def postprocessRC():
f = np.logspace(1, 8)
omega = 2. * np.pi * f
R = 1000.
C = 1e-6
data = OrderedDict()
data['f'] = f
samples = 50
model = "parallel(R, C)"
m = get_equivalent_circuit_model(model)
for i in range(samples):
Ri = 0.05 * R * np.random.randn() + R
Ci = 0.05 * C * np.random.randn() + C
Z = m.eval(omega=omega, R=Ri, C=Ci)
Z += np.random.randn(Z.size)
data['real' + str(i)] = Z.real
data['imag' + str(i)] = Z.imag
pd.DataFrame(data=data).to_csv('test.csv', index=False)
fitter = Fitter('CSV', LogLevel='WARNING')
parameters = {'R': {'value': R}, 'C': {'value': C}}
fitter.run(model, parameters=parameters)
os.remove('test.csv')
return PostProcess(fitter.fit_data)
def fitter2():
f = np.logspace(1, 8)
omega = 2. * np.pi * f
data = OrderedDict()
data['f'] = f
samples = 5
m = get_equivalent_circuit_model(model2)
for i in range(samples):
km1 = 0.05 * km * np.random.randn() + km
em1 = 0.05 * em * np.random.randn() + em
Z = m.eval(omega=omega,
Rc=Rc,
dm=dm,
km=km1,
em=em1,
ecp=ecp,
kcp=kcp,
kmed=kmed,
emed=emed,
p=p,
c0=c0,
k=k,
alpha=alpha)
Z += np.random.randn(Z.size)
data['real' + str(i)] = Z.real
data['imag' + str(i)] = Z.imag
pd.DataFrame(data=data).to_csv('test.csv', index=False)
fitter = Fitter('CSV', LogLevel='WARNING')
os.remove('test.csv')
return fitter
def fitter():
f = np.logspace(1, 8)
omega = 2. * np.pi * f
data = OrderedDict()
data['f'] = f
samples = 5
m = get_equivalent_circuit_model(model)
for i in range(samples):
Ri = 0.05 * R * np.random.randn() + R
Ci = 0.05 * C * np.random.randn() + C
Z = m.eval(omega=omega, R=Ri, C=Ci)
Z += np.random.randn(Z.size)
data['real' + str(i)] = Z.real
data['imag' + str(i)] = Z.imag
pd.DataFrame(data=data).to_csv('test.csv', index=False)
fitter = Fitter('CSV', LogLevel='WARNING')
os.remove('test.csv')
return fitter
# along with this program. If not, see <https://www.gnu.org/licenses/>.
import impedancefitter
import numpy
# parameters
frequencies = numpy.logspace(0, 8)
Rct = 100.
Rs = 20.
Aw = 300.
C0 = 25e-6
# generate model by user-defined circuit
model = 'R_s + parallel(R_ct + W, C)'
lmfit_model = impedancefitter.get_equivalent_circuit_model(model)
Z = lmfit_model.eval(omega=2. * numpy.pi * frequencies,
ct_R=Rct, s_R=Rs,
C=C0, Aw=Aw)
impedancefitter.plot_impedance(2. * numpy.pi * frequencies, Z)
# use pre-implemented circuit
model = 'Randles'
lmfit_model = impedancefitter.get_equivalent_circuit_model(model)
Z2 = lmfit_model.eval(omega=2. * numpy.pi * frequencies,
Rct=Rct, Rs=Rs,
C0=C0, Aw=Aw)
# check that both are equal
if numpy.all(numpy.isclose(Z, Z2)):
print("Both formulations are equal.")
def test_randles_CPE_model():
Z = Z_randles_CPE(omega, Rct, Rs, Aw, k, alpha)
m = get_equivalent_circuit_model(model2)
Z_m = m.eval(omega=omega, ct_R=Rct, s_R=Rs, k=k, alpha=alpha, Aw=Aw)
assert np.all(np.isclose(Z, Z_m))
def test_randles_model():
Z = Z_randles(omega, Rct, Rs, Aw, C0)
m = get_equivalent_circuit_model(model1)
Z_m = m.eval(omega=omega, ct_R=Rct, s_R=Rs, C=C0, Aw=Aw)
assert np.all(np.isclose(Z, Z_m))
def test_in_model():
model = "R + L"
m = get_equivalent_circuit_model(model)
Z = Z_in(omega, L, R)
Z_m = m.eval(omega=omega, L=L * 1e-9, R=R)
assert np.all(np.isclose(Z, Z_m))
def test_loss_model():
model = "parallel(R, parallel(L, C))"
m = get_equivalent_circuit_model(model)
Z = Z_loss(omega, L, C, R)
Z_m = m.eval(omega=omega, L=L * 1e-9, R=R, C=C * 1e-12)
assert np.all(np.isclose(Z, Z_m))
rcParams['figure.figsize'] = [20, 10]
# parameters
f = np.logspace(1, 8)
omega = 2. * np.pi * f
R = 1000.
C = 1e-6
data = OrderedDict()
data['f'] = f
samples = 1000
model = "parallel(R, C)"
m = get_equivalent_circuit_model(model)
# generate random samples
for i in range(samples):
Ri = 0.05 * R * np.random.randn() + R
Ci = 0.05 * C * np.random.randn() + C
Z = m.eval(omega=omega, R=Ri, C=Ci)
# add some noise
Z += np.random.randn(Z.size)
data['real' + str(i)] = Z.real
data['imag' + str(i)] = Z.imag
# save data to file
pd.DataFrame(data=data).to_csv('test.csv', index=False)
