Module « scipy.interpolate »

Classe « RegularGridInterpolator »

Informations générales

Héritage

builtins.object
    RegularGridInterpolator

Définition

class RegularGridInterpolator(builtins.object):

Description _{[extrait de RegularGridInterpolator.__doc__]}

    Interpolation on a regular grid in arbitrary dimensions

    The data must be defined on a regular grid; the grid spacing however may be
    uneven. Linear and nearest-neighbor interpolation are supported. After
    setting up the interpolator object, the interpolation method (*linear* or
    *nearest*) may be chosen at each evaluation.

    Parameters
    ----------
    points : tuple of ndarray of float, with shapes (m1, ), ..., (mn, )
        The points defining the regular grid in n dimensions.

    values : array_like, shape (m1, ..., mn, ...)
        The data on the regular grid in n dimensions.

    method : str, optional
        The method of interpolation to perform. Supported are "linear" and
        "nearest". This parameter will become the default for the object's
        ``__call__`` method. Default is "linear".

    bounds_error : bool, optional
        If True, when interpolated values are requested outside of the
        domain of the input data, a ValueError is raised.
        If False, then `fill_value` is used.

    fill_value : number, optional
        If provided, the value to use for points outside of the
        interpolation domain. If None, values outside
        the domain are extrapolated.

    Methods
    -------
    __call__

    Notes
    -----
    Contrary to LinearNDInterpolator and NearestNDInterpolator, this class
    avoids expensive triangulation of the input data by taking advantage of the
    regular grid structure.

    If any of `points` have a dimension of size 1, linear interpolation will
    return an array of `nan` values. Nearest-neighbor interpolation will work
    as usual in this case.

    .. versionadded:: 0.14

    Examples
    --------
    Evaluate a simple example function on the points of a 3-D grid:

    >>> from scipy.interpolate import RegularGridInterpolator
    >>> def f(x, y, z):
    ...     return 2 * x**3 + 3 * y**2 - z
    >>> x = np.linspace(1, 4, 11)
    >>> y = np.linspace(4, 7, 22)
    >>> z = np.linspace(7, 9, 33)
    >>> xg, yg ,zg = np.meshgrid(x, y, z, indexing='ij', sparse=True)
    >>> data = f(xg, yg, zg)

    ``data`` is now a 3-D array with ``data[i,j,k] = f(x[i], y[j], z[k])``.
    Next, define an interpolating function from this data:

    >>> my_interpolating_function = RegularGridInterpolator((x, y, z), data)

    Evaluate the interpolating function at the two points
    ``(x,y,z) = (2.1, 6.2, 8.3)`` and ``(3.3, 5.2, 7.1)``:

    >>> pts = np.array([[2.1, 6.2, 8.3], [3.3, 5.2, 7.1]])
    >>> my_interpolating_function(pts)
    array([ 125.80469388,  146.30069388])

    which is indeed a close approximation to
    ``[f(2.1, 6.2, 8.3), f(3.3, 5.2, 7.1)]``.

    See also
    --------
    NearestNDInterpolator : Nearest neighbor interpolation on unstructured
                            data in N dimensions

    LinearNDInterpolator : Piecewise linear interpolant on unstructured data
                           in N dimensions

    References
    ----------
    .. [1] Python package *regulargrid* by Johannes Buchner, see
           https://pypi.python.org/pypi/regulargrid/
    .. [2] Wikipedia, "Trilinear interpolation",
           https://en.wikipedia.org/wiki/Trilinear_interpolation
    .. [3] Weiser, Alan, and Sergio E. Zarantonello. "A note on piecewise linear
           and multilinear table interpolation in many dimensions." MATH.
           COMPUT. 50.181 (1988): 189-196.
           https://www.ams.org/journals/mcom/1988-50-181/S0025-5718-1988-0917826-0/S0025-5718-1988-0917826-0.pdf

    

Constructeur(s)

Signature du constructeur	Description
__init__(self, points, values, method='linear', bounds_error=True, fill_value=nan)

Liste des opérateurs

Opérateurs hérités de la classe object

__eq__, __ge__, __gt__, __le__, __lt__, __ne__

Méthodes héritées de la classe object

__delattr__, __dir__, __format__, __getattribute__, __hash__, __init_subclass__, __reduce__, __reduce_ex__, __repr__, __setattr__, __sizeof__, __str__, __subclasshook__