scipy.interpolate.RegularGridInterpolator.__call__#
- RegularGridInterpolator.__call__(xi, method=None)[source]#
Interpolation at coordinates.
- Parameters
- xindarray of shape (…, ndim)
The coordinates to evaluate the interpolator at.
- methodstr
The method of interpolation to perform. Supported are “linear” and “nearest”.
Examples
Here we define a nearest-neighbor interpolator of a simple function
>>> 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]]) array([2., 9.])
We can however evaluate the linear interpolant by overriding the method parameter
>>> interp([[1.5, 1.3], [0.3, 4.5]], method='linear') array([ 4.7, 24.3])