guesses = [1E7, 1E5, 1E6]
C3_guesses = [1E-5, 1E-6, 1E-7, 1E-8]
CPP3_guesses = [0.5]
initial_guesses_5a = []
for R0 in R0_guesses:
for R1 in R1_guesses:
for C1 in C1_guesses:
for CPP in CPP_guesses:
for R2 in R2_guesses:
for C2 in C2_guesses:
for CPP2 in CPP2_guesses:
for R3 in R3_guesses:
for CPP3 in CPP3_guesses:
for C3 in C3_guesses:
initial_guesses_5a.append([
R0, R1, C1, CPP, R2, C2, CPP2,
R3, C3, CPP3
])
num_init_guesses = len(initial_guesses_5a)
print(f"{num_init_guesses} guess combinations for {circuit_str_5a}")
assert all(initial_guesses_5a), "Cannot use 0 for any initial guesses"
chi_sentinal = 0.1
num_remaining = num_init_guesses
fitted_dict = {}
for initial_guess in initial_guesses_5a:
# print(params.voltage, initial_guess)
num_remaining -= 1
circuit = CustomCircuit(circuit=circuit_str_5a,
initial_guess=initial_guess)
circuit.fit(
f,
Z,
global_opt=False,
bounds=(
# R0 R1 C1 CPP1 R2 C2 CPP2 R3 C3 CPP3"
[6800, 0, 1E-9, 0.78, 0, 0, 0.78, 0, 0, 0.48],
[7000, np.inf, 1E-6, 1, np.inf, 1E-5, 1, np.inf, 1E-5, 0.52]))
fitted_params = circuit.parameters_
R0, R1, C1, CPP, R2, C2, CPP2, R3, C3, CPP3 = circuit.parameters_
Z_fit = circuit.predict(f)
dof = len(Z) - len(
initial_guess
) # Degrees of freedom - i.e. number of data points - number of variables
Re_Z = np.real(Z) # Extract real part of Z
Re_Z_fit = np.real(Z_fit) # Extract real part of Z_fit
variance_re = sum(
(Re_Z - Re_Z.mean())**2) / (len(Re_Z) - 1) # Variance
Chi_square_re = sum(((Re_Z - Re_Z_fit)**2) / variance_re)
Im_Z = np.imag(Z) # Extract real part of Z
Im_Z_fit = np.imag(Z_fit) # Extract real part of Z_fit
variance_im = sum(
(Im_Z - Im_Z.mean())**2) / (len(Im_Z) - 1) # Variance
Chi_square_im = sum(((Im_Z - Im_Z_fit)**2) / variance_im)
Zsq = np.sqrt(abs(Re_Z**2 + Im_Z**2))
Zsq_fit = np.sqrt(abs(Re_Z_fit**2 + Im_Z_fit**2))
variance = sum((Zsq - Zsq.mean())**2) / (len(Z) - 1) # Variance
Chi_square = sum(((Zsq - Zsq_fit)**2) / variance)
chi_square_diag = sqrt(Chi_square_re**2 + Chi_square_im**2)
red_Chi_sq = Chi_square / dof # Reduced Chi squared
std_err = np.sqrt(red_Chi_sq) * 100
fitted_dict[tuple(initial_guess)] = (fitted_params, red_Chi_sq, f, Z,
Z_fit, circuit_str_5a,
params.voltage)
divisor = 60 if num_init_guesses > 60 else num_init_guesses
if num_remaining in range(1, num_init_guesses,
int(num_init_guesses / divisor)):
print(f"{initial_guess} - {num_remaining} guesses left")
# if chi_square_diag < chi_sentinal or chi_square_diag < 0.005 or Chi_square_re < chi_re_sentinal or Chi_square_re < 0.0038:
if red_Chi_sq < chi_sentinal:
chi_sentinal = red_Chi_sq * 1.02
print(
f"Fit {params.voltage}, red_chi2={red_Chi_sq:.8f}, chi2_re={Chi_square_re:.3f}, chi2_im={Chi_square_im:.3f}, std_err={std_err:.4f}, circuit={circuit_str_5a}\t {num_remaining} guesses left"
)
fitted_tuple = sorted(fitted_dict.items(), key=lambda item: item[1][1])
sorted_fitted_dict = OrderedDict(fitted_tuple)
# pprint.pprint(sorted_fitted_dict)
return sorted_fitted_dict
def fit(data_dir, filename, plot: str = ""):
array = open_file_numpy(data_dir, filename)
params = get_params(data_dir, filename)
print(array.shape)
data = get_data_numpy(array)
(freq_log, imped_log, phase, imag_imped, real_imped) = data
freq = np.power(10, freq_log)
# np.savetxt("zzz.csv", np.concatenate((freq, real_imped, -imag_imped), axis=1), delimiter=",")
# f, Z = preprocessing.readCSV("zzz.csv")
f = freq[:, 0]
Z = np.array(real_imped + 1j * -imag_imped, dtype=np.complex)[:, 0]
circuit_str_5a = 'R0-p(R1-p(R2,CPE2),CPE1)'
R0_guesses = [6850]
R1_guesses = [1E7, 1E6, 1E8, 5E8]
C1_guesses = [5E-8, 1E-8, 5E-9, 5E-7, 1E-6] # , 5E-9, 5E-8, 1E-8]
CPP_guesses = [0.8, 0.9, 1.0]
R2_guesses = [1E5, 1E6, 1E7, 1E8]
C2_guesses = [5E-6, 1E-6, 1E-7, 1E-8]
CPP2_guesses = [0.8, 0.9, 1.0]
initial_guesses_5a = []
for R0 in R0_guesses:
for R1 in R1_guesses:
for C1 in C1_guesses:
for CPP in CPP_guesses:
for R2 in R2_guesses:
for C2 in C2_guesses:
for CPP2 in CPP2_guesses:
initial_guesses_5a.append([R0, R1, R2, C2, CPP2, C1, CPP])
num_init_guesses = len(initial_guesses_5a)
print(f"{num_init_guesses} guess combinations")
assert all(initial_guesses_5a), "Cannot use 0 for any initial guesses"
chi_sentinal = 1.0
chi_re_sentinal = 1.0
num_remaining = num_init_guesses
fitted_dict = {}
for initial_guess in initial_guesses_5a:
# print(params.voltage, initial_guess)
num_remaining -= 1
circuit = CustomCircuit(circuit=circuit_str_5a, initial_guess=initial_guess)
circuit.fit(f, Z, global_opt=False, bounds=(
# R0 R1 R2 C2 CPP2 C1 CPP1
[6800, 0, 0, 1E-9, 0.78, 1E-9, 0.78],
# [7000, 1E8, 1E-5, 1, 1E8, 1E-5, 1]
[7000, np.inf, np.inf, 1E-5, 1, 1E-5, 1]
))
fitted_params = circuit.parameters_
R0, R1, R2, C2, CPP2, C1, CPP = circuit.parameters_
Z_fit = circuit.predict(f)
# dof = len(Z)-len(initial_guess) # Degrees of freedom - i.e. number of data points - number of variables
Re_Z = np.real(Z) # Extract real part of Z
Re_Z_fit = np.real(Z_fit) # Extract real part of Z_fit
variance_re = sum((Re_Z-Re_Z.mean())**2) / (len(Re_Z)-1) # Variance
Chi_square_re = sum(((Re_Z-Re_Z_fit)**2)/variance_re)
Im_Z = np.imag(Z) # Extract real part of Z
Im_Z_fit = np.imag(Z_fit) # Extract real part of Z_fit
variance_im = sum((Im_Z-Im_Z.mean())**2) / (len(Im_Z)-1) # Variance
Chi_square_im = sum(((Im_Z-Im_Z_fit)**2)/variance_im)
variance = sum((Z-Z.mean())**2) / (len(Z)-1) # Variance
Chi_square = sum(((Z-Z_fit)**2)/variance)
chi_square_diag = sqrt(Chi_square_re**2 + Chi_square_im**2)
red_Chi_sq = Chi_square_re # Reduced Chi squared
fitted_dict[tuple(initial_guess)] = (fitted_params, red_Chi_sq, f, Z, Z_fit, circuit_str_5a, params.voltage)
if num_remaining in range(1, num_init_guesses, int(num_init_guesses / 60)):
print(f"{initial_guess} - {num_remaining} guesses left")
if chi_square_diag < chi_sentinal or chi_square_diag < 0.01 or Chi_square_re < chi_re_sentinal or Chi_square_re < 0.005:
if chi_square_diag < chi_sentinal:
chi_sentinal = chi_square_diag * 1.1
if Chi_square_re < chi_re_sentinal:
chi_re_sentinal = Chi_square_re * 1.1
print(f"Fit {params.voltage}, chi2_re={Chi_square_re:.3f}, chi2_im={Chi_square_im:.3f}, chi2_diag={chi_square_diag:.3f}, circuit={circuit_str_5a}\t {num_remaining} guesses left")
if plot in ("all", "both"):
import matplotlib.pyplot as plt
fig, ax = plt.subplots()
plot_nyquist(ax, Z, fmt='o')
plot_nyquist(ax, Z_fit, fmt='-')
plt.legend(['Data', 'Fit'])
plt.title(f"{params.voltage}_{red_Chi_sq:.4f}")
plt.gcf().text(
0.8, 0.7,
f" R0={R0:.0f}\n\n R1={R1:.0f}\n C1= {C1}\n CPE1={CPP}\n\n R2={R2:.0f}\n C2={C2}\n CPE2={CPP2}\n\n {circuit_str_5a}",
fontsize=12)
fig.set_size_inches(8.5, 5.5, forward=True)
chi_str = f"{red_Chi_sq: .6f}".replace('.', '_')
plt.savefig(Rf'{data_dir}\all\{params.voltage}_chi_{chi_str}_{circuit_str_5a}.png',
bbox_inches='tight', dpi=500, transparent=True)
plt.close()
# plt.show()
fitted_tuple = sorted(fitted_dict.items(), key=lambda item: item[1][1])
sorted_fitted_dict = OrderedDict(fitted_tuple)
# pprint.pprint(sorted_fitted_dict)
return sorted_fitted_dict
