Create surrogate data in python using fourier transform?

Question

I want to create 10,000 sample (random phase but same power spectra) of my original time series using fourier transform. The method I want to opt is given in figure. The actual time series data can be found here Data link

Method details figure_please see this**

so far I understood that I first need to used scipy.fft.fft on my original data..

    import scipy.fft
    import pandas as pd
    import numpy as np
     
    ######

    var=np.loadtxt('DataQ.txt');
    data=pd.DataFrame({'var1':var[:,0],'var2':var[:,1]});
    

##-first part of the method---------

def DFT(x):
   """
   Function to calculate the 
   discrete Fourier Transform 
   of a 1D real-valued signal x
   """
   N = len(x);
   n = np.arange(N);
   k = n.reshape((N, 1));
   e = np.exp(2j * np.pi * k * n / N);
   X = np.dot(e, x);

return X

X=DFT(data['var1'].values);

The second step I need help.

I am looking forward to your reply on the method of generating surrogate data using the method specified in figure.