Python function to calculate a vibration isolator transmissibility when the isolator performance varies in time

Question

Example of Input and Output graph

I am running some experiments on a vibration isolator whose performance varies in time and I am trying to find the process the data.

I have recorded a dataset comprising 3 columns: Time, Input_amplitude and Output_amplitude.

On a Windows machine I am using Anaconda Spyder 5.4 to process the date. First I defined a Pandas dataframe from the measured data, with the index column defined as Time, which is an equally spaced vector.

The transmissibility of a vibration isolator is computed by dividing the output signal FFT by the input signal FFT.

Since my vibration isolator performance varies in time, I assumed I need to use a spectrogram function in order to be able to visualize the change in time.

I created a function using the scipy library signal .spectrogram function, but I am not sure I have defined the function parameters correctly. I am interested only in the performance for frequencies up to 50 Hertz.

Following is the function code:

import numpy as np
from scipy import signal

def Calculate_T(Input_amplitude, Output_amplitude, Time):
    #Compute the sampling frequency
    FS = 1/(Time[1]-Time[0])
    #Define the window range into which the dataset if divided (60 seconds)
    Time_Domain = 60
    #Define the overlapping time ratio between adjacent segments
    Ovrl_Ratio = 0.5
    #Compute the overlapping time range between adjacent segments
    Ovrl_Time_Domain = Time_Domain*Ovrl_Ratio
    #Compute the input and output spectrograms
    Input_frequency, Input_time, spec_input - signal.spectrogram(Input_amplitude, FS, nperseg =   Time_Domain, noverlap = Ovrl_Time_Domain, mode = 'magnitude']
    Output_frequency, Output_time, spec_output - signal.spectrogram(Output_amplitude, FS, nperseg = Time_Domain, noverlap = Ovrl_Time_Domain, mode = 'magnitude']
    #Notify the user regarding the length of the segments into which the dataset is split and the overlapping ratio
    print ('The function splits the data into', Time_Domain*(Time[1]-Time[0]), 'seconds time segments with', Ovrl_Time_Domain*(Time[1]-Time[0]), 'seconds overlapping between adjacent time segments')

    #Compute the transmissibility as the magnitude ratio of the output to input spectra
    Transmissibility = np.abs(spec_output)/np.abs(spec_input)

    #Return the frequency and time vectors and transmissibility
    return Input_frequency, Input_time, Transmissibility}

I would like to know if I used correctly the spectrogram function, especially the nperseg parameters and a way to plot the spectrogram against the initial time vector

Thanks a lot!

Edit: Have added an image of an Input/Output example.