def plot_model_fit_overview(f_i, z_i, model):
#Generate plot data from fitted model
z_fit = model.predict(f_i)
fig = plt.figure(constrained_layout=True, figsize=(12, 5))
gs = fig.add_gridspec(2, 2)
f_ax1 = fig.add_subplot(gs[:, 0])
f_ax1.set_title("Nyquist plot")
f_ax2 = fig.add_subplot(gs[0, 1])
f_ax2.set_title("Bode plot")
f_ax3 = fig.add_subplot(gs[1, 1])
plot_nyquist(f_ax1, z_i, fmt='o')
plot_nyquist(f_ax1, z_fit, fmt='-')
plot_bode((f_ax2, f_ax3), f_i, z_i, ls='', marker='s', label='Data')
plot_bode((f_ax2, f_ax3), f_i, z_fit, ls='-', marker='', label='Fit')
f_ax2.legend()
return gs
def test_plot_bode():
f = [1, 10, 100]
Z = np.array([1, 2, 3]) + 1j*np.array([2, 3, 4])
_, axes = plt.subplots(nrows=2)
axes = plot_bode(axes, f, Z, scale=10)
xs, ys = axes[0].lines[0].get_xydata().T
assert (xs == f).all() and (ys == np.abs(Z)).all()
xs, ys = axes[1].lines[0].get_xydata().T
assert (xs == f).all() and (ys == -np.angle(Z, deg=True)).all()
def plot(self, ax=None, f_data=None, Z_data=None, kind='altair', **kwargs):
""" visualizes the model and optional data as a nyquist,
bode, or altair (interactive) plots
Parameters
----------
ax: matplotlib.axes
axes to plot on
f_data: np.array of type float
Frequencies of input data (for Bode plots)
Z_data: np.array of type complex
Impedance data to plot
kind: {'altair', 'nyquist', 'bode'}
type of plot to visualize
Other Parameters
----------------
**kwargs : optional
If kind is 'nyquist' or 'bode', used to specify additional
`matplotlib.pyplot.Line2D` properties like linewidth,
line color, marker color, and labels.
If kind is 'altair', used to specify nyquist height as `size`
Returns
-------
ax: matplotlib.axes
axes of the created nyquist plot
"""
if kind == 'nyquist':
if ax is None:
fig, ax = plt.subplots(figsize=(5, 5))
if Z_data is not None:
ax = plot_nyquist(ax, Z_data, ls='', marker='s', **kwargs)
if self._is_fit():
if f_data is not None:
f_pred = f_data
else:
f_pred = np.logspace(5, -3)
Z_fit = self.predict(f_pred)
ax = plot_nyquist(ax, Z_fit, ls='-', marker='', **kwargs)
return ax
elif kind == 'bode':
if ax is None:
fig, ax = plt.subplots(nrows=2, figsize=(5, 5))
if f_data is not None:
f_pred = f_data
else:
f_pred = np.logspace(5, -3)
if Z_data is not None:
if f_data is None:
raise ValueError('f_data must be specified if' +
' Z_data for a Bode plot')
ax = plot_bode(ax, f_data, Z_data, ls='', marker='s', **kwargs)
if self._is_fit():
Z_fit = self.predict(f_pred)
ax = plot_bode(ax, f_pred, Z_fit, ls='-', marker='', **kwargs)
return ax
elif kind == 'altair':
plot_dict = {}
if Z_data is not None and f_data is not None:
plot_dict['data'] = {'f': f_data, 'Z': Z_data}
if self._is_fit():
if f_data is not None:
f_pred = f_data
else:
f_pred = np.logspace(5, -3)
Z_fit = self.predict(f_pred)
if self.name is not None:
name = self.name
else:
name = 'fit'
plot_dict[name] = {'f': f_pred, 'Z': Z_fit, 'fmt': '-'}
chart = plot_altair(plot_dict, **kwargs)
return chart
else:
raise ValueError("Kind must be one of 'altair'," +
f"'nyquist', or 'bode' (received {kind})")