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Networks

Introduction

This tutorial gives an overview of the microwave network analysis features of skrf. For this tutorial, and the rest of the scikit-rf documentation, it is assumed that skrf has been imported as rf. Whether or not you follow this convention in your own code is up to you.

In [1]:
import skrf as rf
from pylab import *

If this produces an import error, please see Installation.

Creating Networks

skrf provides an object for a N-port microwave Network. A Network can be created in a number of ways. One way is from data stored in a touchstone file.

In [2]:
from skrf import Network, Frequency

ring_slot = Network('data/ring slot.s2p')

A short description of the network will be printed out if entered onto the command line

In [3]:
ring_slot
Out[3]:
2-Port Network: 'ring slot',  75.0-110.0 GHz, 201 pts, z0=[ 50.+0.j  50.+0.j]

Networks can also be created by directly passing values for the frequency, s-paramters and port impedance z0.

In [4]:
freq = Frequency(1,10,101,'ghz')
ntwk = Network(frequency=freq, s= [-1, 1j, 0], z0=50, name='slippy')
ntwk
Out[4]:
1-Port Network: 'slippy',  1.0-10.0 GHz, 101 pts, z0=[ 50.+0.j]

See network for more information on network creation.

Basic Properties

The basic attributes of a microwave Network are provided by the following properties :

  • Network.s : Scattering Parameter matrix.
  • Network.z0 : Port Characteristic Impedance matrix.
  • Network.frequency : Frequency Object.

The Network object has numerous other properties and methods. If you are using IPython, then these properties and methods can be ‘tabbed’ out on the command line.

In [1]: ring_slot.s<TAB>
ring_slot.line.s              ring_slot.s_arcl         ring_slot.s_im
ring_slot.line.s11            ring_slot.s_arcl_unwrap  ring_slot.s_mag
...

All of the network parameters are represented internally as complex numpy.ndarray. The s-parameters are of shape (nfreq, nport, nport)

In [5]:
shape(ring_slot.s)
Out[5]:
(201, 2, 2)

Slicing

You can slice the Network.s attribute any way you want.

In [6]:
ring_slot.s[:11,1,0]  # get first 10 values of S21
Out[6]:
array([ 0.61345710+0.36678139j,  0.62181940+0.36403169j,
        0.63024301+0.36109574j,  0.63872415+0.3579682j ,
        0.64725874+0.35464377j,  0.65584238+0.35111711j,
        0.66447037+0.34738295j,  0.67313770+0.34343602j,
        0.68183901+0.33927115j,  0.69056862+0.33488321j,
        0.69932050+0.3302672j ])

Slicing by frequency can also be done directly on Network objects like so

In [7]:
ring_slot[0:10] #  Network for the first 10 frequency points
Out[7]:
2-Port Network: 'ring slot',  75.0-76.575 GHz, 10 pts, z0=[ 50.+0.j  50.+0.j]

or with a human friendly string ,

In [8]:
ring_slot['80-90ghz']
Out[8]:
2-Port Network: 'ring slot',  80.075-90.05 GHz, 58 pts, z0=[ 50.+0.j  50.+0.j]

Notice that slicing directly on a Network returns a Network. So, a nice way to express slicing in both dimensions is

In [9]:
ring_slot.s11['80-90ghz']
Out[9]:
1-Port Network: 'ring slot',  80.075-90.05 GHz, 58 pts, z0=[ 50.+0.j]

Ploting

Amongst other things, the methods of the Network class provide convenient ways to plot components of the network parameters,

  • Network.plot_s_db : plot magnitude of s-parameters in log scale
  • Network.plot_s_deg : plot phase of s-parameters in degrees
  • Network.plot_s_smith : plot complex s-parameters on Smith Chart

If you would like to use skrf’s plot styling,

In [10]:
%matplotlib inline
rf.stylely()

To plot all four s-parameters of the ring_slot on the Smith Chart.

In [11]:
ring_slot.plot_s_smith()
../_images/tutorials_Networks_32_0.png

Combining this with the slicing features,

In [12]:
from matplotlib import pyplot as plt

plt.title('Ring Slot $S_{21}$')

ring_slot.s11.plot_s_db(label='Full Band Response')
ring_slot.s11['82-90ghz'].plot_s_db(lw=3,label='Band of Interest')
../_images/tutorials_Networks_34_0.png

For more detailed information about plotting see Plotting.

Operators

Arithmetic Operations

Element-wise mathematical operations on the scattering parameter matrices are accessible through overloaded operators. To illustrate their usage, load a couple Networks stored in the data module.

In [13]:
from skrf.data import wr2p2_short as short
from skrf.data import wr2p2_delayshort as delayshort


short - delayshort
short + delayshort
short * delayshort
short / delayshort

Out[13]:
1-Port Network: 'wr2p2,short',  330.0-500.0 GHz, 201 pts, z0=[ 50.+0.j]

All of these operations return Network types. For example, to plot the complex difference between short and delay_short,

In [14]:
difference = (short- delayshort)
difference.plot_s_mag(label='Mag of difference')
../_images/tutorials_Networks_39_0.png

Another common application is calculating the phase difference using the division operator,

In [15]:
(delayshort/short).plot_s_deg(label='Detrended Phase')
../_images/tutorials_Networks_41_0.png

Linear operators can also be used with scalars or an numpy.ndarray that ais the same length as the Network.

In [16]:
hopen = (short*-1)
hopen.s[:3,...]
Out[16]:
array([[[ 1.-0.j]],

       [[ 1.-0.j]],

       [[ 1.-0.j]]])
In [17]:
rando =  hopen *rand(len(hopen))
rando.s[:3,...]
Out[17]:
array([[[ 0.74475607+0.j]],

       [[ 0.68517524+0.j]],

       [[ 0.12154446+0.j]]])

Cascading and De-embedding

Cascading and de-embeding 2-port Networks can also be done though operators. The cascade function can be called through the power operator, **. To calculate a new network which is the cascaded connection of the two individual Networks line and short,

In [18]:
short = rf.data.wr2p2_short
line = rf.data.wr2p2_line
delayshort = line ** short

De-embedding can be accomplished by cascading the inverse of a network. The inverse of a network is accessed through the property Network.inv. To de-embed the short from delay_short,

In [19]:
short_2 = line.inv ** delayshort

short_2==short
Out[19]:
True

Comparison operators also work with networks.

Connecting Multi-ports

skrf supports the connection of arbitrary ports of N-port networks. It accomplishes this using an algorithm called sub-network growth[1], available through the function connect(). Terminating one port of an ideal 3-way splitter can be done like so,

In [20]:
tee = rf.data.tee
tee
Out[20]:
3-Port Network: 'tee',  330.0-500.0 GHz, 201 pts, z0=[ 50.+0.j  50.+0.j  50.+0.j]

To connect port 1 of the tee, to port 0 of the delay short,

In [21]:
terminated_tee = rf.connect(tee,1,delayshort,0)
terminated_tee
Out[21]:
2-Port Network: 'tee',  330.0-500.0 GHz, 201 pts, z0=[ 50.+0.j  50.+0.j]

Note that this function takes into account port impedances. If two connected ports have different port impedances, an appropriate impedance mismatch is inserted.

Interpolation and Concatenation

A common need is to change the number of frequency points of a Network. To use the operators and cascading functions the networks involved must have matching frequencies, for instance. If two networks have different frequency information, then an error will be raised,

In [22]:
from skrf.data import wr2p2_line1 as line1

line1
Out[22]:
2-Port Network: 'wr2p2,line1',  330.0-500.0 GHz, 101 pts, z0=[ 50.+0.j  50.+0.j]
line1+line

---------------------------------------------------------------------------
IndexError                                Traceback (most recent call last)
<ipython-input-49-82040f7eab08> in <module>()
----> 1 line1+line

/home/alex/code/scikit-rf/skrf/network.py in __add__(self, other)
    500
    501         if isinstance(other, Network):
--> 502             self.__compatable_for_scalar_operation_test(other)
    503             result.s = self.s + other.s
    504         else:

/home/alex/code/scikit-rf/skrf/network.py in __compatable_for_scalar_operation_test(self, other)
    701         '''
    702         if other.frequency  != self.frequency:
--> 703             raise IndexError('Networks must have same frequency. See `Network.interpolate`')
    704
    705         if other.s.shape != self.s.shape:

IndexError: Networks must have same frequency. See `Network.interpolate`

This problem can be solved by interpolating one of Networks allong the frequency axis using Network.resample.

In [23]:
line1.resample(201)
line1
Out[23]:
2-Port Network: 'wr2p2,line1',  330.0-500.0 GHz, 201 pts, z0=[ 50.+0.j  50.+0.j]

And now we can do things

In [24]:
line1+line
Out[24]:
2-Port Network: 'wr2p2,line1',  330.0-500.0 GHz, 201 pts, z0=[ 50.+0.j  50.+0.j]

You can also interpolate from a Frequency object. For example,

In [25]:
line.interpolate_from_f(line1.frequency)
Out[25]:
2-Port Network: 'wr2p2,line',  330.0-500.0 GHz, 201 pts, z0=[ 50.+0.j  50.+0.j]

A related application is the need to combine Networks which cover different frequency ranges. Two Netwoks can be concatenated (aka stitched) together using stitch, which concatenates networks along their frequency axis. To combine a WR-2.2 Network with a WR-1.5 Network,

In [26]:
from skrf.data import wr2p2_line, wr1p5_line

big_line = rf.stitch(wr2p2_line, wr1p5_line)
big_line
Out[26]:
2-Port Network: 'wr2p2,line',  330.0-750.0 GHz, 402 pts, z0=[ 50.+0.j  50.+0.j]

Reading and Writing

For long term data storage, skrf has support for reading and partial support for writing touchstone file format. Reading is accomplished with the Network initializer as shown above, and writing with the method Network.write_touchstone().

For temporary data storage, skrf object can be pickled with the functions skrf.io.general.read and skrf.io.general.write. The reason to use temporary pickles over touchstones is that they store all attributes of a network, while touchstone files only store partial information.

In [27]:
rf.write('data/myline.ntwk',line) # write out Network using pickle
In [28]:
ntwk = Network('data/myline.ntwk') # read Network using pickle

Warning

Pickling methods cant support long term data storage because they require the structure of the object being written to remain unchanged. something that cannot be guarnteed in future versions of skrf. (see http://docs.python.org/2/library/pickle.html)

Frequently there is an entire directory of files that need to be analyzed. rf.read_all creates Networks from all files in a directory quickly. To load all skrf files in the data/ directory which contain the string 'wr2p2'.

In [29]:
dict_o_ntwks = rf.read_all(rf.data.pwd, contains = 'wr2p2')
dict_o_ntwks
Out[29]:
{'wr2p2,delayshort': 1-Port Network: 'wr2p2,delayshort',  330.0-500.0 GHz, 201 pts, z0=[ 50.+0.j],
 'wr2p2,line': 2-Port Network: 'wr2p2,line',  330.0-500.0 GHz, 201 pts, z0=[ 50.+0.j  50.+0.j],
 'wr2p2,line1': 2-Port Network: 'wr2p2,line1',  330.0-500.0 GHz, 101 pts, z0=[ 50.+0.j  50.+0.j],
 'wr2p2,short': 1-Port Network: 'wr2p2,short',  330.0-500.0 GHz, 201 pts, z0=[ 50.+0.j]}

Other Parameters

This tutorial focuses on s-parameters, but other network represenations are available as well. Impedance and Admittance Parameters can be accessed through the parameters Network.z and Network.y, respectively. Scalar components of complex parameters, such as Network.z_re, Network.z_im and plotting methods are available as well.

Other parameters are only available for 2-port networks, such as wave cascading parameters (Network.t), and ABCD-parameters (Network.a)

In [30]:
ring_slot.z[:3,...]
Out[30]:
array([[[ 0.88442687+28.15350224j,  0.94703504+30.46757222j],
        [ 0.94703504+30.46757222j,  1.04344170+43.45766805j]],

       [[ 0.91624901+28.72415928j,  0.98188607+31.09594438j],
        [ 0.98188607+31.09594438j,  1.08168411+44.17642274j]],

       [[ 0.94991736+29.31694632j,  1.01876516+31.74874257j],
        [ 1.01876516+31.74874257j,  1.12215451+44.92215712j]]])
In [31]:
ring_slot.plot_z_im(m=1,n=0)
../_images/tutorials_Networks_74_0.png

Conclusion

There are many more features of Networks that can be found in networks

References

[1] Compton, R.C.; , “Perspectives in microwave circuit analysis,” Circuits and Systems, 1989., Proceedings of the 32nd Midwest Symposium on , vol., no., pp.716-718 vol.2, 14-16 Aug 1989. URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=101955&isnumber=3167