`scipy.interpolate._rgi:RegularGridInterpolator.call`

source: /scipy/interpolate/_rgi.py :375

Signature

def __call__ ( self , xi , method = None , * , nu = None )

Summary

Interpolation at coordinates.

Parameters

xi : ndarray of shape (..., ndim): The coordinates to evaluate the interpolator at.
method : str, optional: The method of interpolation to perform. Supported are "linear", "nearest", "slinear", "cubic", "quintic" and "pchip". Default is the method chosen when the interpolator was created.
nu : sequence of ints, length ndim, optional: If not None, the orders of the derivatives to evaluate. Each entry must be non-negative. Only allowed for methods "slinear", "cubic" and "quintic".
versionadded 1.13

Returns

values_x : ndarray, shape xi.shape[:-1] + values.shape[ndim:]: Interpolated values at xi. See notes for behaviour when xi.ndim == 1.

Notes

In the case that xi.ndim == 1 a new axis is inserted into the 0 position of the returned array, values_x, so its shape is instead (1,) + values.shape[ndim:].

Examples

Here we define a nearest-neighbor interpolator of a simple function

import numpy as np
x, y = np.array([0, 1, 2]), np.array([1, 3, 7])
def f(x, y):
    return x**2 + y**2
data = f(*np.meshgrid(x, y, indexing='ij', sparse=True))
from scipy.interpolate import RegularGridInterpolator
interp = RegularGridInterpolator((x, y), data, method='nearest')

✓

By construction, the interpolator uses the nearest-neighbor interpolation

interp([[1.5, 1.3], [0.3, 4.5]])

✓

We can however evaluate the linear interpolant by overriding the `method` parameter

interp([[1.5, 1.3], [0.3, 4.5]], method='linear')

✓

Aliases

scipy.interpolate.RegularGridInterpolator.__call__