def __fft_filters(sr, fmin, n_bins, bins_per_octave, tuning,
                  filter_scale, norm, sparsity, hop_length=None):

    basis, lengths = filters.constant_q(sr,
                                        fmin=fmin,
                                        n_bins=n_bins,
                                        bins_per_octave=bins_per_octave,
                                        tuning=tuning,
                                        filter_scale=filter_scale,
                                        norm=norm,
                                        pad_fft=True)

    # Filters are padded up to the nearest integral power of 2
    n_fft = basis.shape[1]

    if hop_length is not None and n_fft < 2 * hop_length:
        n_fft = int(2.0 ** (np.ceil(np.log2(2 * hop_length))))

    # normalize by inverse length to compensate for phase invariance
    basis *= lengths.reshape((-1, 1)) / n_fft

    # FFT and retain only the non-negative frequencies
    fft_basis = fft.fft(basis, n=n_fft, axis=1)[:, :(n_fft // 2)+1]

    # normalize as in Parseval's relation, and sparsify the basis
    fft_basis = util.sparsify_rows(fft_basis / n_fft, quantile=sparsity)

    return fft_basis, n_fft, lengths
Delete a script.
dataset/ETHPy150Open bmcfee/librosa/librosa/core/constantq.py/__fft_filters--crossover with--dataset/ETHPy150Open openstack/python-cloudkittyclient/cloudkittyclient/v1/rating/pyscripts/shell.py/do_pyscripts_script_delete