interpn(points, values, xi, method='linear', bounds_error=True, fill_value=nan)

Notes

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.

xi : ndarray of shape (..., ndim)

The coordinates to sample the gridded data at

method : str, optional

The method of interpolation to perform. Supported are "linear" and "nearest", and "splinef2d". "splinef2d" is only supported for 2-dimensional data.

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 :None:None:`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. Extrapolation is not supported by method "splinef2d".

Returns

values_x : ndarray, shape xi.shape[:-1] + values.shape[ndim:]

Interpolated values at input coordinates.

Multidimensional interpolation on regular grids.

See Also

LinearNDInterpolator

Piecewise linear interpolant on unstructured data in N dimensions

NearestNDInterpolator

Nearest neighbor interpolation on unstructured data in N dimensions

RectBivariateSpline

Bivariate spline approximation over a rectangular mesh

RegularGridInterpolator

Linear and nearest-neighbor Interpolation on a regular grid in arbitrary dimensions

Examples

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

>>> from scipy.interpolate import interpn
... def value_func_3d(x, y, z):
...     return 2 * x + 3 * y - z
... x = np.linspace(0, 4, 5)
... y = np.linspace(0, 5, 6)
... z = np.linspace(0, 6, 7)
... points = (x, y, z)
... values = value_func_3d(*np.meshgrid(*points, indexing='ij'))

Evaluate the interpolating function at a point

>>> point = np.array([2.21, 3.12, 1.15])
... print(interpn(points, values, point))
[12.63]

See :

Back References

The following pages refer to to this document either explicitly or contain code examples using this.

scipy.interpolate._interpolate.interp2d scipy.interpolate._interpolate.interpn

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