20047f4faebee3a9f21596bdd24a12df575dcb7a,neurodsp/sim/aperiodic.py,,sim_powerlaw,#Any#Any#Any#Any#,152

Before Change

    //   Note: the delta exponent to be applied is divided by two, as
    //     the FFT output is in units of amplitude not power
    fft_output_rot = rotate_powerlaw(freqs, fft_output, -exponent/2)
    sig = zscore(np.real(np.fft.ifft(fft_output_rot)))

    if f_range is not None:
        sig = filter_signal(sig, fs, infer_passtype(f_range), f_range, **filter_kwargs)

After Change

    // Get the number of samples to simulate for the signal
    //   If filter is to be filtered, with FIR, add extra to compensate for edges
    if f_range and filter_kwargs.get("filt_type", None) != "iir":

        pass_type = infer_passtype(f_range)
        filt_len = compute_filter_length(fs, pass_type,
                                         *check_filter_definition(pass_type, f_range),
                                         n_seconds=filter_kwargs.get("n_seconds", None),
                                         n_cycles=filter_kwargs.get("n_cycles", 3))

        n_samples = int(n_seconds * fs) + filt_len + 1

    else:
        n_samples = int(n_seconds * fs)

    sig = _create_powerlaw(n_samples, fs, exponent)

    if f_range is not None:
        sig = filter_signal(sig, fs, infer_passtype(f_range), f_range,

Instances