Scaling of convolution in python

Question

I'm trying to get the impulse response and transfer function of a filter with an exponential sweep.

To do so, I convolved the output of the filter (measure in my code) with the inverse filter of the sweep (invFilter).

This should give me the impulse response of the filter.

Then I computed the FFT of this impulse response to get the transfer function of the filter.

You can notice that I'm only keeping a part of the impulse response, because my next step is too measure non-linear system with this method (cf Farina's publications).

invFilter = inverseFilter(fmin,fmax,duration)

    print("Calcul de la IR...")
    farinaTempIR = np.convolve(measure,invFilter,mode="full")
    farinaTempIR = farinaTempIR[sweep.size-1:] * 2
    timeFarinaTempIR = np.arange(0,farinaTempIR.size,1) / fs
    plt.figure("IR")
    plt.plot(timeFarinaTempIR,farinaTempIR,'--',label = "Farina Temp")
    plt.legend()

    print("Calcul de la TF...")
    farinaTempTF = np.fft.rfft(farinaTempIR) / farinaTempIR.size * 2
    freqFarinaFreqTF = np.fft.rfftfreq(farinaTempIR.size,1/fs)
    plt.figure("Fonction de transfert")
    plt.semilogx(freqFarinaFreqTF,20*np.log10(np.abs(farinaTempTF)),'--',label = "Farina Temp")
    plt.legend()

But the amplitude of the impulse response isn't correct and so the transfer function (see figures). Maybe I should normalize by a factor, but which one ? number of points? sampling frequency ?

I'm pretty sure the inverse filter is correct. Because when I did the same operation in frequency domain (multiplying the FFT of the measure with the FFT of the inverse filter), I got good results

EDIT : I forget to say, on the figure, the method that gives wrong results is "Farina temp" the other methods are what I'm expecting.