librosa/util/utils.py
def pad_center(data, size, axis=-1, **kwargs):
    '''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
    ------
    ParameterError
        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 ParameterError(('Target size ({:d}) must be '
                              'at least input size ({:d})').format(size, n))

    return np.pad(data, lengths, **kwargs)
def pad_center(data, size, axis=-1, **kwargs):
    '''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
    ------
    ParameterError
        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 ParameterError(('Target size ({:d}) must be '
                              'at least input size ({:d})').format(size, n))

    return np.pad(data, lengths, **kwargs)
def pad_center ( data , size , axis = - 1 , * * kwargs ) : 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 ParameterError ( ( 'Target size ({:d}) must be ' 'at least input size ({:d})' ) . format ( size , n ) ) return np . pad ( data , lengths , * * kwargs )
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
    ------
    ParameterError
        If `size < data.shape[axis]`

    See Also
    --------
    numpy.pad
Wrapper for np . pad to automatically center an array prior to padding . This is analogous to str . center ()