# scipy.interpolate.RegularGridInterpolator¶

class scipy.interpolate.RegularGridInterpolator(points, values, method='linear', bounds_error=True, fill_value=nan)[source]

Interpolation on a regular grid in arbitrary dimensions

The data must be defined on a regular grid; the grid spacing however may be uneven. Linear and nearest-neighbour interpolation are supported. After setting up the interpolator object, the interpolation method (linear or nearest) may be chosen at each evaluation.

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. method : str, optional The method of interpolation to perform. Supported are “linear” and “nearest”. This parameter will become the default for the object’s __call__ method. Default is “linear”. 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 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.

NearestNDInterpolator
Nearest neighbour interpolation on unstructured data in N dimensions
LinearNDInterpolator
Piecewise linear interpolant on unstructured data in N dimensions

Notes

Contrary to LinearNDInterpolator and NearestNDInterpolator, this class avoids expensive triangulation of the input data by taking advantage of the regular grid structure.

New in version 0.14.

References

 [R46] Python package regulargrid by Johannes Buchner, see https://pypi.python.org/pypi/regulargrid/
 [R47] Trilinear interpolation. (2013, January 17). In Wikipedia, The Free Encyclopedia. Retrieved 27 Feb 2013 01:28. http://en.wikipedia.org/w/index.php?title=Trilinear_interpolation&oldid=533448871
 [R48] Weiser, Alan, and Sergio E. Zarantonello. “A note on piecewise linear and multilinear table interpolation in many dimensions.” MATH. COMPUT. 50.181 (1988): 189-196. http://www.ams.org/journals/mcom/1988-50-181/S0025-5718-1988-0917826-0/S0025-5718-1988-0917826-0.pdf

Examples

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

>>> from scipy.interpolate import RegularGridInterpolator
>>> def f(x,y,z):
>>>     return 2 * x**3 + 3 * y**2 - z
>>> x = np.linspace(1, 4, 11)
>>> y = np.linspace(4, 7, 22)
>>> z = np.linspace(7, 9, 33)
>>> data = f(*np.meshgrid(x, y, z, indexing='ij', sparse=True))


data is now a 3D array with data[i,j,k] = f(x[i], y[j], z[k]). Next, define an interpolating function from this data:

>>> my_interpolating_function = RegularGridInterpolator((x,y,z), data)


Evaluate the interpolating function at the two points (x,y,z) = (2.1, 6.2, 8.3) and (3.3, 5.2, 7.1):

>>> pts = np.array([[2.1, 6.2, 8.3], [3.3, 5.2, 7.1]])
>>> my_interpolating_function(pts)
array([ 125.80469388,  146.30069388])


which is indeed a close approximation to [f(2.1, 6.2, 8.3), f(3.3, 5.2, 7.1)].

Methods

 __call__(xi[, method]) Interpolation at coordinates

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