scipy.interpolate.griddata

scipy.interpolate.griddata(points, values, xi, method='linear', fill_value=nan)[source]

Interpolate unstructured N-dimensional data.

New in version 0.9.

Parameters :

points : ndarray of floats, shape (npoints, ndims)

Data point coordinates. Can either be a ndarray of size (npoints, ndim), or a tuple of ndim arrays.

values : ndarray of float or complex, shape (npoints, ...)

Data values.

xi : ndarray of float, shape (..., ndim)

Points where to interpolate data at.

method : {‘linear’, ‘nearest’, ‘cubic’}, optional

Method of interpolation. One of

  • nearest: return the value at the data point closest to the point of interpolation. See NearestNDInterpolator for more details.
  • linear: tesselate the input point set to n-dimensional simplices, and interpolate linearly on each simplex. See LinearNDInterpolator for more details.
  • cubic (1-D): return the value determined from a cubic spline.
  • cubic (2-D): return the value determined from a piecewise cubic, continuously differentiable (C1), and approximately curvature-minimizing polynomial surface. See CloughTocher2DInterpolator for more details.

fill_value : float, optional

Value used to fill in for requested points outside of the convex hull of the input points. If not provided, then the default is nan. This option has no effect for the ‘nearest’ method.

Examples

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