# Ex4: Passivity Evaluation and Enforcement¶

To demonstrate the passivity evaluation and enforcement features of the vector fitting class, the ring slot example 2-port is used, once again. Have a look at the other vector fitting example notebooks for more general explanations of the fitting process.

[1]:

import skrf
import numpy as np
import matplotlib.pyplot as mplt

# load and fit the ring slot network with 3 poles
nw = skrf.data.ring_slot
vf = skrf.VectorFitting(nw)
vf.vector_fit(n_poles_real=3, n_poles_cmplx=0)

# plot fitting results
freqs = np.linspace(0, 200e9, 201)
fig, ax = mplt.subplots(2, 2)
fig.set_size_inches(12, 8)
vf.plot_s_mag(0, 0, freqs=freqs, ax=ax[0][0]) # s11
vf.plot_s_mag(0, 1, freqs=freqs, ax=ax[0][1]) # s12
vf.plot_s_mag(1, 0, freqs=freqs, ax=ax[1][0]) # s21
vf.plot_s_mag(1, 1, freqs=freqs, ax=ax[1][1]) # s22
fig.tight_layout()
mplt.show()


The fitting result does not look too bad. Let’s check the RMS error:

[2]:

vf.get_rms_error()

[2]:

0.003867921017005565


An RMS error of less than 0.05 usually indicates a good fit and confirms our optical inspection. But is the model still passive at all frequencies?

[3]:

vf.is_passive()

[3]:

False


It’s not passive? What’s going on? Wasn’t the original data of the ring slot representing a passive network?

[4]:

nw.is_passive()

[4]:

True


The network data was passive, but the vector fitted model is not. Let’s investigate (and correct?) the problem.

[5]:

# plot singular values of vector fitted scattering matrix
freqs = np.linspace(0, 200e9, 201)
fig, ax = mplt.subplots(1, 1)
fig.set_size_inches(6, 4)
vf.plot_s_singular(freqs=freqs, ax=ax)
fig.tight_layout()
mplt.show()


One of the singular values of the fitted scattering matrix is greater than 1 at some frequencies. This indeed indicates a non-passive model. For further analysis, you can get a list of all frequency bands with passivity violations:

[6]:

vf.passivity_test()

[6]:

array([[0.00000000e+00, 4.48680480e+10],
[8.43461939e+10, 9.84609027e+10]])


The network is not passive in two frequency bands: From dc to about 44.9 GHz, and from 84.3 GHz to 98.5 GHz. Luckily, passivity can be enforced to obtain passive vector fitted model:

[7]:

vf.passivity_enforce()


After passivity enforcement, the network should be passive at all frequencies. Let’s check ourselves:

[8]:

vf.is_passive()

[8]:

True

[9]:

vf.passivity_test()

[9]:

array([], dtype=float64)

[10]:

# plot singular values of vector fitted scattering matrix
freqs = np.linspace(0, 200e9, 201)
fig, ax = mplt.subplots(1, 1)
fig.set_size_inches(6, 4)
vf.plot_s_singular(freqs=freqs, ax=ax)
fig.tight_layout()
mplt.show()


Alright, the model is finally passive. But does it still fit the original network data?

[11]:

# plot fitting results again after passivity enforcement
freqs = np.linspace(0, 200e9, 201)
fig, ax = mplt.subplots(2, 2)
fig.set_size_inches(12, 8)
vf.plot_s_mag(0, 0, freqs=freqs, ax=ax[0][0]) # s11
vf.plot_s_mag(0, 1, freqs=freqs, ax=ax[0][1]) # s12
vf.plot_s_mag(1, 0, freqs=freqs, ax=ax[1][0]) # s21
vf.plot_s_mag(1, 1, freqs=freqs, ax=ax[1][1]) # s22
fig.tight_layout()
mplt.show()


In addition to the visual inspection, let’s check the RMS error again:

[12]:

vf.get_rms_error()

[12]:

0.004754408262762471


Yes, the model still fits the original data very well and the differences to the first non-passive fit from above are insignificant: the rms error is still very low.