def test_cropFreq_both():
filtered_freq, filtered_Z = cropFrequencies(frequencies,
Z_correct,
freqmin=1,
freqmax=1e3)
assert (filtered_freq >= 1).all() and (filtered_freq <= 1e3).all()
def calculate(row):
f, z = pp.cropFrequencies(FREQ, row.complex_z, 200)
f, z = pp.ignoreBelowX(f, z)
circuit = 'p(R1,C1)-p(R2,C2)'
guess = [5e+4, 1e-10, 5e+6, 1e-10]
model = modelling.model_impedance(circuit, guess, f, z)
return modelling.get_resistance(model)
def test_cropFreq_minonly():
filtered_freq, filtered_Z = cropFrequencies(frequencies,
Z_correct,
freqmin=1)
assert (filtered_freq >= 1).all()
def test_cropFreq_maxonly():
filtered_freq, filtered_Z = cropFrequencies(frequencies,
Z_correct,
freqmax=1e3)
assert (filtered_freq <= 1e3).all()
def model_conductivity(freq, complex_z, cutoff, circuit, guess):
"""Takes a row of the dataframe and return the model object, the resistance and the rmse.
Args:
row ([type]): [description]
Returns:
model: the model object
resistance: calculated resistance
rmse: the root mean square error on the resistance
"""
f, z = pp.cropFrequencies(np.array(freq), complex_z, cutoff)
f, z = pp.ignoreBelowX(f, z)
model = model_impedance(circuit, guess, f, z)
rmse = fitting.rmse(z, model.predict(f))
resistance = get_resistance(model)
return model, resistance, rmse
def cole(data,
freq,
temp,
freq_min=200,
freq_max=None,
fit=False,
ax=None,
**kwargs):
"""Creates a Cole-Cole plot (imaginary versus real impedance) at a given temperature. Finds the available data closest to the temperature specified by 'temp'. A linear least squares circle fit can be added by setting fit=True.
:param temp: temperature in degrees C
:type temp: float/int
"""
data = conductivity_only(data)
# for temp in temp_list:
[index, Tval] = index_temp(data.temp, temp)
f, z = pp.cropFrequencies(frequencies=np.array(freq),
Z=data.complex_z.iloc[index],
freqmin=freq_min,
freqmax=freq_max)
f, z = pp.ignoreBelowX(f, z)
p = ax.scatter(np.real(z) / 1000, np.abs(np.imag(z)) / 1000, **kwargs)
if fit:
model = data.model.iloc[index]
predicted = model.predict(np.geomspace(0.001, 2000000, 100))
ax.plot(np.real(predicted) / 1000, np.abs(np.imag(predicted)) / 1000)
ax.add_artist(AnchoredText(model.circuit, loc=2))
if not ax.xaxis.get_label().get_text():
ax.axis('equal')
# ax.axis('square')
ax.set_xlim(left=0)
ax.set_ylim(bottom=0)
ax.set_ylabel(r'$-Im(Z) [{}]$'.format(K_OHM))
ax.set_xlabel(r'$Re(Z) [{}]$'.format(K_OHM))
ax.ticklabel_format(style='sci', scilimits=(-3, 4), axis='both')
ax.set_title('Cole-Cole')
return ax