f5122cdab53605b7b800c96d6700b791b8c9add8,librosa/segment.py,,lag_to_recurrence,#,287

Before Change

    idx_slice = [slice(None)] * lag.ndim
    for i in range(1, t):
        idx_slice[axis] = i
        lag[idx_slice] = np.roll(lag[idx_slice], i)

    sub_slice = [slice(None)] * lag.ndim
    sub_slice[1 - axis] = slice(t)
    return np.ascontiguousarray(lag[sub_slice].T).T


def timelag_filter(function, pad=True, index=0):
    """Filtering in the time-lag domain.

After Change

    sub_slice[1 - axis] = slice(t)
    rec = rec[tuple(sub_slice)]

    if sparse:
        return rec.asformat(lag.format)
    else:
        return np.ascontiguousarray(rec.T).T


def timelag_filter(function, pad=True, index=0):
    """Filtering in the time-lag domain.

    This is primarily useful for adapting image filters to operate on

Instances