interp

An implementation of numpy.interp for sparse arrays.

Thanks to the function dispatch of numpy, this enables interpolation on sparse arrays using the numpy universal function. This function effectively wraps np.interp by calling it on the array data and the fill value. See the numpy documentation for details on the parameters.

Parameters:

Name	Type	Description	Default
x	SparseArray	The x-coordinates at which to evaluate the interpolated values.	required
xp	1-D sequence or SparseArray	The x-coordinates of the data points.	required
fp	1-D sequence or SparseArray	The y-coordinates of the data points, same length as xp.	required
left	float or complex	Value to return for x < xp[0], default is fp[0].	None
right	float or complex	Value to return for x > xp[-1], default is fp[-1].	None
period	None or float	A period for the x-coordinates.	None

Returns:

Name	Type	Description
out	SparseArray	The interpolated values, same shape as x.

See Also

https://numpy.org/doc/stable/reference/generated/numpy.interp.html

Examples:

When interpolating a sparse array, its data and the fill value are interpolated. The returned array is pruned. Therefore, the fill value and the number of nonzero elements might change.

>>> import numpy as np
>>> xp = [1, 2, 3]
>>> fp = [3, 2, 0]
>>> y = np.interp(sparse.COO.from_numpy(np.array([0, 1, 1.5, 2.72, 3.14])), xp, fp)
>>> y.todense()
array([3.  , 3.  , 2.5 , 0.56, 0.  ])
>>> y.fill_value
np.float64(3.0)
>>> y.nnz
3

Source code in sparse/numba_backend/_common.py

def interp(x, xp, fp, left=None, right=None, period=None):
    """
    An implementation of ``numpy.interp`` for sparse arrays.

    Thanks to the function dispatch of numpy, this enables interpolation on sparse arrays
    using the numpy universal function. This function effectively wraps ``np.interp`` by
    calling it on the array data and the fill value. See the numpy documentation for
    details on the parameters.

    Parameters
    ----------
    x : SparseArray
        The x-coordinates at which to evaluate the interpolated values.
    xp : 1-D sequence or SparseArray
        The x-coordinates of the data points.
    fp : 1-D sequence or SparseArray
        The y-coordinates of the data points, same length as ``xp``.
    left : float or complex, optional
        Value to return for ``x < xp[0]``, default is ``fp[0]``.
    right : float or complex, optional
        Value to return for ``x > xp[-1]``, default is ``fp[-1]``.
    period : None or float, optional
        A period for the x-coordinates.

    Returns
    -------
    out : SparseArray
        The interpolated values, same shape as x.

    See Also
    --------
    https://numpy.org/doc/stable/reference/generated/numpy.interp.html

    Examples
    --------
    When interpolating a sparse array, its data and the fill value are interpolated. The
    returned array is pruned. Therefore, the fill value and the number of nonzero
    elements might change.

    >>> import numpy as np
    >>> xp = [1, 2, 3]
    >>> fp = [3, 2, 0]
    >>> y = np.interp(sparse.COO.from_numpy(np.array([0, 1, 1.5, 2.72, 3.14])), xp, fp)
    >>> y.todense()
    array([3.  , 3.  , 2.5 , 0.56, 0.  ])
    >>> y.fill_value
    np.float64(3.0)
    >>> y.nnz
    3
    """
    from ._compressed import GCXS
    from ._coo import COO
    from ._dok import DOK

    # Densify sparse interpolants
    if isinstance(xp, SparseArray):
        xp = xp.todense()
    if isinstance(fp, SparseArray):
        fp = fp.todense()

    def interp_func(xx):
        return np.interp(xx, xp, fp, left=left, right=right, period=period)

    # Shortcut for dense arrays
    if not isinstance(x, SparseArray):
        return interp_func(x)

    # Define output type
    out_kwargs = {}
    out_type = COO
    if isinstance(x, GCXS):
        out_type = GCXS
        out_kwargs["compressed_axes"] = x.compressed_axes
    elif isinstance(x, DOK):
        out_type = DOK

    # Perform interpolation on sparse object
    arr = as_coo(x)
    data = interp_func(arr.data)
    fill_value = interp_func(arr.fill_value)
    return COO(data=data, coords=arr.coords, shape=arr.shape, fill_value=fill_value, prune=True).asformat(
        out_type, **out_kwargs
    )