def test_fit_rc_c_1nF(c_data_1nF):
actual_res, actual_cap, _ = \
fit_impedance(c_data_1nF, model='rc',
model_config='parallel', p0=(1, 1))
desired_res, desired_cap = [1e13, 1e-9 / (2 * np.pi)]
assert_allclose(actual_cap, desired_cap, atol=1e-22)
def test_fit_rc_series_complex(rc_data_complex):
actual_res, actual_cap, impedance_func = \
fit_impedance(rc_data_complex, model='rc',
model_config='series', p0=(1, 1))
desired_res, desired_cap = 1000, 1e-9
assert_allclose(actual_cap, desired_cap, rtol=1e-6)
assert_allclose(actual_res, desired_res, rtol=1e-6)
def test_fit_rc_series(rc_data_series):
data = rc_data_series['data']
actual_res, actual_cap, _ = \
fit_impedance(data, model='rc',
model_config='series', p0=(1, 1))
desired_res, desired_cap = rc_data_series['params']
assert_allclose(actual_cap, desired_cap, rtol=1e-5)
assert_allclose(actual_res, desired_res, rtol=1e-5)
def test_fit_rc_r_only(r_data):
actual_res, actual_cap, _ = \
fit_impedance(r_data, model='rc',
model_config='parallel', p0=(10, 1))
desired_res, desired_cap = [1, 0]
actual_params = np.array([actual_res, actual_cap])
desired_params = np.array([desired_res, desired_cap])
assert_allclose(actual_params, desired_params, atol=1e-10)
def test_fit_rc_parallel(rc_data_parallel):
data = rc_data_parallel['data']
actual_res, actual_cap, impedance_func = \
fit_impedance(data, model='rc',
model_config='parallel', p0=(1, 1))
desired_res, desired_cap = rc_data_parallel['params']
if abs(np.log10(2 * np.pi * desired_res * desired_cap * 100)) > 5:
rtol = 1e-1
else:
rtol = 1e-4
assert_allclose(actual_cap, desired_cap, rtol=rtol)
assert_allclose(actual_res, desired_res, rtol=rtol)
def plot_impedance(data,
fit=True,
model='rc',
model_config='series',
**kwargs):
"""
Plots the magnitude / phase of a set of impedance data.
:param fit: Whether to attempt to fit an impedance model to the data
:param model: Real/reactive model - options are "rc"
:param model_config: Model configuration, either "series" or "parallel"
"""
# The challenge here is that we need to plot two things with the same x axis and different y axes: the magnitude and phase data.
if data.shape[1] == 2:
data_complex = True
z_data = column_from_unit(data, ureg.ohm).to(ureg.ohm).m
phase_data = np.angle(z_data)
magnitude_data = np.abs(z_data)
data_to_plot = pd.DataFrame({
'Frequency (Hz)': data.iloc[:, 0],
'|Z| (ohm)': magnitude_data,
'Phase (deg)': phase_data * 180 / np.pi
})
else:
data_complex = False
phase_data = column_from_unit(data, ureg.rad).to(ureg.deg).m
phase_name = cname_from_unit(data, ureg.rad)
data_to_plot = data.rename(columns={phase_name: 'Phase (deg)'})
data_to_plot['Phase (deg)'] = phase_data
if fit:
_, _, impedance_func = fit_impedance(data)
# This needs to return a functional form for the impedance vs. frequency in addition to the relevant parameters, as it's not clear
freq_data = column_from_unit(data, ureg.Hz)
min_log = np.log10(freq_data.min().to(ureg.Hz).m)
max_log = np.log10(freq_data.max().to(ureg.Hz).m)
freq_theory_data = np.logspace(min_log, max_log, 100)
impedance_theory_data = impedance_func(freq_theory_data)
mag_theory_data = abs(impedance_theory_data)
phase_theory_data = np.angle(impedance_theory_data) * 180 / np.pi
theory_data = pd.DataFrame({
'Frequency (Hz)': freq_theory_data,
'Z (ohm)': mag_theory_data,
'Phase (deg)': phase_theory_data
})
else:
theory_data = None
theory_to_plot = None
subplot_kw = {'xscale': 'log', 'yscale': 'log'}
if theory_data is not None:
theory_to_plot = theory_data.iloc[:, [0, 1]]
kwargs.update(subplot_kw=subplot_kw,
theory_data=theory_to_plot,
theory_name='|Z| (Fit)',
plot_type='scatter')
fig, ax = default_plotter(data_to_plot.iloc[:, [0, 1]], **kwargs)
if theory_data is not None:
theory_to_plot = theory_data.iloc[:, [0, -1]]
kwargs.update(subplot_kw=subplot_kw,
fig=fig,
ax=ax,
theory_data=theory_to_plot,
theory_name='Phase (Fit)',
plot_type='scatter')
fig, ax = default_plotter(data_to_plot.iloc[:, [0, -1]], **kwargs)
prettifyPlot(fig=fig, ax=ax)
return fig, ax