- scipy.interpolate.interpn(points, values, xi, method='linear', bounds_error=True, fill_value=nan)[source]#
Multidimensional interpolation on regular or rectilinear grids.
Strictly speaking, not all regular grids are supported - this function works on rectilinear grids, that is, a rectangular grid with even or uneven spacing.
- pointstuple of ndarray of float, with shapes (m1, ), …, (mn, )
The points defining the regular grid in n dimensions. The points in each dimension (i.e. every elements of the points tuple) must be strictly ascending or descending.
- valuesarray_like, shape (m1, …, mn, …)
The data on the regular grid in n dimensions. Complex data can be acceptable.
- xindarray of shape (…, ndim)
The coordinates to sample the gridded data at
- methodstr, optional
The method of interpolation to perform. Supported are “linear”, “nearest”, “slinear”, “cubic”, “quintic”, “pchip”, and “splinef2d”. “splinef2d” is only supported for 2-dimensional data.
- bounds_errorbool, 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_valuenumber, 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”.
- values_xndarray, shape xi.shape[:-1] + values.shape[ndim:]
Interpolated values at xi. See notes for behaviour when
xi.ndim == 1.
Nearest neighbor interpolation on unstructured data in N dimensions
Piecewise linear interpolant on unstructured data in N dimensions
interpolation on a regular or rectilinear grid in arbitrary dimensions (
interpnwraps this class).
Bivariate spline approximation over a rectangular mesh
interpolation on grids with equal spacing (suitable for e.g., N-D image resampling)
New in version 0.14.
In the case that
xi.ndim == 1a new axis is inserted into the 0 position of the returned array, values_x, so its shape is instead
(1,) + values.shape[ndim:].
If the input data is such that input dimensions have incommensurate units and differ by many orders of magnitude, the interpolant may have numerical artifacts. Consider rescaling the data before interpolation.
Evaluate a simple example function on the points of a regular 3-D grid:
>>> import numpy as np >>> 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]