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# scipy.interpolate.RegularGridInterpolator

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

  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.

  See also

  `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 :Parameters: xi : ndarray of shape (..., ndim) The coordinates to sample the gridded data at method : str The method of interpolation to perform.