__call__#
- RegularGridInterpolator.__call__(xi, method=None, *, nu=None)[source]#
- Interpolation at coordinates. - Parameters:
- xindarray of shape (…, ndim)
- The coordinates to evaluate the interpolator at. 
- methodstr, 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. 
- nusequence 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”. - Added in version 1.13. 
 
- Returns:
- values_xndarray, 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 == 1a 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]]) 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])