TONGUE_KEVIN_JN_python/noise.py

import numpy as np
import scipy.io

def generate_noise_temporal(time, tlength, q, vseed, is_matlab=True):
    """
    Generates correlated noise
    """
    nstep = len(time)
    
    if is_matlab:
        np.random.seed(vseed)
    else:
        # For octave compatibility, load state from mat file
        mat = scipy.io.loadmat(f'randomstate_head/randomState{vseed}.mat')
        np.random.set_state(mat['v'][0])
    
    seed = np.random.randn(nstep)
    
    # Correlation matrix
    time_diff = time[:, np.newaxis] - time[np.newaxis, :]
    Corr = np.exp(- (time_diff ** 2) / (tlength ** 2))
    fpert = Corr @ seed
    
    famp = np.sum(fpert ** 2) / nstep
    fpert = fpert * np.sqrt(q / famp)
    return fpert