def pad_center()

in basic_pitch/layers/nnaudio.py [0:0]

• 9 lines of code
• 2 McCabe index (conditional complexity)

def pad_center(data: np.ndarray, size: int, axis: int = -1, **kwargs: Any) -> np.ndarray:
    """Wrapper for np.pad to automatically center an array prior to padding.
    This is analogous to `str.center()`

    Examples
    --------
    >>> # Generate a vector
    >>> data = np.ones(5)
    >>> librosa.util.pad_center(data, 10, mode='constant')
    array([ 0.,  0.,  1.,  1.,  1.,  1.,  1.,  0.,  0.,  0.])
    >>> # Pad a matrix along its first dimension
    >>> data = np.ones((3, 5))
    >>> librosa.util.pad_center(data, 7, axis=0)
    array([[ 0.,  0.,  0.,  0.,  0.],
           [ 0.,  0.,  0.,  0.,  0.],
           [ 1.,  1.,  1.,  1.,  1.],
           [ 1.,  1.,  1.,  1.,  1.],
           [ 1.,  1.,  1.,  1.,  1.],
           [ 0.,  0.,  0.,  0.,  0.],
           [ 0.,  0.,  0.,  0.,  0.]])
    >>> # Or its second dimension
    >>> librosa.util.pad_center(data, 7, axis=1)
    array([[ 0.,  1.,  1.,  1.,  1.,  1.,  0.],
           [ 0.,  1.,  1.,  1.,  1.,  1.,  0.],
           [ 0.,  1.,  1.,  1.,  1.,  1.,  0.]])

    Parameters
    ----------
    data : np.ndarray
        Vector to be padded and centered
    size : int >= len(data) [scalar]
        Length to pad `data`
    axis : int
        Axis along which to pad and center the data
    kwargs : additional keyword arguments
      arguments passed to `np.pad()`

    Returns
    -------
    data_padded : np.ndarray
        `data` centered and padded to length `size` along the
        specified axis

    Raises
    ------
    ValueError
        If `size < data.shape[axis]`

    See Also
    --------
    numpy.pad
    """

    kwargs.setdefault("mode", "constant")

    n = data.shape[axis]

    lpad = int((size - n) // 2)

    lengths = [(0, 0)] * data.ndim
    lengths[axis] = (lpad, int(size - n - lpad))

    if lpad < 0:
        raise ValueError(("Target size ({:d}) must be at least input size ({:d})").format(size, n))

    return np.pad(data, lengths, **kwargs)