from itertools import combinations
import skrf as rf 
%matplotlib inline
from pylab import * 
rf.stylely()

"""
Explanation: Measuring a Mutiport Device with a 2-Port Network Analyzer
Introduction
This notebook demonstrates a numerical test of the technique described in 
"A Rigorous Technique for Measuring the Scattering Matrix of a Multiport Device with a 2-Port Network Analyzer" [1].
In microwave measurements,  one commonly needs to measure a n-port deveice  with a m-port network analyzer ($ m<n $ of course). Generally, this is done by  terminating each non-measured port with a matched load, and assuming the reflected power is negligable. However, in some cases this may not provide the most accurate results, or even be possible in all measurement environments. The paper above presents an elegent solution to this problem, using impedance renormalization. We'll call it Tippet's technique, because it has a good ring to it.
In Tippets technique, several sub-networks are measured in a similar way as before, but the port terminations are not assumed to be matched. Instead, the terminations just have to be known and no more than one can be completely reflective. So, in general $|\Gamma| \ne 1$. During measurements, each port is terminated with a consistent termination. So port 1 is always terminated with $Z_1$ when not being measured. Once measured, each sub-network is re-normalized to these port impedances. Think about that.  Finally the composit network is contructed, and  may then be re-normalized to the desired system impedance, say $50 ohm $ 

[1] J. C. Tippet and R. A. Speciale, “A Rigorous Technique for Measuring the Scattering Matrix of a Multiport Device with a 2-Port Network Analyzer,” IEEE Transactions on Microwave Theory and Techniques, vol. 30, no. 5, pp. 661–666, May 1982.

Outline of Tippet's Technique
Following the example given in [1], measuring a 4-port network with a 2-port network analyzer.
An outline of the technique: 

Calibrate 2-port network analyzer
Get four known terminations ($Z_1, Z_2, Z_3,Z_4$). No more than one can have  $|\Gamma| = 1$
Measure all combinations of 2-port subnetworks (there are 6). Each port not currently being measured must be terminated with its corresponding load.
Renormalize each subnetwork to the impedances of the loads used to terminate it when note being measured. 
Build composite 4-port, renormalize to VNA impedance. 

Implementation
End of explanation
"""


wg = rf.wr10
wg.frequency.npoints = 101

"""
Explanation: First, we create a Media object, which is used to generate networks for testing. We will use WR-10 Rectangular waveguide.
End of explanation
"""


dut = wg.random(n_ports  = 4,name= 'dut')
dut

"""
Explanation: Next, lets generate a random 4-port network which will be the DUT, that we are trying to measure with out 2-port network analyzer.
End of explanation
"""


loads = [wg.load(.1+.1j),
         wg.load(.2-.2j),
         wg.load(.3+.3j),
         wg.load(.5),
         ]
# construct the impedance array, of shape FXN
z_loads = array([k.z.flatten() for k in loads]).T


"""
Explanation: Now, we need to define the loads used to terminate each port when it is not being measured, note as described in [1] not more than one can be have full reflection, $|\Gamma| = 1$
End of explanation
"""


ports = arange(dut.nports)
port_combos = list(combinations(ports, 2))
port_combos

"""
Explanation: Create required measurement port combinations.  There are 6 different measurements required to measure a 4-port with a 2-port VNA.  In general, #measurements = $n\choose 2$, for n-port DUT on  a 2-port VNA.
End of explanation
"""



composite = wg.match(nports = 4)  # composite network, to be filled.
measured,measured_renorm = {},{}  # measured subnetworks and renormalized sub-networks


# ports  `a` and `b` are the ports we will connect the VNA too
for a,b in port_combos:
    # port `c` and `d` are the ports which we will connect the loads too
    c,d =ports[(ports!=a)& (ports!=b)] 
    
    # determine where `d` will be on four_port, after its reduced to a three_port
    e = where(ports[ports!=c]==d)[0][0]

    # connect loads
    three_port = rf.connect(dut,c, loads[c],0)
    two_port =  rf.connect(three_port,e, loads[d],0)
    
    # save raw and renormalized 2-port subnetworks
    measured['%i%i'%(a,b)] = two_port.copy()
    two_port.renormalize(c_[z_loads[:,a],z_loads[:,b]])
    measured_renorm['%i%i'%(a,b)] = two_port.copy()
    
    # stuff this 2-port into the composite 4-port 
    for i,m in enumerate([a,b]):
        for j,n in enumerate([a,b]):
            composite.s[:,m,n] = two_port.s[:,i,j]
    
    # properly copy the port impedances
    composite.z0[:,a] = two_port.z0[:,0]
    composite.z0[:,b] = two_port.z0[:,1]
    

# finally renormalize from 
composite.renormalize(50)

"""
Explanation: Now to do it. Ok we loop over the port combo's and connect the loads to the right places, simulating  actual measurements. Each raw subnetwork measurement is saved, along with the renormalized subnetwork.   Finally, we stuff the result into the 4-port composit network.
End of explanation
"""


measured_renorm

"""
Explanation: Results
Self-Consistency
Note that 6-measurements of 2-port subnetworks works out to 24-sparameters, and we only need 16. This is because each reflect, s-parameter is measured three-times. As, in [1], we will use this redundent measurement as a check of our accuracy.
The renormalized networks are stored in a dictionary with names based on their port indecies, from this you can see that each have been renormalized to the appropriate z0.
End of explanation
"""


s11_set = rf.NS([measured[k] for k in measured if k[0]=='0'])

figure(figsize = (8,4))
subplot(121)
s11_set .plot_s_db(0,0)
subplot(122)
s11_set .plot_s_deg(0,0)
tight_layout()

"""
Explanation: Plotting all three raw measurements of $S_{11}$, we can see that they are not in agreement. These plots answer to plots 5 and 7 of [1]
End of explanation
"""


s11_set = rf.NS([measured_renorm[k] for k in measured_renorm if k[0]=='0'])

figure(figsize = (8,4))
subplot(121)
s11_set .plot_s_db(0,0)
subplot(122)
s11_set .plot_s_deg(0,0)
tight_layout()

"""
Explanation: However, the renormalized measurements agree perfectly. These plots answer to plots 6 and 8 of [1]
End of explanation
"""


composite == dut

"""
Explanation: Test For Accuracy
Making sure our composite network is the same as our DUT
End of explanation
"""


sum((composite - dut).s_mag)

"""
Explanation: Nice!. How close ?
End of explanation
"""


def tippits(dut, gamma, noise=None):
    '''
    simulate tippits technique on a 4-port dut.
    '''
    ports = arange(dut.nports)
    port_combos = list(combinations(ports, 2))
    
    loads = [wg.load(gamma) for k in ports]
    
    # construct the impedance array, of shape FXN
    z_loads = array([k.z.flatten() for k in loads]).T
    composite = wg.match(nports = dut.nports)  # composite network, to be filled.
    #measured,measured_renorm = {},{}  # measured subnetworks and renormalized sub-networks


    # ports  `a` and `b` are the ports we will connect the VNA too
    for a,b in port_combos:
        # port `c` and `d` are the ports which we will connect the loads too
        c,d =ports[(ports!=a)& (ports!=b)] 

        # determine where `d` will be on four_port, after its reduced to a three_port
        e = where(ports[ports!=c]==d)[0][0]

        # connect loads
        three_port = rf.connect(dut,c, loads[c],0)
        two_port =  rf.connect(three_port,e, loads[d],0)
        
        if noise is not None:
            two_port.add_noise_polar(*noise)
        # save raw and renormalized 2-port subnetworks
        measured['%i%i'%(a,b)] = two_port.copy()
        two_port.renormalize(c_[z_loads[:,a],z_loads[:,b]])
        measured_renorm['%i%i'%(a,b)] = two_port.copy()

        # stuff this 2-port into the composite 4-port 
        for i,m in enumerate([a,b]):
            for j,n in enumerate([a,b]):
                composite.s[:,m,n] = two_port.s[:,i,j]

        # properly copy the port impedances
        composite.z0[:,a] = two_port.z0[:,0]
        composite.z0[:,b] = two_port.z0[:,1]


    # finally renormalize from 
    composite.renormalize(50)
    
    return composite


wg.frequency.npoints = 11
dut = wg.random(4)

#er = lambda gamma: mean((tippits(dut,gamma)-dut).s_mag)/mean(dut.s_mag)
def er(gamma, *args):
    return max(abs(tippits(dut, rf.db_2_mag(gamma),*args).s_db-dut.s_db).flatten())

gammas = linspace(-80,0,11)


title('Error vs $|\Gamma|$')
plot(gammas, [er(k) for k in gammas])
plot(gammas, [er(k) for k in gammas])
semilogy()
xlabel('$|\Gamma|$ of Loads (dB)')
ylabel('Max Error in DUT\'s dB(S)')



figure()

#er = lambda gamma: max(abs(tippits(dut,gamma,(1e-5,.1)).s_db-dut.s_db).flatten())
noise = (1e-5,.1)
title('Error vs $|\Gamma|$ with reasonable noise')
plot(gammas, [er(k, noise) for k in gammas])
plot(gammas, [er(k,noise) for k in gammas])
semilogy()
xlabel('$|\Gamma|$ of Loads (dB)')
ylabel('Max Error in DUT\'s dB(S)')

"""
Explanation: Dang!
Practical Application
This could be used in many ways. In waveguide, one could just make a measurement of a radiating open after a standard two-port calibration (like TRL). Then using Tippets technique, you can  leave each port wide open while not being measured. This way you dont have to buy a bunch of loads. How sweet would that be?
More Complex Simulations
End of explanation
"""


