pad

Performs the equivalent of sparse.SparseArray. Note that this function returns a new array instead of a view.

Parameters:

Name	Type	Description	Default
array	SparseArray	Sparse array which is to be padded.	required
pad_width	(sequence, array_like, int)	Number of values padded to the edges of each axis. ((before_1, after_1), … (before_N, after_N)) unique pad widths for each axis. ((before, after),) yields same before and after pad for each axis. (pad,) or int is a shortcut for before = after = pad width for all axes.	sequence
mode	str	Pads to a constant value which is fill value. Currently only constant mode is implemented	'constant'
constant_values	int	The values to set the padded values for each axis. Default is 0. This must be same as fill value.	required

Returns:

Type	Description
SparseArray	The padded sparse array.

Raises:

Type	Description
NotImplementedError	If mode != 'constant' or there are unknown arguments.
ValueError	If constant_values != self.fill_value

See Also

numpy.pad : NumPy equivalent function

Source code in sparse/numba_backend/_common.py

def pad(array, pad_width, mode="constant", **kwargs):
    """
    Performs the equivalent of [`sparse.SparseArray`][]. Note that
    this function returns a new array instead of a view.

    Parameters
    ----------
    array : SparseArray
        Sparse array which is to be padded.

    pad_width : {sequence, array_like, int}
        Number of values padded to the edges of each axis. ((before_1, after_1), … (before_N, after_N)) unique pad
        widths for each axis. ((before, after),) yields same before and after pad for each axis. (pad,) or int is a
        shortcut for before = after = pad width for all axes.

    mode : str
        Pads to a constant value which is fill value. Currently only constant mode is implemented

    constant_values : int
        The values to set the padded values for each axis. Default is 0. This must be same as fill value.

    Returns
    -------
    SparseArray
        The padded sparse array.

    Raises
    ------
    NotImplementedError
        If mode != 'constant' or there are unknown arguments.

    ValueError
        If constant_values != self.fill_value

    See Also
    --------
    [`numpy.pad`][] : NumPy equivalent function

    """
    if not isinstance(array, SparseArray):
        raise NotImplementedError("Input array is not compatible.")

    if mode.lower() != "constant":
        raise NotImplementedError(f"Mode '{mode}' is not yet supported.")

    if not equivalent(kwargs.pop("constant_values", _zero_of_dtype(array.dtype)), array.fill_value):
        raise ValueError("constant_values can only be equal to fill value.")

    if kwargs:
        raise NotImplementedError("Additional Unknown arguments present.")

    from ._coo import COO

    array = array.asformat("coo")

    pad_width = np.broadcast_to(pad_width, (len(array.shape), 2))
    new_coords = array.coords + pad_width[:, 0:1]
    new_shape = tuple([array.shape[i] + pad_width[i, 0] + pad_width[i, 1] for i in range(len(array.shape))])
    new_data = array.data
    return COO(new_coords, new_data, new_shape, fill_value=array.fill_value)