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speis_plot.py
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speis_plot.py
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import numpy as np
import matplotlib.pyplot as plt
import matplotlib as mpl
from impedance.visualization import plot_nyquist, plot_bode
# The following helper function are modified variants of those implement in impedance.py to enable the meaningful
# plotting of SPEIS data
def plot_speis_nyquist(ax, z, scale=1, units='Ohms', fmt='.-', cutoff=None, **kwargs):
#Generate colormap
colors = plt.cm.get_cmap('RdYlGn')
if cutoff == None:
cutoff = len(z)
#Plot each point
for i in range(len(z)):
ax.plot(z[i][:cutoff].real, -z[i][:cutoff].imag, fmt, color=colors(i/len(z)), **kwargs)
ax.set_aspect('equal')
ax.set_xlabel(r'$Z^{\prime}(\omega)$ ' +
'$[{}]$'.format(units), fontsize=20)
ax.set_ylabel(r'$-Z^{\prime\prime}(\omega)$ ' +
'$[{}]$'.format(units), fontsize=20)
ax.locator_params(axis='x', nbins=5, tight=True)
ax.locator_params(axis='y', nbins=5, tight=True)
ax.grid(b=True, which='major', axis='both', alpha=.5)
limits = -np.log10(scale)
if limits != 0:
ax.ticklabel_format(style='sci', axis='both',
scilimits=(limits, limits))
y_offset = ax.yaxis.get_offset_text()
y_offset.set_size(18)
t = ax.xaxis.get_offset_text()
t.set_size(18)
return ax
def plot_speis_bode(axes, f, z, scale=1, units='Ohms', fmt='.-', **kwargs):
#Generate colormap
colors = plt.cm.get_cmap('RdYlGn')
ax_mag, ax_phs = axes
for i in range(len(z)):
ax_mag.plot(f[i], np.abs(z[i]), fmt, color=colors(i/len(z)), **kwargs)
ax_phs.plot(f[i], -np.angle(z[i], deg=True), fmt, color=colors(i/len(z)), **kwargs)
# ax_mag.plot(f, np.abs(z), fmt, **kwargs)
# ax_phs.plot(f, -np.angle(z, deg=True), fmt, **kwargs)
ax_mag.set_ylabel(r'$|Z(\omega)|$ ' +
'$[{}]$'.format(units), fontsize=20)
ax_phs.set_ylabel(r'$-\phi_Z(\omega)$ ' + r'$[^o]$', fontsize=20)
for ax in axes:
# Set the frequency axes title and make log scale
ax.set_xlabel('f [Hz]', fontsize=20)
ax.set_xscale('log')
# Make the tick labels larger
ax.tick_params(axis='both', which='major', labelsize=14)
# Change the number of labels on each axis to five
ax.locator_params(axis='y', nbins=5, tight=True)
# Add a light grid
ax.grid(b=True, which='major', axis='both', alpha=.5)
limits = -np.log10(scale)
if limits != 0:
ax_mag.ticklabel_format(style='sci', axis='y',
scilimits=(limits, limits))
y_offset = ax_mag.yaxis.get_offset_text()
y_offset.set_size(18)
return axes
#Quick function to generate overview of the dataset with bode and nyquist plot
def plot_speis_overview(f, z):
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_speis_nyquist(f_ax1, z)
plot_speis_bode((f_ax2, f_ax3), f, z)
return gs
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