Insertion Loss Deviation (Python)

The following programming example is written in Python and calculates ILD (Insertion Loss Deviation).

The Python file called from PLTS should contain a pure function definition block. The syntax is as follows:

def functionName( parameters ):

“function_docstring” (optional)

import modules

function_suite

return [expression]

The parameters passed from PLTS are treated as complex column vectors. The return value from the Python function back to PLTS should be a column vector.

def ILD(measFreq, Sdd21):
  "Calculate insertion loss deviation"
  import numpy as np
  from numpy import transpose
  from numpy.linalg import inv
  Sdd21_dB = 20 * np.log10(np.squeeze(Sdd21))
  freq = np.real(measFreq)
  length = len(freq)
  Fb = freq.item(length-1)
  fILmin = 10e6
  fILmax = Fb
  startIndex = 0
  stopIndex = length - 1
  for i in range (length):
      if freq.item(i)>=fILmin:
          startIndex = i
          break
  for i in range (length):
      if freq.item(i) >= fILmax:
          stopIndex = i
          break
  freq_trunc = freq[startIndex:stopIndex+1]
  Sdd21_trunc = Sdd21_dB[startIndex:stopIndex+1]
  freq_trunc = np.matrix(freq_trunc).transpose()
  Sdd21_trunc = np.matrix(Sdd21_trunc).transpose()
  IL = -1 * Sdd21_trunc
  item1 = np.sqrt(freq_trunc*1e-9)
  item2 = freq_trunc*1e-9
  item3 = np.power(freq_trunc*1e-9, 2)
  F = np.array([item1, item2, item3])
  F = np.matrix(F).transpose()
  F_transpose = F.transpose()
  product = np.matrix(F_transpose) * np.matrix(F)
  F_inverse = inv(product)
  temp = np.matmul(F_inverse, F_transpose)
  coeff = np.matmul(temp, np.matrix(IL))
  ILfitted = -1 * np.matmul(F, coeff)
  ILD_temp = np.power(10, 0.05 * (Sdd21_trunc - ILfitted))
  ILD =np.array(ILD_temp).reshape(-1)
  return ILD