### Navigation

- index
- modules |
- modules |
- next |
- previous |
- SciPy v0.12 Reference Guide (DRAFT) »
- Interpolation (`scipy.interpolate`) »

# scipy.interpolate.griddata

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

  Interpolate unstructured N-dimensional data.

  New in version 0.9.

  Parameters : | points : ndarray of floats, shape (N, ndim) Data point coordinates. Can either be an array of size (N, ndim), or a tuple of ndim arrays. values : ndarray of float or complex, shape (N,) Data values. xi : ndarray of float, shape (M, ndim) Points at which to interpolate data. 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

  Suppose we want to interpolate the 2-D function

      >>> def func(x, y):
      >>>     return x*(1-x)*np.cos(4*np.pi*x) * np.sin(4*np.pi*y**2)**2

  on a grid in [0, 1]x[0, 1]

      >>> grid_x, grid_y = np.mgrid[0:1:100j, 0:1:200j]

  but we only know its values at 1000 data points:

      >>> points = np.random.rand(1000, 2)
      >>> values = func(points[:,0], points[:,1])

  This can be done with `griddata` – below we try out all of the interpolation methods:

      >>> from scipy.interpolate import griddata
      >>> grid_z0 = griddata(points, values, (grid_x, grid_y), method='nearest')
      >>> grid_z1 = griddata(points, values, (grid_x, grid_y), method='linear')
      >>> grid_z2 = griddata(points, values, (grid_x, grid_y), method='cubic')

  One can see that the exact result is reproduced by all of the methods to some degree, but for this smooth function the piecewise cubic interpolant gives the best results:

      >>> import matplotlib.pyplot as plt
      >>> plt.subplot(221)
      >>> plt.imshow(func(grid_x, grid_y).T, extent=(0,1,0,1), origin='lower')
      >>> plt.plot(points[:,0], points[:,1], 'k.', ms=1)
      >>> plt.title('Original')
      >>> plt.subplot(222)
      >>> plt.imshow(grid_z0.T, extent=(0,1,0,1), origin='lower')
      >>> plt.title('Nearest')
      >>> plt.subplot(223)
      >>> plt.imshow(grid_z1.T, extent=(0,1,0,1), origin='lower')
      >>> plt.title('Linear')
      >>> plt.subplot(224)
      >>> plt.imshow(grid_z2.T, extent=(0,1,0,1), origin='lower')
      >>> plt.title('Cubic')
      >>> plt.gcf().set_size_inches(6, 6)
      >>> plt.show()

  (Source code)

### Navigation

- index
- modules |
- modules |
- next |
- previous |
- SciPy v0.12 Reference Guide (DRAFT) »
- Interpolation (`scipy.interpolate`) »