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

Before Change

    // Compute the FFT
    fft_output = np.fft.fft(sig)
    freqs = np.fft.fftfreq(len(sig), 1. / fs)

    // Rotate spectrum and invert, zscore to normalize.
    //   Note: the delta exponent to be applied is divided by two, as

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