skrf.network.Network.nf_circle

Network.nf_circle(nf, npoints=181)[source]

Returns loci of noise figure circles for a specified noise figure. The network must have two ports and noise data. The center and radius of the noise figure circle are calculated by the following equations [1].

\[C_{F} = \frac{\Gamma_{opt}}{N + 1}\]

\[R_{F} = \frac{\sqrt{N(N +1 - |\Gamma_{opt}|^2)}}{N + 1}\]

where \(N\) is the noise figure parameter defined by

\[N = \frac{|\Gamma_{s}-\Gamma_{opt}|^2}{1-|\Gamma_{s}|^2} = \frac{F-F_{min}}{4R_{N}/Z_{0}}|1+\Gamma_{opt}|^2\]

Parameters:

nf (float) – Noise figure of network in decibels.
npoints (int, optional) – The number of points on the circumference of the circle. More points result in a smoother circle, but require more computation. Default is 181.

Returns:

nfc – Loci of noise figure circles in complex numbers

Return type:

numpy.ndarray (shape is npoints x f)

Example

>>> import skrf as rf
>>> import matplotlib.pyplot as plt

Create a two-port network object

>>> ntwk = rf.Network('ntwk_noise.s2p')

Calculate the noise figure circles for all the frequencies at a noise figure of 1 dB

>>> nfc = ntwk.nf_circle(nf=1.0)

Plot the circles on the smith chart

>>> rf.plotting.plot_smith(s=nfc, smith_r=1, marker='o')
>>> plt.show()

Slicing the network allows you to specify a frequency

>>> nfc = ntwk['1GHz'].nf_circle(nf=1.0)
>>> rf.plotting.plot_smith(s=nfc, smith_r=1, marker='o')
>>> plt.show()

References