Test out noisy series operations; at least the function runs successfully

sim/test_series.py

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+import numpy as np
+import skrf
+from skrf.media import DistributedCircuit
+import skrf.frequency as freq
+import skrf.network as net
+import skrf.util
+from scipy.interpolate import interp1d
+
+def series(a, b):
+  try:
+    # one side should hopefully be invertible
+    newz = a.z + b.z
+  except e:
+    raise e
+    # not using this part for now
+    ## there's a formula for calculating the inverse of a sum:
+    ## (A + B)^-1 = A^-1 - 1/(1+g)*A^-1B^-1
+    ## where g = trace(BA^-1)
+
+    ## z = y^-1
+    ## y = (za + zb)^-1 = za^-1 - 1/(1+g)*za^-1*zb*za^-1 =
+    ## ya - 1/(1+g)*ya*zb*ya, g = trace(zb*ya)
+    ## so we never need to calculate za
+    #g = np.trace(np.matmul(a.y, b.z), axis1=1, axis2=2)
+    #newy = a.y - 1./(1./+g)*np.matmul(np.matmul(a.y, b.z), a.y)
+    #newz = np.linalg.inv(newy)
+  ret = net.Network.from_z(newz, frequency=a.frequency)
+  noiseless = not a.noisy and not b.noisy
+  if noiseless:
+    return ret
+  if a.noisy:
+    an = a.n
+    af = a.f_noise
+  else:
+    an = np.zeros(b.n.shape)
+    af = b.f_noise
+  if b.noisy:
+    bn = b.n
+    bf = b.f_noise
+  else:
+    bn = np.zeros(a.n.shape)
+    bf = a.f_noise
+
+  if bf != af:
+    # TODO interpolate noise values for b into a frequency ranges
+    # bn = ??? 
+    raise NotImplementedError
+
+  a_real = interp1d(a.frequency.f, a.a.real, 
+          axis=0, kind=net.Network.noise_interp_kind)
+  a_imag = interp1d(a.frequency.f, a.a.imag, 
+          axis=0, kind=net.Network.noise_interp_kind)
+  a_abcd = a_real(af.f) + 1.j * a_imag(af.f)
+
+  b_real = interp1d(b.frequency.f, b.a.real, 
+          axis=0, kind=net.Network.noise_interp_kind)
+  b_imag = interp1d(b.frequency.f, b.a.imag, 
+          axis=0, kind=net.Network.noise_interp_kind)
+  b_abcd = b_real(bf.f) + 1.j * b_imag(bf.f)
+
+  # calculate noise
+  # based on https://onlinelibrary.wiley.com/doi/pdf/10.1002/9781119073093.app3
+  a1 = 1
+  b1 = (b_abcd[:,0,0] - a_abcd[:,0,0])/(a_abcd[:,1,0] + b_abcd[:,1,0])
+  c1 = 0
+  d1 = b_abcd[:,1,0]/(a_abcd[:,1,0] + b_abcd[:,1,0])
+
+  a2 = 1
+  b2 = (a_abcd[:,0,0] - b_abcd[:,0,0])/(a_abcd[:,1,0] + b_abcd[:,1,0])
+  c2 = 0
+  d2 = a_abcd[:,1,0]/(a_abcd[:,1,0] + b_abcd[:,1,0])
+
+  ee = (a1*np.conj(a1)*an[:,0,0] + b1*np.conj(b1)*bn[:,1,1] +
+          a2*np.conj(a2)*bn[:,1,1] + b2*np.conj(b2)*bn[:,1,1] +
+      a1*np.conj(b1)*an[:,0,1] + b1*np.conj(a1)*an[:,1,0] +
+          a2*np.conj(b2)*bn[:,0,1] + b2*np.conj(a2)*bn[:,1,0])
+  ii = (c1*np.conj(c1)*an[:,0,0] + d1*np.conj(d1)*an[:,1,1] +
+          c2*np.conj(c2)*bn[:,0,0] + d2*np.conj(d2)*bn[:,1,1] +
+      c1*np.conj(d1)*an[:,0,1] + d1*np.conj(c1)*an[:,1,0] +
+        c2*np.conj(d2)*bn[:,0,1] + d2*np.conj(c2)*bn[:,1,0])
+  ei = (a1*np.conj(c1)*an[:,0,0] + b1*np.conj(d1)*an[:,1,1] + 
+          a2*np.conj(c2)*bn[:,0,0] + b2*np.conj(d2)*bn[:,1,1] + 
+      a1*np.conj(d1)*an[:,0,1] + b1*np.conj(c1)*an[:,1,0] + 
+        a2*np.conj(d2)*bn[:,0,1] + b2*np.conj(c2)*bn[:,1,0])
+  ie = np.conj(ei)
+
+  ret.noise = np.moveaxis(np.array([[ee, ei], [ie, ii]]), 2, 0)
+  ret.noise_freq = af
+  return ret
+
+f = freq.Frequency(0.4, 2, 1001)
+tem = DistributedCircuit(f, z0=50)
+
+bjt = net.Network('BFU520_Spar_NF_400MHz-2GHz/BFU520_05V0_005mA_NF_SP.s2p').interpolate(f)
+inductor = tem.shunt_inductor(1e-9)
+
+print(series(bjt, inductor).n)