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 = np.power(10, 0.05 * (Sdd21_trunc - ILfitted))
   return ILD